Prediction and prevention of over-temperature risk of Li-ion power batteries based on the critical heat transfer coefficient and intervention time

Abstract

• The critical heat transfer coefficient ( h cr ) is defined to indicate that the heat dissipation capability is equal to the heat generated by the battery. • A quantitative criterion ( h < h cr ) is proposed to predict the over-temperature risk of batteries. • The intervention time ( τ intv ) is specified as the time when the battery reaches the over-temperature threshold. • A response surface analysis is conducted to determine intervention methods to prevent over-temperature risk. In recent years, research on high-performance battery thermal management systems (BTMSs) has increased to improve the safety and reliability of electric vehicle (EV) batteries. However, some underlying concepts have not been clearly explained. For example, which conditions are considered safe, and what about not？ This study focuses on the prediction and prevention of battery over-temperature risk using the finite element method. The critical heat transfer coefficient ( h cr ) is proposed as a quantitative criterion for risk prediction since it provides sufficient information on the battery operation, such as the dynamic operating parameters and the battery’s safety temperature. The intervention time is specified, and intervention methods are suggested to prevent risky conditions. Three parameters were found to have a substantial impact on risk prevention, namely, the equivalent heat transfer coefficient ( h ), representing the maximum heat transfer capacity of the heat dissipation system, the ambient temperature ( T ab ), representing the coolant temperature of the heat dissipation system, and the discharge current rate ( C rate ), representing the current level during discharge. A response surface analysis was performed using the maximum operating temperature of the battery ( T max ) as the response variable and three influencing factors as the input variables. The results show that safety zones can be found for each of the three influencing factors. For example, for the case considered in this work, the optimum T ab is around 281.15 K and should not exceed 305.15 K; the C rate should be below under 5C and should never exceed 8C; h should be greater than 50 W·m −2 ·K −1 . Suggestions are provided for choosing risk prevention methods according to the response characteristics of T max . The proposed method provides a novel approach for the research and design of intelligent BTMSs.