Proposal and Validation of a SOC Estimation Algorithm of LiFePO₄ Battery Packs for Traction Applications

An accurate onboard State-of-Charge (SOC) estimation is one of the key functions a Battery Management System (BMS) has to perform in order to provide the optimal performance management of the battery system under control. In this framework, this paper presents a proposal of an Enhanced Coulomb Counting (CC) State-of-Charge estimation algorithm based on Constant Voltage Charge Detection (CVCD) and Open Circuit Voltage (OCV) model for LiFePO4 batteries. Designed for onboard BMS implementation, it is characterized by its simplicity and operability in wide operating conditions (under diverse load profiles, temperatures, SOC ranges, etc.). The description of the algorithm at both, cell and battery-module level is detailed in the paper. Furthermore, its on-line experimental validation and scope determination is tested under three different traction applications and cell specimens in an own-developed real time validation platform: 2.5 Ah cells (Type A) in a residential elevator application, 8 Ah cells (Type B) in a pure electric on-road vehicle application and 100 Ah cells (Type C) in an electric railway vehicle application. According to the achieved results, the accuracy and versatility of the algorithm for different operating scenarios is certainly proven. In the worst case scenario the algorithm is capable of keeping the SOC estimation of the system under test stabilized around 5% of error.