Advanced Electric Vehicle Technology

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Topic Information

Dear Colleagues,

The electric vehicle is a very exciting topic of research and industrial development. In the last 10 years, rapid development in both electric vehicle technology and commercial activities has been witnessed. The number of research papers, webinars, tutorial courses, and PhD graduates in this field has increased rapidly, and commercial electric vehicles have increased in both sales and models. The electric vehicle is also a hot topic in the news and internet media. Research on battery, energy storage, packaging, and chargers still requires a lot of research effort, while other associated technologies such as Vehicle to X, new motors, and actuators are now replacing all the conventional mechanical and hydraulic systems in a vehicle. Because of the demand in smart cities and robotic activities, new control methods and autonomous driving are now being applied and developed in most vehicles. All the automotive enterprises have gradually changed their models into electric versions. The associated infrastructure, government policy, and standards have evolved, making this an exciting topic of research. Electric vehicle technology has been extended to the vessel, underwater vehicles, air transport, and space vehicles. The technology is not only restricted to electrical, electronic, and computer engineering but also extended to multidisciplinary research. We are launching this Special Issue because the electric vehicle market is expanding rapidly, and the next 50 years are anticipated to serve as a transition period from fossil fuel vehicles to electric vehicles. Indeed, the next 20 years are critical for electric vehicle development, which is why we are inviting you to submit a paper to report, discuss, and predict the research development in this exciting research topic.

Prof. Dr. Eric Cheng
Prof. Dr. Junfeng Liu
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Batteries batteries	4.6	4.0	2015	19.7 Days	CHF 2700
Designs designs	-	3.9	2017	21.7 Days	CHF 1600
Energies energies	3.0	6.2	2008	16.8 Days	CHF 2600
Sustainability sustainability	3.3	6.8	2009	19.7 Days	CHF 2400
World Electric Vehicle Journal wevj	2.6	4.5	2007	16.2 Days	CHF 1400

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Published Papers (53 papers)

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16 pages, 5735 KiB  

Open AccessEditor’s ChoiceArticle

Investigation of Low-Frequency Data Significance in Electric Vehicle Drivetrain Durability Development

by Mingfei Li, Fabian Kai-Dietrich Noering, Yekta Öngün, Michael Appelt and Roman Henze

World Electr. Veh. J. 2024, 15(3), 88; https://doi.org/10.3390/wevj15030088 - 28 Feb 2024

Cited by 1 | Viewed by 1573

Abstract

The digitalization of the automotive industry presents significant potential for technical advantages, such as the online collection of customer driving data. These data can be used for customer-oriented development to improve the durability of components or systems. However, due to current limitations in [...] Read more.

The digitalization of the automotive industry presents significant potential for technical advantages, such as the online collection of customer driving data. These data can be used for customer-oriented development to improve the durability of components or systems. However, due to current limitations in data transfer, the sampling frequency is typically lower than that of classic dataloggers. This paper examines the importance of low-frequency data in the development of drivetrain durability and investigates the extent to which these data can be utilized for a drivetrain durability analysis. Real driving data were utilized as a database to demonstrate the impact of downsampling on data significance, with the deviation in damage serving as the criteria. The findings suggest that low-frequency data, when available in sufficient quantities, can provide valuable information for predicting durability in rollover and time at level classification. The deviation in the damage prediction is less than 2% for distances exceeding 5000 km. However, low-frequency data are not suitable for rainflow analysis. Finally, the database size was adjusted to assess the statistical stability of the durability prediction. A larger dataset typically reduces variance. The paper presents evidence for the quality and usability of cloud data in drivetrain durability design. Cloud data from a significant number of customer vehicles can be used for certain analyses of representative customer load collectives, which can reduce development time and costs. Full article

(This article belongs to the Topic Advanced Electric Vehicle Technology)

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21 pages, 4647 KiB  

Open AccessArticle

High Efficiency Dual-Active-Bridge Converter with Triple-Phase-Shift Control for Battery Charger of Electric Vehicles

by Shih-hao Kuo, Huang-Jen Chiu, Che-Wei Chiang, Ta-Wei Huang, Yu-Chen Chang, Serafin Bachman, Szymon Piasecki, Marek Jasinski and Marek Turzyński

Energies 2024, 17(2), 354; https://doi.org/10.3390/en17020354 - 10 Jan 2024

Cited by 1 | Viewed by 2982

Abstract

An optimal modulation scheme with triple-phase-shift (TPS) control could increase the efficiency in the entire load range for a dual-active-bridge (DAB) converter under wide output voltage range conditions. Therefore, this study proposes a convergent approach to TPS mode selection, coupled with an optimal [...] Read more.

An optimal modulation scheme with triple-phase-shift (TPS) control could increase the efficiency in the entire load range for a dual-active-bridge (DAB) converter under wide output voltage range conditions. Therefore, this study proposes a convergent approach to TPS mode selection, coupled with an optimal modulation scheme, ensuring the circuit’s efficiency over the entire range in the realm of a high-power and high-efficiency battery charger for electric vehicles. The convergent approach to TPS mode selection also reduces the numerous cases for small-signal analysis through general average modeling. After verifying the small-signal models under various voltage transfer ratios and load conditions to verify the stability, a converter prototype with a rated power of 15 kW is built and tested. Thus, a peak efficiency of 97.7% can be achieved. Full article

(This article belongs to the Topic Advanced Electric Vehicle Technology)

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24 pages, 10269 KiB  

Open AccessArticle

Design of a Misalignment-Tolerant Inductor–Capacitor–Capacitor-Compensated Wireless Charger for Roadway-Powered Electric Vehicles

by Mustafa Abdulhameed, Eiman ElGhanam, Ahmed H. Osman and Mohamed S. Hassan

Sustainability 2024, 16(2), 567; https://doi.org/10.3390/su16020567 - 9 Jan 2024

Cited by 6 | Viewed by 1759

Abstract

Dynamic wireless charging (DWC) systems enable electric vehicles (EVs) to receive energy on the move, without stopping at charging stations. Nonetheless, the energy efficiency of DWC systems is affected by the inherent misalignments of the mobile EVs, causing fluctuations in the amount of [...] Read more.

Dynamic wireless charging (DWC) systems enable electric vehicles (EVs) to receive energy on the move, without stopping at charging stations. Nonetheless, the energy efficiency of DWC systems is affected by the inherent misalignments of the mobile EVs, causing fluctuations in the amount of energy transmitted to the EVs. In this work, a multi-coil secondary-side inductive link (IL) design is proposed with independent double-D (DD) and quadrature coils to reduce the effect of coupling fluctuations on the power received during misalignments. Dual-sided inductor–capacitor–capacitor (LCC) compensation networks are utilized with power and current control circuits to provide a load-independent, constant current output at different misalignment conditions. The LCC compensation components are tuned to maximize the power transferred at the minimum acceptable coupling point, $k_{min}$. This compensates for the leaked energy during misalignments and minimizes variations in the operating frequency during zero-phase angle (ZPA) operation. Simulations reveal an almost constant output power for different lateral misalignment (LTMA) values up to $\pm 200$ mm for a 25 kW system, with a power transfer efficiency of $90\%$. A close correlation between simulation and experimental results is observed. Full article

(This article belongs to the Topic Advanced Electric Vehicle Technology)

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17 pages, 6644 KiB  

Open AccessArticle

A Deep Learning Approach to Improve the Control of Dynamic Wireless Power Transfer Systems

by Manuele Bertoluzzo, Paolo Di Barba, Michele Forzan, Maria Evelina Mognaschi and Elisabetta Sieni

Energies 2023, 16(23), 7865; https://doi.org/10.3390/en16237865 - 1 Dec 2023

Cited by 6 | Viewed by 1814

Abstract

In this paper, an innovative approach for the fast estimation of the mutual inductance between transmitting and receiving coils for Dynamic Wireless Power Transfer Systems (DWPTSs) is implemented. To this end, a Convolutional Neural Network (CNN) is used; an image representing the geometry [...] Read more.

In this paper, an innovative approach for the fast estimation of the mutual inductance between transmitting and receiving coils for Dynamic Wireless Power Transfer Systems (DWPTSs) is implemented. To this end, a Convolutional Neural Network (CNN) is used; an image representing the geometry of two coils that are partially misaligned is the input of the CNN, while the output is the corresponding inductance value. Finite Element Analyses are used for the computation of the inductance values needed for CNN training. This way, thanks to a fast and accurate inductance estimated by the CNN, it is possible to properly manage the power converter devoted to charge the battery, avoiding the wind up of its controller when it attempts to transfer power in poor coupling conditions. Full article

(This article belongs to the Topic Advanced Electric Vehicle Technology)

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16 pages, 1221 KiB  

Open AccessArticle

Technological Alternatives for Electric Propulsion Systems in the Waterway Sector

by John E. Candelo-Beccera, Leonardo Bohórquez Maldonado, Edwin Paipa Sanabria, Hernán Vergara Pestana and José Jiménez García

Energies 2023, 16(23), 7700; https://doi.org/10.3390/en16237700 - 22 Nov 2023

Cited by 3 | Viewed by 3666

Abstract

The trend in the development of maritime and river propulsion systems is to make a transition from hydrocarbon to more environmentally friendly solutions. This contributes positively to the solution of the problems identified by the International Maritime Organization (IMO) regarding the high emissions [...] Read more.

The trend in the development of maritime and river propulsion systems is to make a transition from hydrocarbon to more environmentally friendly solutions. This contributes positively to the solution of the problems identified by the International Maritime Organization (IMO) regarding the high emissions of polluting gases emitted by maritime transportation. Currently, there is a wide variety of systems available due to the development of mobility technologies focused on decarbonization. This paper presents an analysis of technological alternatives for boats with electromobility applications and propulsion systems in the waterway field. First, a description of the operation of boats with electric motors, the different energy sources, and the alternative propulsion options is presented. Then, the electromobility technologies are characterized, analyzing the different configurations between the power source and the propulsion system. The results show a comparative table of technologies and their advantages and disadvantages. In addition, the most environmentally friendly technologies present significant challenges for large-scale implementation due to their recent development. In the short term, hybrid systems technologies present advantages over the others, as current systems are available, with the addition of equipment with higher efficiency and lower environmental impact. Full article

(This article belongs to the Topic Advanced Electric Vehicle Technology)

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25 pages, 13842 KiB  

Open AccessArticle

Research into the Peculiarities of the Individual Traction Drive Nonlinear System Oscillatory Processes

by Alexander V. Klimov, Baurzhan K. Ospanbekov, Andrey V. Keller, Sergey S. Shadrin, Daria A. Makarova and Yury M. Furletov

World Electr. Veh. J. 2023, 14(11), 316; https://doi.org/10.3390/wevj14110316 - 20 Nov 2023

Cited by 6 | Viewed by 1801

Abstract

Auto-oscillations may occur in moving vehicles in the area where the tire interacts with the support base. The parameters of such oscillations depend on the sliding velocity in the contact patch. As they negatively affect the processes occurring in the electric drive and [...] Read more.

