电池(电)
电池组
电流(流体)
热的
电气工程
计算机科学
汽车工程
拓扑(电路)
工程类
物理
热力学
功率(物理)
作者
Yi Xie,Xi Wang,Xiaosong Hu,Wei Li,Yangjun Zhang,Xianke Lin
出处
期刊:IEEE Transactions on Power Electronics
[Institute of Electrical and Electronics Engineers]
日期:2022-01-01
卷期号:37 (1): 1027-1043
被引量:13
标识
DOI:10.1109/tpel.2021.3102292
摘要
Large battery packs are used in electric vehicles. Heat is generated when the battery pack is being used. Therefore, it is necessary to predict battery heat generation. An enhanced electro-thermal model is developed to describe the temperature distribution inside a battery pack. It combines the dynamic resistance model and the current distribution model. The resistance model is affected by the thermal and electrical parameters, while the current distribution model considers the interaction between cell status and current variation in the parallel branch. The proposed model can accurately predict the temperature change of cells in the pack under static and dynamic current conditions. Experiments are conducted to validate the prediction accuracy. Most of the average absolute errors (AE ave ) between the predicted value and test value displayed on the experimental device do not exceed 0.4 °C under static current conditions, and all of them are below 0.1 °C under dynamic current conditions. The two existing models, namely the state-of-charge (SOC)-dependent resistance model [R(SOC)] and SOC-T-dependent resistance model [R(SOC, T)], have AE ave values of 1.6 and 0.54 °C when the pack is discharged at 0.5 C. In contrast, the AE ave value achieved by our proposed model is 0.4 °C. Under dynamic current conditions, the maximum AE ave s are 0.42 °C for the R(SOC) model, 0.26 °C for the R(SOC, T) model, and 0.16 °C for the proposed model. These results demonstrate that the proposed model provides more accurate predictions of the temperature rise inside the pack than the popular existing models.
科研通智能强力驱动
Strongly Powered by AbleSci AI