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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

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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
Dynamic Prediction of Pure Electric Vehicle Battery State of Health by Optimized and Graded T-S Fuzzy Control
投稿时间：2018-05-04
DOI：10.15918/j.tbit1001-0645.2019.06.010
中文关键词:SOH累计充电循环次数计量法二分查表法T-S模糊控制动态模型
English Keywords:SOHmeasuring method of accumulative charge cycle timesbinary look-up table methodT-S fuzzy controldynamic model
基金项目:国家自然科学基金资助项目（61463020）；江西省自然科学基金资助项目（20151BAB206034）
作者单位
陈德海江西理工大学 电气工程与自动化学院, 江西, 赣州 341000
华铭江西理工大学 电气工程与自动化学院, 江西, 赣州 341000
邹争明江西理工大学 电气工程与自动化学院, 江西, 赣州 341000
任永昌江西理工大学 电气工程与自动化学院, 江西, 赣州 341000
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中文摘要:
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.
English Summary:
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.
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