Auto-oscillations may occur in moving vehicles in the area where the tire interacts with the support base. The parameters of such oscillations depend on the sliding velocity in the contact patch. As they negatively affect the processes occurring in the electric drive and the mechanical transmission, reducing their energy efficiency, such processes can cause failures in various elements. This paper aims to conduct a theoretical study into the peculiarities of oscillatory processes in the nonlinear system and an experimental study of the auto-oscillation modes of an individual traction drive. It presents the theoretical basis used to analyze the peculiarities of oscillation processes, including their onset and course, the results of simulation mathematical modeling and the experimental studies into the oscillation phenomena in the movement of the vehicle towards the supporting base. The practical value of this study lies in the possibility to use the results in the development of algorithms for the exclusion of auto-oscillation phenomena in the development of vehicle control systems, as well as to use the auto-oscillation processes onset and course analysis methodology to design the electric drive of the driving wheels. Full article

(This article belongs to the Topic Advanced Electric Vehicle Technology)

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18 pages, 5295 KiB  

Open AccessArticle

Real-Time Energy Management Strategy of Hydrogen Fuel Cell Hybrid Electric Vehicles Based on Power Following Strategy–Fuzzy Logic Control Strategy Hybrid Control

by Ke Zou, Wenguang Luo and Zhengjie Lu

World Electr. Veh. J. 2023, 14(11), 315; https://doi.org/10.3390/wevj14110315 - 20 Nov 2023

Cited by 11 | Viewed by 2810

Abstract

Fuel cell hybrid electric vehicles have the advantages of zero emission, high efficiency and fast refuelling, etc. and are one of the key directions for vehicle development. The energy management problem of fuel cell hybrid electric vehicles is the key technology for power [...] Read more.

Fuel cell hybrid electric vehicles have the advantages of zero emission, high efficiency and fast refuelling, etc. and are one of the key directions for vehicle development. The energy management problem of fuel cell hybrid electric vehicles is the key technology for power distribution. The traditional power following strategy has the advantage of a real-time operation, but the power correction is usually based only on the state of charge of a lithium battery, which causes the operating point of the fuel cell to be in the region of a low efficiency. To solve this problem, this paper proposes a hybrid power-following-fuzzy control strategy, where a fuzzy logic control strategy is used to optimise the correction module based on the power following strategy, which regulates the state of charge while correcting the output power of the fuel cell towards the efficient operating point. The results of the joint simulation with Matlab + Advisor under the Globally Harmonised Light Vehicle Test Cycle Conditions show that the proposed strategy still ensures the advantages of real-time energy management, and for the hydrogen fuel cell, the hydrogen consumption is reduced by 13.5% and 4.1% compared with the power following strategy and the fuzzy logic control strategy, and the average output power variability is reduced by 14.6% and 5.1%, respectively, which is important for improving the economy of the whole vehicle and prolonging the lifetime of fuel cell. Full article

(This article belongs to the Topic Advanced Electric Vehicle Technology)

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16 pages, 3070 KiB  

Open AccessArticle

Adaptation of Deep Network in Transfer Learning for Estimating State of Health in Electric Vehicles during Operation

by Wenbin Zheng, Xinyu Zhou, Chenyu Bai, Di Zhou and Ping Fu

Batteries 2023, 9(11), 547; https://doi.org/10.3390/batteries9110547 - 7 Nov 2023

Cited by 6 | Viewed by 2358

Abstract

Battery state of health (SOH) is a significant metric for evaluating battery life and predicting battery safety. Currently, SOH research is largely based on laboratory data, with a dearth of research on electric vehicle (EV) operating data. Due to the difficulty in obtaining [...] Read more.

Battery state of health (SOH) is a significant metric for evaluating battery life and predicting battery safety. Currently, SOH research is largely based on laboratory data, with a dearth of research on electric vehicle (EV) operating data. Due to the difficulty in obtaining complete charge data under EV operating conditions, this study presents a SOH estimation method utilizing deep network adaptation. First, a data-driven approach is employed to extract voltage, current, state of charge (SOC), and incremental capacity (IC) data features. To compensate for the lack of aging information in the EV operation data domain, transfer learning is employed to construct the SOH estimation model. Additionally, to resolve inconsistent data distribution between the source laboratory battery data domain and the target EV operation data domain, an adaptive layer is added to the network, and adaptation of deep network (ADN) is utilized to enhance the model’s performance. Finally, the model is validated using electric bus operational data. Results indicate that this model’s average Mean Absolute Error (MAE) is less than 3.0%, and, compared to support vector machine (SVM) regression and Gaussian Process Regression (GPR) algorithms, the MAE is reduced by 27.7% and 38.4%, respectively. Full article

(This article belongs to the Topic Advanced Electric Vehicle Technology)

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18 pages, 8494 KiB  

Open AccessArticle

Assessment of an Electric Vehicle Drive Cycle in Relation to Minimised Energy Consumption with Driving Behaviour: The Case of Addis Ababa, Ethiopia, and Its Suburbs

by Tatek Mamo, Girma Gebresenbet, Rajendiran Gopal and Bisrat Yoseph

World Electr. Veh. J. 2023, 14(11), 302; https://doi.org/10.3390/wevj14110302 - 31 Oct 2023

Viewed by 2849

Abstract

Battery electric vehicles (BEV) are suitable alternatives for achieving energy independence and meeting the criteria for reducing greenhouse emissions in the transportation sector. Evaluating their performance and energy consumption in the real-data driving cycle (DC) is important. The purpose of this work is [...] Read more.

Battery electric vehicles (BEV) are suitable alternatives for achieving energy independence and meeting the criteria for reducing greenhouse emissions in the transportation sector. Evaluating their performance and energy consumption in the real-data driving cycle (DC) is important. The purpose of this work is to develop a BEV DC for the interlinked urban and suburban route of Addis Ababa (AA) in Ethiopia. In this study, a new approach of micro-trip random selection-to-rebuild with behaviour split (RSBS) was implemented, and its effectiveness was compared via the k-means clustering method. When comparing the statistical distribution of velocity and acceleration with measured real data, the RSBS cycle shows a minimum error of 2% and 2.3%, respectively. By splitting driving behaviour, aggressive drivers were found to consume more energy because of frequent panic stops and subsequent acceleration. In braking mode, coast drivers were found to improve the regenerative braking possibility and efficiency, which can extend the range by 10.8%, whereas aggressive drivers could only achieve 3.9%. Also, resynthesised RSBS with the percentage of behaviour split and its energy and power consumption were compared with standard cycles. A significant reduction of 14.57% from UDDS and 8.9% from WLTC-2 in energy consumption was achieved for the AA and its suburbs DC, indicating that this DC could be useful for both the city and suburbs. Full article

(This article belongs to the Topic Advanced Electric Vehicle Technology)

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21 pages, 2743 KiB  

Open AccessArticle

Parameter Optimization of the Power and Energy System of Unmanned Electric Drive Chassis Based on Improved Genetic Algorithms of the KOHONEN Network

by Weina Wang, Shiwei Xu, Hong Ouyang and Xinyu Zeng

World Electr. Veh. J. 2023, 14(9), 260; https://doi.org/10.3390/wevj14090260 - 14 Sep 2023

Viewed by 1504

Abstract

For unmanned electric drive chassis parameter optimization problems, an unmanned electric drive chassis model containing power systems and energy systems was built using CRUISE, and as the traditional genetic algorithm is prone to falling into the local optima, an improved isolation niche genetic [...] Read more.

For unmanned electric drive chassis parameter optimization problems, an unmanned electric drive chassis model containing power systems and energy systems was built using CRUISE, and as the traditional genetic algorithm is prone to falling into the local optima, an improved isolation niche genetic algorithm based on KOHONEN network clustering (KIGA) is proposed. The simulation results show that the proposed KIGA can reasonably divide the initial niche populations. Compared with the traditional genetic algorithm (GA) and the isolation niche genetic algorithm (IGA), KIGA can achieve faster convergence and a better global search ability. The comprehensive performance of the unmanned electric drive chassis in terms of power and economy was increased by 8.26% with a set of better solutions. The results show that simultaneous power system and energy system parameter optimization can enhance unmanned electric drive chassis performance and that KIGA is an efficient method for optimizing the parameters of unmanned electric drive chassis. Full article

(This article belongs to the Topic Advanced Electric Vehicle Technology)

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31 pages, 9970 KiB  

Open AccessFeature PaperArticle

Real-Time Implementation and Control of Multi-Source Electric Vehicle Traction Motor under Various Drive Conditions

by Achikkulath Prasanthi, Hussain Shareef, Rachid Errouissi, Madathodika Asna and Addy Wahyudie

Energies 2023, 16(18), 6447; https://doi.org/10.3390/en16186447 - 6 Sep 2023

Cited by 2 | Viewed by 1546

Abstract

The hybridization of multiple energy sources is crucial for electric vehicles to deliver the same performance as modern fossil fuel-based automobiles. This paper presents a torque and speed control strategy for an electric vehicle DC traction motor by regulating the power flow from [...] Read more.

The hybridization of multiple energy sources is crucial for electric vehicles to deliver the same performance as modern fossil fuel-based automobiles. This paper presents a torque and speed control strategy for an electric vehicle DC traction motor by regulating the power flow from two energy sources, namely battery and supercapacitor systems, to resist unpredictable disturbances. A control system with three control loops is applied to regulate the speed of the traction motor. The outer speed control loop is a nonlinear state feedback controller with a disturbance observer, which is capable of handling non-linear systems with unpredictable disturbances. The inner voltage and current control loop are precisely tuned PI controllers. Using an adaptive energy management method that varies depending on the state of charge of the source, the total reference current needed to support the load demand is split between two sources. A laboratory prototype system model is generated, and the performance is analyzed under motoring and regenerative braking conditions. For monitoring and controlling the system, the dSPACE DS1202 real-time simulator is employed. The performance of the proposed control system is evaluated and it is found that the settling time to settle within a tolerance band of 1% is 0.75 s. Full article

(This article belongs to the Topic Advanced Electric Vehicle Technology)

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23 pages, 4719 KiB  

Open AccessReview

Recent Advances in Battery Pack Polymer Composites

by Brian Azzopardi, Abdul Hapid, Sunarto Kaleg, Sudirja, Djulia Onggo and Alexander C. Budiman

Energies 2023, 16(17), 6223; https://doi.org/10.3390/en16176223 - 27 Aug 2023

Cited by 9 | Viewed by 4832

Abstract

The use of a polymer composite material in electric vehicles (EVs) has been extensively investigated, especially as a substitute for steel. The key objective of this manuscript is to provide an overview of the existing and emerging technologies related to the application of [...] Read more.

The use of a polymer composite material in electric vehicles (EVs) has been extensively investigated, especially as a substitute for steel. The key objective of this manuscript is to provide an overview of the existing and emerging technologies related to the application of such a composite, especially for battery pack applications, in which its high strength-to-weight ratio, corrosion resistance, design flexibility, and durability are advantageous compared to any metal in general. This study explores the key considerations in the design and fabrication of composites, including base material selection, structural design optimization, reinforcement material, manufacturing processes, and integration with battery systems. The paper also discusses the performance characteristics of composite battery pack structures, such as mechanical properties, thermal management, safety aspects, and environmental sustainability. This study aims to contribute to sharpening the direction of future research and innovations in the area of composite battery pack technology. Full article

(This article belongs to the Topic Advanced Electric Vehicle Technology)

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21 pages, 5575 KiB  

Open AccessArticle

Assessing the Dynamic Performance and Energy Efficiency of  Pure Electric Car with Optimal Gear Shifting

by Qiang Zhao, Shengming Zhou, Yongheng Yue, Bohang Liu, Qin Xie and Na Zhang

Energies 2023, 16(16), 6044; https://doi.org/10.3390/en16166044 - 18 Aug 2023

Viewed by 2048

Abstract

Traditional pure electric cars generally adopt single-speed transmission for cost consideration. However, with the renewal and iteration of technology, small electric cars are all developed in the direction of power performance and environmental protection. Gear shifting makes it possible for the motor to [...] Read more.

