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Critical review and functional safety of a battery management system for large‑scale lithium‑ion battery pack technologies

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International Journal of Coal Science & Technology 2022年第3期9卷 1-17页

作者： K.W.See Guofa Wang Yong Zhang Yunpeng Wang Lingyu Meng Xinyu Gu Neng Zhang K.C.Lim L.Zhao Bin Xie Azure Mining Technology CCTEG 821 Pacifc HighwaySydneyNSW 2067Australia China Coal Research Institute Beijing 100013China China Mining Products Safety Approval and Certifcation Center Beijing 100013China School of Mechanical Electronic and Information Engineering China University of Mining and TechnologyBeijing 100083China Beijing Tianma Intelligent Control Technology Co. LtdBeijing 101399China Australian Institute for Innovative Materials Institute for Superconducting and Electronic MaterialsUniversity of WollongongInnovation CampusWollongongNSW 2500Australia

The battery management system(BMS)is the main safeguard of a battery system for electric propulsion and machine *** is tasked to ensure reliable and safe operation of battery cells connected to provide high currents at high voltage *** addition to efectively monitoring all the electrical parameters of a battery pack system,such as the voltage,current,and temperature,the BMS is also used to improve the battery performance with proper safety measures within the *** growing acceptance of lithium-ion batteries,major industry sectors such as the automotive,renewable energy,manufacturing,construction,and even some in the mining industry have brought forward the mass transition from fossil fuel dependency to electric powered machinery and redefned the world of energy ***,the functional safety considerations,which are those relating to automatic protection,in battery management for battery pack technologies are particularly important to ensure that the overall electrical system,regardless of whether it is for electric transportation or stationary energy storage,is in accordance with high standards of safety,reliability,and *** the system or product fails to meet functional and other safety requirements on account of faulty design or a sequence of failure events,then the environment,people,and property could be *** paper analyzed the details of BMS for electric transportation and large-scale energy storage systems,particularly in areas concerned with hazardous *** analysis covers the aspect of functional safety that applies to BMS and is in accordance with the relevant industrial standards.A comprehensive evaluation of the components,architecture,risk reduction techniques,and failure mode analysis applicable to BMS operation was also *** article further provided recommendations on safety design and performance optimization in relation to the overall BMS integration.

关键词： battery management system Functional safety Hazardous area Lithium-ion batteries Failure mode analysis Electric transportation Large-scale energy storage

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Implementation for a cloud battery management system based on the CHAIN framework

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Energy and AI 2021年第3期5卷 133-140页

作者： Shichun Yang Zhengjie Zhang Rui Cao Mingyue Wang Hanchao Cheng Lisheng Zhang Yinan Jiang Yonglin Li Binbin Chen Heping Ling Yubo Lian Billy Wu Xinhua Liu School of Transportation Science and Engineering Beihang UniversityBeijingChina BYD Co.Ltd. 3009 BYD RoadPingshan DistrictShenzhenChina Dyson School of Design Engineering Imperial College LondonLondonUnited Kingdom The Faraday Institution Quad OneHarwell Science and Innovation CampusDidcotUnited Kingdom

An intelligent battery management system is a crucial enabler for energy storage systems with high power output,increased safety and long *** recent developments in cloud computing and the proliferation of big data,machine learning approaches have begun to deliver invaluable insights,which drives adaptive control of battery management systems(BMS)with improved *** this paper,a general framework utilizing an end-edge-cloud architecture for a cloud-based BMS is proposed,with the composition and function of each link ***-based BMS leverages from the Cyber Hierarchy and Interactional Network(CHAIN)framework to provide multi-scale insights,more advanced and efficient algorithms can be used to realize the state-of-X es-timation,thermal management,cell balancing,fault diagnosis and other functions of traditional BMS *** battery intelligent monitoring and management platform can visually present battery performance,store working-data to help in-depth understanding of the microscopic evolutionary law,and provide support for the development of control ***,the cloud-based BMS requires more effects on the multi-scale inte-grated modeling methods and remote upgrading capability of the controller,these two aspects are very important for the precise management and online upgrade of the *** utility of this approach is highlighted not only for automotive applications,but for any battery energy storage system,providing a holistic framework for future intelligent and connected battery management.

关键词： battery CHAIN Cloud battery management system SOX estimation end-edge-cloud architecture

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New battery management system for multi-cell li-ion battery packs

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Journal of Southeast University(English Edition) 2009年第2期25卷 185-188页

作者：陈琛金津何乐年浙江大学超大规模集成电路设计研究所杭州310027

This paper proposes a new battery management system （BMS） based on a master-slave control mode for multi-cell li-ion battery packs. The proposed BMS can be applied in li-ion battery packs with any cell number. The whole system is composed of a master processor and a string of slave manager cells （SMCs）. Each battery cell corresponds to an SMC. Unlike the conventional BMS, the proposed one has a novel method for communication, and it collects the battery status information in a direct and simple way. An SMC communicates with its adjacent counterparts to transfer the battery information as well as the commands from the master processor. The nethermost SMC communicates with the master processor directly. This method allows the battery management chips to be implemented in a standard CMOS （ complementary metal-oxide-semiconductor transistor） process. A testing chip is fabricated in the CSMC 0.5 μm 5 V N-well CMOS process. The testing results verify that the proposed method for data communication and the battery management system can protect and manage multi-cell li-ion battery packs.

