Simplified Battery Modeling for Accelerated Kalman Filter SoC Estimation

The accuracy of state of charge (SoC) estimation is critical for optimizing battery performance and ensuring reliability in energy storage systems. The Kalman filter is a generally used method for SoC estimation due to its powerful handling of noise and uncertainty. However, its computational complexity can limit real-time applications. This paper introduces a simplified battery modeling framework to accelerate the Kalman filter for SoC estimation. By leveraging reduced-order equivalent circuit models and efficient parameter identification techniques, the proposed approach balances computational efficiency with estimation accuracy. Simulation results demonstrate that the simplified model achieves comparable SoC estimation performance to traditional methods while significantly reducing computational overhead. This advancement facilitates real-time implementation in embedded systems and supports the broader adoption of electric vehicles and renewable energy systems.