电池的荷电状态(state of charge,SOC)的准确快速估计与电池安全管理、延长生命周期、确定再制造阈值等密切相关,优秀的SOC估计算法至少具有准确、稳定、适用性强、估计快四大性质.文中分析了几种常用SOC估算方法,单一的安时积分法和开路电压法虽然操作简单,但精确度不足,神经网络法和滤波法的通用性和精确性较好,但神经网络法需要大量数据,投入成本较大,滤波法对模型精度依赖性较强.最后针对现有SOC 估算技术,提出未来研究重点:(1)联系电池制造工艺,实现从制造商到SOC 估计的一致性;(2)考虑电池的工作环境,实现外部数据与内在反应的联系;(3)考虑电池类型的多样性,提高估算方法的通用性.
Research Status of State of Charge Estimation for Li-ion Power Batteries
The accurate and rapid estimation of the state of charge(SOC)of the battery is closely related to battery safety management,extending the life cycle,determining the remanufacturing threshold,etc.and the excellent SOC estimation algorithm has at least four properties:accurate,stable,applicable and fast estimation.In this paper,several commonly used SOC estimation methods are analyzed,although the single ampere-hour integral method and open-circuit voltage method are simple to operate,but the accuracy is insufficient,the versatility and accuracy of the neural network method and the filter method are better,but the neural network method requires a large amount of data,the input cost is large,and the filtering method has a strong dependence on the accuracy of the model.Finally,according to the existing SOC estimation technology,the future research priorities are proposed:(1)linking the battery manufacturing process to achieve the consistency of SOC estimation from the manufacturer to the SOC;(2)considering the working environment of the battery to realize the connection between external data and internal reaction;(3)considering the diversity of battery types to improve the versatility of estimation methods.
lithium ion batterypower batterystate of chargeestimatealgorithm
万方数据
维普
