采用似然比检测的电动车锂离子电池传感器故障检测方法

刘盼芝; 巫春玲; 李艳波; 郭国防

doi:10.7652/xjtuxb202601021

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采用似然比检测的电动车锂离子电池传感器故障检测方法

专题　“双碳”背景下的污染控制与资源化利用 | 更新时间：2026-01-15

- 采用似然比检测的电动车锂离子电池传感器故障检测方法
- Fault Detection Method for Lithium-Ion Battery Sensors in Electric Vehicles Using Likelihood Ratio Test
- 西安交通大学学报 2026年60卷第1期页码：211-222
- 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：
  
  作者简介：刘盼芝（1980—），女，副教授；
  李艳波（通信作者），男，教授。
- 基金信息：
  
  国家自然科学基金资助项目(52172324);陕西省重点研发计划资助项目。(211432220216)
- DOI：10.7652/xjtuxb202601021
  中图分类号： U469.72
- 收稿：2025-05-06，
  
  纸质出版：2026-01-10
- 稿件说明：
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刘盼芝, 巫春玲, 李艳波, 等. 采用似然比检测的电动车锂离子电池传感器故障检测方法[J]. 西安交通大学学报, 2026,60(1):211-222.

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.
刘盼芝, 巫春玲, 李艳波, 等. 采用似然比检测的电动车锂离子电池传感器故障检测方法[J]. 西安交通大学学报, 2026,60(1):211-222. DOI： 10.7652/xjtuxb202601021.

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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