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Electricity and Electronics in Intelligent Battery Management Systems of Electric Vehicles

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A special issue of Batteries (ISSN 2313-0105). This special issue belongs to the section "Battery Modelling, Simulation, Management and Application".

Deadline for manuscript submissions: closed (15 January 2024) | Viewed by 18688

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Special Issue Editors

Dr. Xinhua Liu

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Guest Editor

School of Transportation Science and Engineering, Beihang University, Beijing 100191, China
Interests: energy material design; battery microstructure modeling; cloud-based battery system management
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Zhenhai Gao

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Guest Editor

State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130022, China
Interests: automotive integration and bionics; smart batteries; battery safety
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Shichun Yang

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School of Transportation Science and Engineering, Beihang University, Beijing 100191, China
Interests: electric vehicle; hybird power system; powertrain; control strategy; battery management system; automobile emission control

Dr. Cheng Zhang

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Guest Editor

Centre for Advanced Low Carbon Propulsion Systems (C-ALPS), Coventry University, Coventry, UK
Interests: battery energy storage system; design of experiment for battery characterisation; model and simulation; parameter optimisation; adaptive state and parameter estimation; control algorithms
Special Issues, Collections and Topics in MDPI journals

Dr. Shen Li

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Guest Editor

Department of Mechanical Engineering, Imperial College London, London SW7 2AZ, UK
Interests: battery management systems

Dr. Siyan Chen

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Guest Editor

State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130022, China
Interests: state estimation and safety protection of lithium-ion power battery

Special Issue Information

Dear Colleagues,

Power batteries have been used various types of electric vehicles (EVs). They can facilitate the decarbonization of the transport sector, which is crucial in order for many countries to meet their emission reduction targets. Battery is usually the main performance bottleneck in EVs, and concerns over the safety, driving range, and lifespan of the battery system are usually the main obstacles to EV rollout. The battery management system (BMS), an electronic control unit managing the real-time battery operation, has important functions, including battery monitoring, modelling, parameter and state estimation, control, diagnosis, and prognosis, etc. Therefore, the BMS plays a key role in the battery’s and EV’s performance. Developing advanced battery management technologies, such as novel model and control algorithms, cloud computing, artificial intelligence, etc., is vital to enhance the safety, efficiency, reliability, and lifespan of the battery system, which are critical to the EV’s performance.

This Special Issue focuses on emerging technologies and recent breakthroughs in the battery management system in automotive applications. Research articles, review articles, as well as short communications are welcomed.

Topics of interest include, but are not limited to:

State of X estimation (SOC, SOH .etc)
Battery monitoring, prognostic and diagnostic of power batteries
Battery modeling, remaining useful lifetime models and evaluations
Battery system model
Battery thermal management systems
Battery fast charging: modeling, estimation and control strategies
Battery recycling/repurposing
Battery full life-span management
Application of novel technologies, such as artificial intelligence, data-driven technology, cloud computing and controlling, cyber physical systems, etc., in the battery management system for electric vehicles.

Dr. Xinhua Liu
Prof. Dr. Zhenhai Gao
Prof. Dr. Shichun Yang
Dr. Cheng Zhang
Dr. Shen Li
Dr. Siyan Chen
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. Batteries is an international peer-reviewed open access monthly 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 2700 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

lithium ion battery
electric vehicle
battery modeling

Published Papers (4 papers)

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Research

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21 pages, 6589 KiB

Open AccessArticle

Prediction of the Remaining Useful Life of Lithium-Ion Batteries Based on the 1D CNN-BLSTM Neural Network

by Jianhui Mou, Qingxin Yang, Yi Tang, Yuhui Liu, Junjie Li and Chengcheng Yu

Batteries 2024, 10(5), 152; https://doi.org/10.3390/batteries10050152 - 30 Apr 2024

Viewed by 581

Abstract

Lithium-ion batteries are currently widely employed in a variety of applications. Precise estimation of the remaining useful life (RUL) of lithium-ion batteries holds significant function in intelligent battery management systems (BMS). Therefore, in order to increase the fidelity and stabilization of predicting the RUL of lithium-ion batteries, in this paper, an innovative strategy for RUL prediction is proposed by integrating a one-dimensional convolutional neural network (1D CNN) and a bilayer long short-term memory (BLSTM) neural network. Feature extraction is carried out through the input capacity data of the model using 1D CNN, and these deep features are used as the input of the BLSTM. The memory function of the BLSTM is applied to retain key information in the database and to better understand the coupling relationship among consecutive time series data along the time axis, thereby effectively predicting the RUL trends of lithium-ion batteries. Two different types of lithium-ion battery datasets from NASA and CALCE were used to verify the effectiveness of the proposed method. The results show that the proposed method achieves higher prediction accuracy, demonstrates stronger generalization capabilities, and effectively reduces prediction errors compared to other methods. Full article

(This article belongs to the Special Issue Electricity and Electronics in Intelligent Battery Management Systems of Electric Vehicles)