Traditional pure electric cars generally adopt single-speed transmission for cost consideration. However, with the renewal and iteration of technology, small electric cars are all developed in the direction of power performance and environmental protection. Gear shifting makes it possible for the motor to work in a more efficient range, which possibly improves the performance of the entire powertrain. In this paper, a small electric car is designed, its power parameters are matched, and the energy-saving space and effect brought by adding multiple-gear shifting transmissions are discussed. To begin, the power-matching design was carried out, and then the transmission ratio was determined by particle swarm optimization. Finally, the power performance and fuel economy of this designed car equipped with different types of transmissions were analyzed and compared through simulation experiments. The results show that the electric car equipped with two-speed transmission has improvements in most important indicators, among which the acceleration time of 0 to 100 km/h is decreased by 17.7%, and the power consumption is reduced by 1.8%. To sum up, the feasibility of applying multiple-gear shifting to small electric cars is verified, and the experimental results provide a valuable reference for the development of electric cars. Full article

(This article belongs to the Topic Advanced Electric Vehicle Technology)

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19 pages, 17433 KiB  

Open AccessEditor’s ChoiceArticle

Electric Analysis of the Maritime Application High-Frequency Magnetohydrodynamic Thruster

by Kin Lung Jerry Kan, Ka Wai Eric Cheng and Hai-Chen Zhuang

Energies 2023, 16(16), 6021; https://doi.org/10.3390/en16166021 - 17 Aug 2023

Cited by 4 | Viewed by 2407

Abstract

A magnetohydrodynamic (MHD) thruster is the next-generation electric jet engine for maritime applications. It eliminates the moving mechanical components that make the noises and reduces physical harm to sea creatures. This paper finds that aluminum electrodes have higher conductivity and less capacitive value [...] Read more.

A magnetohydrodynamic (MHD) thruster is the next-generation electric jet engine for maritime applications. It eliminates the moving mechanical components that make the noises and reduces physical harm to sea creatures. This paper finds that aluminum electrodes have higher conductivity and less capacitive value in a KCl solution than the 316 stainless steel and zinc in MHD applications. Further, the AC operation can mitigate the power loss during electrolysis and power loss while on the water. The new optimal coil design with the enclosed-type ferrite layout of the MHD thruster is addressed by this simulation study. The AC operation and electric drive with a Lorentz force analysis will be demonstrated. Lastly, a verification experiment that pushes the KCl solution at 3 cm/s will be interpreted by the prototype to display the electric operation detail. Full article

(This article belongs to the Topic Advanced Electric Vehicle Technology)

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21 pages, 1786 KiB  

Open AccessArticle

Computer Vision for DC Partial Discharge Diagnostics in Traction Battery Systems

by Ronan Sangouard, Ivo Freudenberg and Maximilian Kertel

World Electr. Veh. J. 2023, 14(8), 222; https://doi.org/10.3390/wevj14080222 - 15 Aug 2023

Viewed by 1772

Abstract

The tendency towards thin insulation layers in traction battery systems presents new challenges regarding insulation quality and service life. Phase-resolved DC partial discharge diagnostics can help to identify defects. Furthermore, different root causes are characterized by different patterns. However, to industrialize the procedure, [...] Read more.

The tendency towards thin insulation layers in traction battery systems presents new challenges regarding insulation quality and service life. Phase-resolved DC partial discharge diagnostics can help to identify defects. Furthermore, different root causes are characterized by different patterns. However, to industrialize the procedure, there is the need for an automatic pattern recognition system. This paper shows how methods from computer vision can be applied to DC partial discharge diagnostics. The derived system is self-learning, needs no tedious manual calibration, and can identify defects within a matter of seconds. Thus, the combination of computer vision and phase-resolved DC partial discharge diagnostics provides an industrializable system for detecting insulation faults and identifying their root causes. Full article

(This article belongs to the Topic Advanced Electric Vehicle Technology)

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24 pages, 10728 KiB  

Open AccessEditor’s ChoiceArticle

Performance Prediction of a 4WD High-Performance Electric Vehicle Using a Model-Based Torque-Vectoring Approach

by Rafael Serralvo Neto, Joao Bruno Palermo, Renato Giacomini, Michele Rodrigues, Fabio Delatore, Giovana Betoni Rossi, Milene Galeti and Rudolf Theoderich Bühler

World Electr. Veh. J. 2023, 14(7), 183; https://doi.org/10.3390/wevj14070183 - 13 Jul 2023

Cited by 1 | Viewed by 3794

Abstract

Electric vehicles (EVs) enable the integration of powertrains with multiple motors, allowing for the adjustment of torque delivered to each wheel. This approach permits the implementation of torque vectoring techniques (TV) to enhance the vehicle’s stability and cornering response, providing better control of [...] Read more.

Electric vehicles (EVs) enable the integration of powertrains with multiple motors, allowing for the adjustment of torque delivered to each wheel. This approach permits the implementation of torque vectoring techniques (TV) to enhance the vehicle’s stability and cornering response, providing better control of yaw moments. This study utilizes comprehensive telemetry data and an advanced simulator model to assess the influence of torque vectoring (TV) on a Formula SAE (Society of Automotive Engineers) competition vehicle. The telemetry data were collected from a fully instrumented 2WD car, which was then employed to calibrate the simulation model. The calibrated model was subsequently utilized to predict the performance enhancements that could be achieved by implementing a 4WD system. The methodology proved to be a valuable contribution to vehicle design development. This approach also helps evaluate the potential advantages of torque vectoring for drivers with limited experience. Full article

(This article belongs to the Topic Advanced Electric Vehicle Technology)

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16 pages, 5317 KiB  

Open AccessArticle

Performance Analysis of Two Receiver Arrangements for Wireless Battery Charging System

by Abhay Kumar, Rupesh Kumar Jha, Manuele Bertoluzzo, Chetan B. Khadse, Swati Jaiswal, Gourang Mulay and Amritansh Sagar

Designs 2023, 7(4), 92; https://doi.org/10.3390/designs7040092 - 6 Jul 2023

Cited by 1 | Viewed by 1512

Abstract

Two different arrangements for Wireless Battery Charging Systems (WBCSs) with a series-parallel resonant topology have been analyzed in this paper. The first arrangement charges the battery by controlling the receiver-side rectifier current and voltage without a chopper, while the second arrangement charges it [...] Read more.

Two different arrangements for Wireless Battery Charging Systems (WBCSs) with a series-parallel resonant topology have been analyzed in this paper. The first arrangement charges the battery by controlling the receiver-side rectifier current and voltage without a chopper, while the second arrangement charges it with a chopper while keeping the chopper input voltage constant. The comparison of these two arrangements is made based on their performance on various figures of merit, such as the sizing factor of both the supply voltage source and receiver coil, overall system efficiency, power-transfer ratio, receiver efficiency, and cost estimation. Later, the simulated study is verified by the experimental setup designed to charge the electric vehicle. Full article

(This article belongs to the Topic Advanced Electric Vehicle Technology)

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27 pages, 5956 KiB  

Open AccessArticle

H–H Configuration of Modular EV Powertrain System Based on the Dual Three-Phase BLDC Motor and Battery-Supercapacitor Power Supply System

by Ihor Shchur and Valentyn Turkovskyi

World Electr. Veh. J. 2023, 14(7), 173; https://doi.org/10.3390/wevj14070173 - 29 Jun 2023

Cited by 1 | Viewed by 1662

Abstract

A modular approach to the construction of electric machines, drive systems, power supply systems is a new direction of modern technology development. Especially, the modular approach is promising for electric vehicles due to such positive aspects as increased efficiency, fault tolerance, overall reliability, [...] Read more.

A modular approach to the construction of electric machines, drive systems, power supply systems is a new direction of modern technology development. Especially, the modular approach is promising for electric vehicles due to such positive aspects as increased efficiency, fault tolerance, overall reliability, safety, enhanced control capabilities, etc. In this work, the modular approach is comprehensively applied to an EV powertrain system, which includes a dual three-phase (DTP) BLDC motor with two machine modules of an asymmetric configuration, two battery modules and a supercapacitor module (SCM). The proposed H–H configuration of modular EV powertrain system includes four voltage source inverters that combine the power modules with the open ends of the windings (OEW) of the module machine armature, and provide control of their operation. Based on the developed mode system of the OEW machine module operation for EV traction and braking, a general control algorithm for the proposed configuration of the modular EV powertrain system has been developed. It combines the control of the operating modes with the functions of maintaining the required SOC level of the SCM and equalizing the SOCs of the two battery modules. The conducted simulation and experimental studies confirmed the workability and effectiveness of the proposed solutions. Full article

(This article belongs to the Topic Advanced Electric Vehicle Technology)

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19 pages, 7947 KiB  

Open AccessArticle

Coordinated Control Strategy for Drive Mode Switching of Double Rotor In-Wheel Motor Based on MPC and Control Allocation

by Junmin Li, Junchang Wang, Jianhao Liu and Chongyang Ren

World Electr. Veh. J. 2023, 14(5), 132; https://doi.org/10.3390/wevj14050132 - 20 May 2023

Cited by 3 | Viewed by 2001

Abstract

To overcome the problems existing in the practical application of traditional in-wheel motors used for electric vehicles, an integrated double rotor in-wheel motor was proposed, which can realize three drive modes to meet variable operating condition requirements of the vehicle. The process of [...] Read more.

To overcome the problems existing in the practical application of traditional in-wheel motors used for electric vehicles, an integrated double rotor in-wheel motor was proposed, which can realize three drive modes to meet variable operating condition requirements of the vehicle. The process of switching between different drive modes affects the ride comfort of a vehicle. Taking the mode switching from a single inner motor drive to a dual-motor coupling drive as a research object, a dynamic modeling method of drive mode switching based on the switching system was proposed. According to the critical conditions of each state transition, the switching rules expressed by the segmental constant function were designed. At the engagement stage of electromagnetic clutch II, the torque coordination control strategy based on model predictive control (MPC) and control allocation was proposed. The simulation results show that the proposed strategy can effectively reduce the impact degree of a vehicle and the slipping-friction work of the clutch on the premise of ensuring the fast response of mode switching and the steady increase in vehicle speed. The switching quality of the mode-switching process is effectively improved. In addition, the drive mode switching control of the double rotor in-wheel motor prototype was tested, which proves its ability to operate in multi-drive mode. Full article

(This article belongs to the Topic Advanced Electric Vehicle Technology)

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25 pages, 3824 KiB  

Open AccessEditor’s ChoiceArticle

Location of the Interurban Fast Charging Infrastructure for Electric Vehicles Using the Methodology for Calculating the Maximum Distance between Fast Charges (MDFC) and Simulation: A Case Study in Ecuador

by Luis Buenaño, Hugo Torres and Efrén Fernández

World Electr. Veh. J. 2023, 14(5), 129; https://doi.org/10.3390/wevj14050129 - 19 May 2023

Cited by 1 | Viewed by 2875

Abstract

This study determines the location of the minimum fast charging infrastructure for electric vehicles in the interurban route Riobamba–Quito in Ecuador using the methodology of the maximum distance between fast charges (MDFC). From the application of the method, a MDFC of 60 km [...] Read more.

