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

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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

更新时间：2025-08-12

- 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
- Automotive Lithium-ion Battery Sensor Fault Diagnosis Method Based on Likelihood Ratio Test
- 默认刊物名称 2025年
- 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：
  中图分类号： U469.72
- 收稿：2025-05-06，
  
  修回：2025-07-18，
  
  录用：2025-07-24，
- 稿件说明：
移动端阅览
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/OL]. 默认刊物名称, 2025.

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.
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/OL]. 默认刊物名称, 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

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