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Next Generation Energy and Propulsion Systems for Transportation Electrification

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A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "E: Electric Vehicles".

Deadline for manuscript submissions: closed (15 November 2023) | Viewed by 26770

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Special Issue Editors

Dr. Jinhao Meng

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Guest Editor

School of Electrical Engineering, Xi'an Jiaotong University, Xi’an 710049, China
Interests: battery management systems; battery state estimation; fast charging control; battery impedance measurement
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Prof. Dr. Dongdong Zhao

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School of Automation, Northwestern Polytechnical University, Xi’an 710072, China
Interests: fuel cell system; battery energy management; electric machines and drives
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Dr. Daniel Stroe

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Guest Editor

AAU Energy, Aalborg University, 9220 Aalborg, Denmark
Interests: energy storage; lithium-ion batteries; battery performance and lifetime testing; accelerated aging; battery performance/degradation modeling; state-of-charge estimation; state-of-health estimation; remaining-useful-lifetime prediction; aging mechanisms
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Dr. Ji Wu

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Guest Editor

Department of Automotive Engineering, Hefei University of Technology, Hefei 230000, China
Interests: modeling, estimation, control, and optimization for lithium-ion batteries
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Dr. Qian Xiao

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Guest Editor

School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China
Interests: distribution network; microgrid; multilevel converter; battery energy storage system
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Dr. Zeyu Chen

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Guest Editor

School of Mechanical Engineering and Automation, Northeastern University, Shenyang 110819, China
Interests: optimization of electric vehicle operation and control; safety management of lithium-ion battery; battery management system; thermal management; self-heating of energy storage

Dr. Yanan Wang

E-Mail Website
Guest Editor

School of Mechanical Engineering, Shandong University, Jinan 250061, China
Interests: performance analysis and simulation of lithium-ion battery; design of battery and thermal management system; fast charge technology of electric vehicles

Dr. Ruixin Yang

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Guest Editor

Department of Vehicle Engineering, School of Mechanical Engineering, Beijing Institute of Technology, No.5 Zhongguancun Street, Beijing 100081, China
Interests: electric/hybrid vehicles; battery management system; battery safety management; energy storage system; fault diagnosis

Dr. Stefano Bracco

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Guest Editor

Electrical, Electronics and Telecommunication Engineering and Naval Architecture Department (DITEN), University of Genoa, Via Opera Pia 11a, 16145 Genoa, Italy
Interests: optimization and simulation of microgrids and nanogrids; smart charging of electric vehicles and V2G technologies; power systems management
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Special Issue Information

Dear Colleagues,

Electrified transportation has been an urgent mission for enabling a carbon neural society in the near future. The revolution of e-mobility has witnessed the booming development of emerging technologies such as new energy vehicles, electric aircraft, electric ships, etc. Therefore, cutting-edge products are frequently available for next-generation energy and propulsion systems of e-mobility, including solid-state batteries, hydrogen fuel cells, and new structural electric machines. This way, advanced design and energy management of next-generation energy and propulsion systems will definitely benefit the transportation electrification process.

Within this scope, battery, fuel cell, and electric motor technologies and their applications are critical for the transportation revolution. This Special Issue solicits papers on new research achievements in the recently emerging and cross-disciplinary field of enabling energy management and propulsion system control for e-mobilities. Topics of interest of this Special Issue include but are not limited to the following:

Advanced battery thermal/energy management system design and applications;
Fuel cell, batteries, and electric machine modelling;
Advanced control, fault diagnosis, and performance analysis of fuel cell, batteries, and electric machines;
Power converters for energy and propulsion systems;
Microgrids in electrified transportation systems;
Management of interactions between electric vehicles and grids;
Hardware in the loop of the energy and propulsion system for transportation;
Fast charge for energy supply in electrified systems;
Hybrid energy systems for e-mobility.

Dr. Jinhao Meng
Prof. Dr. Dongdong Zhao
Dr. Daniel Stroe
Dr. Ji Wu
Dr. Qian Xiao
Dr. Zeyu Chen
Dr. Yanan Wang
Dr. Ruixin Yang
Dr. Stefano Bracco
Guest Editors

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Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Energies is an international peer-reviewed open access semimonthly journal published by MDPI.