This study determines the location of the minimum fast charging infrastructure for electric vehicles in the interurban route Riobamba–Quito in Ecuador using the methodology of the maximum distance between fast charges (MDFC). From the application of the method, a MDFC of 60 km and a basic highway charging infrastructure (BHCI) of six stations are obtained. The location is calculated by measuring the MDFC on the road using the desktop application Google Earth Pro. The proposal is validated by means of a mathematical model in Simulink, and two simulation scenarios are proposed. In the first one, the initial state of charge (SOC) is 95% and represents an EV with complete charging patterns, while in the second one, the initial SOC is 65% and represents incomplete charging patterns. The results indicate that for both simulation scenarios, the EV KIA SOUL 2016 can perform the specified round-trip routes using the proposed BHCI performing two fast charges of 20 min each way. In all cases, SOC values during operation remain above 20%. The results obtained allow us to establish that the proposed BHCI and its location are sufficient to allow the studied EV to complete the route. Full article

(This article belongs to the Topic Advanced Electric Vehicle Technology)

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21 pages, 1122 KiB  

Open AccessArticle

Exploring Factors Affecting People’s Willingness to Use a Voice-Based In-Car Assistant in Electric Cars: An Empirical Study

by Jing Liu, Fucheng Wan, Jinzhi Zou and Jiaqi Zhang

World Electr. Veh. J. 2023, 14(3), 73; https://doi.org/10.3390/wevj14030073 - 14 Mar 2023

Cited by 6 | Viewed by 5184

Abstract

Voice-based digital assistants are growing in popularity and have been acknowledged as a crucial part of in-car interaction. Currently, academic attention is being paid to various voice assistant scenarios. However, sparse literature focuses on the adoption of voice assistants within the in-vehicle context. [...] Read more.

Voice-based digital assistants are growing in popularity and have been acknowledged as a crucial part of in-car interaction. Currently, academic attention is being paid to various voice assistant scenarios. However, sparse literature focuses on the adoption of voice assistants within the in-vehicle context. The objective of this paper is to examine key factors influencing people’s willingness to use voice assistance in electric cars. First, eight general variables were identified based on the literature review, as well as four demographic variables. These factors were then integrated to construct a hypothetical research model. After that, we carried out an empirical study to examine the structural relationships in the model based on the questionnaire survey results (N = 427). The hypothesis testing results indicated that most path relationships among variables were validated. Finally, we discussed the research findings and developed corresponding design strategies to enhance user acceptance towards in-car voice assistants, both from designers’ and car enterprises’ viewpoints. This article offers valuable theoretical and practical implications for the development of such technologies. Full article

(This article belongs to the Topic Advanced Electric Vehicle Technology)

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24 pages, 4851 KiB  

Open AccessArticle

Vertical-Longitudinal Coupling Effect Investigation and System Optimization for a Suspension-In-Wheel-Motor System in Electric Vehicle Applications

by Ze Zhao, Lei Zhang, Jianyang Wu, Liang Gu and Shaohua Li

Sustainability 2023, 15(5), 4168; https://doi.org/10.3390/su15054168 - 25 Feb 2023

Cited by 2 | Viewed by 2194

Abstract

In-wheel-motor-drive electric vehicles have attracted enormous attention due to its potentials of improving vehicle performance and safety. Road surface roughness results in forced vibration of in-wheel-motor (IWM) and thus aggravates the unbalanced electric magnetic force (UEMF) between its rotor and stator. This can [...] Read more.

In-wheel-motor-drive electric vehicles have attracted enormous attention due to its potentials of improving vehicle performance and safety. Road surface roughness results in forced vibration of in-wheel-motor (IWM) and thus aggravates the unbalanced electric magnetic force (UEMF) between its rotor and stator. This can further compromise vertical and longitudinal vehicle dynamics. This paper presents a comprehensive study to reveal the coupled vertical–longitudinal effect on suspension-in-wheel-motor systems (SIWMS) along with a viable optimization procedure to improve ride comfort and handling performance. First, a UEMF model is established to analyze the mechanical–electrical–magnetic coupling relationship inside an IWM. Then a road–tire–ring force (RTR) model that can capture the transient tire–road contact patch and tire belt deformation is established to accurately describe the road–tire and tire–rotor forces. The UEMF and the RTRF model are incorporated into the quarter-SIWMS model to investigate the coupled vertical–longitudinal vehicle dynamics. Through simulation studies, a comprehensive evaluation system is put forward to quantitatively assess the effects during braking maneuvers under various road conditions. The key parameters of the SIWMS are optimized via a multi-optimization method to reduce the adverse impact of UEMF. Finally, the multi-optimization method is validated in a virtual prototype which contains a high-fidelity multi-body model. The results show that the longitudinal acceleration fluctuation rate and the slip ratio signal-to-noise ratio are reduced by 5.07% and 6.13%, respectively, while the UEMF in the vertical and longitudinal directions varies from 22.2% to 34.7%, respectively, and is reduced after optimization. Thus, the negative coupling effects of UEMF are minimized while improving the ride comfort and handling performance. Full article

(This article belongs to the Topic Advanced Electric Vehicle Technology)

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17 pages, 763 KiB  

Open AccessArticle

Non-Linear Model Predictive Control Using CasADi Package for Trajectory Tracking of Quadrotor

by Mohamed Elhesasy, Tarek N. Dief, Mohammed Atallah, Mohamed Okasha, Mohamed M. Kamra, Shigeo Yoshida and Mostafa A. Rushdi

Energies 2023, 16(5), 2143; https://doi.org/10.3390/en16052143 - 22 Feb 2023

Cited by 21 | Viewed by 6507

Abstract

In this paper, we present the development of a non-linear model predictive controller for the trajectory tracking of a quadrotor using the CasADi optimization framework. The non-linear dynamic model of the quadrotor was derived using Newton–Euler equations, and the control algorithm and drone [...] Read more.

In this paper, we present the development of a non-linear model predictive controller for the trajectory tracking of a quadrotor using the CasADi optimization framework. The non-linear dynamic model of the quadrotor was derived using Newton–Euler equations, and the control algorithm and drone dynamics were wrapped in Matlab. The proposed controller was tested by simulating the tracking of a 3D helical reference trajectory, and its efficiency was evaluated in terms of numerical performance and tracking accuracy. The results showed that the proposed controller leads to faster computational times, approximately 20 times faster than the Matlab toolbox (nlmpc), and provides better tracking accuracy than both the Matlab toolbox and classical PID controller. The robustness of the proposed control algorithm was also tested and verified under model uncertainties and external disturbances, demonstrating its ability to effectively eliminate tracking errors. Full article

(This article belongs to the Topic Advanced Electric Vehicle Technology)

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18 pages, 24568 KiB  

Open AccessArticle

Temperature Field Analysis and Cooling Structure Optimization for Integrated Permanent Magnet In-Wheel Motor Based on Electromagnetic-Thermal Coupling

by Qiang Wang, Rui Li, Ziliang Zhao, Kui Liang, Wei Xu and Pingping Zhao

Energies 2023, 16(3), 1527; https://doi.org/10.3390/en16031527 - 3 Feb 2023

Cited by 5 | Viewed by 2865

Abstract

Aiming at the impact of heat generation and temperature rise on the driving performance of a permanent magnet (PM) motor, taking the PM in-wheel motor (IWM) for electric vehicles as an object, research is conducted into the temperature distribution of the electromagnetic–thermal effect [...] Read more.

Aiming at the impact of heat generation and temperature rise on the driving performance of a permanent magnet (PM) motor, taking the PM in-wheel motor (IWM) for electric vehicles as an object, research is conducted into the temperature distribution of the electromagnetic–thermal effect and cooling structure optimization. Firstly, the electromagnetic–thermal coupling model considering electromagnetic harmonics is established using the subdomain model and Bertotti’s iron loss separation theory. Combined with the finite element (FE) simulation model established by Ansoft Maxwell software platform, the winding copper loss, stator core loss and PM eddy current loss under the action of complex magnetic flux are analyzed, and the transient temperature distribution of each component is obtained through coupling. Secondarily, the influence of the waterway structure parameters on the heat dissipation effect of the PM-IWM is analyzed by the thermal-fluid coupled relationship. On the basis, the optimization design of waterway structure parameters is carried out to improve the heat dissipation effect of the cooling system based on the proposed chaotic mapping ant colony algorithm with metropolis criterion. The comparison before and after optimization shows that the temperature of key components is significantly improved, the average convection heat transfer coefficient (CHTC) is increased by 23.57%, the peak temperature of stator is reduced from 95.47 °C to 82.73 °C, and the peak temperature of PM is decreased by 14.26%, thus the demagnetization risk in the PM is improved comprehensively. The research results can provide some theoretical and technical support for the structural optimization of water-cooled dissipation in the PM motor. Full article

(This article belongs to the Topic Advanced Electric Vehicle Technology)

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19 pages, 5231 KiB  

Open AccessArticle

A Method for Abnormal Battery Charging Capacity Diagnosis Based on Electric Vehicles Operation Data

by Fang Li, Yongjun Min, Ying Zhang and Chen Wang

Batteries 2023, 9(2), 103; https://doi.org/10.3390/batteries9020103 - 2 Feb 2023

Cited by 4 | Viewed by 3629

Abstract

Overcharging due to an abnormal charging capacity is one of the most common causes of thermal runaway (TR). This study proposes a method for diagnosing abnormal battery charging capacity based on electric vehicle (EV) data. The proposed method can obtain the fault frequency [...] Read more.

Overcharging due to an abnormal charging capacity is one of the most common causes of thermal runaway (TR). This study proposes a method for diagnosing abnormal battery charging capacity based on electric vehicle (EV) data. The proposed method can obtain the fault frequency and output the corresponding state of charge (SOC) when a fault occurs. First, a machine-learning-based data cleaning framework is developed to overcome the limitations of the interpolation method. Then, offline training is implemented, based on big vehicle operation data and an improved Gaussian process regression (GPR). Thereafter, online monitoring of the discrete capacity increment (DCI) is used to identify the abnormal charging capacity. The abnormal charging capacity fault is identified by the absolute error between the GPR outputs and the true DCI, and the thresholds are determined using a Box–Cox transformation with a value of 3$\sigma$. The diagnostic results indicate that the abnormal charging capacity of the TR vehicle is identified two months in advance, and the fault frequency of the abnormal and normal vehicles is 0.5221 and 0.0311, respectively. EV operation data and various methods are used to validate the robustness and applicability of the proposed method. Full article

(This article belongs to the Topic Advanced Electric Vehicle Technology)

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20 pages, 8304 KiB  

Open AccessArticle

Design of a Recommender System with Safe Driving Mode Based on State-of-Function Estimation in Electric Vehicle Drivetrains with Battery/Supercapacitor Hybrid Energy Storage System

by Farshid Naseri, Sepehr Karimi, Ebrahim Farjah and Peyman Setoodeh

Designs 2023, 7(1), 25; https://doi.org/10.3390/designs7010025 - 1 Feb 2023

Cited by 4 | Viewed by 2300

Abstract

The performance of electric vehicle (EV) drivetrains depends on the power capability of individual components, including the battery pack, motor drive, and electric motor. To ensure safety, maximum power must be limited by considering the constraint of the weakest component in the drivetrain. [...] Read more.

