基于凸优化-寿命参数退化机理模型的锂离子电池剩余使用寿命预测
|
摘要
针对锂离子电池寿命预测中模型普适性差、预测精度不足等问题,提出一种基于凸优化-寿命参数退化机理模型的锂离子电池剩余使用寿命RUL预测方法。首先构造锂离子电池实际容量与其循环周期的退化机理模型。对锂离子电池寿命试验数据进行凸优化降噪处理;基于预处理得到的可靠性较高的数据,采用最小二乘法对所建机理模型的参数进行辨识,从而得到精确的模型表达式,实现锂离子电池RUL的预测。基于NASA锂离子电池数据集预测并评估锂离子电池的RUL,预测结果验证了模型良好的通用性,误差范围为4%左右。
To solve the problems in the life prediction of lithium-ion battery,such as poor model universality and low prediction accuracy,a prediction method for the remaining useful life(RUL)of lithium-ion battery is proposed based on the convex optimization-life parameter degradation mechanism model. First,the degradation mechanism model with the actual capacity of lithium-ion battery and its cycle is constructed. Then,the life test data of lithium-ion battery are processed by convex optimization for noise reduction;based on the data with higher reliability obtained based on preprocessing,the parameters of the mechanism model are identified using the least squares method,thus the exact expression of the model is obtained,which realizes the prediction of lithium-ion battery's RUL. Finally,based on the NASA lithium-ion battery data set,the lithium-ion battery's RUL is predicted and evaluated. The prediction results show that the proposed model is of good generality,and the error range is about 4%.
To solve the problems in the life prediction of lithium-ion battery,such as poor model universality and low prediction accuracy,a prediction method for the remaining useful life(RUL)of lithium-ion battery is proposed based on the convex optimization-life parameter degradation mechanism model. First,the degradation mechanism model with the actual capacity of lithium-ion battery and its cycle is constructed. Then,the life test data of lithium-ion battery are processed by convex optimization for noise reduction;based on the data with higher reliability obtained based on preprocessing,the parameters of the mechanism model are identified using the least squares method,thus the exact expression of the model is obtained,which realizes the prediction of lithium-ion battery's RUL. Finally,based on the NASA lithium-ion battery data set,the lithium-ion battery's RUL is predicted and evaluated. The prediction results show that the proposed model is of good generality,and the error range is about 4%.
引文
[1] Liu Zhenbao,Sun Gaoyuan,Bu Shuhui,et al. Particle learn?ing framework for estimating the remaining useful life of lithium-ion batteries[J]. IEEE Trans on Instrumentation and Measurement,2017,66(2):280-293.
[2] Aung H,Low K S,Shu T G. State-of-charge estimation of lithium-ion battery using square root spherical unscented Kalman filter(Sqrt-UKFST)in nanosatellite[J]. IEEE Trans on Power Electronics,2015,30(9):4774-4783.
[3] Wang D,Yang F,Tsui K L,et al. Remaining useful life prediction of lithium-ion batteries based on spherical cu?bature particle filter[J]. IEEE Trans on Instrumentation&Measurement,2016,65(6):1282-1291.
[4] Zhang Jingliang,Lee Jay. A review on prognostics and health monitoring of Li-ion battery[J]. Journal of Power Sources,2011,196(15):6007-6014.
[5] Liu Datong,Xie Wei,Liao Haitao,et al. An integrated probabilistic approach to lithium-ion battery remaining useful life estimation[J]. IEEE Trans on Instrumentation and Measurement,2015,64(3):660-670.
[6] Alvarez Anton J C,Garcia Nieto P J,Blanco Viejo C,et al.Support vector machines used to estimate the battery state of charge[J]. IEEE Trans on Power Electronics,2013,28(12):5919-5926.
[7] Richardson R R,Osborne M A,Howey D A. Gaussian pro?cess regression for forecasting battery state of health[J].Journal of Power Sources,2017,357:209-219.
[8] Wu Ji,Zhang Chenbin,Chen Zonghai,et al. An online method for lithium-ion battery remaining useful life esti?mation using importance sampling and neural networks[J].Applied Energy,2016,173:134-140.
[9] Zhang Chenghui,Zhang Yun,Li Yan. A novel battery state-of-health estimation method for hybrid electric vehi?cles[J]. IEEE/ASME Trans on Mechatronics,2015,20(5):2604-2612.
[10] Chen Lin,LüZhiqiang,Lin Weilong,et al. A new state-ofhealth estimation method for lithium-ion batteries through the intrinsic relationship between ohmic internal resis?tance and capacity[J]. Measurement,2018,116:586-595.
[11] Zou Yuan,Hu Xiaosong,Ma Hongmin,et al. Combined state of charge and state of health estimation over lithiumion battery cell cycle lifespan for electric vehicles[J]. Jour?nal of Power Sources,2015,273:793-803.
