电动车辆锂离子电池传感器故障诊断方法
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中图分类号:

U463.63

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国家自然科学基金资助项目(51807071);重庆市技术预见与制度创新项目(cstc2020jsyj-ydxwtAX0006)。


Fault diagnosis of lithium-ion battery sensors for electric vehicles
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    摘要:

    为降低锂离子电池传感器故障对电动车辆安全与性能的影响,提出了一种基于观测器的电池传感器故障诊断方法。结合锂离子电池电热耦合动态模型,构建2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,结果表明,该方法能够及时准确地对锂离子电池单体3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 order to reduce the impact of lithium-ion battery sensor faults on the safety and performance of electric vehicles, an observer-based fault diagnosis scheme was presented to detect and isolate battery sensor faults in this paper. The proposed scheme constructed 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 generated the residual. Then the residuals were evaluated by statistical cumulative sum(CUSUM) test that determined the presence of the faults. According to the respond of two residuals, the fault diagnosis and isolation (FDI) of the current sensor, the voltage sensor and the surface temperature sensor could be realized. The proposed scheme was tested to verify its effectiveness. The result shows that the proposed scheme can diagnose and locate three kinds of lithium-ion battery cell sensor faults in time and accurately, demonstrating excellent performance and easy implementation.

    参考文献
    [1] Hu X S, Zou C F, Zhang C P, et al. Technological developments in batteries:a survey of principal roles, types, and management needs[J]. IEEE Power and Energy Magazine, 2017, 15(5):20-31.
    [2] 王震坡,孙逢春.电动车辆动力电池系统及应用技术[M].北京:机械工业出版社, 2012.Wang Z P, Sun F C. Electric vehicle power battery system and application technology[M]. Beijing:China Machine Press, 2012.(in Chinese)
    [3] 张友鹏,朱涛伟,赵斌.基于模糊定性趋势分析的JTC综合故障诊断方法[J].重庆大学学报, 2019, 42(3):65-75.Zhang Y P, Zhu T W, Zhao B. Comprehensive fault diagnosis method for jointless track circuit based on fuzzy qualitative trend analysis[J]. Journal of Chongqing University, 2019, 42(3):65-75.(in Chinese)
    [4] 李嫄源,袁梅,王瑶,等. SVM与PSO相结合的电机轴承故障诊断[J].重庆大学学报, 2018, 41(1):99-107.Li Y Y, Yuan M, Wang Y, et al. Fault diagnosis of motor bearings based on SVM and PSO[J]. Journal of Chongqing University, 2018, 41(1):99-107.(in Chinese)
    [5] 柯炎,樊波,谢一静,等.基于小波包分析和Elman神经网络的军用电源智能故障诊断[J].重庆大学学报, 2019, 42(9):67-73.Ke Y, Fan B, Xie Y J, et al. Fault diagnosis of military power based on wavelet packet analysis and Elman neural network[J]. Journal of Chongqing University, 2019, 42(9):67-73.(in Chinese)
    [6] Lombardi W, Zarudniev M, Lesecq S, et al. Sensors fault diagnosis for a BMS[C]//2014 European Control Conference (ECC). June 24-27, 2014, Strasbourg, France. IEEE, 2014:952-957.
    [7] Liu Z T, He H W, Ahmed Q, et al. Structural analysis based fault detection and isolation applied for a lithium-ion battery pack[J]. IFAC-PapersOnLine, 2015, 48(21):1465-1470.
    [8] Xu J, Wang J, Li S Y, et al. A method to simultaneously detect the current sensor fault and estimate the state of energy for batteries in electric vehicles[J]. Sensors (Basel, Switzerland), 2016, 16(8):E1328.
    [9] Dey S, Mohon S, Pisu P, et al. Sensor fault detection, isolation, and estimation in lithium-ion batteries[J]. IEEE Transactions on Control Systems Technology, 2016, 24(6):2141-2149.
    [10] He H W, Liu Z T, Hua Y. Adaptive extended Kalman filter based fault detection and isolation for a lithium-ion battery pack[J]. Energy Procedia, 2015, 75:1950-1955.
    [11] Liu Z T, He H W. 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.
    [12] Liu Z T, Ahmed Q, Zhang J Y, et al. Structural analysis based sensors fault detection and isolation of cylindrical lithium-ion batteries in automotive applications[J]. Control Engineering Practice, 2016, 52:46-58.
    [13] 胡晓松,唐小林.电动车辆锂离子动力电池建模方法综述[J].机械工程学报, 2017, 53(16):20-31.Hu X S, Tang X L. Review of modeling techniques for lithium-ion traction batteries in electric vehicles[J]. Journal of Mechanical Engineering, 2017, 53(16):20-31.(in Chinese)
    [14] 刘真通.基于模型的纯电动车辆动力系统故障诊断研究[D].北京:北京理工大学, 2016.Liu Z T. Model-based fault diagnosis of electrified driven powertrains in pure electric vehicles[D]. Beijing:Beijing Institute of Technology, 2016.(in Chinese)
    [15] 宋丽,魏学哲,戴海峰,等.锂离子电池单体热模型研究动态[J].汽车工程, 2013, 35(3):285-291.Song L, Wei X Z, Dai H F, et al. A review on the research of thermal models for lithium ion battery cell[J]. Automotive Engineering, 2013, 35(3):285-291.(in Chinese)
    [16] Lin X F, Perez H E, Mohan S, et al. A lumped-parameter electro-thermal model for cylindrical batteries[J]. Journal of Power Sources, 2014, 257:1-11.
    [17] Sidhu A, Izadian A, Anwar S. Adaptive nonlinear model-based fault diagnosis of Li-ion batteries[J]. IEEE Transactions on Industrial Electronics, 2015, 62(2):1002-1011.
    [18] Chen C, Xiong R, Yang R X, et al. State-of-charge estimation of lithium-ion battery using an improved neural network model and extended Kalman filter[J]. Journal of Cleaner Production, 2019, 234:1153-1164.
    [19] Liu Z T, He H W. Model-based sensor fault diagnosis of a lithium-ion battery in electric vehicles[J]. Energies, 2015, 8(7):6509-6527.
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许俊雄,冯飞,邓忠伟.电动车辆锂离子电池传感器故障诊断方法[J].重庆大学学报,2022,45(6):27-39.

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  • 收稿日期:2020-12-30
  • 最后修改日期:2021-04-23
  • 在线发布日期: 2022-06-18
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