Abstract：In order to accurately estimate the state of health (SOH) of lithium-ion batteries, this paper proposes a new estimation method based on improved grid search (GS) and generalized regression neural network (GRNN). Firstly, data processing is performed on the data set, and effective characteristic data including voltage and current are extracted through correlation analysis method. Secondly, a regression model based on improved grid search and generalized regression neural network is proposed to estimate the health of the battery. Finally, two public data sets of lithium-ion batteries are used to verify the proposed estimation method. Experimental results prove that this method has the advantages of accuracy, generalization performance and reliability compared with other estimation methods.
姚远, 陈志聪, 吴丽君, 程树英, 林培杰. 一种基于改进网格搜索和广义回归神经网络的锂离子电池健康状态估计方法[J]. 电气技术, 2021, 22(7): 32-37.
YAO Yuan, CHEN Zhicong, WU Lijun, CHENG Shuying, LIN Peijie. A health state estimation method for lithium-ion battery based on improved grid rearch and generalized regression neural network. Electrical Engineering, 2021, 22(7): 32-37.
[1] 杨若岑, 冬雷, 廖晓钟, 等. 电池剩余容量估算方法综述[J]. 电气技术, 2019, 20(10): 1-5, 57. [2] 李建林, 马会萌, 惠东. 储能技术融合分布式可再生能源的现状及发展趋势[J]. 电工技术学报, 2016, 31(14): 1-10, 20. [3] 孙冬, 许爽. 梯次利用锂电池健康状态预测[J]. 电工技术学报, 2018, 33(9): 2121-2129. [4] 颜湘武, 邓浩然, 郭琪, 等. 基于自适应无迹卡尔曼滤波的动力电池健康状态检测及梯次利用研究[J]. 电工技术学报, 2019, 34(18): 3937-3948. [5] WEI Jingwen, DONG Guangzhong, CHEN Zonghai.Remaining useful life prediction and state of health diagnosis for lithium-ion batteries using particle filter and support vector regression[J]. IEEE Transactions on Industrial Electronics, 2018, 65(7): 5634-5643. [6] GUO Peiyao, CHENG Ze, YANG Lei.A data-driven remaining capacity estimation approach for lithium-ion batteries based on charging health feature extraction[J]. Journal of Power Sources, 2019, 412: 442-450. [7] 吕航, 刘承志, 尹栋, 等. 深海动力磷酸铁锂电池组均衡方案设计优化[J]. 电工技术学报, 2016, 31(19): 232-238. [8] 郭永芳, 黄凯, 李志刚. 基于短时搁置端电压压降的快速锂离子电池健康状态预测[J]. 电工技术学报, 2019, 34(19): 3968-3978. [9] DONG Guangzhong, CHEN Zonghai, WEI Jingwen, et al.Battery health prognosis using brownian motion modeling and particle filtering[J]. IEEE Transactions on Industrial Electronics, 2018, 65(11): 8646-8655. [10] QU Jiantao, LIU Feng, MA Yuxiang, et al.A neural-network based method for RUL prediction and SOH monitoring of lithium-ion battery[J]. IEEE Access, 2019, 7: 87178-87191. [11] WU Ji, WANG Yujie, ZHANG Xu, et al.A novel state of health estimation method of Li-ion battery using group method of data handling[J]. Journal of Power Sources, 2016, 327: 457-464. [12] DAI Houde, ZHAO Guangcai, LIN Mingqiang, et al.A novel estimation method for the state of health of lithium-ion battery using prior knowledge-based neural network and Markov chain[J]. IEEE Transactions on Industrial Electronics, 2019, 66(10): 7706-7716. [13] TIAN Jinpeng, XIONG Rui, YU Quanqing.Fractional-model-based incremental capacity analysis for degrad-ation state recognition of lithium-ion batteries[J]. IEEE Transactions on Industrial Electronics, 2019, 66(2): 1576-1584. [14] WANG F K, MAMO T.A hybrid model based on support vector regression and differential evolution for remaining useful lifetime prediction of lithium-ion batteries[J]. Journal of Power Sources, 2018, 401: 49-54. [15] XIONG Rui, LI Linlin, TIAN Jinpeng.Towards a smarter battery management system: a critical review on battery state of health monitoring methods[J]. Journal of Power Sources, 2018, 405: 18-29. [16] 彭周宁, 林培杰, 赖云锋, 等. 基于混合灰色关联分析-广义回归神经网络的光伏电站短期功率预测[J]. 电气技术, 2019, 20(10): 11-18. [17] YANG Duo, ZHANG Xu, PAN Rui, et al.A novel Gaussian process regression model for state-of-health estimation of lithium-ion battery using charging curve[J]. Journal of Power Sources, 2018, 384: 387-395.
2021, Vol. 22 
