面向SOC估计的计及温度和循环次数的锂离子电池组合模型
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TH39

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工业和信息化部民用飞机专项(MJ2017D26)、安徽省自然科学基金面上项目(1808085MF200)资助


A Lithiumion battery combined model considering temperature and cycle times for SOC estimation
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    摘要:

    锂离子电池荷电状态(SOC)估计的准确性依赖于精确的电池模型,为此提出一种基于改进的Shepherd模型并耦合温度和循环次数因素的锂离子电池组合模型(SCM)。将Shepherd模型受温度和循环次数影响的满电开路电压、极化常数、可用容量、内阻等参数进行热建模和循环损失建模,同时将模型参数辨识方法简化为仅需两组不同环境温度下放电实验数据的非线性最小二乘法。通过对不同循环次数的锂离子电池在不同温度环境下模拟电动汽车实际工况,进行放电实验,并结合扩展卡尔曼滤波算法实现对SCM模型和ECM模型的SOC动态估计。仿真和实验结果表明所提模型相对误差小于15%,SOC估计误差小于3%,从而验证了所提出模型的优越性。

    Abstract:

    The state of charge (SOC) estimation accuracy for the lithiumion battery depends heavily on the battery model. Therefore, a combined model (Shepherdbased combined model, SCM) based on the improved Shepherd model coupled with temperature and cycle numbers is proposed. The thermal modeling and cyclic loss modeling of parameters, such as open circuit voltage, polarization constant, available capacity, and internal resistance in the Shepherd model, are considered in this paper. The identification method for model parameters is simplified to a nonlinear least squares method that only requires two sets of discharge experimental data at different temperatures. The discharge experiment is implemented by using lithiumion batteries of different cycle numbers and the actual working conditions of the electric vehicle are simulated by setting the lithiumion battery at different temperatures. The SOC dynamic estimations of SCM model and ECM model are implemented by using the extended Kalman filter algorithm. Simulation and experimental results show that the relative error of the proposed model is less than 15% and the SOC estimation error is less than 3%. The superiority of the proposed model is verified.

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刘征宇,朱诚诚,尤勇,姚利阳.面向SOC估计的计及温度和循环次数的锂离子电池组合模型[J].仪器仪表学报,2019,40(11):117-127

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  • 在线发布日期: 2022-01-08
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