Abstract
In this paper, we propose a digital twin model for battery management systems (BMS). We first discuss the corresponding concepts about the digital twin model of battery management systems. Then, the state-of-charge (SoC) and state-of-health (SoH) estimation algorithms are presented in an integrated fashion for the monitoring and prognostics. Concretely, the extended Kalman filter algorithm (EKF) is used in this paper for the estimation of SoC, which improves the robustness of digital twin model, and the particle swarm optimization algorithm (PSO) is used in this paper for the estimation of SoH. The embedded system platforms are introduced to implement the proposed digital twin model. In the end of this paper, by using the experimental data obtained from the actual circuit experiment and using the Simulink module of MATLAB to simulate the digital twin model proposed in this paper, we verified that the digital twin model proposed in this paper for BMS has good performance in the Gaussian white noise condition.
This work is supported by National Natural Science Foundation of China (No. 61803394). Mi Zhou, Lu Bai, and Jiaxuan Lei contribute equally to this work.
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Zhou, M., Bai, L., Lei, J., Wang, Y., Li, H. (2022). A Digital Twin Model for Battery Management Systems: Concepts, Algorithms, and Platforms. In: Yao, J., Xiao, Y., You, P., Sun, G. (eds) The International Conference on Image, Vision and Intelligent Systems (ICIVIS 2021). Lecture Notes in Electrical Engineering, vol 813. Springer, Singapore. https://doi.org/10.1007/978-981-16-6963-7_102
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