Thermal Optimization Strategies for Li-Ion Batteries: Predictive Temperature Algorithm

Abstract

Performance, safety, and longevity of batteries are all strongly impacted by thermal management, which is an essential component of battery design and operation. This work examines how accurate temperature control can result in significant improvements in performance and reliability with a focus on battery thermal heating. Predicting the temperature achieved by the battery during operation not only avoids conditions that lead to thermal runaway but also guarantees that the battery is used optimally within an optimal temperature range. Within the optimal temperature range, several advantages are observed. First, battery efficiency improves significantly as electrochemical processes occur more efficiently. Furthermore, by lowering the possibility of short circuits and improving overall battery safety, thermal stability aids in the prevention of undesirable phenomena like dendrite growth. By lessening the deterioration brought on by thermal degradation processes, thermal optimization also affects battery longevity. Based on experimental tests, a finite element method (FEM) model is developed. A model for thermal runaway propagation is established by combining thermal runaway and conduction models with an Arrhenius law-based combustion model. The study employed a cylindrical Li-ion cell to conduct tests, taking into account three parameters: discharge rate (CRate), ambient temperature (Tamb), and initial battery temperature (T0). An algorithm based on the three variables was developed using the simulation results. The algorithm enables the accurate prediction of rising battery temperature during use, facilitating the setting of an optimal maximum discharge rate considering initial and ambient temperatures, thereby ensuring optimal performance within the desired temperature range.