基于日常片段充电数据的锂电池健康状态实时评估方法研究
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摘要
实时评估电动汽车动力锂电池的健康状态(stateof health,SOH)对电动汽车的维护至关重要。针对实际应用中电动汽车电池具有放电容量测量不稳定、测试负载重,操作不方便等问题。该文首先研究基于充电容量计算电池健康状态的可行性。然后,建立充电容量SOH模型将电池充电容量的估算转换为电池全充所需时间的估算。由于锂电池实际充电时的数据是片段的,提出基于扩展卡尔曼滤波和高斯过程回归的全充时间估算算法,解决了片段充电数据预测电池实时全充时间的问题。最后,通过实验仿真,验证了高斯过程扩展卡尔曼滤波在锂电池健康状态评估中的针对性、有效性和实时性。
The real-time estimation of the state of health(SOH) for lithium batteries is extremely important for the maintenance of electric vehicles. In view of the fact that the battery of electric vehicle has instability, heavy load and inconvenient operation when measuring the discharge capacity, this paper firstly studied the feasibility of calculating SOH based on charge capacity. Then, the charge capacity based SOH model was established which converts the battery charge capacity to the time required for the full charge of the battery. Because the data of lithium battery is usually fragmented, a full charge time estimation algorithm based on the fusion of Gauss Process Regression and extended Kalman filter was proposed, which solves the estimation problem of real-time charging time of fragment charging data. Finally, through the experimental simulation, it is proved that the extended Kalman filter-Gauss Process Regression is pertinent, effective and real-time in the evaluation of the SOH of lithium battery.
The real-time estimation of the state of health(SOH) for lithium batteries is extremely important for the maintenance of electric vehicles. In view of the fact that the battery of electric vehicle has instability, heavy load and inconvenient operation when measuring the discharge capacity, this paper firstly studied the feasibility of calculating SOH based on charge capacity. Then, the charge capacity based SOH model was established which converts the battery charge capacity to the time required for the full charge of the battery. Because the data of lithium battery is usually fragmented, a full charge time estimation algorithm based on the fusion of Gauss Process Regression and extended Kalman filter was proposed, which solves the estimation problem of real-time charging time of fragment charging data. Finally, through the experimental simulation, it is proved that the extended Kalman filter-Gauss Process Regression is pertinent, effective and real-time in the evaluation of the SOH of lithium battery.
引文
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[22]Rasmussen C E,Williams C K I.Gaussian processes for machine learning[M].Cambridge,MA:MIT Press,2006.
[2]Zhang Jingliang,Lee J.A review on prognostics and health monitoring of Li-ion battery[J].Journal of Power Sources,2011,196(15):6007-6014.
[3]Lin Cheng,Tang Aihua,Wang Weiwei.A review of SOHestimation methods in lithium-ion batteries for electric vehicle applications[J].Energy Procedia,2015,75:1920-1925.
[4]Andre D,Appel C,Soczka-Guth T,et al.Advanced mathematical methods of SOC and SOH estimation for lithium-ion batteries[J].Journal of Power Sources,2013,224:20-27.
[5]Hu Xiaosong,Jiang Jiuchun,Cao Dongpu,et al.Battery health prognosis for electric vehicles using sample entropy and sparse Bayesian predictive modeling[J].IEEETransactions on Industrial Electronics,2016,63(4):2645-2656.
[6]刘婷婷,朱文东,刘广一.基于深度学习的文本分类研究进展[J].电力信息与通信技术,2018,16(3):1-7.Liu Tingting,Zhu Wendong,Liu Guangyi.Advances in deep learning based text classification[J].Electric Power Information and Communication Technology,2018,16(3):1-7(in Chinese).
[7]贺倩.人工智能技术的发展与应用[J].电力信息与通信技术,2017,15(9):32-37.He Qian.Development and application of artificial intelligence technology[J].Electric Power Information and Communication Technology,2017,15(9):32-37(in Chinese).
[8]Long Bing,Xian Weiming,Jiang Lin,et al.An improved autoregressive model by particle swarm optimization for prognostics of lithium-ion batteries[J].Microelectronics Reliability,2013,53(6):821-831.
[9]Liu Datong,Luo Yue,Liu Jie,et al.Lithium-ion battery remaining useful life estimation based on fusion nonlinear degradation AR model and RPF algorithm[J].Neural Computing and Applications,2014,25(3-4):557-572.
[10]Lin H T,Liang T J,Chen S M.Estimation of battery state of health using probabilistic neural network[J].IEEETransactions on Industrial Informatics,2013,9(2):679-684.
[11]Eddahech A,Briat O,Bertrand N,et al.Behavior and state-of-health monitoring of Li-ion batteries using impedance spectroscopy and recurrent neural networks[J].International Journal of Electrical Power&Energy Systems,2012,42(1):487-494.
[12]Andre D,Nuhic A,Soczka-Guth T,et al.Comparative study of a structured neural network and an extended Kalman filter for state of health determination of lithium-ion batteries in hybrid electricvehicles[J].Engineering Applications of Artificial Intelligence,2013,26(3):951-961.
[13]Zhou Jie,He Zhiwei,Gao Mingyu,et al.Battery state of health estimation using the generalized regression neural network[C]//2015 8th International Congress on Image and Signal Processing.Shenyang:IEEE,2016:1396-1400.
[14]Xie Jiayu,Li Weiqing,Hu Yan.Aviation lead-acid battery state-of-health assessment using PSO-SVM technique[C]//2014 IEEE 5th International Conference on Software Engineering and Service Science.Beijing:IEEE,2014:344-347.
[15]Klass V,Behm M,Lindbergh G.A support vector machine-based state-of-health estimation method for lithium-ion batteries under electric vehicle operation[J].Journal of Power Sources,2014,270:262-272.
[16]Hu Chao,Youn B D,Chung J.A multiscale framework with extended Kalman filter for lithium-ion battery SOCand capacity estimation[J].Applied Energy,2012,92:694-704.
[17]Plett G L.Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs:Part 3.State and parameter estimation[J].Journal of Power Sources,2004,134(2):277-292.
[18]Miao Qiang,Xie Lei,Cui Hengjuan,et al.Remaining useful life prediction of lithium-ion battery with unscented particle filter technique[J].Microelectronics Reliability,2013,53(6):805-810.
[19]Olivares B E,Munoz M A C,Orchard M E,et al.Particle-filtering-based prognosis framework for energy storage devices with a statistical characterization of state-of-health regeneration phenomena[J].IEEETransactions on Instrumentation and Measurement,2013,62(2):364-376.
[20]Liu Datong,Pang Jingyue,Zhou Jianbao,et al.Prognostics for state of health estimation of lithium-ion batteries based on combination Gaussian process functional regression[J].Microelectronics Reliability,2013,53(6):832-839.
[21]Julier S J,Uhlmann J K.New extension of the Kalman filter to nonlinear systems[C]//Proceedings Volume 3068,Signal Processing,Sensor Fusion,and Target Recognition VI.Orlando,FL,United States:SPIE,1997,3068:182-194.
[22]Rasmussen C E,Williams C K I.Gaussian processes for machine learning[M].Cambridge,MA:MIT Press,2006.