关键词： battery management system CMOS integrated circuits master-slave control

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A new state of charge determination method for battery management system

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Journal of Harbin Institute of Technology(New Series) 2004年第6期11卷 624-630页

作者：朱春波王铁成 HURLEY W G School of Electrical Engineering and Automation Harbin Institute of Technology Harbin 150001 China Power Electronics Research Centre National University of Ireland Galway Ireland

State of Charge (SOC) determination is an increasingly important issue in battery technology. In addition to the immediate display of the remaining battery capacity to the user, precise knowledge of SOC exerts additional control over the charging/discharging process which in turn reduces the risk of over-voltage and gassing, which degrade the chemical composition of the electrolyte and plates. This paper describes a new approach to SOC determination for the lead-acid battery management system by combining Ah-balance with an EMF estimation algorithm, which predicts the battery’s EMF value while it is under load. The EMF estimation algorithm is based on an equivalent-circuit representation of the battery, with the parameters determined from a pulse test performed on the battery and a curve-fitting algorithm by means of least-square regression. The whole battery cycle is classified into seven states where the SOC is estimated with the Ah-balance method and the proposed EMF based algorithm. Laboratory tests and results are described in detail in the paper.

关键词： state of charge battery battery management system

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Thermal & battery management system Design and Optimization for Power Lithium-ion battery

Thermal & Battery Management System Design and Optimization ...

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中国化学会第30届学术年会-第三十分会：化学电源

作者：沈佳妮贺益君马紫峰上海交通大学化学化工学院

The thermal management system(TMS)&battery management system(BMS)have been receiving increasing attention due to the wide application of power lithium ion batteries on the electric vehicles and energy storage *** temperature non-uniformity in the battery pack adversely affects the overall cell lifespan as well as the available cell *** degree of cell temperature rise reduces the lifespan of the Li-ion battery by approximately 2 months over an operating temperature range of 300℃.TMS&BMS design and optimization strategic are the most important issues for the development of power Lithium-ion battery *** thermal design criteria established are to keep the maximum cell temperature below 35℃for the worst thermal conditions and to keep cell temperature differences always below 5℃to achieve a full *** research emphasis of BMS has been put to the accurate estimation of state of charge(SOC),state of health(SOH)and parameters for the thermal management system(TMS).The electrochemical impedance spectroscopy(EIS)has proved uniquely excellent test technology because of its fast and non-damaged *** EIS of 20 Ah batteries systems in two different connections(in parallel and series)under various temperature and SOC were *** outputs provided a fast detection method for SOC estimation and TMS parameters.A multiscale Gaussian Process Regression modeling method is proposed to tackle accurate SOH estimation problems.

关键词： Thermal management system battery management system SOC

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Key Technologies in Lithium-ion battery management system of Electric Vehicles:Challenges and Recommendations

Key Technologies in Lithium-ion Battery Management System of...

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第19届中国系统仿真技术及其应用学术年会

作者：汪玉洁 Chen Zong-hai Department of Automation University of Science and Technology of China

The lithium-ion batteries,with their excellent performance,have been widely used in electric vehicles and hybrid electric *** an indispensable part of most electric vehicles and hybrid electric vehicles,the battery management system is designed to provide monitoring,diagnosis,control and protecting functions to enhance the operation of the battery *** and more attentions have been paid to lithium-ion battery management technology in recent *** present paper,through the analysis of literatures and in combination with our practical work,gives a brief introduction to the key technologies in battery management system such as lithium-ion battery performance modeling,theory and methods of estimation of the state-of-charge,state-of-energy,state-of-power and state-ofhealth,and other techniques for equalization,charging and fault diagnoses,in hopes of providing some inspirations to the research of the lithium-ion battery management.

关键词： battery management system system Modeling and Simulation State Estimation system Equalization

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Application of Digital Twin in Smart battery management systems

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Chinese Journal of Mechanical Engineering 2021年第4期34卷 1-19页

作者： Wenwen Wang Jun Wang Jinpeng Tian Jiahuan Lu Rui Xiong Advanced Energy Storage and Application Group National Engineering Laboratory for Electric VehiclesSchool of Mechanical EngineeringBeijing Institute of TechnologyBeijing 100081China

Lithium-ion batteries have always been a focus of research on new energy vehicles,however,their internal reactions are complex,and problems such as battery aging and safety have not been fully *** view of the research and preliminary application of the digital twin in complex systems such as aerospace,we will have the opportunity to use the digital twin to solve the bottleneck of current battery ***,this paper arranges the development history,basic concepts and key technologies of the digital twin,and summarizes current research methods and challenges in battery modeling,state estimation,remaining useful life prediction,battery safety and ***,based on digital twin we describe the solutions for battery digital modeling,real-time state estimation,dynamic charging control,dynamic thermal management,and dynamic equalization control in the intelligent battery management *** also give development opportunities for digital twin in the battery *** we summarize the development trends and challenges of smart battery management.

