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Energies
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Key Functionalities in Battery Management Systems for Transportation Electrification
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Key Functionalities in Battery Management Systems for Transportation Electrification

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "D2: Electrochem: Batteries, Fuel Cells, Capacitors".

Deadline for manuscript submissions: closed (25 February 2025) | Viewed by 4232

Special Issue Editors

Dr. Qiao Wang

E-Mail Website
Guest Editor
1. National Engineering Research Center for Highway Maintenance Equipment, Chang'an University, Xi'an 710064, China
2. Chair for Electrochemical Energy Conversion and Storage Systems, Institute for Power Electronics and Electrical Drives (ISEA), RWTH Aachen University, 52074 Aachen, Germany
Interests: battery management; battery modelling; machine learning; battery degradation diagnosis and prognosis
Dr. Zhongwei Deng

E-Mail Website
Guest Editor
School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
Interests: parameters identification; state estimation; health and safety management
Special Issues, Collections and Topics in MDPI journals
Dr. Yue Wu

E-Mail Website
Guest Editor
School of Electronic Information, Central South University, Changsha 410075, China
Interests: energy management; electrified vehicle; energy storage system; optimal control
Dr. Yunhong Che

E-Mail Website
Guest Editor
Department of Energy, Aalborg University, 9220 Aalborg, Denmark
Interests: battery modeling and monitoring; health prognostics and maintenance; energy storage
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Driven by concerns about global warming and policy directives, the development of carbon-neutral economics through electric mobility is gaining increasing popularity. Lithium-ion batteries are among the most promising options for transportation electrification due to their high energy density, long cycle life, and low self-discharge rate. However, the application of lithium-ion batteries in electric vehicles still faces many challenges. Battery degradation and safety, unmeasurable state of charge, slow charging rate, and difficulty in fault diagnosis and prognosis all pose great challenges for transportation electrification. Battery management systems are essential to ensure the high efficiency, reliability, and safety of lithium-ion batteries in operation. The key functionalities of battery management systems include state estimation, degradation diagnosis and prognosis, fault diagnosis and prediction, and safe and efficient charge/discharge control.

Advanced key functionalities in battery management systems can potentially improve not only the usage of batteries but also the manufacturing based on positive feedback in field operation. With this in mind, this Special Issue aims to develop advanced key functionalities in battery management systems for electric vehicles. Original and high-quality research, reviews, and perspectives are invited for publication. Potential topics include, but are not limited to, the following:

  • Battery modelling with physics-based modelling and artificial intelligence;
  • State estimation in electrode, cell, module, and pack level;
  • Battery degradation diagnosis, prognosis, and optimization;
  • Battery thermal monitoring, management design, and control strategy;
  • Safety mechanisms, early warning, diagnosis, and prediction;
  • Advanced experiment and characterization;
  • Emerging sensor technologies for battery management;
  • Series/parallel analysis and cell balancing for battery pack;
  • Advanced energy management for electric vehicle.

Dr. Qiao Wang
Dr. Zhongwei Deng
Dr. Yue Wu
Dr. Yunhong Che
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

  • electric vehicles
  • battery management systems
  • key functionalities
  • physics-based modelling
  • artificial intelligence

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Published Papers (5 papers)

