Accurate battery temperature prediction using self-training neural networks within embedded system

被引：0
作者：
Fan, Xinyuan [1 ,2 ]
Zhang, Weige [1 ]
Qi, Hongfeng [2 ]
Zhou, Xingzhen [1 ]
机构：
[1] Department of Electrical Engineering, Beijing Jiaotong University, Shang Yuan Cun No. 3, Beijing
[2] CRRC Industrial Institute Corporation Limited, No. 1, East Road, Automobile Museum, Fengtai District, Beijing
来源：
Energy | 2024年 / 313卷
关键词：
Battery temperature; Embedded system; Neural network; Self-training; Temperature prediction;
D O I：
10.1016/j.energy.2024.134031
中图分类号：
学科分类号：
摘要：
The temperature of lithium-ion batteries is an essential factor in the performance and safety of the battery pack. By predicting batteries’ temperature, the battery management system (BMS) can optimize the strategy in advance, improve the precision of temperature control, and enhance the battery pack's performance. A self-training feedforward neural network is proposed as a means of predicting the battery surface temperature 300 s later. By extracting knowledge-driven features from current and voltage data, the structure of the proposed method is greatly simplified, facilitating implementation in a real BMS. The model is capable of self-training and parameter updating at the edge side, addressing the issue of poor generalizability associated with pre-trained models. The proposed method was validated under a variety of temperatures and operating conditions. The root-mean-square error (RMSE) of battery temperature prediction is 0.55 °C for constant ambient temperature and 0.64 °C for varying ambient temperature. Predicting 100 battery temperatures takes only 94 ms, enabling the BMS of electric vehicles to realize real-time temperature prediction. © 2024 Elsevier Ltd
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  • [1] Waseem M., Ahmad M., Parveen A., Suhaib M., Battery technologies and functionality of battery management system for EVs: current status, key challenges, and future prospectives, J Power Sources, 580, (2023)
  • [2] Ghaeminezhad N., A review on lithium-ion battery thermal management system techniques: A control-oriented analysis, Appl Therm Eng, (2023)
  • [3] He L., Review of thermal management system for battery electric vehicle, J Energy Storage, (2023)
  • [4] Tian J., Xiong R., Shen W., State-of-health estimation based on differential temperature for lithium ion batteries, IEEE Trans Power Electron, 35, 10, pp. 10363-10373, (2020)
  • [5] Xia Q., Ren Y., Wang Z., Yang D., Yan P., Wu Z., Et al., Safety risk assessment method for thermal abuse of lithium-ion battery pack based on multiphysics simulation and improved bisection method, Energy, 264, (2023)
  • [6] Piao N., Gao X., Yang H., Guo Z., Hu G., Cheng H.-M., Et al., Challenges and development of lithium-ion batteries for low temperature environments, eTransportation, 11, (2022)
  • [7] Bodenes L., Naturel R., Martinez H., Dedryvere R., Menetrier M., Croguennec L., Et al., Lithium secondary batteries working at very high temperature: capacity fade and understanding of aging mechanisms, J Power Sources, 236, pp. 265-275, (2013)
  • [8] Cho G., Wang M., Kim Y., Kwon J., Su W., A physics-informed machine learning approach for estimating lithium-ion battery temperature, IEEE Access, 10, pp. 88117-88126, (2022)
  • [9] The importance of a temp sensor on every cell in an EV battery - Dukosi, (2024)
  • [10] Wang Y., Chen X., Li C., Yu Y., Zhou G., Wang C., Et al., Temperature prediction of lithium-ion battery based on artificial neural network model, Appl Therm Eng, 228, (2023)
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