State of charge estimation and analysis of lithium-ion batteries in electric forklifts under dynamic load conditions

This thesis presents a comparative analysis of various State of Charge (SOC) estimation methods for lithium-ion batteries used in electric forklift applications. Compared to traditional lead-acid batteries, lithium-ion technology offers advantages such as faster charging, maintenance-free operation, and longer cycle life. Therefore, Battery Management Systems (BYS) play a critical role in the safe and efficient operation of these batteries. In this study, several SOC estimation techniques (OCV, Coulomb Counting, EKF, UKF, AEKF) were analyzed through both real-world field tests and MATLAB/Simulink-based simulations. The field tests were conducted using a customized track based on the VDI 2198 standard, employing a 51.2V 554Ah LFP battery-powered forklift. Parameters such as current, voltage, temperature, and SOC were continuously monitored, and the estimation accuracy of each method was evaluated using statistical metrics (MAE, RMSE, MAPE). The results indicate that model-based and hybrid methods outperform traditional approaches in dynamic forklift operations. This study provides a scientific basis for selecting SOC estimation strategies in the transition from lead-acid to lithium-ion battery systems in industrial forklifts. Keywords: Forklift, state of charge, SOC estimation, lithium-ion battery, BYS, Kalman filter