Fault Detection Method for Lithium-Ion Battery Sensors in Electric Vehicles Using Likelihood Ratio Test

LIU Panzhi; WU Chunling; LI Yanbo; GUO Guofang

doi:10.7652/xjtuxb202601021

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Fault Detection Method for Lithium-Ion Battery Sensors in Electric Vehicles Using Likelihood Ratio Test

Special Topic Pollution Control and Resource Utilization in the “Dual Carbon”Context | 更新时间：2026-01-15

- Fault Detection Method for Lithium-Ion Battery Sensors in Electric Vehicles Using Likelihood Ratio Test
- Journal of Xi'an Jiaotong University Vol. 60, Issue 1, Pages: 211-222(2026)
- 作者机构：
  
  长安大学能源与电气工程学院，710064，西安
  长安大学陕西省交通新能源开发、应用与汽车节能重点实验室，710064，西安
  陕西重型汽车有限公司，710200，西安
- 作者简介：
- 基金信息：
- DOI：10.7652/xjtuxb202601021
  CLC： U469.72
- Received：06 May 2025，
  
  Published：10 January 2026
- 稿件说明：
移动端阅览
LIU Panzhi, WU Chunling, LI Yanbo, et al. Fault Detection Method for Lithium-Ion Battery Sensors in Electric Vehicles Using Likelihood Ratio Test[J]. Journal of Xi'an Jiaotong University, 2026, 60(1): 211-222.
DOI：

LIU Panzhi, WU Chunling, LI Yanbo, et al. Fault Detection Method for Lithium-Ion Battery Sensors in Electric Vehicles Using Likelihood Ratio Test[J]. Journal of Xi'an Jiaotong University, 2026, 60(1): 211-222. DOI： 10.7652/xjtuxb202601021.

摘要

为了降低电池管理系统中传感器故障对电动汽车性能的影响，设计了一种采用似然比检测的故障诊断方法。首先，建立电池等效电路模型，并进行参数辨识，使用扩展卡尔曼滤波（EKF）算法，对所建立的电池模型进行状态估计；接着，根据状态估计结果计算电池端电压残差，并结合电池荷电状态估计，使用似然比检测方法处理残差数据，对电池传感器故障进行诊断，提高故障检测方法的效率；最后，使用电池工况实验数据验证算法的有效性和适应性。结果表明，设计的方法能及时准确地检测到2种传感器故障。对于给定的测试条件，电压故障时根据端电压残差检测所需诊断时间在20 s内；根据荷电状态（SOC）残差检测所需诊断时间在100 s内；电流故障时根据端电压残差检测所需诊断时间在20 s内；根据SOC残差检测所需诊断时间在400 s内。同样测试条件下，采用传统的累积和方法（CUSUM）的诊断时间分别是20 s内、445 s以上、150 s以上和1000 s以上，可见设计的方法可以明显缩短诊断时间。同时，该方法对故障有较好的敏感性，可以检测传感器小幅度故障，当电压故障信号为正常电压1%时仍可以实现故障检测。另外，当电压和电流传感器的发生多次故障时，该方法仍能实现故障检测目标。

Abstract

To mitigate the impact of sensor faults in battery management systems on electric vehicle performance，a fault diagnosis method based on likelihood ratio test theory is designed. First，an equivalent circuit model of the battery is established and parameter identification is performed.The extended Kalman filter (EKF)algorithm is used for state estimation of the established battery model.Subsequently，based on the state estimation results，the terminal voltage residual of the battery is calculated.Considering the state of charge (SOC)estimation，the likelihood ratio test method is applied to process the residual data，enabling diagnosis of battery sensor faults and improving the efficiency of the fault detection method.Finally，the effectiveness and adaptability of the algorithm are verified using battery operating condition experimental data.The results show that this method can timely and accurately detect two types of sensor faults.Under the given test conditions，for voltage faults，the diagnosis time required based on terminal voltage residuals is within 20 s；based on SOC residuals，it is within 100 s.For current faults，the diagnosis time based on terminal voltage residuals is within 20 s；based on SOC residuals，it is within 400 s.Under the same test conditions，the diagnosis time using the traditional cumulative sum (CUSUM)method is within 20 s，over 445 s，over 150 s，and over 1000 s，respectively.This demonstrates that the proposed method significantly reduces diagnosis time.Additionally，the method exhibits good sensitivity to faults，enabling the detection of minor sensor faults.It can still detect faults when the voltage fault signal is as low as 1% of the normal voltage value.Moreover，the method remains effective in detecting faults even when multiple faults occur in voltage and current sensors.

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references

ISERMANN R.Model-based fault-detection and diagnosis-status and applications[J].Annual Reviews in Control,2005,29(1):71-85.

KANG Yongzhe,DUAN Bin,ZHOU Zhongkai,et al.Online multi-fault detection and diagnosis for battery packs in electric vehicles[J].Applied Energy, 2020,259:114170.

LIU Zhentong, HE Hongwen. Model-based sensor fault diagnosis of a lithium-ion battery in electric vehicles[J].Energies,2015,8(7):6509-6527.

LIU Zhentong,HE Hongwen.Sensor fault detection and isolation for a lithium-ion battery pack in electric vehicles using adaptive extended Kalman filter[J]. Applied Energy,2017,185(Part 2):2033-2044.

