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Energy Management Systems of Electric Vehicles: New Trends and Dynamic Futures

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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: 20 August 2024 | Viewed by 7681

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Dr. Atriya Biswas

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Guest Editor

McMaster Automotive Resource Center (MARC), McMaster University, Hamilton, ON L8P 0A6, Canada
Interests: reinforcement learning; intelligent control of electrified vehicles; hybrid electric vehicles; energy management systems; next generation transportation; hardware-in-the-loop implementation

Special Issue Information

Dear Colleagues,

The Guest Editor is welcoming submissions to a Special Issue of Energies entitled “Energy Management Systems of Electric Vehicles: New Trends and Dynamic Futures”.

In a global drive to expedite the adoption of battery electric vehicles (BEVs) and phase out conventional internal combustion engines (ICEs) reliant on fossil fuels, governments worldwide are intensifying their commitment to sustainability. The ascendancy of BEVs in the automotive landscape is undeniable, with a remarkable 43% growth in the market share recorded in 2020 compared to 2019. Notably, in 2020, BEVs constituted two-thirds of all new electric car registrations. Despite their emission-free profiles, energy independence from fossil fuels, and minimal noise pollution, BEVs grapple with notable challenges that necessitate mitigation for further market penetration. These challenges encompass the limited availability of public recharging infrastructure, pricing competitiveness vis-à-vis traditional ICE vehicles, extended recharging durations, and the ever-lingering concern of range anxiety.

To bolster the travel range, operational efficiency, and dynamic performance of BEVs, both academia and the automotive industry have been actively proposing an array of solutions centered around intelligent and innovative energy management systems (EMS) within the powertrain domain. This Special Issue serves as a platform not only for the dissemination of cutting-edge advancements in intelligent and innovative EMS for BEVs, but also for the exploration of futuristic energy management paradigms. These forward-looking frameworks incorporate elements such as the Internet of Things (IoT), vehicle-to-everything (V2X) connectivity, onboard predictive optimization, and reinforcement learning.

Furthermore, this Special Issue extends an invitation to comprehensive review articles that span both contemporary and visionary EMSs for BEVs.

Topics of interest for publication include, but are not limited to, the following:

Integrated thermal and energy management system
Energy management of multi-motor battery electric vehicle
Reinforcement learning-based EMSs for BEVs
Impact of EMS in designing multi-speed BEVs
Traffic predictive EMSs for BEVs and range extension
Multi-objective optimization-based EMSs for BEVs
EMSs for ICE-based and fuel-cell range-extended electric vehicles
EMSs formulation for long-haul battery electric trucks
Energy savings of BEVs in connected driving scenario
Regenerative braking efficiency/ energy maximization in BEVs

Dr. Atriya Biswas
Guest Editor

Manuscript Submission Information

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.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

energy management system
range extension
electric vehicles
charging management
integrated thermal and energy management
dual-motor electric vehicle
regenerative braking efficiency
multi-motor electric vehicle
net-zero vehicles

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Published Papers (11 papers)

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Research

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16 pages, 2846 KiB

Open AccessArticle

Vehicle Acceleration and Speed as Factors Determining Energy Consumption in Electric Vehicles

by Edward Kozłowski, Piotr Wiśniowski, Maciej Gis, Magdalena Zimakowska-Laskowska and Anna Borucka

Energies 2024, 17(16), 4051; https://doi.org/10.3390/en17164051 - 15 Aug 2024

Abstract

Energy consumption in electric vehicles is a key element of their operation, determining energy efficiency and one of its main indicators, i.e., range. Therefore, in this article, mathematical models were developed to evaluate the impact of selected factors on energy consumption in electric vehicles. The phenomenon of energy recuperation was also examined. The study used data from mileage measurements of the electric vehicle (EV) driving on a motorway and in built-up areas. The results obtained showed a strong correlation between acceleration, vehicle speed, battery power, and energy consumption. In urban conditions, engine RPM and vehicle speed had an additional impact on energy consumption. Findings from this study can be used to optimize vehicle acceleration control modules to increase their range, develop eco-driving styles for EV drivers, and better understand the energy efficiency factors of EVs. Full article

