基于粒子群算法估计实际工况下锂电池SOH

提出一种基于粒子群算法和锂电池经验容量模型的对电池实际工况下的健康状态进行估计的新方法.建立了电动汽车实际运行工况下充电曲线特征与电池健康度的线性模型.辅以电池经验容量模型，使之符合监督学习的实际情况并能够用计算机对参数进行拟合.以美国航天航空局电池老化数据建立训练集与验证集，对模型进行训练，并对训练好的模型进行实验验证.实验表明SOH估计误差都在7%以下，在实际工况中能够快速对电动汽车锂电池的健康度进行准确估计. 

Estimation of Lithium Battery SOH Under Actual Operating Conditions Based on Particle Swarm Optimization

A new method was proposed based on the particle swarm algorithm and the empirical capacity model of lithium batteries to estimate the state of health (SOH) of the battery under actual operating conditions. A linear model was established for charging curve characteristics and battery health under electric vehicle operating conditions. A battery empirical capacity model was supplied to make it conform to the actual situation of supervised learning and to be able to fit the parameters with a computer. Based on NASA''s battery aging data, a training set and a validation set were established, training the model and verifying the trained model experimentally. Results show that, the SOH estimation error can reduce to less than 7%. In actual working conditions, the health of lithium batteries of electric vehicles can be accurately estimated quickly.