The performance of electric vehicle (EV) drivetrains depends on the power capability of individual components, including the battery pack, motor drive, and electric motor. To ensure safety, maximum power must be limited by considering the constraint of the weakest component in the drivetrain. While there exists a large body of work that discusses state-of-power (SoP) estimation for individual components, there is no work that considers all the components’ limiting factors at once. Moreover, research on how to use these limits to adjust the performance at the system level has been rare. In this paper, the SoPs of the components are used to estimate the state-of-function (SoF) of the EV drivetrain. The SoF is defined as the maximum charge/discharge power that can be sourced and/or sunk by the drivetrain without violating the safety limits of its components. The component-level SoP estimations are fulfilled using several digital algorithms based on recursive least-squares (RLS) and Kalman filters (KFs), as well as by taking into account specific limiting conditions such as high driving altitude and ambient temperatures. An EV driven by a hybrid energy storage system based on a battery/supercapacitor, and a permanent-magnet synchronous motor is considered the use case. Based on the drivetrain SoF estimation, we propose two de-rating schemes to ensure that the drivetrain safety limits will be respected: adaptive cruise control and adaptive adjustment of pedal sensitivity. The de-rating schemes are introduced to a so-called recommender system that is implemented in MATLAB/STATEFLOW. The recommender system provides advisory feedback to the driver to switch to a different driving mode to ensure safety. The simulation results over a standard drive cycle using MATLAB/SIMULINK and STATEFLOW show the effectiveness of the proposed design at both component and system levels. The paper also proposes an implementation concept for the integration of the proposed recommender system into the advanced driver assistance system (ASAS). Full article

(This article belongs to the Topic Advanced Electric Vehicle Technology)

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19 pages, 1562 KiB  

Open AccessEditor’s ChoiceArticle

Design and Implementation of Reinforcement Learning for Automated Driving Compared to Classical MPC Control

by Ahmad Reda and József Vásárhelyi

Designs 2023, 7(1), 18; https://doi.org/10.3390/designs7010018 - 29 Jan 2023

Cited by 5 | Viewed by 3288

Abstract

Many classic control approaches have already proved their merits in the automotive industry. Model predictive control (MPC) is one of the most commonly used methods. However, its efficiency drops off with increase in complexity of the driving environment. Recently, machine learning methods have [...] Read more.

Many classic control approaches have already proved their merits in the automotive industry. Model predictive control (MPC) is one of the most commonly used methods. However, its efficiency drops off with increase in complexity of the driving environment. Recently, machine learning methods have been considered an efficient alternative to classical control approaches. Even with successful implementation of reinforcement learning in real-world applications, it is still not commonly used compared to supervised and unsupervised learning. In this paper, a reinforcement learning (RL)-based framework is suggested for application in autonomous driving systems to maintain a safe distance. Additionally, an MPC-based control model is designed for the same task. The behavior of the two controllers is compared and discussed. The trained RL model was deployed on a low-end FPGA-in-the-loop (field-programmable gate array in-the-loop). The results showed that the two controllers responded efficiently to changes in the environment. Specifically, the response of the RL controller was faster, at approximately 1.75 s, than that of the MPC controller, while the MPC provided better overshooting performance (approximately 1.3 m/s less) in terms of following the reference speeds. The reinforcement-learning model showed efficient behavior after being deployed on the FPGA with ($4.9\times {10}^{-6})$ m${}^2/$s as a maximum deviation compared to MATLAB Simulink. Full article

(This article belongs to the Topic Advanced Electric Vehicle Technology)

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17 pages, 3411 KiB  

Open AccessArticle

Research on the Stability Control Strategy of Distributed Electric Vehicles Based on Cooperative Reconfiguration Allocation

by Jian Ou, Dehai Yan, Yong Zhang, Echuan Yang and Dong Huang

World Electr. Veh. J. 2023, 14(2), 31; https://doi.org/10.3390/wevj14020031 - 27 Jan 2023

Cited by 5 | Viewed by 2196

Abstract

Aiming at the problem of body instability caused by actuator failure in a distributed electric vehicle drive system, a fault-tolerant control strategy of longitudinal and lateral force cooperative reconstruction with active steering control was proposed, and a layered control structure was adopted based [...] Read more.

Aiming at the problem of body instability caused by actuator failure in a distributed electric vehicle drive system, a fault-tolerant control strategy of longitudinal and lateral force cooperative reconstruction with active steering control was proposed, and a layered control structure was adopted based on the vehicle model. In the upper controller, the resultant force and torque are calculated according to the vehicle parameter state and MPC algorithm; the lower controller is the cooperative reconfiguration allocation layer, and the minimum tire load rate, longitudinal and lateral force constraints and front wheel angle control are considered. Finally, offline simulation experiments and hardware-in-the-loop experiments are completed to verify the effectiveness and real-time performance of the designed strategy. The results show that the designed strategy can significantly improve the driving stability and safety of the vehicle when the actuator fails. Full article

(This article belongs to the Topic Advanced Electric Vehicle Technology)

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15 pages, 5480 KiB  

Open AccessArticle

Analysis of the Electric Vehicle Charging Stations Effects on the Electricity Network with Artificial Neural Network

by Kadir Olcay and Nurettin Çetinkaya

Energies 2023, 16(3), 1282; https://doi.org/10.3390/en16031282 - 25 Jan 2023

Cited by 9 | Viewed by 3569

Abstract

In this study, the effects of electric vehicles, whose usage rate is increasing day by day in the world, on the existing electricity grid have been studied. EV charging stations and similar non-linear loads cause various harmful effects on power systems such as [...] Read more.

In this study, the effects of electric vehicles, whose usage rate is increasing day by day in the world, on the existing electricity grid have been studied. EV charging stations and similar non-linear loads cause various harmful effects on power systems such as phase imbalances, the effect of harmonic formation, energy quality, voltage, and current imbalance. The study focuses on the harmonic effects of EV charging stations at the point where they are connected to the grid and at lower voltage levels by using IEEE 6-, 14-bus, and 30-bus test power systems. In addition to the existing loads in these grid systems, the effects on the grid as a result of drawing electrical energy from the grid for charging electric vehicles are investigated. These effects have shown how these charging stations on the grid have changed, considering the fact that the number of electric vehicles and the number of charging stations increased over the years when a single electric vehicle provided energy from the grid, and the grid was not renewed. The response of the network to the increase in the load that will occur in addition to the current loads, its harmonic effects, and the effects of the current grid on the increase in the electric vehicle growth rate over the years have been predicted and examined by using artificial neural networks. Solution suggestions are presented for power networks in similar situations. Full article

(This article belongs to the Topic Advanced Electric Vehicle Technology)

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19 pages, 3802 KiB  

Open AccessArticle

Experimental and Numerical Studies on the Effect of Lithium-Ion Batteries’ Shape and Chemistry on Heat Generation

by Piyatida Trinuruk, Warongkorn Onnuam, Nutthanicha Senanuch, Chinnapat Sawatdeejui, Papangkorn Jenyongsak and Somchai Wongwises

Energies 2023, 16(1), 264; https://doi.org/10.3390/en16010264 - 26 Dec 2022

Cited by 6 | Viewed by 2995

Abstract

Data sets of internal resistances and open-circuit voltage of a particular battery are needed in ANSYS Fluent program to predict the heat generation accurately. However, one set of available data, called Chen’s original, does not cover all types and shapes of batteries. Therefore, [...] Read more.

Data sets of internal resistances and open-circuit voltage of a particular battery are needed in ANSYS Fluent program to predict the heat generation accurately. However, one set of available data, called Chen’s original, does not cover all types and shapes of batteries. Therefore, this research was intended to study the effects of shapes and polarization chemistries on heat generation in Li-ion batteries. Two kinds of material chemistries (nickel manganese cobalt oxide, NMC, and lithium iron phosphate, LFP) and three forms (cylindrical, pouch, and prismatic) were studied and validated with the experiment. Internal resistance was unique to each cell battery. Differences in shapes affected the magnitude of internal resistance, affecting the amount of heat generation. Pouch and prismatic cells had lower internal resistance than cylindrical cells. This may be the result of the forming pattern, in which the anode, cathode, and separator are rolled up, making electrons difficult to move. In contrast, the pouch and prismatic cells are formed as sandwich layers, resulting in electrons moving easily and lowering the internal resistance. The shapes and chemistries did not impact the entropy change. All batteries displayed exothermic behavior during a lower SOC that gradually became endothermic behavior at around 0.4 SOC onwards. Full article

(This article belongs to the Topic Advanced Electric Vehicle Technology)

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25 pages, 5934 KiB  

Open AccessFeature PaperEditor’s ChoiceReview

A Comprehensive Review of Machine-Integrated Electric Vehicle Chargers

by Uvais Mustafa, Rishad Ahmed, Alan Watson, Patrick Wheeler, Naseer Ahmed and Parmjeet Dahele

Energies 2023, 16(1), 129; https://doi.org/10.3390/en16010129 - 22 Dec 2022

Cited by 7 | Viewed by 5472

Abstract

Electric Vehicles are becoming increasingly popular due to their environment friendly operation. As the demand for electric vehicles increases, it has become quite important to explore their charging strategies. Since charging and traction do not normally occur simultaneously and the power electronics converters [...] Read more.

Electric Vehicles are becoming increasingly popular due to their environment friendly operation. As the demand for electric vehicles increases, it has become quite important to explore their charging strategies. Since charging and traction do not normally occur simultaneously and the power electronics converters for both operations have some similarities, the practice of integrating both charging and traction systems is becoming popular. These types of chargers are termed ‘Integrated Chargers’. The aim of this paper is to review the available literature on the integrated chargers and present a critical analysis of the pros and cons of different integrated charging architectures. Integrated chargers for electric vehicles with three-phase permanent magnet synchronous machines, multi-phase machines and switched reluctance machines were compared. The challenges with the published integrated chargers and the future aspect of the work were been discussed. Full article

(This article belongs to the Topic Advanced Electric Vehicle Technology)

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17 pages, 6676 KiB  

Open AccessArticle

Recurrent Neural Network-Based Nonlinear Optimization for Braking Control of Electric Vehicles

by Jiapeng Yan, Huifang Kong and Zhihong Man

Energies 2022, 15(24), 9486; https://doi.org/10.3390/en15249486 - 14 Dec 2022

Cited by 3 | Viewed by 1912

Abstract

In this paper, electro-hydraulic braking (EHB) force allocation for electric vehicles (EVs) is modeled as a constrained nonlinear optimization problem (NOP). Recurrent neural networks (RNNs) are advantageous in many folds for solving NOPs, yet existing RNNs’ convergence usually requires convexity with calculation of [...] Read more.

In this paper, electro-hydraulic braking (EHB) force allocation for electric vehicles (EVs) is modeled as a constrained nonlinear optimization problem (NOP). Recurrent neural networks (RNNs) are advantageous in many folds for solving NOPs, yet existing RNNs’ convergence usually requires convexity with calculation of second-order partial derivatives. In this paper, a recurrent neural network-based NOP solver (RNN-NOPS) is developed. It is seen that the RNN-NOPS is designed to drive all state variables to asymptotically converge to the feasible region, with loose requirement on the NOP’s first-order partial derivative. In addition, the RNN-NOPS’s equilibria are proved to meet Karush–Kuhn–Tucker (KKT) conditions, and the RNN-NOPS behaves with a strong robustness against the violation of the constraints. The comparative studies are conducted to show RNN-NOPS’s advantages for solving the EHB force allocation problem, it is reported that the overall regenerative energy of RNN-NOPS is 15.39% more than that of the method for comparison under SC03 cycle. Full article

(This article belongs to the Topic Advanced Electric Vehicle Technology)

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9 pages, 3035 KiB  

Open AccessArticle

Simple Diagnosis of Lifetime Characteristics of Used Automotive Storage Battery Cells

by Norihiro Shimoi and Kazuyuki Tohji

Energies 2022, 15(23), 8814; https://doi.org/10.3390/en15238814 - 22 Nov 2022

Cited by 3 | Viewed by 1298

Abstract

In constructing a nanogrid for the effective use of renewable energy, such as solar power, the use of storage batteries is considered as a stabilizer for capturing renewable energy and outputting it in an energy-saving manner. Storage batteries that are included in a [...] Read more.

