优化分级T-S模糊控制动态估计纯电动汽车电池健康状态

针对纯电动汽车动力电池健康状态（state of health，SOH）预测中非线性影响因素多、算法繁杂、难以在单片机开发平台中实现等难点，首先利用累计充电循环次数计量法得到使用循环次数，将SOH与使用循环次数、内阻变化量、电压降值的相关非线性关系转换成离散的二维数据表，依据使用条件，采用二分查表法获得不同估计方法下SOH值；再将使用循环次数、电压降值和内阻变化量作为输入量，以相应SOH的权重作为输出，利用T-S模糊控制建立SOH动态预测模型，根据权重和边界条件计算得到SOH.仿真结果表明，所提方法最大预测误差4.3%，响应时间55 ms内，预测效果比现有方法显著提高. 

Dynamic Prediction of Pure Electric Vehicle Battery State of Health by Optimized and Graded T-S Fuzzy Control

Due to the state of health (SOH) prediction of pure electric vehicle power battery relates to many non-linear factors and complicated algorithms, it is difficult to accomplish in singlechip platform. In order to overcome the difficulty, a new method was proposed. Firstly, a method for counting the accumulative charging cycles was used to calculate the number of battery use cycles. Then the nonlinear relationship between SOH and the number of cycles, the variation of internal resistance and the value of voltage drop were transformed into a discrete two-dimensional datasheet. According to the use conditions, the SOH values under different estimation methods could be obtained by using the binary look-up table method. Secondly, taking the number of cycles, voltage drop and internal resistance variation as input and the weight of corresponding SOH as output, a SOH dynamic prediction model was established based on T-S fuzzy control. According to the weights and boundary conditions, the SOH could be calculated. The simulation results show that the proposed method has a maximum prediction error of 4.3% and a response time of 55 ms, and the prediction effect is much better than that of the existing methods.