Comparative analysis of SOC estimation methods for lithium-ion batteries in industrial electric forklift applications: experimental and simulation-based study

Abstract Accurate state-of-charge (SOC) estimation is essential for the safe, efficient, and reliable operation of lithium-ion batteries in industrial electric forklifts. Although numerous SOC estimation methods have been investigated for electric vehicle (EV) applications, their comparative performance under realistic forklift duty cycles remains insufficiently studied. Industrial electric forklifts are characterized by repetitive start-stop operation, abrupt load variations associated with acceleration and lifting, regenerative braking, and limited rest periods, which pose significant challenges for accurate real-time SOC estimation. Therefore, this study presents a systematic experimental and simulation-based evaluation of SOC estimation techniques. Field tests were conducted on an industrial electric forklift equipped with a 51.2 V, 554 Ah lithium iron phosphate (LFP) battery and operated according to the VDI 2198 duty cycle. In parallel, MATLAB/Simulink models were developed using equivalent-circuit parameters identified through Hybrid Pulse Power Characterization (HPPC) tests. Open Circuit Voltage (OCV) and Coulomb Counting (CC) were considered as baseline SOC estimation approaches, while Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF), and Adaptive Extended Kalman Filter (AEKF) were evaluated using dynamic current profiles. Although CC closely followed the reference SOC under controlled C5 test conditions, its practical applicability remains sensitive to initial SOC uncertainty, cumulative current measurement errors, and capacity variations. Among the evaluated model-based approaches, the UKF provided the closest agreement with the reference SOC under real-world forklift operation, achieving an MAE of 0.852%, an RMSE of 1.070%, and a MAPE of 1.719%. The findings demonstrate that nonlinear model-based SOC estimation methods, particularly the UKF, can provide consistent SOC tracking under the dynamic load conditions encountered in industrial forklift applications.