Fuzzy logic-controlled online state-of-health (SOH) prediction in large format LiMn2O4 cell for energy storage system (ESS) applications

This paper investigates a new approach based on the fuzzy logic-controlled methodology that is suitable for analyzing and evaluating large format LiMn₂O₄ cell performance via online state-of-health (SOH) prediction for energy storage system (ESS) applications. The proposed method for online SOH prediction is comprised of two parts. First of all, the values of the cell resistance R and maximum cell capacity Q_max are calculated from four factors such as voltage, current, time, and temperature that were measured during charge-discharge sequence at unknown temperature. Specifically, in order to minimize of SOH jump and drops with temperature variation, temperature compensation for R normalization is efficiently implemented. Then, using two values R and Q_max previously calculated at unknown temperature, present SOH of an arbitrary LiMn₂O₄ cell can be predicted using the defined fuzzy-logic inference system. The main advantage of this approach is wide parameters tuning possibility for good correspondence of SOH decay with time, and the possibility to perform suitable online SOH estimation. The proposed model used as part of either a Matlab/Simulink model or an integral part of the battery management system (BMS)-micro controller unit (MCU) of STM32F105VC, was verified by the comparison with experimental data of Samsung SDI 60Ah LiMn₂O₄ cell and a method for SOH prediction by measuring cell capacity during fully charging/ discharging sequence.