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

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 1940

Special Issue Editor

Dr. Atriya Biswas

E-Mail Website
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

Published Papers (4 papers)

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Research

25 pages, 16782 KiB  
Article
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 326
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 [...] Read more.
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  
Article
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
Viewed by 243
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 [...] Read more.
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  
Article
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 509
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 [...] Read more.
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  
Article
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 469
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 [...] Read more.
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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