(This article belongs to the Special Issue Energy Management Systems of Electric Vehicles: New Trends and Dynamic Futures)

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20 pages, 2476 KiB

Open AccessArticle

Real-Time Implementable Integrated Energy and Cabin Temperature Management for Battery Life Extension in Electric Vehicles

by Mattia Mauro, Atriya Biswas, Carlo Fiorillo, Hao Wang, Ezio Spessa, Federico Miretti, Ryan Ahmed, Angelo Bonfitto and Ali Emadi

Energies 2024, 17(13), 3185; https://doi.org/10.3390/en17133185 - 28 Jun 2024

Viewed by 588

Abstract

Among many emerging technologies, battery electric vehicles (BEVs) have emerged as a prominent and highly supported solution to stringent emissions regulations. However, despite their increasing popularity, key challenges that might jeopardize their further spread are the lack of charging infrastructure, battery life degradation, and the discrepancy between the actual and promised all-electric driving range. The primary focus of this paper is to formulate an integrated energy and thermal comfort management (IETM) strategy. This strategy optimally manages the electrical energy required by the heating, ventilation, and air conditioning (HVAC) unit, the most impacting auxiliary in terms of battery load, to minimize battery life degradation over any specific drive cycle while ensuring the actual cabin temperature hovers within the permissible tolerance limit from the reference cabin temperature and the driver-requested traction power is always satisfied. This work incorporates a state-of-health (SOH) estimation model, a high-fidelity cabin thermodynamics model, and an HVAC model into the forward-approach simulation model of a commercially available BEV to showcase the impact and efficacy of the proposed IETM strategy for enhancing battery longevity. The instantaneous optimization problem of IETM is solved by the golden-section search method leveraging the convexity of the objective function. Simulated results under different driving scenarios show that the improvement brought by the proposed ITEM controller can minimize battery health degradation by up to 4.5% and energy consumption by up to 2.8% while maintaining the cabin temperature deviation within permissible limits from the reference temperature. Full article

(This article belongs to the Special Issue Energy Management Systems of Electric Vehicles: New Trends and Dynamic Futures)

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18 pages, 13225 KiB

Open AccessFeature PaperArticle

AC Direct Charging for Electric Vehicles via a Reconfigurable Cascaded Multilevel Converter

by Giulia Tresca and Pericle Zanchetta

Energies 2024, 17(10), 2428; https://doi.org/10.3390/en17102428 - 19 May 2024

Viewed by 569

Abstract

This paper presents a charging architecture for the Reconfigurable Cascaded Multilevel converter, which was specifically designed for electric vehicle (EV) powertrain applications. The RCMC topology is capable of executing power conversion and actively managing battery systems concurrently. The active battery management is achieved using the Reconfigurable Battery Module, which regulates the serial connection of cells via a switch pattern. In this paper, the RCMC is directly interfaced with an AC three-phase power system, facilitating the dynamic control over battery cells charging. Its inherent design allows for the implementation of various charging algorithms, customizable to specific requirements, without necessitating additional intermediary power stages. Firstly, an overview of the RCMC topology is given, and an analysis to define the optimal filter inductance is carried out. Subsequently, after the AC system characteristics are explained, two charging algorithms are presented and described: one prioritizes State of Charge (SOC) balancing among battery cells, while the other focuses on minimizing power losses. Moreover, a time estimation computation for the RCMC is carried out considering a two-level AC charging station. The result is compared with the time required for a conventional battery pack. The results show a reduction of 10 s in charging time for a mere 20% increase in SOC. Finally, the experimental setup is presented and used to validate the efficacy of the proposed algorithms. Full article

(This article belongs to the Special Issue Energy Management Systems of Electric Vehicles: New Trends and Dynamic Futures)

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27 pages, 3850 KiB

Open AccessArticle

Policy Assessment for Energy Transition to Zero- and Low-Emission Technologies in Pickup Trucks: Evidence from Mexico

by Julieth Stefany Garcia, Laura Milena Cárdenas, Jose Daniel Morcillo and Carlos Jaime Franco