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Keywords

transportation electrification
energy storage system
propulsion system
battery
fuel cell
electric machine
power electronics
control
energy management
diagnosis
vehicle to grid

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Published Papers (11 papers)

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Research

21 pages, 3898 KiB

Open AccessArticle

Non-Iterative Coordinated Optimisation of Power–Traffic Networks Based on Equivalent Projection

by Wei Dai, Zhihong Zeng, Cheng Wang, Zhijie Zhang, Yang Gao and Jun Xu

Energies 2024, 17(8), 1899; https://doi.org/10.3390/en17081899 - 16 Apr 2024

Viewed by 757

Abstract

The exchange of sensitive information between power distribution networks (PDNs) and urban transport networks (UTNs) presents a difficulty in ensuring privacy protection. This research proposes a new collaborative operation method for a coupled system. The scheme takes into account the schedulable capacity of electric vehicle charging stations (EVCSs) and locational marginal prices (LMPs) to handle the difficulty at hand. The EVCS hosting capacity model is built and expressed as the feasible area of charging power, based on AC power flow. This model is then used to offer information on the real schedulable capacity. By incorporating the charging loads into the coupling nodes between PDNs and UTNs, the issue of coordinated operation is separated and becomes equal to the optimal problem involving charging loads. Based on this premise, the most efficient operational cost of PDNs is transformed into a comparable representation of cost information in PDNs. This representation incorporates LMP information that guides charging decisions in UTNs. The suggested collaborative scheduling methodology in UTNs utilises the collected projection information from the static traffic assignment (STA) to ensure data privacy protection and achieve non-iterative calculation. Numerical experiments are conducted to illustrate that the proposed method, which uses a smaller amount of data, achieves the same level of optimality as the coordinated optimisation. Full article

(This article belongs to the Special Issue Next Generation Energy and Propulsion Systems for Transportation Electrification)

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23 pages, 1740 KiB

Open AccessFeature PaperArticle

Design of Zonal E/E Architectures in Vehicles Using a Coupled Approach of k-Means Clustering and Dijkstra’s Algorithm

by Jonas Maier and Hans-Christian Reuss

Energies 2023, 16(19), 6884; https://doi.org/10.3390/en16196884 - 29 Sep 2023

Cited by 4 | Viewed by 2306

Abstract

Electromobility and autonomous driving has started a transformation in the automotive industry, resulting in new requirements for vehicle systems. Due to its functions, the electrical/electronic (E/E) architecture is one of the essential systems. Zonal E/E architecture is a promising approach to tackle this issue. The research presented in this paper describes a methodology for determining the optimal number of zones, the position of the zone control units (ZCU), and the assignment of electric components to these zones and ZCUs. Therefore, the design of the power supply and the wiring harness is essential. This approach aims to identify the most suitable system architecture for a given vehicle geometry and a set of electric components. For this purpose, the assignment of electric components is accomplished by k-means clustering, and Dijkstra’s algorithm is used to optimize the cable routing. As ZCUs will be the hubs for the in-vehicle data and information transport in zonal architectures, their position and their number are crucial for the architecture and wiring harness development. Simulations show a suitable zonal architecture reduces wiring harness length as well as weight and brings functional benefits. However, the number of zones must be chosen with care, as there may also be functional limitations. Full article

(This article belongs to the Special Issue Next Generation Energy and Propulsion Systems for Transportation Electrification)

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21 pages, 7068 KiB

Open AccessArticle

Improved Battery Balancing Control Strategy for Reconfigurable Converter Systems

by Guangwei Wan, Qiang Zhang, Menghan Li, Siyuan Li, Zehao Fu, Junjie Liu and Gang Li

Energies 2023, 16(15), 5619; https://doi.org/10.3390/en16155619 - 26 Jul 2023

Cited by 2 | Viewed by 1426

Abstract

In order to address the issue of battery cell disparity in lithium-ion battery systems, battery balancing techniques are required. This paper proposes an improved battery balancing strategy within a reconfigurable converter system. The strategy is based on the state of charge (SOC) of batteries, and utilizes the reconfigurable converter system to transfer energy from battery modules with high SOC to those with lower SOC. Additionally, it allows for battery module balancing while supplying power to loads. A MATLAB/Simulink simulation model with five batteries was built to validate the effectiveness of the proposed balancing strategy under unloaded and loaded conditions. The simulation results demonstrate that the proposed strategy achieves more efficient and accurate battery module balancing compared to the previous balancing modes. Full article