[12] Saha Bhaskar,Goebel Kai. Modeling Li-ion battery capaci?ty depletion in a particle filtering framework[C]//Annual Conference of the Prognostics and Health Management So?ciety. San Diego,USA,2009.
[13] Li Limin,Wang Zhongsheng,Jiang Hongkai. Storage bat?tery remaining useful life prognosis using improved un?scented particle filter[J]. Proceedings of the Institution of Mechanical Engineers Part O:Journal of Risk and Reli?ability,2015,229(1):52-61.
[14] Wang Dong,Miao Qiang,Pecht Michael. Prognostics of lithium-ion batteries based on relevance vectors and a con?ditional three-parameter capacity degradation model[J].Journal of Power Sources,2013,239:253-264.
[15] Tseng Kuo-Hsin,Liang Jin-Wei,Chang Wunching,et al.Regression models using fully discharged voltage and in?ternal resistance for state of health estimation of lithiumion batteries[J]. Energies,2015,8(4):2889-2907.
[16] Yamazaki I,Nooshabadi S,Tomov S,et al. Structureaware linear solver for realtime convex optimization for embedded systems[J]. IEEE Embedded Systems Letters,2017,9(3):61-64.
[17] Guha A,Patra A. State of health estimation of lithium-ion batteries using capacity fade and internal resistance growth models[J]. IEEE Trans on Transportation Electrifi?cation,2018,4(1):135-146.
[2] Aung H,Low K S,Shu T G. State-of-charge estimation of lithium-ion battery using square root spherical unscented Kalman filter(Sqrt-UKFST)in nanosatellite[J]. IEEE Trans on Power Electronics,2015,30(9):4774-4783.
[3] Wang D,Yang F,Tsui K L,et al. Remaining useful life prediction of lithium-ion batteries based on spherical cu?bature particle filter[J]. IEEE Trans on Instrumentation&Measurement,2016,65(6):1282-1291.
[4] Zhang Jingliang,Lee Jay. A review on prognostics and health monitoring of Li-ion battery[J]. Journal of Power Sources,2011,196(15):6007-6014.
[5] Liu Datong,Xie Wei,Liao Haitao,et al. An integrated probabilistic approach to lithium-ion battery remaining useful life estimation[J]. IEEE Trans on Instrumentation and Measurement,2015,64(3):660-670.
[6] Alvarez Anton J C,Garcia Nieto P J,Blanco Viejo C,et al.Support vector machines used to estimate the battery state of charge[J]. IEEE Trans on Power Electronics,2013,28(12):5919-5926.
[7] Richardson R R,Osborne M A,Howey D A. Gaussian pro?cess regression for forecasting battery state of health[J].Journal of Power Sources,2017,357:209-219.
[8] Wu Ji,Zhang Chenbin,Chen Zonghai,et al. An online method for lithium-ion battery remaining useful life esti?mation using importance sampling and neural networks[J].Applied Energy,2016,173:134-140.
[9] Zhang Chenghui,Zhang Yun,Li Yan. A novel battery state-of-health estimation method for hybrid electric vehi?cles[J]. IEEE/ASME Trans on Mechatronics,2015,20(5):2604-2612.
[10] Chen Lin,LüZhiqiang,Lin Weilong,et al. A new state-ofhealth estimation method for lithium-ion batteries through the intrinsic relationship between ohmic internal resis?tance and capacity[J]. Measurement,2018,116:586-595.
[11] Zou Yuan,Hu Xiaosong,Ma Hongmin,et al. Combined state of charge and state of health estimation over lithiumion battery cell cycle lifespan for electric vehicles[J]. Jour?nal of Power Sources,2015,273:793-803.
[12] Saha Bhaskar,Goebel Kai. Modeling Li-ion battery capaci?ty depletion in a particle filtering framework[C]//Annual Conference of the Prognostics and Health Management So?ciety. San Diego,USA,2009.
[13] Li Limin,Wang Zhongsheng,Jiang Hongkai. Storage bat?tery remaining useful life prognosis using improved un?scented particle filter[J]. Proceedings of the Institution of Mechanical Engineers Part O:Journal of Risk and Reli?ability,2015,229(1):52-61.
[14] Wang Dong,Miao Qiang,Pecht Michael. Prognostics of lithium-ion batteries based on relevance vectors and a con?ditional three-parameter capacity degradation model[J].Journal of Power Sources,2013,239:253-264.
[15] Tseng Kuo-Hsin,Liang Jin-Wei,Chang Wunching,et al.Regression models using fully discharged voltage and in?ternal resistance for state of health estimation of lithiumion batteries[J]. Energies,2015,8(4):2889-2907.
[16] Yamazaki I,Nooshabadi S,Tomov S,et al. Structureaware linear solver for realtime convex optimization for embedded systems[J]. IEEE Embedded Systems Letters,2017,9(3):61-64.
[17] Guha A,Patra A. State of health estimation of lithium-ion batteries using capacity fade and internal resistance growth models[J]. IEEE Trans on Transportation Electrifi?cation,2018,4(1):135-146.