Estimation of battery SOC based on improved EKF algorithm | |
Shi G(石刚)![]() ![]() | |
2016 | |
会议名称 | 2016 IEEE Information Technology, Networking, Electronic and Automation Control Conference, ITNEC 2016 |
会议日期 | May 20-22, 2016 |
会议地点 | Chongqing, China |
关键词 | EKF neural network battery SOC Thevenin circuit Li-ion battery |
页码 | 151-154 |
通讯作者 | 石刚 |
中文摘要 | This paper studies the estimation of the state of lithium battery (SOC), and develops an improved extended Kalman filter algorithm for this problem. To compensate deficiencies of the simple polynomial fitting, the neural network algorithm firstly is adopted to simulate the relation curve between the SOC and the parameters of circuit model, which is constructed based on Thevenin circuit. And then the state space equation among the battery's SOC and the voltage of the ends of the RC loop is established, also does the measurement equation which is based on the battery output voltage. In addition, extended Kalman is applied to estimate battery SOC. In the last, the effectiveness of the proposed method is verified using an experimental testing, and the results show that our method can estimate the SOC more accurately comparing with the standard extended Kalman algorithm. |
收录类别 | EI ; CPCI(ISTP) |
产权排序 | 1 |
会议主办者 | Chongqing Global Union Academy of Science and Technology; Global Union Academy of Science and Technology; IEEE Beijing Section |
会议录 | Proceedings of 2016 IEEE Information Technology, Networking, Electronic and Automation Control Conference, ITNEC 2016
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会议录出版者 | IEEE |
会议录出版地 | Piscataway, NJ, USA |
语种 | 英语 |
ISBN号 | 978-1-4673-9192-4 |
WOS记录号 | WOS:000389505000032 |
内容类型 | 会议论文 |
源URL | [http://ir.sia.cn/handle/173321/19283] ![]() |
专题 | 沈阳自动化研究所_工业控制网络与系统研究室 |
推荐引用方式 GB/T 7714 | Shi G,Zhao W,Han ZH,et al. Estimation of battery SOC based on improved EKF algorithm[C]. 见:2016 IEEE Information Technology, Networking, Electronic and Automation Control Conference, ITNEC 2016. Chongqing, China. May 20-22, 2016. |
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