| 基于PNGV模型的锂离子电池荷电状态估计 |
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| 引用本文： | 柳新,陈自强.基于PNGV模型的锂离子电池荷电状态估计[J].装备环境工程,2023,20(11):81-90. |
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| 作者姓名： | 柳新 陈自强 |
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| 作者单位： | 上海交通大学 海洋工程国家重点实验室，上海 200240 |
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| 摘 要： | 目的 提升不同老化情况下的锂离子电池荷电状态(SOC)估计精度。方法 基于PNGV模型(Partnership for a New Generation of Vehicles)，对锂离子电池SOC进行估计。首先通过双线性变换对PNGV模型进行离散化，采用带有遗忘因子的递归最小二乘法(FFRLS)，对电池模型参数进行在线辨识，利用卡尔曼滤波(EKF)算法进行SOC估计，并通过动态工况验证SOC估计精度。结果 以多种误差指标考察不同循环下的试验数据，在不同电池老化状态下具有较好的预测精度。相比基于Thevenin模型的算法，基于PNGV模型的算法可以将SOC平均绝对误差减少约60%，同时也可以将SOC估计最大绝对误差波动范围降低53.8%。结论 本算法引入PNGV模型后，解决了基于Thevenin模型算法误差大、不稳定的问题，提升了动力电池系统在不同老化环境下的适应性。
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| 关 键 词： | 锂离子电池 荷电状态估计 PNGV模型 带遗忘因子的最小二乘法 卡尔曼滤波 动态工况中图分类号:TM912 文献标识码:A 文章编号:1672-9242(2023)11-0081-10 |
| 收稿时间： | 2023/6/12 0:00:00 |
| 修稿时间： | 2023/9/6 0:00:00 |
State of Charge Estimation of Lithium-ion Batteries Based on PNGV Model |
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| LIU Xin,CHEN Zi-qiang.State of Charge Estimation of Lithium-ion Batteries Based on PNGV Model[J].Equipment Environmental Engineering,2023,20(11):81-90. |
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| Authors: | LIU Xin CHEN Zi-qiang |
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| Affiliation: | State Key Laboratory of Ocean Engineering Shanghai Jiao Tong University, Shanghai 200240, China |
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| Abstract: | The work aims to improve the accuracy of state of charge (SOC) estimation for lithium-ion batteries under different aging conditions. The SOC of lithium-ion batteries was estimated based on a PNGV (Partnership for a New Generation of Vehicles) model. Firstly, the PNGV model was discretized through bilinear transformation, and the recursive least squares method with forgetting factor (FFRLS) was used for online identification of battery model parameters. The Kalman filter (EKF) algorithm was used for SOC estimation, and the accuracy of SOC estimation was verified through dynamic operating conditions. By examining experimental data under different cycles using multiple error indicators, it showed good prediction accuracy under different battery aging states. Compared with the algorithm based on the Thevenin model, the algorithm based on the PNGV model could reduce the average absolute error of SOC by about 60%. At the same time, it could also reduce the fluctuation range of the maximum absolute error of SOC estimation by 53.8%. After introducing the PNGV model, this algorithm solves the problem of high error and instability based on the Thevenin model algorithm, and improves the adaptability of the power battery system in different aging environments. |
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| Keywords: | lithium-ion battery state of charge estimation PNGV model FFRLS kalman filtering dynamic operating conditions |
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