Energies 2024, 17(10), 2386; https://doi.org/10.3390/en17102386 - 15 May 2024

Viewed by 763

Abstract

The transport sector is under scrutiny because of its significant greenhouse gas emissions. Essential strategies, particularly the adoption of zero- and low-emission vehicles powered by electricity, are crucial for mitigating emissions in road transport. Pickups, which are integral to Mexico’s fleet, contribute to such emissions. Thus, implementing effective policies targeting pickups is vital for reducing air pollution and aligning with Mexico’s decarbonization objectives. This paper presents a simulation model based on system dynamics to represent the adoption process of zero- and low-emission vehicles, with a focus on pickups and utilizing data from the Mexican case. Three policy evaluation scenarios are proposed based on the simulation model: business as usual; disincentives for zero- and low-emission vehicles; and incentives for unconventional vehicles. One of the most significant findings from this study is that even in a scenario with a greater number of vehicles in circulation, if the technology is fully electric, the environmental impact in terms of emissions is lower. Additionally, a comprehensive sensitivity analysis spanning a wide spectrum is undertaken through an extensive computational process, yielding multiple policy scenarios. The analysis indicates that to achieve a maximal reduction in the country’s emissions, promoting solely hybrid electric vehicles and plug-in hybrid electric vehicles is advisable, whereas internal combustion engines, vehicular natural gas, and battery electric vehicles should be discouraged. Full article

(This article belongs to the Special Issue Energy Management Systems of Electric Vehicles: New Trends and Dynamic Futures)

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21 pages, 2750 KiB

Open AccessArticle

Model-Predictive-Control-Based Centralized Disturbance Suppression Strategy for Distributed Drive Electric Vehicle

by Aiping Tan, Lixiao Gao and Yanfeng Chen

Energies 2024, 17(10), 2268; https://doi.org/10.3390/en17102268 - 8 May 2024

Viewed by 761

Abstract

This paper presents a centralized disturbance suppression strategy for distributed drive electric vehicles which is based on model predictive direct motion control. This strategy is capable of addressing issues such as parameter uncertainties and external disturbances in vehicles. Firstly, the paper provides a brief introduction to model predictive direct motion control. Secondly, it analyzes the impact of vehicle parameter uncertainties and external disturbances on the mathematical model. Finally, a centralized disturbance suppression strategy based on a sliding mode observer is proposed. Simulation results demonstrate that this strategy exhibits excellent disturbance rejection capabilities. Full article

(This article belongs to the Special Issue Energy Management Systems of Electric Vehicles: New Trends and Dynamic Futures)

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25 pages, 16782 KiB

Open AccessArticle

Mean Field Game-Based Algorithms for Charging in Solar-Powered Parking Lots and Discharging into Homes a Large Population of Heterogeneous Electric Vehicles

by Samuel M. Muhindo

Energies 2024, 17(9), 2118; https://doi.org/10.3390/en17092118 - 29 Apr 2024

Viewed by 828

Abstract

An optimal daily scheme is presented to coordinate a large population of heterogeneous battery electric vehicles when charging in daytime work solar-powered parking lots and discharging into homes during evening peak-demand hours. First, we develop a grid-to-vehicle strategy to share the solar energy available in a parking lot between vehicles where the statistics of their arrival states of charge are dictated by an aggregator. Then, we develop a vehicle-to-grid strategy so that vehicle owners with a satisfactory level of energy in their batteries could help to decongest the grid when they return by providing backup power to their homes at an aggregate level per vehicle based on a duration proposed by an aggregator. Both strategies, with concepts from Mean Field Games, would be implemented to reduce the standard deviation in the states of charge of batteries at the end of charging/discharging vehicles while maintaining some fairness and decentralization criteria. Realistic numerical results, based on deterministic data while considering the physical constraints of vehicle batteries, show, first, in the case of charging in a parking lot, a strong to slight decrease in the standard deviation in the states of charge at the end, respectively, for the sunniest day, an average day, and the cloudiest day; then, in the case of discharging into the grid, over three days, we observe at the end the same strong decrease in the standard deviation in the states of charge. Full article

