Observer-based Fault Diagnosis of Lithium-ion Battery Sensors for Electric Vehicles
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Affiliation:
School of Automotive Engineering,Chongqing University
Fund Project:
Supported by National Natural Science Foundation of China (No. 51807071).
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摘要:
提出了一种基于观测器的电动车辆锂离子电池传感器的故障诊断方法。结合锂离子电池电热耦合动态模型,构建了两个扩展卡尔曼滤波(Extended Kalman Filter, EKF)观测器,估计电池单体的状态量,对比状态量估计值与传感器测量值以生成残差,并使用累计和(Cumulative sum, CUSUM)测试方法进行残差评价,根据残差组合的不同响应情况实现锂离子电池电流传感器、电压传感器以及表面温度传感器故障的诊断与分离(Fault diagnosis and isolation, FDI)。在不同的传感器故障情况下对诊断方法进行测试并分析了有效性,结果表明,该方法能够及时准确地对锂离子电池单体三种传感器故障进行诊断与定位,性能表现优异且易于实施。
Abstract:
In this paper, an observer-based fault diagnosis scheme was presented to detect and isolate lithium-ion battery sensor faults for electric vehicles. The proposed scheme constructs two extended Kalman filter (EKF) observers in combination with the coupling electro-thermal dynamic model of Li-ion battery to realize state estimation. The difference between the estimated value and the sensor measured value generates the residual. Then the residuals are evaluated by statistical cumulative sum(CUSUM) test that determines the presence of the faults. According to the responding of two residuals, the current sensor, the voltage sensor and the surface temperature sensor faults can be isolated. Fault diagnosis and isolation (FDI) scheme are tested and analyzed for effectiveness in the event of different sensor faults. The result shows that the scheme can diagnose and locate three kinds of lithium-ion battery cell sensor faults in time and accurately, and has Excellent performance and easy implementation.
