Specialized State Space Battery Modeling for EKF-Based SOC Estimation in Photovoltaic Battery Storage Systems

The use of Kalman filter (KF) algorithms for state of charge (SOC) estimation in photovoltaic battery energy storage systems (PV-BESS) has received comparatively less attention than their widespread application in electric vehicles (EVs). However, unlike EV batteries, the charging behavior of a PV-BESS is significantly influenced by weather conditions. Consequently, modifying the KF algorithm to incorporate weather-related factors may lead to better accuracy. To explore this potential, we developed specialized state space models (SSMs) for the extended Kalman filter (EKF) algorithm, specifically tailored for SOC estimation in PV-BESS. The SSMs integrates key environmental variables, including ambient temperature, solar irradiance and humidity. For model validation, we built a custom-designed data acquisition (DAQ) system to collect real-world data. The design, technical specifications, and cost evaluation of the DAQ system are presented in detail. A two-level illustration approach is adopted to provide a comprehensive overview of the estimator performance. First, case studies under specific charging and discharging conditions highlight estimation errors at each time step and the corresponding root mean square error (RMSE). Second, a generalized evaluation presents the mean, median, maximum, and minimum RMSE values across different estimation scenarios, along with their corresponding confidence intervals. The results exhibits that the specialized EKF estimators outperform the baselines during charging, reducing RMSE by 3% and 4.75% for the combined and Rint models, respectively. During discharging, baseline models yield a mean RMSE of 3–4% across discharge rates.