(This article belongs to the Special Issue Energy Management Systems of Electric Vehicles: New Trends and Dynamic Futures)

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18 pages, 3182 KiB

Open AccessArticle

Designing a Real-Time Implementable Optimal Adaptive Cruise Control for Improving Battery Health and Energy Consumption in EVs through V2V Communication

by Carlo Fiorillo, Mattia Mauro, Atriya Biswas, Angelo Bonfitto and Ali Emadi

Energies 2024, 17(9), 1986; https://doi.org/10.3390/en17091986 - 23 Apr 2024

Cited by 1 | Viewed by 625

Abstract

Battery electric vehicles (BEVs) face challenges like their limited all-electric range, the discrepancy between promised and actual energy efficiency, and battery health degradation, despite their environmental benefits. This article proposes an optimal adaptive cruise control (OACC) framework by leveraging ideal vehicle-to-vehicle communication to address these challenges. In a connected vehicle environment, where it is assumed that the Ego vehicle’s vehicle control unit (VCU) accurately knows the speed and position of the Leading vehicle, the VCU can optimally plan the acceleration trajectory for a short-term future time window through a model predictive control (MPC) framework tailored to BEVs. The primary objective of the OACC is to reduce the energy consumption and battery state-of-health degradation of a BEV. The Chevrolet Spark 2015 is chosen as the BEV platform used to validate the effectiveness of the proposed OACC. Simulations conducted under urban and highway driving conditions, as well as under communication delay and infused noise, resulted in up to a 3.7% reduction in energy consumption and a 9.7% reduction in battery state-of-health (SOH) degradation, demonstrating the effectiveness and robustness of the proposed OACC. Full article

(This article belongs to the Special Issue Energy Management Systems of Electric Vehicles: New Trends and Dynamic Futures)

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20 pages, 5663 KiB

Open AccessArticle

Research on Precise Tracking Control of Gear-Shifting Actuator for Non-Synchronizer Automatic Mechanical Transmission Based on Sleeve Trajectory Planning

by Xiangyu Gongye, Changqing Du, Longjian Li, Cheng Huang, Jinhai Wang and Zhengli Dai

Energies 2024, 17(5), 1092; https://doi.org/10.3390/en17051092 - 25 Feb 2024

Viewed by 718

Abstract

The Non-Synchronizer Automated Mechanical Transmission (NSAMT) demonstrates a straightforward structure and cost-effectiveness; however, the primary obstacle to its widespread application lies in NSAMT shift control. The implementation of active angle alignment effectively addresses the issue of shifting quality, but achieving active angle alignment necessitates precise tracking of the planned shifting curve by the gear-shifting actuator. To tackle the control problem of accurate tracking for NSAMT shift actuators, this paper initially analyzes the structure and shift characteristics of the NSAMT. Based on this analysis, a physical model is established using Amesim, incorporating a drive motor, two-gear NSAMT, shift actuator, sleeve, and DC motor model. An extended state observer (ESO) is designed to mitigate unknown interference within the system. Furthermore, an active angle alignment control algorithm based on “zero speed difference” and “zero angle difference” for double target tracking is constructed while planning the axial motion trajectory of the sleeve. The Backstepping algorithm is employed to successfully track and regulate this planned trajectory. Finally, through Hardware-in-the-Loop testing, we validate our proposed control strategy, which demonstrates consistent results with simulation outcomes, thereby affirming its effectiveness. Full article

(This article belongs to the Special Issue Energy Management Systems of Electric Vehicles: New Trends and Dynamic Futures)

