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Advances in Battery Technologies for Electric Vehicles
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Advances in Battery Technologies for Electric Vehicles

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

Deadline for manuscript submissions: 25 September 2025 | Viewed by 4911

Special Issue Editors

Dr. Minella Bezha

E-Mail Website
Guest Editor
Power System Analysis Laboratory, International Infrastructure System Research Center, Kyotonabe Campus, Doshisha University, 1-3, Miyakodani, Tatara, Kyotanabe, Kyoto 610-0394, Japan
Interests: hardware and software embedded projects; next gen EV technology; battery and advance charging system; VGI-V2X system; efficient embedded circuits; renewable energy generation; infrastructure; real time diagnosis; data mining; development of artificial intelligence and robotics; power electronic circuits; practical applied physics; high voltage; transdisciplinary science and engineering fields
Prof. Dr. Naoto Nagaoka

E-Mail Website
Guest Editor
Power System Analysis Lab, Director of International Infrastructure System Research Center, Department of Electrical & Electronic Engineering, Kyotonabe Campus, Doshisha University, 1-3, Miyakodani, Tatara, Kyotanabe, Kyoto 610-0394, Japan
Interests: complex and advance infrastructure; power electronic circuits; high voltage; numerical simulations; AI algorithm; artificial intelligence and robotics; next gen EV technology; electromagnetism; embedded systems; renewable energy generation telecommunication system; lightning protections

Special Issue Information

Dear Colleagues,

EVs are, without doubt, the future and the solution of zero emission mobility, and even beyond that. By 2030, it is estimated that EVs will represent more than 62% of vehicles sold globally. Billions of dollars are spent globally on the research, testing, and manufacture of next-generation EV battery technology, which can enable higher energy density, higher power input/output, lighter materials, longer lifetimes, and safer operation, and require more abundant and eco-friendly minerals to be mined and recycled. In order to reach these goals, academics, scientist, vehicle manufacturers, and research institutes are working together to investigate, design, integrate, and validate the best and optimal solution for future EV batteries. The aim of this Special Issue of Energies is to propose, explore, introduce, discuss, and clarify research innovation and theoretical and practical industrial concepts within complex system engineering, which includes new battery pack design, smart battery management, advanced thermal-electrical and mechanical management, new battery chemistry for EVs, new battery electrodes and electrolyte materials, solid state batteries, advanced fast charging system, and re-usability of the EV battery pack for 2nd, 3rd, or nth life applications. Specific areas of interest include, but are not limited to:

  • New concepts in vehicle energy storage design, including the use of hybrid or mixed technology systems (e.g., NMC and LFP battery combinations) within both first-life and second-life applications.
  • New concepts in energy management optimization and energy storage system design within electrified vehicles with greater levels of autonomy and connectivity.
  • New fast-charging technology enabling extremely fast times, with the ability to handle immense power density without compromising the lifecycle of the battery pack.
  • Innovative machine learning model-based investigation, and optimization for battery design, as well as fast and accurate (SoH) state of health and (SoC) state of charge estimation.
  • The design, verification, and implementation of enhanced algorithms and models for battery control and monitoring, including new methods in state of charge estimation, state of health estimation, fast charge management, and active balancing.
  • Novel methods of thermal management, including the creation of new models, the use of new materials, and their integration within the broader thermal management requirements of the vehicle.
  • Next-generation VGI (vehicle grid integration) systems based on the optimization of EV battery lifetime, grid sustainability, and load flow efficiency, as well as micro-grid integration of EVs,
  • New proposal for telematics, big data mining, and machine learning for the performance analysis, diagnosis, and management of energy storage and integrated systems.

Dr. Minella Bezha
Prof. Dr. Naoto Nagaoka
Guest Editors

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 optimization
  • new battery pack design
  • battery material systems modeling
  • accurate SoH estimation
  • on board testing battery
  • new smart battery management systems
  • advance balancing cell design
  • battery mix chemistry in one single EV pack
  • drive-cycle creation
  • micro grid analysis based on a pool of EVs
  • innovative thermal-electric management
  • energy storage ageing and degradation
  • system testing and verification
  • vehicle-to-grid integration scenarios
  • super fast charging technology
  • life-cycle assessment
  • second-life energy storage applications
  • AI tools and optimization in EV tech
  • optimal battery lifetime during fast charging

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Published Papers (3 papers)