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28 pages, 11763 KiB

Open AccessArticle

Novel Bidirectional Isolated DC/DC Converter with High Gain Ratio and Wide Input Voltage for Electric Vehicle Storage Systems

by Yu-En Wu and Chen-Han Tai

Batteries 2022, 8(11), 240; https://doi.org/10.3390/batteries8110240 - 15 Nov 2022

Cited by 6 | Viewed by 3052

Abstract

This study proposes a novel bidirectional isolated DC/DC converter with a high gain ratio and wide input voltage for electric vehicle (EV) storage systems. The DC bus of an EV can be used to charge its battery, and the battery pack can discharge energy to the DC bus through the bidirectional converter when the DC bus lacks power. The input voltage range of the proposed converter is 24 to 58 V on the low-voltage side, which meets the voltage specifications of most servers and batteries on the market. The proposed topology is verified through design, simulation, and implementation, and voltage gain, voltage stress, and current stress are investigated. The control bidirectional converter is simple. Only one set of complementary signals is required for step-up and step-down modes, which greatly reduces costs. The converter also features a continuous current at the low-voltage side, a leakage inductance function for energy recovery, and zero-voltage switching (ZVS) on certain switches, which can prevent voltage spikes on the switches and increase the efficiency of the proposed converter. A bidirectional converter with a total power of 1 kW is used to verify the topology’s feasibility and practicability. The power at the low-voltage side was 24–58 V, and the maximum efficiency in step-up mode was 94.5%, 96.5%, and 94.8%, respectively; the maximum efficiency in step-down mode was 94.4%, 95.4%, and 93.7%, respectively. Full article

(This article belongs to the Special Issue Electricity and Electronics in Intelligent Battery Management Systems of Electric Vehicles)

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16 pages, 2917 KiB

Open AccessArticle

The Dilemma of C-Rate and Cycle Life for Lithium-Ion Batteries under Low Temperature Fast Charging

by Zhenhai Gao, Haicheng Xie, Xianbin Yang, Wanfa Niu, Shen Li and Siyan Chen

Batteries 2022, 8(11), 234; https://doi.org/10.3390/batteries8110234 - 11 Nov 2022

Cited by 12 | Viewed by 7767

Abstract

Electric vehicles (EVs) in severe cold regions face the real demand for fast charging under low temperatures, but low-temperature environments with high C-rate fast charging can lead to severe lithium plating of the anode material, resulting in rapid degradation of the lithium-ion battery (LIB). In this paper, by constructing an electrode–thermal model coupling solid electrolyte interphase (SEI) growth and lithium plating, the competition among different factors of capacity degradation under various ambient temperatures and C-rates are systematically analyzed. In addition, the most important cause of rapid degradation of LIBs under low temperatures are investigated, which reveal the change pattern of lithium plating with temperature and C-rate. The threshold value and kinetic law of lithium plating are determined, and a method of lithium-free control under high C-rate is proposed. Finally, by studying the average aging rate of LIBs, the reasons for the abnormal attenuation of cycle life at lower C-rates are ascertained. Through the chromaticity diagram of the expected life of LIBs under various conditions, the optimal fast strategy is explored, and its practical application in EVs is also discussed. This study can provide a useful reference for the development of high-performance and high-safety battery management systems to achieve fine management. Full article

(This article belongs to the Special Issue Electricity and Electronics in Intelligent Battery Management Systems of Electric Vehicles)

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Review

Jump to: Research

37 pages, 3874 KiB

Open AccessReview

Battery State of Health Estimate Strategies: From Data Analysis to End-Cloud Collaborative Framework

by Kaiyi Yang, Lisheng Zhang, Zhengjie Zhang, Hanqing Yu, Wentao Wang, Mengzheng Ouyang, Cheng Zhang, Qi Sun, Xiaoyu Yan, Shichun Yang and Xinhua Liu

Batteries 2023, 9(7), 351; https://doi.org/10.3390/batteries9070351 - 1 Jul 2023

Cited by 10 | Viewed by 6209

Abstract

Lithium-ion batteries have become the primary electrical energy storage device in commercial and industrial applications due to their high energy/power density, high reliability, and long service life. It is essential to estimate the state of health (SOH) of batteries to ensure safety, optimize better energy efficiency and enhance the battery life-cycle management. This paper presents a comprehensive review of SOH estimation methods, including experimental approaches, model-based methods, and machine learning algorithms. A critical and in-depth analysis of the advantages and limitations of each method is presented. The various techniques are systematically classified and compared for the purpose of facilitating understanding and further research. Furthermore, the paper emphasizes the prospect of using a knowledge graph-based framework for battery data management, multi-model fusion, and cooperative edge-cloud platform for intelligent battery management systems (BMS). Full article

(This article belongs to the Special Issue Electricity and Electronics in Intelligent Battery Management Systems of Electric Vehicles)

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