In constructing a nanogrid for the effective use of renewable energy, such as solar power, the use of storage batteries is considered as a stabilizer for capturing renewable energy and outputting it in an energy-saving manner. Storage batteries that are included in a battery management system that includes their reuse in a vehicle are expected to be discharged into the market in large quantities over their long lifetime. Storage battery modules obtained from an unspecified number of electric vehicles (EVs), hybrid vehicles (HVs) and plug-in hybrid vehicles (PHVs) will vary in their charge/discharge capacity from module to module and it is crucial to determine the stability in terms of the state of charge and the state of health of the modules before their reuse. However, in an automotive storage battery module, multiple battery cells are connected in series or in parallel, and there is no established method of managing the variation in the output of each battery cell. Therefore, in this study, we propose an accurate charge–discharge state estimation technique for each cell using impedance characteristic evaluation based on an electrochemical method as a simple and quick method of grasping the charge–discharge performance of storage batteries equipped in a vehicle. Full article

(This article belongs to the Topic Advanced Electric Vehicle Technology)

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18 pages, 3748 KiB  

Open AccessReview

Driver Identification Methods in Electric Vehicles, a Review

by Dengfeng Zhao, Junjian Hou, Yudong Zhong, Wenbin He, Zhijun Fu and Fang Zhou

World Electr. Veh. J. 2022, 13(11), 207; https://doi.org/10.3390/wevj13110207 - 3 Nov 2022

Cited by 5 | Viewed by 3184

Abstract

Driver identification is very important to realizing customized service for drivers and road traffic safety for electric vehicles and has become a research hotspot in the field of modern automobile development and intelligent transportation. This paper presents a comprehensive review of driver identification [...] Read more.

Driver identification is very important to realizing customized service for drivers and road traffic safety for electric vehicles and has become a research hotspot in the field of modern automobile development and intelligent transportation. This paper presents a comprehensive review of driver identification methods. The basic process of driver identification task is proposed as four steps, the advantages and disadvantages of different data sources for driver identification are analyzed, driver identification models are divided into three categories, and the characteristics and research progress of driver identification models are summarized, which can provide a reference for further research on driver identification. It is concluded that on-board sensor data in the natural driving state is objective and accurate and could be the main data source for driver identification. Emerging technologies such as big data, artificial intelligence, and the internet of things have contributed to building a deep learning hybrid model with high accuracy and robustness and representing an important gradual development trend of driver identification methods. Developing a driver identification method with high accuracy, real-time performance, and robustness is an important development goal in the future. Full article

(This article belongs to the Topic Advanced Electric Vehicle Technology)

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20 pages, 1524 KiB  

Open AccessArticle

A Review of Economic Incentives to Promote Decarbonization Alternatives in Maritime and Inland Waterway Transport Modes

by Clara Paola Camargo-Díaz, Edwin Paipa-Sanabria, Julian Andres Zapata-Cortes, Yamileth Aguirre-Restrepo and Edgar Eduardo Quiñones-Bolaños

Sustainability 2022, 14(21), 14405; https://doi.org/10.3390/su142114405 - 3 Nov 2022

Cited by 14 | Viewed by 4204

Abstract

Public policies and economic incentives are widely used as a strategy to stimulate the use of green technologies and low-emission practices in the waterborne transport sector. Since the Paris Agreement, countries have been encouraged to implement more strategies to reduce greenhouse gas emissions [...] Read more.

Public policies and economic incentives are widely used as a strategy to stimulate the use of green technologies and low-emission practices in the waterborne transport sector. Since the Paris Agreement, countries have been encouraged to implement more strategies to reduce greenhouse gas emissions and to build resilience against climate change impacts in developing countries. This article presents a literature review on policies, regulations, and programs that represent economic incentives to promote alternatives to decarbonize maritime and inland waterway transport in sixteen countries, including Colombia. More than one hundred thirty sources of information were reviewed, including official portals of governments, port authorities and organizations, and scientific articles; therefore, the incentives found were grouped into three categories: project financing, differentiated port tariffs, and incentives to cover onshore power service fees. As a result of this review, it was found that differentiated port tariffs were the most common type of incentive. Finally, the specific case of Colombia was analyzed, which provides a deeper perspective of current policies and measures aimed at encouraging the decarbonization of waterborne transport and compares them with the international panorama. Full article

(This article belongs to the Topic Advanced Electric Vehicle Technology)

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23 pages, 10282 KiB  

Open AccessArticle

Smart Energy Management Strategy for Microgrids Powered by Heterogeneous Energy Sources and Electric Vehicles’ Storage

by Poornachandra Reddy Madhavaram and Manimozhi M

Energies 2022, 15(20), 7739; https://doi.org/10.3390/en15207739 - 19 Oct 2022

Cited by 8 | Viewed by 2044

Abstract

The rapid growths of power demand and renewable resources have led to numerous challenges. Constructing more resilient microgrids (MGs) provides an opportunity to avoid dependency on the main grid. This article proposes an innovative Energy Management Strategy (EMS) for microgrids that uses non-conventional [...] Read more.

The rapid growths of power demand and renewable resources have led to numerous challenges. Constructing more resilient microgrids (MGs) provides an opportunity to avoid dependency on the main grid. This article proposes an innovative Energy Management Strategy (EMS) for microgrids that uses non-conventional energy sources such as solar power, wind power, and the storage of electric vehicles (EVs). Numerous studies have been published on MG EMSs using storage; however, in real-time scenarios, predominant factors limit their straightforward implementation. In this article, an attempt is made to address key aspects of EV storage exploitation to support MGEMSs. Minimizing the total MG energy cost is the key objective, considering EV battery longevity and technical limitations. The proposed EMS was implemented in three layers: Optimal Storage Distribution (OSD), Optimal Power Usage (OPU), and EV Selection (EVS). A novel probabilistic approach was implemented in the EVS process (using a Fuzzy Logic Controller (FLC)) to minimize battery degradation. Various case studies were analyzed in a grid-connected MG by implementing the proposed EMS. Full article

(This article belongs to the Topic Advanced Electric Vehicle Technology)

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30 pages, 22440 KiB  

Open AccessArticle

Analyzing Performance Effects of Neural Networks Applied to Lane Recognition under Various Environmental Driving Conditions

by Tatiana Ortegon-Sarmiento, Sousso Kelouwani, Muhammad Zeshan Alam, Alvaro Uribe-Quevedo, Ali Amamou, Patricia Paderewski-Rodriguez and Francisco Gutierrez-Vela

World Electr. Veh. J. 2022, 13(10), 191; https://doi.org/10.3390/wevj13100191 - 17 Oct 2022

Cited by 3 | Viewed by 2969

Abstract

Lane detection is an essential module for the safe navigation of autonomous vehicles (AVs). Estimating the vehicle’s position and trajectory on the road is critical; however, several environmental variables can affect this task. State-of-the-art lane detection methods utilize convolutional neural networks (CNNs) as [...] Read more.

Lane detection is an essential module for the safe navigation of autonomous vehicles (AVs). Estimating the vehicle’s position and trajectory on the road is critical; however, several environmental variables can affect this task. State-of-the-art lane detection methods utilize convolutional neural networks (CNNs) as feature extractors to obtain relevant features through training using multiple kernel layers. It makes them vulnerable to any statistical change in the input data or noise affecting the spatial characteristics. In this paper, we compare six different CNN architectures to analyze the effect of various adverse conditions, including harsh weather, illumination variations, and shadows/occlusions, on lane detection. Among all the aforementioned adverse conditions, harsh weather in general and snowy night conditions particularly affect the performance by a large margin. The average detection accuracy of the networks decreased by 75.2%, and the root mean square error (RMSE) increased by 301.1%. Overall, the results show a noticeable drop in the networks’ accuracy for all adverse conditions because the features’ stochastic distributions change for each state. Full article

(This article belongs to the Topic Advanced Electric Vehicle Technology)

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25 pages, 5020 KiB  

Open AccessEditor’s ChoiceArticle

Preliminary Sizing of Electric-Propulsion Powertrains for Concept Aircraft Designs

by Josin Hu and Julian Booker

Designs 2022, 6(5), 94; https://doi.org/10.3390/designs6050094 - 13 Oct 2022

Cited by 5 | Viewed by 4302

Abstract

The drive towards a greener and more sustainable future is encouraging the aviation industry to move towards increasing electrification of its fleet. The development of electric propulsion technologies also requires new approaches to assess their viability in novel configurations. A methodology is proposed [...] Read more.

The drive towards a greener and more sustainable future is encouraging the aviation industry to move towards increasing electrification of its fleet. The development of electric propulsion technologies also requires new approaches to assess their viability in novel configurations. A methodology is proposed which consists of four sub-procedures; powertrain modelling, performance analysis, aerodynamic modelling, and sizing. This approach initially considers powertrain modelling using AIAA symbol representations, and a review of the available literature establishes state-of-the-art component values of efficiency, specific power, specific energy, and specific fuel consumption. The sizing procedure includes a mission and aerodynamic analysis to determine the energy and power requirements, and it relies on a mass regression model based on full-electric, hybrid, VTOL and fixed-wing aircraft found in the literature. The methodology has been applied to five case studies which are representative of a wide range of missions and configurations. Their predicted masses from the sizing procedure have been validated against their actual masses. The predicted total mass shows generally good agreement with the actual values, and in addition, accurate values for active mass have been predicted. A sensitivity analysis of the sizing procedure suggests that future work may include a more accurate analysis of aerodynamics and mission if the methodology were to be applied for selecting aircraft concepts. Full article

(This article belongs to the Topic Advanced Electric Vehicle Technology)

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18 pages, 3417 KiB  

Open AccessArticle

Time vs. Capacity—The Potential of Optimal Charging Stop Strategies for Battery Electric Trucks

by Maximilian Zähringer, Sebastian Wolff, Jakob Schneider, Georg Balke and Markus Lienkamp

Energies 2022, 15(19), 7137; https://doi.org/10.3390/en15197137 - 28 Sep 2022

Cited by 13 | Viewed by 2547

Abstract

The decarbonization of the transport sector, and thus of road-based transport logistics, through electrification, is essential to achieve European climate targets. Battery electric trucks offer the greatest well-to-wheel potential for CO₂ saving. At the same time, however, they are subject to restrictions [...] Read more.

The decarbonization of the transport sector, and thus of road-based transport logistics, through electrification, is essential to achieve European climate targets. Battery electric trucks offer the greatest well-to-wheel potential for CO₂ saving. At the same time, however, they are subject to restrictions due to charging events because of their limited range compared to conventional trucks. These restrictions can be kept to a minimum through optimal charging stop strategies. In this paper, we quantify these restrictions and show the potential of optimal strategies. The modeling of an optimal charging stop strategy is described mathematically as an optimization problem and solved by a genetic algorithm. The results show that in the case of long-distance transport using trucks with battery capacities lower than 750 kWh, a time loss is to be expected. However, this can be kept below 20 min for most battery capacities by optimal charging stops and sufficient charging infrastructure. Full article

(This article belongs to the Topic Advanced Electric Vehicle Technology)

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16 pages, 505 KiB  

Open AccessArticle

Attitudes of Drivers towards Electric Vehicles in Kuwait

by Andri Ottesen, Sumayya Banna and Basil Alzougool

Sustainability 2022, 14(19), 12163; https://doi.org/10.3390/su141912163 - 26 Sep 2022

Cited by 15 | Viewed by 4824

Abstract

Although researchers have started to examine the landscape of electric vehicles (EVs) around the world, very little research has examined this phenomenon in Kuwait. In addition, limited research has explored it among drivers. Kuwait constitutes a very promising market for EVs because there [...] Read more.