(This article belongs to the Special Issue Next Generation Energy and Propulsion Systems for Transportation Electrification)

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16 pages, 1767 KiB

Open AccessArticle

Lithium-Ion Battery Health State Prediction Based on VMD and DBO-SVR

by Chunling Wu, Juncheng Fu, Xinrong Huang, Xianfeng Xu and Jinhao Meng

Energies 2023, 16(10), 3993; https://doi.org/10.3390/en16103993 - 9 May 2023

Cited by 31 | Viewed by 2922

Abstract

Accurate estimation of the state-of-health (SOH) of lithium-ion batteries is a crucial reference for energy management of battery packs for electric vehicles. It is of great significance in ensuring safe and reliable battery operation while reducing maintenance costs of the battery system. To eliminate the nonlinear effects caused by factors such as capacity regeneration on the SOH sequence of batteries and improve the prediction accuracy and stability of lithium-ion battery SOH, a prediction model based on Variational Modal Decomposition (VMD) and Dung Beetle Optimization -Support Vector Regression (DBO-SVR) is proposed. Firstly, the VMD algorithm is used to decompose the SOH sequence of lithium-ion batteries into a series of stationary mode components. Then, each mode component is treated as a separate subsequence and modeled and predicted directly using SVR. To address the problem of difficult parameter selection for SVR, the DBO algorithm is used to optimize the parameters of the SVR model before training. Finally, the predicted values of each subsequence are added and reconstructed to obtain the final SOH prediction. In order to verify the effectiveness of the proposed method, the VMD-DBO-SVR model was compared with SVR, Empirical Mode Decomposition-Support Vector Regression (EMD-SVR), and VMD-SVR methods for SOH prediction of batteries based on the NASA dataset. Experimental results show that the proposed model has higher prediction accuracy and fitting degree, with prediction errors all within 1% and better robustness. Full article

(This article belongs to the Special Issue Next Generation Energy and Propulsion Systems for Transportation Electrification)

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18 pages, 11877 KiB

Open AccessArticle

Luenberger Observer-Based Microgrid Control Strategy for Mixed Load Conditions

by Yong Cheng and Cong Li

Energies 2022, 15(10), 3655; https://doi.org/10.3390/en15103655 - 16 May 2022

Cited by 5 | Viewed by 1872

Abstract

In this paper, a Luenberger observer-based microgrid control strategy is proposed to enhance the power quality of microgrids, when the grid loads are mixed and strongly non-linear. To improve performance under this condition, a Luenberger observer is designed for three phase power grids. On the basis of the observer, the components of different frequencies and sequences of voltages and currents are obtained accurately. The virtual impedance of different frequencies and sequences is designed, which makes the equivalent line impedance meet the power-sharing condition, reducing the fundamental negative sequence voltages and harmonic voltages. The active power droop equation, meanwhile, is proposed, where the bus voltage is modified. The value range of virtual impedance is discussed in the complex frequency domain. The proposed control strategy does not require any communication lines, so the hardware structure is simplified. The simulations and experiments are provided to verify the effectiveness of the proposed method. Full article

(This article belongs to the Special Issue Next Generation Energy and Propulsion Systems for Transportation Electrification)

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21 pages, 5045 KiB

Open AccessArticle

Parametric Investigation on the Performance of a Battery Thermal Management System with Immersion Cooling

by Yuxin Zhou, Zhengkun Wang, Zongfa Xie and Yanan Wang

Energies 2022, 15(7), 2554; https://doi.org/10.3390/en15072554 - 31 Mar 2022

Cited by 34 | Viewed by 4327

Abstract

Lithium-ion batteries will generate a large amount of heat during high-rate charging and discharging. By transferring the heat to the environment in time, the batteries can be kept in a suitable temperature range. This allows them to work normally, prolongs their cycle life, and reduces the risk of thermal runaway. Immersion cooling is a simple and efficient thermal management method. In this paper, a battery thermal management system (BTMS) with immersion cooling was designed by immersing the lithium-ion cells in the non-conductive coolant—dimethyl silicone oil. The electric–thermal coupled model was adopted to obtain the heat production and temperature distribution of the cell during discharging, and the performance of the system was obtained by numerical calculation. It was found that, compared with natural cooling, immersion cooling could significantly reduce both the maximum temperature (MAT) of the cell and the temperature of the tabs during the 3C discharging process. However, the maximum temperature difference (MATD) of the cell was significantly increased. To solve this problem, the effects of the flow rate, viscosity, specific heat capacity, and thermal conductivity of the coolant on the performance of immersion cooling were further investigated and discussed, including the MAT and MATD of the cell, and the pressure drop of the coolant. The method and results could provide references for the design and application of the BTMS with immersion cooling in the future. Full article

