Automotive Lithium-ion Battery Sensor Fault Diagnosis Method Based on Likelihood Ratio Test

LIU Panzhi; WU Chunling; LI Yanbo; GUO Guofang

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Automotive Lithium-ion Battery Sensor Fault Diagnosis Method Based on Likelihood Ratio Test

更新时间：2025-08-12

- Automotive Lithium-ion Battery Sensor Fault Diagnosis Method Based on Likelihood Ratio Test
- Moren Journal (2025)
- 作者机构：
  
  1.陕西省交通新能源开发、应用与汽车节能重点实验室,陕西省西安市710064
  2.长安大学能源与电气工程学院,陕西省西安市710064
  3.陕西汽车重型有限公司,陕西省西安市710299
- 作者简介：
- 基金信息：
- DOI：
  CLC： U469.72
- Received：06 May 2025，
  
  Revised：2025-07-18，
  
  Accepted：24 July 2025，
- 稿件说明：
移动端阅览
LIU Panzhi, WU Chunling, LI Yanbo, et al. Automotive Lithium-ion Battery Sensor Fault Diagnosis Method Based on Likelihood Ratio Test[J/OL]. Moren Journal, 2025.
DOI：

LIU Panzhi, WU Chunling, LI Yanbo, et al. Automotive Lithium-ion Battery Sensor Fault Diagnosis Method Based on Likelihood Ratio Test[J/OL]. Moren Journal, 2025. DOI：

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理论的故障诊断方法。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（extended kalman filter，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，该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20s内；根据荷电状态（state of charge，SOC）残差检测所需诊断时间在100s内；电流故障时根据端电压残差检测所需诊断时间在20s内；根据SOC残差检测所需诊断时间在400s内。上述测试条件下，采用传统的累积和方法（CUSUM）的诊断时间分别是20s内、445s以上、150s以上和1000s以上，可见本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值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

In order to reduce the impact of sensor faults in battery management system(BMS) on the performance of electric vehicles

a fault diagnosis method using the likelihood ratio detection theory was designed. Firstly

establish the equivalent circuit model of the battery and conduct parameter identification. Use the extended kalman filter (EKF) algorithm to estimate the state of the established battery model. Then

the terminal voltage residual of the battery is calculated based on estimated states. Next

the residual data is processed using the likelihood ratio detection method to diagnose the faults of the battery sensor. Finally

the validity and adaptability of the algorithm are verified using experimental data. The results show that

this method can detect two types of sensor faults in a timely. For the given test conditions

when there is a voltage fault

the diagnosis time is within 20 seconds based on the terminal voltage residual

and it is within 100 seconds based on the SOC residual. When there is a current fault

the diagnosis time is within 20 seconds based on the terminal voltage residual

and the diagnosis time based on the state of charge(SOC) residual is within 400 seconds. While under the same test conditions

the diagnostic times using cumulative sum(CUSUM) method are within 20s

over 445s

over 150s and over 1000s respectively. This method can significantly shorten the diagnostic time. Meanwhile

this method can detect minor faults of the sensor. Fault detection can still be achieved when the voltage fault signal is 1% of the normal voltage value. In addition

when multiple faults occur sensors

this method can still achieve the fault detection goal.

关键词

Keywords

references

Isermann R . Model-based fault-detection and diagnosis status and applications [J ] . Ann Rev Control 2005 , 29 ( 1 ): 71 – 85 .

Kang Y , Duan B , Zhou Z , Shang Y , Zhang C . Online multi-fault detection and diagnosis for battery packs in electric vehicles [J ] . Applied Energy 2020 , 259 : 1 - 16 . https://doi.org /10.1016/j.apenergy.2019.114170 https://doi.org/10.1016/j.apenergy.2019.114170 .

Liu Z , He H . Model-based sensor fault diagnosis of a lithium-ion battery in electric vehicles [J ] . Energies 2015 , 8 ( 7 ): 9 – 27 .

Liu Z , He H . Sensor fault detection and isolation for a lithium-ion battery pack in electric vehicles using adaptive extended kalman filter [J ] . Applied Energy 2017 , 185 : 2033 – 2044 .

Xiong R , Yu Q , Shen W , Lin C , Sun F . A sensor fault diagnosis method for a lithium-ion battery pack in electric vehicles [J ] . IEEE Trans Power Electron 2019 , 34 ( 10 ): 9709 – 9718 .

Yu Q , Wan C , Li J , Xiong R , Chen Z . A model-based sensor fault diagnosis scheme for batteries in electric vehicles [J ] . Energies 2021 , 14 ( 4 ): 1 - 15 .