Although researchers have started to examine the landscape of electric vehicles (EVs) around the world, very little research has examined this phenomenon in Kuwait. In addition, limited research has explored it among drivers. Kuwait constitutes a very promising market for EVs because there is a need to lower GHG emissions and improve the air quality in Kuwait. This study therefore explored the attitudes of conventional car internal combustion engine (ICE) drivers towards EVs in Kuwait, particularly identifying attributes, features, enablers, and barriers of EVs that are considered important by potential consumers in Kuwait. This study utilized a mixed method approach in terms of quantitative data and qualitative data from a sample of 472 drivers to accomplish the main objectives of this study. The study showed that more than half of participants would buy an EV within the next 3 years, and they would buy if several conditions were met. That includes a cheaper purchase price with the assistance of policies controlled by the government along with the availability of suitable infrastructure for EVs relating to charging stations, fast lanes, and free parking spaces. More than 40% of participants would also seriously start thinking about buying an EV if the gas/fuel prices increased by between 50 and 199%. More than 40% of participants thought that EVs are safe in relation to fire and car crashes. Furthermore, approximately half of participants would pay 6–20% more for an EV that is both environmentally friendly and much quicker than gasoline cars. In addition, participants would also prefer EVs over gasoline cars in the future for their environmental, economic, and technological values. More importantly, the study yielded many significant findings, such as the demanded and preferred features of EVs and reflections on the readiness of the Kuwaiti market. Full article

(This article belongs to the Topic Advanced Electric Vehicle Technology)

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15 pages, 4567 KiB  

Open AccessArticle

Research on Energy Management Strategy of Fuel Cell Electric Tractor Based on Multi-Algorithm Fusion and Optimization

by Hongtu Yang, Yan Sun, Changgao Xia and Hongdang Zhang

Energies 2022, 15(17), 6389; https://doi.org/10.3390/en15176389 - 1 Sep 2022

Cited by 29 | Viewed by 2752

Abstract

To solve the serious pollution problems of traditional fuel tractors and the short continuous operation time of pure electric tractors, a hybrid tractor with fuel cell as the primary power source and battery as the auxiliary power source is proposed. A novel energy [...] Read more.

To solve the serious pollution problems of traditional fuel tractors and the short continuous operation time of pure electric tractors, a hybrid tractor with fuel cell as the primary power source and battery as the auxiliary power source is proposed. A novel energy management strategy was also designed, which integrates thermostat control strategy, power following strategy, and fuzzy logic control. The energy management strategy utilizes the advantages of different algorithms and realizes the rational distribution of fuel cell and battery output power. The system economy and fuel cell durability are improved by the tabu search algorithm. The simulation results show that the proposed energy management strategy can work well in different SOC states and reduce the fuel cell’s power fluctuations. The tractor is equipped with 960 g of hydrogen, the initial state of charge (SOC) is 90%, and it can operate continuously for 2.65 h. Full article

(This article belongs to the Topic Advanced Electric Vehicle Technology)

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23 pages, 15344 KiB  

Open AccessArticle

Octovalve Thermal Management Control for Electric Vehicle

by Alex Wray and Kambiz Ebrahimi

Energies 2022, 15(17), 6118; https://doi.org/10.3390/en15176118 - 23 Aug 2022

Cited by 16 | Viewed by 18767

Abstract

In the pursuit of more efficient vehicles on the world’s roads, the vehicle thermal management system has become a limiting factor when it comes to EV range and battery life. In extreme climates, if the thermal system cannot pull down or warm up [...] Read more.

In the pursuit of more efficient vehicles on the world’s roads, the vehicle thermal management system has become a limiting factor when it comes to EV range and battery life. In extreme climates, if the thermal system cannot pull down or warm up the EV powertrain in a timely manner, the battery is at serious risk of capacity loss or accelerated degradation. As waste heat is inherently limited with EVs, the way in which we provide the heat for warm-up must be as efficient as possible to reduce the load on the battery. In this paper, a revolutionary waste heat recovery (WHR) thermal management system designed by Tesla, nicknamed the ‘Octovalve’, is described, modelled, and simulated. This paper contributes to collective knowledge by presenting an in-depth breakdown of the key operating modes and outlining the potential benefits. Modelled in the multidomain Simulink Simscape software, the octovalve’s performance is directly compared to a typical EV WHR thermal management system. The system under analysis is shown to significantly reduce EV energy consumption and battery load during warm-up but at the cost of overall warm-up time. Unlike any other WHR system found in literature, this system has a heat pump with can perform air conditioning and heat pump tasks simultaneously, which is shown to have a remarkable impact on energy efficiency and battery life. Full article

(This article belongs to the Topic Advanced Electric Vehicle Technology)

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28 pages, 6688 KiB  

Open AccessArticle

Deep Learning LSTM Recurrent Neural Network Model for Prediction of Electric Vehicle Charging Demand

by Jaikumar Shanmuganathan, Aruldoss Albert Victoire, Gobu Balraj and Amalraj Victoire

Sustainability 2022, 14(16), 10207; https://doi.org/10.3390/su141610207 - 17 Aug 2022

Cited by 48 | Viewed by 5674

Abstract

The immense growth and penetration of electric vehicles has become a major component of smart transport systems; thereby decreasing the greenhouse gas emissions that pollute the environment. With the increased volumes of electric vehicles (EV) in the past few years, the charging demand [...] Read more.

The immense growth and penetration of electric vehicles has become a major component of smart transport systems; thereby decreasing the greenhouse gas emissions that pollute the environment. With the increased volumes of electric vehicles (EV) in the past few years, the charging demand of these vehicles has also become an immediate requirement. Due to which, the prediction of the demand of electric vehicle charging is of key importance so that it minimizes the burden on the electric grids and also offers reduced costs of charging. In this research study, an attempt is made to develop a novel deep learning (DL)-based long-short term memory (LSTM) recurrent neural network predictor model to carry out the forecasting of electric vehicle charging demand. The parameters of the new deep long-short term memory (DLSTM) neural predictor model are tuned for its optimal values using the classic arithmetic optimization algorithm (AOA) and the input time series data are decomposed so as to maintain their features using the empirical mode decomposition (EMD). The novel EMD—AOA—DLSTM neural predictor modeled in this study overcomes the vanishing and exploding gradients of basic recurrent neural learning and is tested for its superiority on the EV charging dataset of Georgia Tech, Atlanta, USA. At the time of simulation, the best results of 97.14% prediction accuracy with a mean absolute error of 0.1083 and a root mean square error of 2.0628 × 10⁻⁵ are attained. Furthermore, the mean absolute error was evaluated to be 0.1083 and the mean square error pertaining to 4.25516 × 10⁻¹⁰. The results prove the efficacy of the prediction metrics computed with the novel deep learning LSTM neural predictor for the considered dataset in comparison with the previous techniques from existing works. Full article

(This article belongs to the Topic Advanced Electric Vehicle Technology)

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16 pages, 2963 KiB  

Open AccessArticle

Coordinated Path Following Control of 4WID-EV Based on Backstepping and Model Predictive Control

by Chenning Wang, Ren He, Zhecheng Jing and Shijun Chen

Energies 2022, 15(15), 5728; https://doi.org/10.3390/en15155728 - 6 Aug 2022

Cited by 5 | Viewed by 2070

Abstract

A path following control strategy for a four-wheel-independent-drive electrical vehicle (4WID-EV) based on backstepping and model predictive control is presented, which can ensure the accuracy of path following and maintain vehicle stability simultaneously. Firstly, a 2-DOF vehicle dynamic model and a path following [...] Read more.

A path following control strategy for a four-wheel-independent-drive electrical vehicle (4WID-EV) based on backstepping and model predictive control is presented, which can ensure the accuracy of path following and maintain vehicle stability simultaneously. Firstly, a 2-DOF vehicle dynamic model and a path following error model are built and the desired yaw rate is obtained through backstepping. Then, a model predictive controller is adopted to track the desired yaw rate and obtain the optimal front wheel steering and external yaw moment. Meanwhile, an optimal torque distribution algorithm is carried out to allocate it to each tire. Finally, the effectiveness and superiority of the strategy is validated via CarSim–Simulink joint simulation. Results show that the strategy has higher following accuracy, smaller sideslip angle, and better yaw rate tracking. Full article

(This article belongs to the Topic Advanced Electric Vehicle Technology)

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19 pages, 6735 KiB  

Open AccessArticle

A High-Power-Density Active-Clamp Converter with Integrated Planar Transformer

by Dae-Woo Lee, Ji-Hoon Lim, Dong-In Lee and Han-Shin Youn

Energies 2022, 15(15), 5609; https://doi.org/10.3390/en15155609 - 2 Aug 2022

Cited by 8 | Viewed by 3543

Abstract

This paper proposes an active-clamp forward-flyback (ACFF) converter with an integrated planar transformer for wide-input voltage and high-output current applications, such as low-voltage direct-current (LDC) converters in electric vehicles. An integrated planar transformer that consists of a forward-flyback transformer, single primary winding, and [...] Read more.

This paper proposes an active-clamp forward-flyback (ACFF) converter with an integrated planar transformer for wide-input voltage and high-output current applications, such as low-voltage direct-current (LDC) converters in electric vehicles. An integrated planar transformer that consists of a forward-flyback transformer, single primary winding, and efficient structure of secondary windings is adopted for the proposed converter, and since this transformer is implemented with a common four-layer printed circuit board (PCB) winding, a high power density and low cost of the proposed converter can be achieved. In addition, due to the low leakage inductance induced by the planar transformer, a reduced commutation period can be achieved, and it is possible to increase the switching frequency resulting in low volume of transformer. Although the integrated planar transformer has relatively high conduction loss, the active-clamp topology can significantly reduce the conduction loss on switches compared with widely used full-bridge (FB) converters because it only utilizes two switches and shows the low circulating current. As a result, the proposed converter with an integrated planar transformer has strengths in high power density and cost competitiveness without degraded efficiency, and it is a very attractive topology for LDC converters and other applications that require wide-input voltage and high-output current. Full article

(This article belongs to the Topic Advanced Electric Vehicle Technology)

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16 pages, 6625 KiB  

Open AccessArticle

Numerical Analysis of E-Machine Cooling Using Phase Change Material

by Jayakumar Selvan and Sreekanth Manavalla

Energies 2022, 15(15), 5594; https://doi.org/10.3390/en15155594 - 2 Aug 2022

Cited by 7 | Viewed by 2218

Abstract

The cooling of E-machines was investigated using phase change material (PCM). The PCM is widely used in the cooling of electronic components because of its heat-absorbing and cooling properties. In this study, PCM OM35 (50:50) and OM35 (60:40) were used for the cooling [...] Read more.

