基于AMEsim的锂电池健康状态估计模型实验研究
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TN701

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Experimental study on estimation model of health state of lithium battery based on AMEsim
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    摘要:

    新能源汽车动力电池健康状态(state of health,SOH)是一个表征电池性能优良的重要评价指标。针对准确估计18650锂电池健康状态这一目标需求,在锂电池单体数学模型的基础上,通过其等效电路模型分析影响锂电池健康状态的因素,采用通用非线性模型(gneral nonlinear model,GNL)电池等效电路和扩展卡尔曼滤波算法,在AMEsim仿真环境下搭建了锂电池SOH估计模型,并对18650锂电池进行充放电循环实验,将采集到的数据集导入AMEsim估计模型的数据模块中进行算法仿真。仿真实验结果表明,SOH估算误差小于8%,建立的锂电池SOH估计模型满足估算精度高,响应速度快的目标需求。

    Abstract:

    State of health (SOH) is an important indicator of good battery performanceNew energy vehicle. Aiming at the target requirement of accurately estimating the health status of 18650 lithium battery, based on the mathematical model of lithium battery cell, the equivalent circuit model is used to analyze the factors affecting the health status of lithium battery, and the universal nonlinear battery equivalent circuit model is adopted. Gneral nonlinear model (GNL) and extended Kalman filter algorithm, the SOH estimation model of lithium battery was built in AMEsim simulation environment, and the charge and discharge cycle experiment of 18650 lithium battery was carried out. The collected data set was imported into the data module of AMEsim estimation model. The simulation results show that the SOH estimation error is less than 8%, and the established lithium battery SOH estimation model meets the target demand for High estimation accuracy and fast response.

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孙俏,张晓宇.基于AMEsim的锂电池健康状态估计模型实验研究[J].电子测量技术,2019,42(9):102-106

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  • 在线发布日期: 2021-08-23
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