An adaptive state of charge estimator for lithium‐ion batteries

This study presents a data-driven approach in conjunction with an adaptive extended Kalman filter (AEKF) to estimate lithium-ion batteries' state of charge (SOC) online. The Thevenin battery model is used evaluate the effects using voltage and current. advantages Lagrange multiplier method are utilized battery. continuously decreases error adjust gain AEKF for accurate SOC estimation. Various current profiles such as hybrid pulse test, dynamic stress Beijing test verify proposed approach's adaptability, robustness, accuracy. It observed that outperforms other methodologies (recursive least square–AEKF forgetting factor recursive square–AEKF) due its high accuracy (mean average 0.32%). Additionally, exhibits robustness convergence speed despite deliberate erroneous initialization parameters, thus indicating applicability online estimation applications.