Abstract
The high voltage rating of the battery pack requires that it has good insulation properties. Once an insulation fault occurs, it will not only cause a fire, but also poses a threat to equipment and personal safety, therefore, the study of insulation fault diagnosis methods for power storage systems is of great importance. This paper firstly proposes an equivalent model for battery pack insulation fault diagnosis based on the signal injection method; then uses a double Kalman filter algorithm to identify the model parameters to improve the identification accuracy, and at the same time makes an estimate of the end voltage and charge state; finally, the lithium battery pack is tested and verified using the hybrid power pulse characteristics experiment, and the results show that the maximum absolute error of the output voltage of the battery pack is 3.48 mV, and the maximum absolute value of the error in the prediction of the charge state is 0.0005 which improves the recognition accuracy and prediction accuracy of the parameters effectively.
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Acknowledgements
This work was supported by the National Natural Science Foundation of China under Grant 52077027 and the Shenyang Key Research and Development Project (No. 2022021000014).
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Duan, L., Wang, D., Sun, G., Ni, Y., Song, K., Li, Y. (2024). A New Method of Lithium Battery Insulation Fault Diagnosis Based on Double Kalman Filter. In: Dong, X., Cai, L.C. (eds) The Proceedings of 2023 4th International Symposium on Insulation and Discharge Computation for Power Equipment (IDCOMPU2023). IDCOMPU 2023. Lecture Notes in Electrical Engineering, vol 1101. Springer, Singapore. https://doi.org/10.1007/978-981-99-7401-6_37
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DOI: https://doi.org/10.1007/978-981-99-7401-6_37
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