Yu Quanqing , Dai Lei , Xiong Rui , Chen Zeyu , Zhang Xin , Shen Weixiang . Current sensor fault diagnosis method based on an improved equivalent circuit battery model [J ] . Applied Energy . 2022 , 310 : 1 - 15 .

He Hongwen , Liu Zhentong , Hua Yin . Adaptive Extended Kalman Filter Based Fault Detection and Isolation for a Lithium-Ion Battery Pack [C ] . 7th International Conference on Applied Energy, ICAE 2015 , 2015 , 75 : 1950 - 1955 .

XU Jun , WANG Jing , LI Shiyin , 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 ): 13 - 28 .

TIAN Jiaqiang , ANG 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 ： 1 - 13 .

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+37 .

SUN Lizhen ， ZHAO Lele . Comparison of Nonlinear Curve Fitting Based on MATLAB and 1stOpt [J ] . SmartTech Innovations , 2020 , ( 31 ): 28 - 30+37 .

李春 , 刘泽民 , 陈恒杰 , 薛善增 . 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 ( 05 ): 117 - 123 .

LI Chun , LIU Zemin , CHEN Hengji , XUE Shan-zeng . Application of PeakFit and 1stOpt in Data Processing of the Frank-Hertz Experiment [J ] . Physical Experiment of College , 2018 , 31 ( 05 ): 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 ( 05 ): 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 ( 05 ): 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 ] . 西安理工大学 , 2019 .

QIU Xinyu . Robust parameter identification for equivalent circuit models and soc estimation of lithium batteries [D ] . Xi’an University of Technology , 2019 .

胡雯博 . 储能锂电池荷电状态估计算法研究 [D ] . 长安大学 , 2023 .

Hu Wenbo . Research on State of Charge estimation Algorithm of Lithium-ion Battery [D ] . Chang’an University , 2023 .

陈晓飞 , 蒋淑霞 , 崔祥波 , 赵文卓 , 王思思 . 基于融合EKF与改进DELM的锂电池SOC实时估计 [J ] . 电源学报 , 2024 : 1 - 14 .

Chen Xiaofei , Jiang Shuxia , Cui Xiangbo , Zhao Wenzhuo , Wang Sisi . Real-time SOC estimation for lithium batteries based on fusing EKF and improved DELM [J ] . Journal of Power Supply , 2024 : 1 - 14 .

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

DAI Lei . Research on Fault Diagnosis Methods of for Vehicle Power Batteries Current Sensors [D ] . Harbin Institute of Technology , 2022 .

闫林杰 , 郝程鹏 , 殷超然 , 孙苇轩 , 侯朝焕 . 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 ( 03 ): 443 - 452 .

YAN Linjie , HAO Chengpeng , YIN Chaoran , SUN Weixuan , HOU Chaohuan . Modified Generalized Likelihood Ratio Test Detection Based on a Symmetrically Spaced Linear Array in Partially Homogeneous Environments [J ] . Journal of Radars , 2021 , 10 ( 03 ): 443 - 452 .

Liu Boxuan , Cui Zhongma , Lu Zhen . Marine target cfar detection method based on superpixel difference degree [C ] . Journal of Physics : Conference Series , 2024 , 2906 ( 1 ): 1 - 13 .

Liang Zhihuan , Jin Yanghao , Liang Buge , Mo Jinjun . A Modified CA-CFAR Multi-human Detection Algorithm in Complex Environment Using Radar [C ] . 2024 International Conference on Electronic Engineering and Information Systems, EEISS 2024 , 2024 : 93 - 97 .

Fangdan Zheng , Yinjiao Xing , Jiuchun Jiang , Bingxiang Sun , Jonghoon Kim , Michael Pecht . Influence of different open circuit voltage tests on state of charge online estimation for lithium-ion batteries . Applied Energy , 2016 , 183 : 513 – 525 .

Yinjiao Xing , Wei He , Michael Pecht , Kwok Leung Tsui . State of Charge Estimation of Lithium-Ion Batteries Using the Open-Circuit Voltage at Various Ambient Temperatures . Applied Energy , 2014 , 113 : 106 - 115 .

Wei He , Nicholas Williard , Chaochao Chen , Michael Pecht . State of Charge Estimation for Li-Ion Batteries Using Neural Network Modeling and Unscented Kalman Filter-based Error Cancellation . 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 .

ZHAO Hua . Research on anomaly detection algorithm for industrial control system [D ] . Beijing : Metallurgical Automation Research and Design Institute , 2013 .

BASSEVILLE M ， NIKIFOROV I. V . Detection of Abrupt Changes：Theory and Application [M ] . New Jersey，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 non-parametric CUSUM intrusion detection method based on industrial control model [J ] . JOURNAL OF SOUTHEAST UNIVER -SITY ( Natural Science Edition) , 2012 ， 42 ( S1 ): 55 - 59 .

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