| 基于EKF-AH联合算法的锂离子电池荷电状态估算 |
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| 引用本文： | 陈盛华. 基于EKF-AH联合算法的锂离子电池荷电状态估算[J]. 电源技术, 2019, 43(1): 103-106 |
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| 作者姓名： | 陈盛华 |
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| 作者单位： | 河北工业大学省部共建电工装备可靠性与智能化国家重点实验室,天津300130;河北工业大学河北省电磁场与电器可靠性重点实验室,天津300130 |
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| 摘 要： | 估算算法先进性与否是影响锂离子电池荷电状态(SOC)估算准确度的重要因素。用扩展卡尔曼滤波(EKF)算法估算锂离子电池SOC时在低容量区和估算后期误差较大,为此将EKF算法和安时积分法(AH)相结合,提出EKF-AH联合算法。选用恒流放电及动态工况对联合算法进行实验验证。结果表明,在两个实验工况下对SOC的估算误差分别小于2%和3%。因此EKF-AH相比于EKF,估算精度提高。
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| 关 键 词： | 锂离子电池 荷电状态估算 扩展卡尔曼 安时积分法 |
Charge state estimation of lithium-ion batteries based on EKF-AH joint algorithm |
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| CHEN Sheng-hua. Charge state estimation of lithium-ion batteries based on EKF-AH joint algorithm[J]. Chinese Journal of Power Sources, 2019, 43(1): 103-106 |
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| Authors: | CHEN Sheng-hua |
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| Affiliation: | (State Key Laboratory of Reliability and Intelligence of Electrical Equipment,Hebei University of Technology,Tianjin 300130,China;Key Laboratory of Electromagnetic Field and Electrical Apparatus Reliability of Hebei Province,Hebei University of Technology, Tianjin 300130,China) |
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| Abstract: | CHEN Sheng-hua(State Key Laboratory of Reliability and Intelligence of Electrical Equipment,Hebei University of Technology,Tianjin 300130,China;Key Laboratory of Electromagnetic Field and Electrical Apparatus Reliability of Hebei Province,Hebei University of Technology, Tianjin 300130,China) |
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| Keywords: | lithium-ion battery SOC estimation extended Kalman filter ampere-hour integration |
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