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基于UKF的铅酸蓄电池SOC估算策略 被引量:2

SOC Estimation of Lead-Acid Batteries Based on the Unscented-Kalman Filtering
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摘要 为了估算铅酸蓄电池的荷电状态,以Thevenin模型为基础,建立了数学关系简单,易于工程实现的状态空间模型。在此基础上对模型进行处理,采用无味卡尔曼滤波算法实现了对电池荷电状态的估算。仿真结果表明该模型能较好地体现电池特性,无味卡尔曼滤波算法在估算中可保持很好的精度。实验结果与真实值的误差不超过5%,满足电动汽车对荷电状态误差8%的使用要求,验证了此估算策略的可靠性和可行性。 A strategy of estimating the state of charge based on the Unscented-Kalman Filter( UKF) is presented. A state space model of the lead-acid battery derived from the venin model is proposed,which has the advantage of simplicity and is easy to implement. The model is well managed,and the Unscented-Kalman Filter is applied in the estimation. The simulation results show that the model can reflect the battery characteristics, and the Unscented-Kalman Filter can give an excellent precision in the estimation. The SOC estimation error using the proposed method is lower than 5%,which meets the requirements for the SOC estimation error of the electric vehicles.The reliability and feasibility of the method are validated.
作者 胡振宇 吴雷
出处 《江南大学学报(自然科学版)》 CAS 2015年第5期567-571,共5页 Joural of Jiangnan University (Natural Science Edition) 
基金 2012年省产学研创新项目(BY20120690)
关键词 无味卡尔曼滤波算法 荷电状态 铅酸蓄电池 Unscented-Kalman Filtering state of charg lead-acid batteries
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