Research on improving the low-temperature performance of lithium-ion battery based on electromagnetic induction heating method

Abstract

Aimed to improve the performance degradation of lithium-ion batteries and hence electric vehicles in cold weather, the multi-objective optimization heating strategy based on the improved non-dominated sorting genetic algorithm (IMNSGA-Ⅱ) is proposed to achieve a good tradeoff among the heating rate, temperature uniformity and energy consumption of the electromagnetic induction heating method that induces a large amount of eddy current loss inside the battery in order to quickly warm up it through the “electricity-magnetism-electricity-heat” energy conversion, as well as enhanced battery’s operating behavior, thereby improving its low-temperature performance. Firstly, an electrochemical-thermal multiphysics coupling model based on electromagnetic heat is established to depict the battery’s temperature and internal heat generation distribution during the heating process, and the improved ionic concentration distribution inside the battery, usable capacity and power performances after the heating, compared with the existing battery models, the proposed model can accurately characterize the battery’s induction heating performance, internal electromagnetic field and induction heat distributions in the heating process, and analyze the battery voltage output and microscopic electrochemical mass transfer. Secondly, based on the contradictory relation among each temperature-rising evaluation indictor, the heating strategy mentioned above is introduced to optimize the coil parameters, thereby enhancing the induction heating performance. Finally, under the optimal coil parameters, the battery can be heated from −30 ℃ to 20 ℃ within 1030 s, and either usable capacity or power can be recovered to the approximately room-temperature level. Therefore, the proposed heating method has a substantial potential to improve the environmental adaptability of battery.