基于随机片段数据的锂电池状态估计方法

针对锂电池状态估计通常只能采集到不完整的放电数据,导致难以准确判断锂电池状态的问题,提出一种基于随机片段数据的锂电池状态估计方法.以固定健康状态(SOH)差为间隔构建老化数据库,利用随机片段数据进行匹配,并采用粒子群优化算法进行求解,从而判断对应的锂电池初始荷电状态(SOC)及SOH等信息;基于二阶戴维南等效电路模型对锂电池进行建模,并对其参数进行辨识;基于状态匹配结果与所建模型,利用扩展卡尔曼滤波对锂电池SOC进行估计,获得锂电池的剩余放电时间等状态信息.利用锂电池单体放电数据进行实验验证,实验与仿真结果表明:与传统方法相比,所提方法具有较高的稳定性和准确率.

State estimation method of lithium battery based on random fragment data

Aiming at the problem that the state estimation of lithium battery can only collect incomplete discharging data, which makes it difficult to accurately judge the state of lithium battery, a state estimation method of lithium battery based on random fragment data is proposed. The aging database is constructed with fixed SOH(State Of Health) difference as the interval, the random fragment data is matched, and the particle swarm optimization algorithm is used to solve the problem, so as to judge the corresponding information such as initial SOC(State Of Charge) and SOH of lithium battery. The lithium battery is modeled based on second-order Thevenin equivalent circuit model, and its parameters are identified. Based on the state matching results and the established model, the extended Kalman filter is used to estimate the SOC of lithium battery, and the state information such as remaining discharging time of lithium battery is obtained. The discharging data of lithium battery is used for experimental verification, and the experimental and simulative results show that the proposed method has higher stability and accuracy compared with the traditional methods.