State of Temperature Estimation of Li-Ion Batteries Using 3rd Order Smooth Variable Structure Filter

The Battery Management System plays a critical role in ensuring the longevity, safety, and optimal performance of batteries by performing state of charge and health estimation, thermal management, cell balancing, and charge control. Thermal management is a crucial component that is responsible for temperature monitoring and control, managing heat generation and dissipation, preventing thermal runaway, and optimizing battery performance. This paper includes several original contributions. (1) A four-state lumped thermal model is introduced to model the core and surface temperatures of the battery. (2) Accordingly, various characterization tests were conducted on a lithium-ion Prismatic battery to log the thermal behavior of the battery. The third-order Equivalent Circuit Model is used to calculate the generated heat inside the cell using the measured physical parameters such as voltage, and current. (3) Machine learning methods like Particle Swarm Optimization and Genetic Algorithm are used and compared to determine the parameters of the thermal model. (4) A novel, reliable 3rd order Smooth Variable Structure Filter is suggested in this work and evaluated against the Extended Kalman Filter, SVSF, and 2nd-order SVSF. The proposed strategy demonstrated higher accuracy compared to the abovementioned filters.