(This article belongs to the Special Issue Next Generation Energy and Propulsion Systems for Transportation Electrification)

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16 pages, 5381 KiB

Open AccessArticle

Analysis of Acoustic Characteristics under Battery External Short Circuit Based on Acoustic Emission

by Nan Zhou, Xiulong Cui, Changhao Han and Zhou Yang

Energies 2022, 15(5), 1775; https://doi.org/10.3390/en15051775 - 28 Feb 2022

Cited by 7 | Viewed by 2583

Abstract

The safety of power batteries has received more and more attention in promoting electric vehicles. The external short circuit is particularly prominent as an abnormal and harmful event of a battery, and the exploration of in-situ low-cost detection technology for such an event is the starting point of this paper. By building an experimental bench that could detect the external short circuit of the battery and obtain the acoustic, electrode, and temperature responses, the resulting acoustic analysis would establish an internal connection with the electrode and temperature measurement when the external short circuit occurs. The respective acoustic response characteristics of different initial battery states of charge were analyzed by selecting appropriate acoustic characteristic parameters in the time and frequency domains. The acoustic measurement could represent the battery abnormality synchronously like the electrode measurement, and the results of the damage and rearrangement of the internal of the battery are easy to characterize through a moderate amplification of the acoustic response. The different initial state of charge (SOC) state reflects noticeable differences in the acoustic characteristics. Therefore, it is considered that the acoustic emission technology might have potential battery condition assessment capabilities and be a tool for in-situ battery fault diagnosis. Full article

(This article belongs to the Special Issue Next Generation Energy and Propulsion Systems for Transportation Electrification)

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23 pages, 5282 KiB

Open AccessArticle

Robust Errorless-Control-Targeted Technique Based on MPC for Microgrid with Uncertain Electric Vehicle Energy Storage Systems

by Yalin Liang, Yuyao He and Yun Niu

Energies 2022, 15(4), 1398; https://doi.org/10.3390/en15041398 - 15 Feb 2022

Cited by 5 | Viewed by 1504

Abstract

Regarding the microgrid with large-scale electric vehicle (EV) energy storage systems working at the vehicle-to-grid (V2G) mode, uncertain factors (e.g., the number of EVs feeding the microgrid shifts frequently) make the system unfixed, leading to the fact that it is difficult to precisely determine the real-time droop coefficients of the system, thereby degrading the performance of the traditional inverter control strategies that rely on the droop coefficients. To solve the problem, this paper proposes an errorless-control-targeted double control loop (DCL) technique based on robust MPC to control the microgrid with EV energy storage systems without using droop coefficients. Firstly, the structure of the DCL method is developed, with each component in the structure detailed. Compared to the traditional control strategies, the novel one regards the frequency, voltage, and currents as the control objectives instead of active/inactive power. It deserves to be mentioned that the frequency and voltage are regulated by proportional-integral controllers, while the currents are regulated by the finite control set model predictive control (FCS-MPC) method. Secondly, the impacts of system parameter uncertainties on the prediction accuracy of the FCS-MPC controller are analyzed clearly, illustrating that it is necessary to develop effective techniques to enhance the robustness of the controller. Thirdly, sliding mode observers (SMO) based on a novel hyperbolic function are constructed to detect the real-time disturbances, which can be used to generate voltage compensations by using automatic disturbance regulators. Then, the voltage compensations are adopted to establish a modified predicting plant model (PPM) used for the FCS-MPC controller. By using the proposed SMO-based disturbance detection and compensation techniques, the MPC controller gains a strong robustness against parameter uncertainties. Finally, a simulation is conducted on a microgrid system to verify the effectiveness of the proposed techniques, and the obtained results are compared with the traditional virtual synchronous machine (VSG) strategy relying on droop coefficients. Full article

(This article belongs to the Special Issue Next Generation Energy and Propulsion Systems for Transportation Electrification)