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18 pages, 941 KiB

Open AccessFeature PaperArticle

Reliability Assessment of Integrated Power and Road System for Decarbonizing Heavy-Duty Vehicles

by Wei Zuo and Kang Li

Energies 2024, 17(4), 934; https://doi.org/10.3390/en17040934 - 17 Feb 2024

Viewed by 702

Abstract

With the continual expansion of urban road networks and global commitments to net zero, electric vehicles (EVs) have been considered to be the most viable solution to decarbonize the transportation sector. In recent years, the electric road system (ERS) has been introduced and piloted in a few countries and regions to decarbonize heavy-duty vehicles. However, little research has been carried out on its reliability. This paper fills the gap and investigates the reliability of electric truck power supply systems for electric road (ETPSS–ER), which considers both the power system and truck traffic networks. First, a brief introduction of electric roads illustrates the working principle of EV charging on roads. Then, an optimized electric truck (ET) travel pattern model is built, based on which the corresponding ET charging load demand, including both static charging and dynamic charging, is conducted. Then, based on the new ET travel pattern model, a daily travel-pattern-driven Monte Carlo simulation-based reliability assessment method for ETPSS–ER system is presented. Case studies based on the IEEE RBTS system shows that ETs driving on ERS systems can meet the daily travel demands. The case studies also examine the impacts of increasing number of ETs, extra wind power, and battery energy storage systems (BESS) on the reliability of ERS power systems. Full article

(This article belongs to the Special Issue Energy Management Systems of Electric Vehicles: New Trends and Dynamic Futures)

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Review

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29 pages, 8533 KiB

Open AccessReview

A Review of Modular Electrical Sub-Systems of Electric Vehicles

by Ahmed Darwish, Mohamed A. Elgenedy and Barry W. Williams

Energies 2024, 17(14), 3474; https://doi.org/10.3390/en17143474 - 15 Jul 2024

Viewed by 540

Abstract

Climate change risks have triggered the international community to find efficient solutions to reduce greenhouse gas (GHG) emissions mainly produced by the energy, industrial, and transportation sectors. The problem can be significantly tackled by promoting electric vehicles (EVs) to be the dominant technology in the transportation sector. Accordingly, there is a pressing need to increase the scale of EV penetration, which requires simplifying the manufacturing process, increasing the training level of maintenance personnel, securing the necessary supply chains, and, importantly, developing the charging infrastructure. A new modular trend in EV manufacturing is being explored and tested by several large automotive companies, mainly in the USA, the European Union, and China. This modular manufacturing platform paves the way for standardised manufacturing and assembly of EVs when standard scalable units are used to build EVs at different power scales, ranging from small light-duty vehicles to large electric buses and trucks. In this context, modularising EV electric systems needs to be considered to prepare for the next EV generation. This paper reviews the main modular topologies presented in the literature in the context of EV systems. This paper summarises the most promising topologies in terms of modularised battery connections, propulsion systems focusing on inverters and rectifiers, modular cascaded EV machines, and modular charging systems. Full article

(This article belongs to the Special Issue Energy Management Systems of Electric Vehicles: New Trends and Dynamic Futures)

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26 pages, 7323 KiB

Open AccessReview

Review of Switched Reluctance Motor Converters and Torque Ripple Minimisation Techniques for Electric Vehicle Applications

by Ali Abdel-Aziz, Mohamed Elgenedy and Barry Williams

Energies 2024, 17(13), 3263; https://doi.org/10.3390/en17133263 - 3 Jul 2024

Viewed by 688

Abstract

This paper presents a review of the most common power converters and torque ripple minimisation approaches for switched reluctance motors (SRMs). Unlike conventional three-phase AC motors, namely squirrel cage induction motors and permanent magnet synchronous motors, which require a typical three-phase inverter for operation, the switched reluctance motor requires a different topology power converter for reliable and efficient operation. In addition, due to the non-linear, discrete nature of SRM torque production, torque ripple is severely pronounced, which is undesirable in servo applications like electric vehicles. Hence, deploying a proper torque control function for smooth and quiet motor operation is crucial. This paper sheds light over the most popular SRM power converters as well as torque ripple minimisation methods, and it suggests an optimal SRM drive topology for EV applications. Full article

(This article belongs to the Special Issue Energy Management Systems of Electric Vehicles: New Trends and Dynamic Futures)

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