XIONG Rui,YU Quanqing,SHEN Weixiang,et al. A sensor fault diagnosis method for a lithium-ion battery pack in electric vehicles[J].IEEE Transactions on Power Electronics,2019,34(10):9709-9718.

YU Quanqing,WAN Changjiang,LI Junfu,et al.A model-based sensor fault diagnosis scheme for batteries in electric vehicles[J].Energies,2021,14(4):829.

YU Quanqing,DAI Lei,XIONG Rui,et al.Current sensor fault diagnosis method based on an improved equivalent circuit battery model[J].Applied Energy, 2022,310:118588.

HE Hongwen,LIU Zhentong,HUA Yin.Adaptive extended Kalman filter based fault detection and isolation for a lithium-ion battery pack[J].Energy Procedia,2015,75:1950-1955.

XU Jun,WANG Jing,LI Shiying,et al.A method to simultaneously detect the current sensor fault and estimate the state of energy for batteries in electric vehicles[J].Sensors,2016,16(8):1328.

TIAN Jiaqiang,WANG Yujie,CHEN Zonghai.Sensor fault diagnosis for lithium-ion battery packs based on thermal and electrical models[J].International Journal of Electrical Power & Energy Systems,2020, 121 :106087.

孙立珍,赵乐乐.基于MATLAB和1stOpt的非线性曲线拟合比较[J].现代计算机,2020(31):28-30.

SUN Lizhen,ZHAO Lele.Comparison of nonlinear curve fitting based on MATLAB and 1stOpt[J]. Modern Computer,2020(31):28-30.

李春,刘泽民,陈恒杰,等.PeakFit、1stOpt在弗兰克-赫兹实验数据处理中的应用[J].大学物理实验, 2018,31(5):117-123.

LI Chun,LIU Zemin,CHEN Hengjie,et al.Application of PeakFit and 1stOpt in data processing of the Frank-Hertz experiment[J].Physical Experiment of College,2018,31(5):117-123.

颜鲁林.基于1stOpt软件下的铅酸电池放电曲线模型研究[J].甘肃高师学报,2018,23(5):5-7.

YAN Lulin.A study on lead-acid battery discharge curve based on the 1stOpt software[J].Journal of Gansu Normal Colleges,2018,23(5):5-7.

邱新宇.稳健的锂电池等效电路模型参数辨识及荷电状态估计研究[D].西安:西安理工大学,2019.

胡雯博.储能锂电池荷电状态估计算法研究[D].西安:长安大学,2023.

陈晓飞,蒋淑霞,崔祥波,等.基于融合EKF与改进DELM的锂电池SOC实时估计[J/OL].电源学报, (2024-08-23)[2025-04-25].https://link.cnki.net/urlid/12.1420.TM.20240822.1755.006.

CHEN Xiaofei,JIANG Shuxia,CUI Xiangbo,et al. Real-time SOC estimation for lithium batteries based on fusing EKF and improved DELM[J/OL].Journal of Power Supply.(2024-08-23)[2025-04-25].https://link.cnki.net/urlid/12.1420.TM.20240822.1755.006.

代磊.车用动力电池电流传感器故障诊断方法研究[D].哈尔滨:哈尔滨工业大学,2022.

闫林杰,郝程鹏,殷超然,等.部分均匀环境下适用于空间对称线阵的修正广义似然比检测方法[J].雷达学报,2021 ,10(3):443-452.

YAN Linjie,HAO Chengpeng,YIN Chaoran,et al. Modified generalized likelihood ratio test detection based on a symmetrically spaced linear array in partially homogeneous environments[J].Journal of Radars, 2021 ,10(3):443-452.

LIU Boxuan,CUI Zhongma,LU Zhen.Marine target CFAR detection method based on superpixel difference degree[C]//Journal of Physics:Conference Series.Bristol,United Kingdom:IOP Publishing,2024:012020.

LIANG Zhihuan,JIN Yanghao,LIANG Buge,et al. A modified CA-CFAR multi-human detection algorithm in complex environment using radar[C]//2024 International Conference on Electronic Engineering and Information Systems (EEISS). Piscataway, NJ, USA:IEEE,2024:93-97.

ZHENG Fangdan,XING Yinjiao,JIANG Jiuchun,et al.Influence of different open circuit voltage tests on state of charge online estimation for lithium-ion batteries[J].Applied Energy,2016,183:513-525.

XING Yinjiao,HE Wei,PECHT M,et al.State of charge estimation of lithium-ion batteries using the opencircuit voltage at various ambient temperatures[J]. Applied Energy,2014,113:106-115.

HE Wei,WILLIARD N,CHEN Chaochao,et al. State of charge estimation for Li-Ion batteries using neural network modeling and unscented Kalman filterbased error cancellation[J].International Journal of Electrical Power & Energy Systems, 2014, 62:783-791 .

赵华.工业控制系统异常检测算法研究[D].北京:冶金自动化研究设计院,2013.

BASSEVILLE M,NIKIFOROV I V.Detection of abrupt changes:theory and application[M].Upper Saddle River,NJ,USA:Prentice Hall,1993.

张云贵,赵华,王丽娜.基于工业控制模型的非参数CUSUM入侵检测方法[J].东南大学学报(自然科学版),2012,42(S1):55-59.

ZHANG Yungui,ZHAO Hua,WANG Lina.A nonparametric CUSUM intrusion detection method based on industrial control model[J].Journal of Southeast University(Natural Science Edition),2012,42 (S1):55-59.

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