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24 pages, 12675 KiB

Open AccessArticle

Electrification of LPT in Algeciras Bay: A New Methodology to Assess the Consumption of an Equivalent E-Bus

by Carola Leone, Giorgio Piazza, Michela Longo and Stefano Bracco

Energies 2021, 14(16), 5117; https://doi.org/10.3390/en14165117 - 19 Aug 2021

Cited by 6 | Viewed by 2151

Abstract

The present paper proposes a new methodology to aid the electrification process of local public transport (LPT). In more detail, real drive cycles of traditional buses currently in use are evaluated together with other data to simulate the consumption of equivalent e-buses (electric buses) with similar characteristics. The results are then used in order to design the best charging infrastructure. The proposed methodology is applied to the case study of Algeciras Bay, where a specific line of LPT is considered. Real measurements are used as data for the simulation model, and the average consumption of an equivalent e-bus is obtained for different operating conditions. Based on these results, different sizes and locations for fast-charging infrastructure are proposed, and the size of the depot charging system is defined trying to maintain the current buses timetable. Finally, some future developments of the present work are presented by considering other bus lines that may benefit from the introduction of the defined charging systems. Full article

(This article belongs to the Special Issue Next Generation Energy and Propulsion Systems for Transportation Electrification)

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16 pages, 3713 KiB

Open AccessArticle

A Multi-Model Probability Based Two-Layer Fusion Modeling Approach of Supercapacitor for Electric Vehicles

by Bo Huang, Yuting Ma, Chun Wang, Yongzhi Chen and Quanqing Yu

Energies 2021, 14(15), 4644; https://doi.org/10.3390/en14154644 - 30 Jul 2021

Cited by 5 | Viewed by 1606

Abstract

The improvement of the supercapacitor model redundancy is a significant method to guarantee the reliability of the power system in electric vehicle application. In order to enhance the accuracy of the supercapacitor model, eight conventional supercapacitor models were selected for parameter identification by genetic algorithm, and the model accuracies based on standard diving cycle are further discussed. Then, three fusion modeling approaches including Bayesian fusion, residual normalization fusion, and state of charge (SOC) fragment fusion are presented and compared. In order to further improve the accuracy of these models, a two-layer fusion model based on SOC fragments is proposed in this paper. Compared with other fusion models, the root mean square error (RMSE), maximum error, and mean error of the two-layer fusion model can be reduced by at least 23.04%, 8.70%, and 30.13%, respectively. Moreover, the two-layer fusion model is further verified at 10, 25, and 40 °C, and the RMSE can be correspondingly reduced by 60.41%, 47.26%, 23.04%. The results indicate that the two-layer fusion model proposed in this paper achieves better robustness and accuracy. Full article

(This article belongs to the Special Issue Next Generation Energy and Propulsion Systems for Transportation Electrification)

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22 pages, 9362 KiB

Open AccessArticle

An Open Circuit Voltage Model Fusion Method for State of Charge Estimation of Lithium-Ion Batteries

by Quanqing Yu, Changjiang Wan, Junfu Li, Lixin E, Xin Zhang, Yonghe Huang and Tao Liu

Energies 2021, 14(7), 1797; https://doi.org/10.3390/en14071797 - 24 Mar 2021

Cited by 30 | Viewed by 3781

Abstract

The mapping between open circuit voltage (OCV) and state of charge (SOC) is critical to the lithium-ion battery management system (BMS) for electric vehicles. In order to solve the poor accuracy in the local SOC range of most OCV models, an OCV model fusion method for SOC estimation is proposed. According to the characteristics of the experimental OCV–SOC curve, the method divides SOC interval (0, 100%) into several sub-intervals, and respectively fits the OCV curve segments in each sub-interval to obtain a corresponding number of OCV sub-models with local high precision. After that, the OCV sub-models are fused through the continuous weight function to obtain fusional OCV model. Regarding the OCV curve obtained from low-current OCV test as the criterion, the fusional OCV models of LiNiMnCoO₂ (NMC) and LiFePO₄ (LFP) are compared separately with the conventional OCV models. The comparison shows great fitting accuracy of the fusional OCV model. Furthermore, the adaptive cubature Kalman filter (ACKF) is utilized to estimate SOC and capacity under a dynamic stress test (DST) at different temperatures. The experimental results show that the fusional OCV model can effectively track the performance of the OCV–SOC curve model. Full article

(This article belongs to the Special Issue Next Generation Energy and Propulsion Systems for Transportation Electrification)

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