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Research

29 pages, 11635 KiB  
Article
A Feed-Forward Back-Propagation Neural Network Approach for Integration of Electric Vehicles into Vehicle-to-Grid (V2G) to Predict State of Charge for Lithium-Ion Batteries
by Alice Cervellieri
Energies 2024, 17(23), 6107; https://doi.org/10.3390/en17236107 - 4 Dec 2024
Cited by 1 | Viewed by 810
Abstract
The accurate prediction and efficient management of the State of Charge (SoC) of electric vehicle (EV) batteries are critical challenges in the integration of vehicle-to-grid (V2G) systems within multi-energy microgrid (MMO) models. Inaccurate SoC estimation can lead to inefficiencies, increased costs, and potential [...] Read more.
The accurate prediction and efficient management of the State of Charge (SoC) of electric vehicle (EV) batteries are critical challenges in the integration of vehicle-to-grid (V2G) systems within multi-energy microgrid (MMO) models. Inaccurate SoC estimation can lead to inefficiencies, increased costs, and potential disruptions in power generation. This paper addresses the problem of optimizing SoC estimation to enhance the reliability and efficiency of V2G scheduling and MMO coordination. In this work, we develop a Feed-Forward Back-Propagation Network (FFBPN) using MATLAB 2024 software, employing the Levenberg–Marquardt algorithm and varying the number of hidden neurons to achieve better performance; performance was measured by the maximum coefficient of determination (R2) and the minimum mean squared error (MSE). Utilizing the NASA Prognostics Center of Excellence (PCoE) dataset, we validate the model’s capability to accurately predict the life cycle of EV batteries. Our proposed FFBPN model demonstrates superior performance compared to existing methods from the literature, offering significant implications for future V2G system developments. The comparison between training, validation, and testing phases underscores the model’s validity and precisely identifies the characteristic curves of FFBPN, showcasing its potential to enhance profitability, efficiency, production, energy savings, and minimize environmental impact. Full article
(This article belongs to the Special Issue Advances in Battery Technologies for Electric Vehicles)
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25 pages, 1040 KiB  
Article
Optimal Vehicle-to-Grid Strategies for Energy Sharing Management Using Electric School Buses
by Ruengwit Khwanrit, Saher Javaid, Yuto Lim, Chalie Charoenlarpnopparut and Yasuo Tan
Energies 2024, 17(16), 4182; https://doi.org/10.3390/en17164182 - 22 Aug 2024
Cited by 5 | Viewed by 1431
Abstract
In today’s power systems, electric vehicles (EVs) constitute a significant factor influencing electricity dynamics, with their important role anticipated in future smart grid systems. An important feature of electric vehicles is their dual capability to both charge and discharge energy to/from their battery [...] Read more.
In today’s power systems, electric vehicles (EVs) constitute a significant factor influencing electricity dynamics, with their important role anticipated in future smart grid systems. An important feature of electric vehicles is their dual capability to both charge and discharge energy to/from their battery storage. Notably, the discharge capability enables them to offer vehicle-to-grid (V2G) services. However, most V2G research focuses on passenger cars, which typically already have their own specific usage purposes and various traveling schedules. This situation may pose practical challenges in providing ancillary services to the grid. Conversely, electric school buses (ESBs) exhibit a more predictable usage pattern, often deployed at specific times and remaining idle for extended periods. This makes ESBs more practical for delivering V2G services, especially when prompted by incentive price signals from grid or utility companies (UC) requesting peak shaving services. In this paper, we introduce a V2G energy sharing model focusing on ESBs in various schools in a single community by formulating the problem as a leader–follower game. In this model, the UC assumes the role of the leader, determining the optimal incentive price to offer followers for discharging energy from their battery storage. The UC aims to minimize additional costs from generating energy during peak demand. On the other hand, schools in a community possessing multiple ESBs act as followers, seeking the optimal quantity of discharged energy from their battery storage. They aim to maximize utility by responding to the UC’s incentive price. The results demonstrate that the proposed model and algorithm significantly aid the UC in reducing the additional cost of energy generation during peak periods by 36% compared to solely generating all electricity independently. Furthermore, they substantially reduce the utility bills for schools by up to 22.6% and lower the peak-to-average ratio of the system by up to 9.5%. Full article
(This article belongs to the Special Issue Advances in Battery Technologies for Electric Vehicles)
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19 pages, 5620 KiB  
Article
Research on Quantitative Diagnosis of Dendrites Based on Titration Gas Chromatography Technology
by Kai Yang, Hongchang Cai, Suran Li, Yu Wang, Xue Zhang, Zhenxuan Wu, Yilin Lai, Minella Bezha, Klara Bezha, Naoto Nagaoka, Yuejiu Zheng and Xuning Feng
Energies 2024, 17(10), 2409; https://doi.org/10.3390/en17102409 - 17 May 2024
Viewed by 1214
Abstract
Lithium plating can cause capacity fade and thermal runaway safety issues in lithium-ion batteries. Therefore, accurately detecting the amount of lithium plating on the surface of the battery’s negative electrode is crucial for battery safety. This is especially crucial in high-energy-density applications such [...] Read more.
Lithium plating can cause capacity fade and thermal runaway safety issues in lithium-ion batteries. Therefore, accurately detecting the amount of lithium plating on the surface of the battery’s negative electrode is crucial for battery safety. This is especially crucial in high-energy-density applications such as battery energy storage systems or in electric vehicles (EVs). Early detection of lithium plating is crucial for evaluation of reliability and longevity. It also serves as a method for early diagnostics in practical industrial applications or infrastructure, such as EV transportation. This can enhance its impact on customers. This study validates the effectiveness of titration gas chromatography (TGC) technology in quantitatively detecting lithium plating on graphite negative electrodes in lithium-ion batteries. The results show that it can detect a minimum of 2.4 μmol of metallic lithium. Compared with the heating direct current resistance and reference electrode methods, which can be used to perform only qualitative dendrite detection, TGC has a wider range of detection. Compared with the nuclear magnetic resonance (NMR) method with higher quantitative detection accuracy, the maximum difference between the detection results of the two methods was only 7.2%, but the TGC method had lower cost and higher implementation convenience. In summary, among various dendrite detection methods, the TGC method can not only realize the effective quantitative detection of lithium plating, but also comprehensively consider its detection range, implementation convenience, cost, and detection accuracy, indicating that it is suitable for engineering applications and has the prospect of realizing large-scale quantitative detection of lithium plating in lithium-ion batteries. Full article
(This article belongs to the Special Issue Advances in Battery Technologies for Electric Vehicles)
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