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Research

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27 pages, 6691 KiB  
Article
Efficient Hybrid Deep Learning Model for Battery State of Health Estimation Using Transfer Learning
by Jinling Ren, Misheng Cai and Dapai Shi
Energies 2025, 18(6), 1491; https://doi.org/10.3390/en18061491 - 18 Mar 2025
Viewed by 353
Abstract
Achieving accurate battery state of health (SOH) estimation is crucial, but existing methods still face many challenges in terms of data quality, computational efficiency, and cross-scenario generalization capabilities. This study proposes a hybrid deep learning framework incorporating transfer learning to address these challenges. [...] Read more.
Achieving accurate battery state of health (SOH) estimation is crucial, but existing methods still face many challenges in terms of data quality, computational efficiency, and cross-scenario generalization capabilities. This study proposes a hybrid deep learning framework incorporating transfer learning to address these challenges. The framework integrates inception depthwise convolution (IDC), channel reduction attention (CRA) mechanism, and staged training strategy to improve the accuracy and generalization ability of SOH estimation. The IDC module of the proposed model is capable of extracting battery degradation time series features from multiple scales while reducing the computational overhead. The CRA module effectively reduces the computational complexity and memory usage of global feature capture by compressing the channel dimensions. A well-designed pre-training/fine-tuning two-stage training strategy achieves accurate cross-scene SOH estimation by utilizing large-scale source-domain data to learn generalized aging features and then uses a small amount of new data to quickly fine-tune the base model. The proposed method is validated using two publicly available datasets, including 54 nickel cobalt manganese oxide (NCM) cells and 16 nickel manganese cobalt oxide (NMC) cells. The experimental results show that the root mean square error (RMSE) of the model on the NCM and NMC datasets is 0.522% and 0.283%, respectively, with a coefficient of determination (R2) not less than 0.98 and mean absolute percentage error (MAPE) of 0.431% and 0.22%, respectively. The proposed method not only achieves high-precision SOH estimation among the same type of batteries but also demonstrates strong generalization ability under different battery chemistries and scenarios. Full article
(This article belongs to the Special Issue Key Functionalities in Battery Management Systems for Transportation Electrification)
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14 pages, 5732 KiB  
Article
Data-Driven Energy Consumption Analysis and Prediction of Real-World Electric Vehicles at Low Temperatures: A Case Study Under Dynamic Driving Cycles
by Yifei Zhao, Hang Liu, Jinsong Li, Hongli Liu and Bin Li
Energies 2025, 18(5), 1239; https://doi.org/10.3390/en18051239 - 3 Mar 2025
Viewed by 590
Abstract
Accurate analysis and prediction of low-temperature energy consumption in pure electric vehicles can provide a reliable reference for energy optimization strategies, thereby alleviating range anxiety. Here, we propose a data-driven energy consumption analysis and prediction approach for real-world electric vehicles in cold conditions. [...] Read more.
Accurate analysis and prediction of low-temperature energy consumption in pure electric vehicles can provide a reliable reference for energy optimization strategies, thereby alleviating range anxiety. Here, we propose a data-driven energy consumption analysis and prediction approach for real-world electric vehicles in cold conditions. Specifically, the dataset was divided into multiple kinematic segments by the fixed-step intercept method, and principal component analysis was applied on segment parameters, showing the average speed and acceleration time had the greatest impact on energy consumption at −7 °C. Then, a Bayesian optimized XGBoost model, with the two factors above as input, was constructed to predict the cumulative driving and total energy consumption. This method was validated with two different types of pure electric vehicles under different dynamic driving cycles. The results demonstrated that the model could predict low-temperature energy consumption accurately, with all mean relative errors less than 3%. Full article
(This article belongs to the Special Issue Key Functionalities in Battery Management Systems for Transportation Electrification)
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25 pages, 6800 KiB  
Article
Deep-Fuzzy Logic Control for Optimal Energy Management: A Predictive and Adaptive Framework for Grid-Connected Microgrids
by Muhammed Cavus, Dilum Dissanayake and Margaret Bell
Energies 2025, 18(4), 995; https://doi.org/10.3390/en18040995 - 19 Feb 2025
Cited by 3 | Viewed by 838
Abstract
This paper introduces a novel energy management framework, Deep-Fuzzy Logic Control (Deep-FLC), which combines predictive modelling using Long Short-Term Memory (LSTM) networks with adaptive fuzzy logic to optimise energy allocation, minimise grid dependency, and preserve battery health in grid-connected microgrid (MG) systems. Integrating [...] Read more.
This paper introduces a novel energy management framework, Deep-Fuzzy Logic Control (Deep-FLC), which combines predictive modelling using Long Short-Term Memory (LSTM) networks with adaptive fuzzy logic to optimise energy allocation, minimise grid dependency, and preserve battery health in grid-connected microgrid (MG) systems. Integrating LSTM-based predictions provides foresight into system parameters such as state of charge, load demand, and battery health, while fuzzy logic ensures real-time adaptive control. Results demonstrate that Deep-FLC achieves a 25.7% reduction in operational costs compared to the conventional system and a 17.5% saving cost over the Fuzzy Logic Control (FLC) system. Additionally, Deep-FLC delivers the highest battery efficiency of 61% and constraints depth of discharge to below 2% per time step, resulting in a reduction of the state of health degradation to less than 0.2% over 300 h. By combining predictive analytics with adaptive control, this study addresses the limitations of standalone approaches and establishes Deep-FLC as a robust, efficient, and sustainable energy management solution. Key novel contributions include the integration of advanced prediction mechanisms with fuzzy control and its application to battery-integrated grid-connected MG systems. Full article
(This article belongs to the Special Issue Key Functionalities in Battery Management Systems for Transportation Electrification)
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9 pages, 8524 KiB  
Article
Hydrothermal Synthesis of Hierarchical Cage-like Co9S8 Microspheres Composed of Nanosheets as High-Capacity Anode Materials
by Haomiao Yang, Lehao Liu, Junfeng Ma, Jinkui Zhang and Qiaomu Zhang
Energies 2024, 17(22), 5553; https://doi.org/10.3390/en17225553 - 7 Nov 2024
Cited by 2 | Viewed by 757
Abstract
Co9S8 is considered to be one of the most promising anode materials because of its high theoretical capacity. In this work, hierarchical cage-like Co9S8 microspheres composed of well-crystallized nanosheets are successfully synthesized at 180 °C by a [...] Read more.
Co9S8 is considered to be one of the most promising anode materials because of its high theoretical capacity. In this work, hierarchical cage-like Co9S8 microspheres composed of well-crystallized nanosheets are successfully synthesized at 180 °C by a hydrothermal method using KOH and disodium ethylenediamine tetraacetate (Na2EDTA) as a mineralizer and a complexing agent, respectively. X-ray diffraction and scanning electron microscopy measurements show that KOH is beneficial in promoting the crystallization and development of Co9S8, avoiding the formation of impurities, while Na2EDTA is conducive to the generation of cage-like microspheres with the micro/nano architecture and better crystallization. The unique hierarchical cage-like micro/nano architecture can effectively relieve the volume change in the cycling process, and the well-crystallized Co9S8 nanosheets in the cage-like microspheres can offer much more active sites for Li+ accommodation, and thus the hierarchical cage-like Co9S8 microspheres composed of well-crystallized nanosheets show superior cycling stability and rate capability, e.g., a high capacity of 303.5 mAh g−1 after 1000 cycles at a high rate of 1.0 A g−1. This work provides a new approach for improving the electrochemical performance of LIBs by constructing a hierarchical anode material. Full article
(This article belongs to the Special Issue Key Functionalities in Battery Management Systems for Transportation Electrification)
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Review

