摘要
碳酸铁锂电池在使用过程中,各项参数会发生规律性变化,其折旧率的规律是反映锂电池能量的重要指标。通过建立电池模型,研究了锂电池剩余电量的估计方法,列举了常见的电池SOC(电池的荷电状态,State of Charge)的估计方法,分析了各种方法的优点和局限性,提出基于人工神经网络模型的SOC预测方法,并在车仿真软件ADVISOR上进行仿真分析。最后通过动力电池SOC的仿真结果,给出了电动汽车动力电池SOH(电池的寿命状态,State of Health)的预测方法,提出通过曲线拟合模型进行预测,得出电池衰减速度的规律。
During operation of iron carbonate lithium batteries, their parameters are changed regularly, and depreciation rate rule is an important index reflecting the energy capacity of the battery. Through establishment of a battery model, this article studies the methods for estimating remaining capacity of the lithium battery. Estimation methods for common SOC ( state of charge) batteries are enumerated, and advantages and limitations of various methods are analyzed. Then, it presents an estimation method based on the artificial neural network model. Simulation analysis is made through ADVISOR aboard simulation software. Finally, with the help of the simulation results of the SOC power battery, it gives an estimation method for the SOH ( state of health) of the vehicle power battery. It proposes that prediction should be made through a curve-fitting model to obtain the regularity of delay rate of the battery.
出处
《电气自动化》
2017年第2期52-55,共4页
Electrical Automation
关键词
锂电池
折旧率
荷电状态
人工神经网络
寿命状态
lithium battery
depreciation rate
state of charge
artificial neural network
state of health