A Temperature Control Framework for Battery Based on Digital Twin

Lithium batteries are widely used in electric vehicles, mobile devices, and renewable energy storage. However, lithium batteries generate heat during operation, and excessive temperature can lead to battery performance degradation, shortened life, and even safety hazards. Therefore, designing an efficient and reliable temperature control framework is the key to realizing lithium batteries' safe and efficient operation. This paper proposes a digital twin-based battery temperature control framework, which includes two parts: digital twin modeling of battery based on the Long Short-Term Memory method and temperature control strategy based on reinforcement learning. Through the application of digital twin technology, the simulation, monitoring, and optimization of the thermal management system of lithium batteries can be realized to improve their performance and lifetime. The experimental results show that the cooling time of the system is reduced by 6%, and the temperature difference on the surface of the battery is reduced by 2.97K.