The cooling of E-machines was investigated using phase change material (PCM). The PCM is widely used in the cooling of electronic components because of its heat-absorbing and cooling properties. In this study, PCM OM35 (50:50) and OM35 (60:40) were used for the cooling of E-Machines, which are commonly known as electric vehicle motors. Three different configurations, viz. no rib, two ribs, and four ribs, were studied to understand the impact of thermal behaviour on bracket cooling. The ribs were added in between the brackets to enhance the heat transfer. Numerical simulations were performed using the volume of fluid multiphase analysis approach to model the behaviour of phase change inside the brackets for 18 KW E-Machines. Based on the study, a four rib configuration showed good performance compared to no rib and two rib configurations, and heat transfer improved by 6%. Heat transfer is thus improved by increasing the number of ribs placed between the brackets. The cyclic heat load was applied to the best performing ribs to study the impact of different PCM materials OM35 (50:50) and OM35 (60:40). Full article

(This article belongs to the Topic Advanced Electric Vehicle Technology)

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16 pages, 936 KiB  

Open AccessArticle

Factors Influencing Battery Electric Vehicle Adoption in Thailand—Expanding the Unified Theory of Acceptance and Use of Technology’s Variables

by Phasiri Manutworakit and Kasem Choocharukul

Sustainability 2022, 14(14), 8482; https://doi.org/10.3390/su14148482 - 11 Jul 2022

Cited by 45 | Viewed by 8616

Abstract

The replacement of combustion cars with battery electric cars can help to support sustainable transport and renewable energy in green transportation in Thailand; however, the diffusion rates of electric cars are still low. The purpose of this study is to examine factors that [...] Read more.

The replacement of combustion cars with battery electric cars can help to support sustainable transport and renewable energy in green transportation in Thailand; however, the diffusion rates of electric cars are still low. The purpose of this study is to examine factors that influence Thai car owners’ adoption of battery electric vehicles. The proposed model expands the Unified Theory of Acceptance and Use of Technology (UTAUT) as the theoretical framework. Data were collected through an online questionnaire survey completed by 403 participants in Bangkok and the vicinity and analyzed using partial least squares structural equation modeling (PLS-SEM). The result showed that purchase intention is significantly and positively influenced by performance expectancy, effort expectancy, social influence, hedonic motivation, and environmental concern. In contrast, purchase intention is not significantly influenced by price value and policy measures. Use behavior is positively influenced by purchase intention. Facilitating conditions do not significantly influence purchase intention and use behavior. Moreover, only the age variable was found to have significant effects on purchase behavior. This study suggests that governments with incentive policies and electric car manufacturers should focus on improving cars to increase battery electric car adoption. Full article

(This article belongs to the Topic Advanced Electric Vehicle Technology)

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15 pages, 4542 KiB  

Open AccessArticle

High-Performance Flywheel Hybrid Powertrain

by Hong Li, Jiangwei Chu and Shufa Sun

Sustainability 2022, 14(13), 8076; https://doi.org/10.3390/su14138076 - 1 Jul 2022

Cited by 4 | Viewed by 2195

Abstract

The high efficiency of the flywheel hybrid powertrain, as well as its power characteristics, can help to meet high energy/power conversion needs, which may prove to be promising. Moreover, the flywheel hybrid powertrain may reduce dependence on batteries. This paper presents the EC-BERS [...] Read more.

The high efficiency of the flywheel hybrid powertrain, as well as its power characteristics, can help to meet high energy/power conversion needs, which may prove to be promising. Moreover, the flywheel hybrid powertrain may reduce dependence on batteries. This paper presents the EC-BERS in order to capture more mechanical power than its rated power, and to reduce the charge/discharge cycles of the battery. In this new energy recovery system, maximum torque can be obtained in the higher speed zone, leading to two marked improvements in terms of improving the braking efficiency. The working point of the system changes and shifts to the high-speed zone to meet the maximum torque at higher speeds. Furthermore, this powertrain can transfer the vehicle kinetic energy into the flywheel directly in the same form. Only the slip energy needs to be dealt with in the electrical form, which is beneficial to prolonging the battery life. Two typical systems were emulated under the same conditions to verify this feature, and a small prototype was designed to prove the concept. Full article

(This article belongs to the Topic Advanced Electric Vehicle Technology)

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28 pages, 12323 KiB  

Open AccessArticle

A 5G-Based VR Application for Efficient Port Management

by Yi Han, Wenhao Wang, Nanxi Chen, Yi Zhong, Ruichun Zhou, Haoyu Yan, Jun Wang and Yulei Bai

World Electr. Veh. J. 2022, 13(6), 101; https://doi.org/10.3390/wevj13060101 - 9 Jun 2022

Cited by 7 | Viewed by 4522

Abstract

In recent years, the throughput of cargo ports has increased rapidly. It is urgent to improve the operating efficiency of ports for its increasing demands. Both industry and academia have shown great interest in adopting 5G and Virtual Reality (VR) technologies to improve [...] Read more.

In recent years, the throughput of cargo ports has increased rapidly. It is urgent to improve the operating efficiency of ports for its increasing demands. Both industry and academia have shown great interest in adopting 5G and Virtual Reality (VR) technologies to improve the efficiency and safety of industrial operations. However, such technologies have not been well explored in port operations. This paper proposes a 5G-based VR smart port to support intelligent management for five typical port operations. The overall architecture of the smart port and its key processes, major advantages, and limitations are discussed in this paper. An application of the proposed smart port system is demonstrated. The performance study based on network Quality of Service (QoS) and Quality of user Experience (QoE) proves the feasibility of the proposed architecture. The architecture supports efficient interaction in real-time, making comprehensive decisions, and staff training. The smart port architecture is suitable for tasks of high working intensity and can dramatically increase operational efficiency. Full article

(This article belongs to the Topic Advanced Electric Vehicle Technology)

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23 pages, 1232 KiB  

Open AccessArticle

Path Planning and Guidance Laws of a Formula Student Driverless Car

by Solange D. R. Santos, José Raul Azinheira, Miguel Ayala Botto and Duarte Valério

World Electr. Veh. J. 2022, 13(6), 100; https://doi.org/10.3390/wevj13060100 - 9 Jun 2022

Cited by 3 | Viewed by 4553

Abstract

Autonomous driving has been a topic of great interest in several areas, of which motor racing is no exception. The aim of this work is the autonomous control of the future Formula Student Lisboa vehicle, by implementing different strategies for control and path [...] Read more.

Autonomous driving has been a topic of great interest in several areas, of which motor racing is no exception. The aim of this work is the autonomous control of the future Formula Student Lisboa vehicle, by implementing different strategies for control and path planning, with the purpose of minimising race lap times. These strategies are tested in simulation, using a realistic model of the prototype. The approach followed involves the decoupling of the lateral and longitudinal subsystems and obtaining the reference path using artificial potential fields, combined with a two-pass algorithm developed to generate a speed profile. In this way, a sub-optimal solution is reached that adequately portrays the expected behaviour of a human driver while respecting traction conditions. The process of generating the speed reference requires prior knowledge of the track layout. This assumption is then eased for obstacle avoidance, i.e., for a scenario where, in addition to the track limits, unknown static obstacles are present. A decoupled control approach is followed controlling each of the two subsystems individually. Full article

(This article belongs to the Topic Advanced Electric Vehicle Technology)

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44 pages, 8616 KiB  

Open AccessReview

Towards Safer and Smarter Design for Lithium-Ion-Battery-Powered Electric Vehicles: A Comprehensive Review on Control Strategy Architecture of Battery Management System

by Bragadeshwaran Ashok, Chidambaram Kannan, Byron Mason, Sathiaseelan Denis Ashok, Vairavasundaram Indragandhi, Darsh Patel, Atharva Sanjay Wagh, Arnav Jain and Chellapan Kavitha

Energies 2022, 15(12), 4227; https://doi.org/10.3390/en15124227 - 8 Jun 2022

Cited by 29 | Viewed by 8975

Abstract

As the battery provides the entire propulsion power in electric vehicles (EVs), the utmost importance should be ascribed to the battery management system (BMS) which controls all the activities associated with the battery. This review article seeks to provide readers with an overview [...] Read more.

As the battery provides the entire propulsion power in electric vehicles (EVs), the utmost importance should be ascribed to the battery management system (BMS) which controls all the activities associated with the battery. This review article seeks to provide readers with an overview of prominent BMS subsystems and their influence on vehicle performance, along with their architectures. Moreover, it collates many recent research activities and critically reviews various control strategies and execution topologies implied in different aspects of BMSs, including battery modeling, states estimation, cell-balancing, and thermal management. The internal architecture of a BMS, along with the architectures of the control modules, is examined to demonstrate the working of an entire BMS control module. Moreover, a critical review of different battery models, control approaches for state estimation, cell-balancing, and thermal management is presented in terms of their salient features and merits and demerits allowing readers to analyze and understand them. The review also throws light on modern technologies implied in BMS, such as IoT (Internet of Things) and cloud-based BMS, to address issues of battery safety. Towards the end of the review, some challenges associated with the design and development of efficient BMSs for E-mobility applications are discussed and the article concludes with recommendations to tackle these challenges. Full article

(This article belongs to the Topic Advanced Electric Vehicle Technology)

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14 pages, 4895 KiB  

Open AccessArticle

Research on Motor Speed Control Method Based on the Prevention of Vehicle Rollover

by Fuhuai Jiang, Mengyuan Dong, Yuezhen Fan and Qingchun Wang

Energies 2022, 15(10), 3609; https://doi.org/10.3390/en15103609 - 15 May 2022

Cited by 9 | Viewed by 2503

Abstract

Vehicle driving safety is an important performance indicator for vehicles, and there is still much room for development in the active safety control of electric vehicles. Vehicles are susceptible to rollover when making sharp turns or overtaking at high speed. In order to [...] Read more.

Vehicle driving safety is an important performance indicator for vehicles, and there is still much room for development in the active safety control of electric vehicles. Vehicles are susceptible to rollover when making sharp turns or overtaking at high speed. In order to improve the anti-rollover stability of electric vehicles (EV), this study proposes a real-time motor control strategy, mainly according to the acquisition of vehicle attitude data and real-time monitoring of the vehicle’s operating state. The lateral load transfer rate was defined as the vehicle rollover evaluation index. When the real-time rollover indicator exceeded the limit safety threshold this article set, the motor speed would be reduced through active control to avoid rollover or reduce the risk of rollover. Both simulation results in Carsim and Simulink showed that the motor control strategy is highly reliable and real-time capable, and the active safety of EV was improved significantly. Full article

(This article belongs to the Topic Advanced Electric Vehicle Technology)

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18 pages, 12230 KiB  

Open AccessArticle

Genetically Optimized Extended Kalman Filter for State of Health Estimation Based on Li-Ion Batteries Parameters

by Claudio Rossi, Carlo Falcomer, Luca Biondani and Davide Pontara

Energies 2022, 15(9), 3404; https://doi.org/10.3390/en15093404 - 6 May 2022

Cited by 15 | Viewed by 2463

Abstract

The state of health (SOH) is among the most important parameters to be monitored in lithium-ion batteries (LIB) because it is used to know the residual functionality in any condition of aging. The paper focuses on the application of the extended Kalman filter [...] Read more.

The state of health (SOH) is among the most important parameters to be monitored in lithium-ion batteries (LIB) because it is used to know the residual functionality in any condition of aging. The paper focuses on the application of the extended Kalman filter (EKF) for the identification of the parameters of a cell model, which are required for the correct estimation of the SOH of the cell. This article proposes a methodology for tuning the covariance matrices of the EKF by using an optimization process based on genetic algorithms (GA). GAs are able to solve the minimization problems for the non-linear functions, and they are better than other optimization algorithms such as gradient descent to avoid the local minimum. To validate the proposed method, the cell parameters obtained from the EKF are compared with a reference model, in which the parameters have been determined with proven procedures. This comparison is carried out with different cells and in the whole range of the cell’s SOH, with the aim of demonstrating that a single tuning procedure, based on the proposed GA process, is able to guarantee good accuracy in the estimation of the cell parameters at all stages of the cell’s life. Full article

(This article belongs to the Topic Advanced Electric Vehicle Technology)

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