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20 pages, 524 KiB  
Review
Deep Learning for State of Health Estimation of Lithium-Ion Batteries in Electric Vehicles: A Systematic Review
by Chenyuan Liu, Heng Li, Kexin Li, Yue Wu and Baogang Lv
Energies 2025, 18(6), 1463; https://doi.org/10.3390/en18061463 - 17 Mar 2025
Viewed by 719
Abstract
Electric vehicles (EVs) play a crucial role in addressing the energy crisis and mitigating the greenhouse effect. Lithium-ion batteries are the primary energy storage medium for EVs due to their numerous advantages. State of health (SOH) is a critical parameter for managing the [...] Read more.
Electric vehicles (EVs) play a crucial role in addressing the energy crisis and mitigating the greenhouse effect. Lithium-ion batteries are the primary energy storage medium for EVs due to their numerous advantages. State of health (SOH) is a critical parameter for managing the health of lithium-ion batteries, and accurate SOH estimation forms the foundation of battery management systems (BMS), ensuring the safe operation of EVs. Data-driven deep learning techniques are attracting significant attention because of their strong ability to model complex nonlinear relationships, which makes them highly suitable for SOH estimation in lithium-ion batteries. This paper provides a comprehensive introduction to the common deep learning techniques used for SOH estimation of lithium-ion batteries, with a focus on model architectures. It systematically reviews the application of various deep learning algorithms in SOH estimation in recent years. Building on this, the paper offers a detailed comparison of these deep learning methods and discusses the current challenges and future directions in this field, with the aim of providing an extensive review of the role of deep learning in SOH estimation. Full article
(This article belongs to the Special Issue Key Functionalities in Battery Management Systems for Transportation Electrification)
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