Numerical study on air-cooled battery thermal management system considering the sheer altitude effect

Due to energy shortage and environmental pollution, vehicles and aircraft powered by Li-ion batteries have now received widespread attention. Among various types of battery thermal management systems (BTMSs), the air-cooled BTMS is still the preferred choice due to its affordability, longevity, and simplicity. To ensure the reliable operation of electric vehicles and aircraft at different altitudes, it is extremely meaningful and significant to study the thermal behavior of batteries at different altitudes. Therefore, for the investigation of altitude impact, this paper firstly proposes an indirect decoupling method to address the limitations of using ambient temperature as a single variable. Based on this, several different configurations of air-cooled BTMS have been investigated through numerical simulation. Then, for the tapering-type BTMS with the best thermal performance, the battery behavior at different altitudes is investigated and the influence law of sheer altitude factor is summarized. Subsequently, to address the thermal performance issues at higher altitudes, this research proposes three optimization measures, which include increasing inlet velocity, decreasing inlet temperature, and incorporating phase change material (PCM) layers, respectively. Ultimately, the entropy weight-TOPSIS method is adopted to seek the optimal measure at different parameters. The results indicate that as altitude increases from the standard altitude to 4000 m, the maximum temperature rises significantly, exceeding the permissible temperature range of Li-ion batteries. Besides, in order to effectively confine the maximum temperature within the permissible temperature range at an altitude of 4000 m, the inlet velocity should be increased by at least 2 m/s or the inlet temperature should be reduced by at least 3 °C. Among all optimized solutions, the solution with the addition of 0.4 mm PCM layers is the best based on the comprehensive evaluation of multiple indicators. © 2024 Elsevier Ltd