关键词： Digital twin battery management system battery model Remaining useful life prediction Dynamic control

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State of Health Estimation of LiFePO_(4) Batteries for battery management systems

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Computers, Materials & Continua 2022年第11期73卷 3149-3164页

作者： Areeb Khalid Syed Abdul Rahman Kashif Noor Ul Ain Ali Nasir Department of Electrical Engineering University of Engineering and TechnologyLahorePakistan Department of Electrical Engineering University of Central PunjabLahorePakistan

When considering the mechanism of the batteries,the capacity reduction at storage(when not in use)and cycling(during use)and increase of internal resistance is because of degradation in the chemical composition inside the *** optimize battery usage,a battery management system(BMS)is used to estimate possible aging effects while different load profiles are requested from the *** is specifically seen in a case when the vehicle is connected to the net(online through BMS).During this process,the BMS chooses the optimized load profiles based on the least aging effects on the battery *** major focus of this paper is to design an algorithm/model for lithium iron phosphate(LiFePO4)*** model of the batteries is based on the accelerated aging test data(data from the beginning of life till the end of life).The objective is to develop an algorithm based on the actual battery trend during the whole life of the *** the analysis of the test data,the complete trend of the battery aging and the factors on which the aging is depending on is identified,the aging model can then be recalibrated to avoid any differences in the production process during cell *** validation of the model was carried out at the end by utilizing different driving profiles at different C-rates and different ambient temperatures.A Linear and non-linear model-based approach is used based on statistical *** parameterization was carried out by dividing the data into small chunks and estimating the parameters for the individual ***-adaptive characteristic map using a lookup table was also *** nonlinear model was chosen as the best candidate among all other approaches for longer validation of 8-month data with real driving data set.

关键词： Aging model state of health lithium-ion cells battery management system state of charge battery modeling

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Multi-Scale Prediction of RUL and SOH for Lithium-Ion Batteries Based on WNN-UPF Combined Model

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Chinese Journal of Electronics 2021年第1期30卷 26-35页

作者： JIA Jianfang WANG Keke PANG Xiaoqiong SHI Yuanhao WEN Jie ZENG Jianchao School of Electrical and Control Engineering North University of China School of Data Science and Technology North University of China

The prediction of Remaining useful life(RUL) and the estimation of State of health(SOH) are extremely important issues for operating performance of Lithium-ion(Li-ion) batteries in the battery management system(BMS). A multi-scale prediction approach of RUL and SOH is presented, which combines Wavelet neural network(WNN) with Unscented particle filter(UPF)model. The capacity degradation data of Li-ion batteries are decomposed into the low-frequency degradation trend and high-frequency fluctuation components by Discrete wavelet transform(DWT). Based on the WNN-UPF model, the long-term RUL of Li-ion batteries is predicted with the low-frequency degradation trend data. The high-frequency fluctuation data and RUL prediction results are integrated effectively to estimate the short-term SOH of Li-ion batteries. The experimental results show that the proposed method achieves high accuracy and strong robustness, even if the prediction starting point is set to the early stage of Li-ion batteries’ lifespan.

关键词： low-frequency degradation trend data WNN-UPF model Kalman filters high-frequency fluctuation components secondary cells RUL prediction results prediction starting point short-term SOH high-frequency fluctuation data remaining life assessment multiscale prediction approach wavelet neural nets particle filtering (numerical methods) Unscented particle filter model power engineering computing WNN-UPF combined model battery management systems capacity degradation data Lithium-ion batteries discrete wavelet transforms long-term RUL battery management system Li-ion batteries

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Physics-based battery SOC estimation methods:Recent advances and future perspectives

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Journal of Energy Chemistry 2024年第2期89卷 27-40,I0003页

作者： Longxing Wu Zhiqiang Lyu Zebo Huang Chao Zhang Changyin Wei College of Mechanical Engineering Anhui Science and Technology UniversityChuzhou 233100AnhuiChina Hubei Key Laboratory of Power system Design and Test for Electrical Vehicle Hubei University of Arts and ScienceXiangyang 441053HubeiChina School of Internet Anhui UniversityHefei 230039AnhuiChina School of Mechanical and Electrical Engineering Guilin University of Electronic TechnologyGuilin 541004GuangxiChina

The reliable prediction of state of charge(SOC)is one of the vital functions of advanced battery management system(BMS),which has great significance towards safe operation of electric *** far,the empirical model-based and data-driven-based SOC estimation methods of lithium-ion batteries have been comprehensively discussed and reviewed in various ***,few reviews involving SOC estimation focused on electrochemical mechanism,which gives physical explanations to SOC and becomes most attractive candidate for advanced *** this reason,this paper comprehensively surveys on physics-based SOC algorithms applied in advanced ***,the research progresses of physical SOC estimation methods for lithium-ion batteries are thoroughly discussed and corresponding evaluation criteria are carefully ***,future perspectives of the current researches on physics-based battery SOC estimation are *** insights stated in this paper are expected to catalyze the development and application of the physics-based advanced BMS algorithms.

关键词： Lithium-ion batteries State of charge Electrochemical model battery management system

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