STATE OF HEALTH (SOH) ESTIMATION FOR EMBEDDED BATTERY MANAGEMENT SYSTEMS: A COMPARATIVE ANALYSIS OF COMPUTATIONALLY EFFICIENT METHODS

Accurate State of Health (SOH) estimation is critically important for the safe and long-term operation of lithium-ion batteries. However, the implementation of estimation algorithms in embedded Battery Management Systems (BMS) is significantly limited by available computational resources. This paper presents a scientifically grounded comparative analysis of two computationally efficient machine learning methods – Support Vector Regression (SVR) and Random Forest – to determine the optimal algorithm for practical implementation, with a dual focus on prediction accuracy and computational efficiency. Based on an open dataset, systematic hyperparameter tuning with GridSearchCV and cross-validation was conducted for both models. The performance of the final, optimized models was evaluated by accuracy metrics (RMSE, MAE) and indicators of suitability for embedded systems (forecasting time, model size). The results showed that even after thorough optimization, the SVR model demonstrated higher prediction accuracy (RMSE 2.67% vs. 3.08% for Random Forest). An even more significant advantage was found in the efficiency analysis: SVR was found to be almost 170 times faster (forecast time of 0.119 ms vs 20.570 ms) and 200 times more compact (model size 8.6 kB vs 1718.6 kB). It is concluded that, for the problem of estimating SOH based on cyclic data, the SVR model is the optimal candidate for practical implementation in embedded BMS. It offers the best balance of high accuracy and minimal hardware resource requirements, outperforming Random Forest across all key criteria for engineering practice.