锂离子电池荷电状态的在线融合估计方法

为提高安时积分法对荷电状态估计的精度,解决其估计误差随时间不断增大的问题,采用极限学习机算法建立了安时积分法的误差预测模型,该模型以电池工作电流作为输入,对应的安时积分法荷电状态估计误差作为输出,将误差预测模型与安时积分法进行融合,对安时积分法的荷电状态估计值进行校正,形成了安时积分法和极限学习机方法融合的锂离子电池荷电状态在线估计方法.仿真分析结果表明,相比安时积分法,融合方法可有效减小荷电状态估计误差,克服安时积分法估计误差随时间不断增大的问题.

An online fusion estimation method forstate of charge of lithium ion batteries

The SOC is one of the important parameters in battery management system. In order to improve the accuracy of the ampere-hour integral method for estimation of SOC and solve the problem that estimation error increases with time, an error prediction model of ampere-hour integration method is established in this paper by using Extreme Learning Machine Algorithm. The model takes working current of battery as input and the corresponding ampere-hour integration method SOC estimation error as output. The error prediction model is integrated with the ampere-hour integration method to correct estimated value of the SOC of the ampere-hour integration method. An on-line estimation method of SOC of lithium-ion battery based on Ampere-hour integration method and Extreme Learning Machine Algorithm is therefore developed. Results from our simulation show that, compared with the ampere-time integration method, the integrated method can effectively reduce estimation errors of the SOC and overcome the problem that the estimation errors resulting from the ampere-time integration method increases with time.