An Improved Method Based on Support Vector Regression with Application Independent Training for State of Charge Estimation

Nowadays, lithium-ion (Li-ion) is among the most used chemistry for batteries and shows an increasing market growth rate; however, to reduce failure or safety risks, the battery state-of-charge (SoC) must be accurately monitored and predicted by a suitable battery management system (BMS). Artificial intelligence (AI) techniques have been extensively applied to this field with good results. Typically, AIs are trained on dynamic profile data, emulating battery charging and discharging cycles related to the application under test. In this article, a novel approach is presented: application-independent constant current profiles are used to train a support vector regression (SVR) algorithm. To enhance the estimation accuracy, the output of the obtained SVR model was postprocessed. Finally, an error correction algorithm was applied to further reduce the estimation error. The system is validated over test cycles, representing different application scenarios for the battery cell operations. For the development of the proposed approach, a total of 105 constant current discharge profiles for the training and 20 realistic test cycles for the validation have been considered, including standard automotive cycles and a generic battery-powered power tool. The performance in the SoC estimation resulted in a root-mean-square error (RMSE) of 0.94% and a mean absolute error (MAE) of 0.75% over all the test cycles. Error metrics are comparable to those obtained for SoC estimation AI algorithms based on traditional approaches using application-dependent battery profiles for the training phase.