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| State of Charge Estimation of Lithium-ion Batteries Based on PNGV Model |
| Received:June 12, 2023 Revised:September 06, 2023 |
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| DOI：10.7643/issn.1672-9242.2023.11.011 |
| KeyWord:lithium-ion battery state of charge estimation PNGV model FFRLS kalman filtering dynamic operating conditions |
| Author | Institution |
| LIU Xin |
State Key Laboratory of Ocean Engineering Shanghai Jiao Tong University, Shanghai , China |
| CHEN Zi-qiang |
State Key Laboratory of Ocean Engineering Shanghai Jiao Tong University, Shanghai , 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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