Robust Observer Design for Mitigating the Impact of Unknown Disturbances on State of Charge Estimation of Lithium Iron Phosphate Batteries Using Fractional Calculus

In recent times, lithium iron phosphate (LFP) batteries have found wide usage in electric vehicles (EVs). Efficient utilization of the storage capacity of any battery used in EVs is heavily dependent on the accuracy of state of charge (SOC) estimation. The classical SOC-OCV (open circuit voltage) based approach for estimation of SOC is quite ineffective under rapid variation in operating condition of EVs. The impact of parameter variations and unknown disturbances due to widely varying environmental conditions and drive terrain, demands a robust observer for SOC estimation. In this regard, this paper proposes a novel SOC estimation technique using a nonlinear fractional-order unknown input observer (FOUIO). The scheme based on the combined framework of fractional calculus and state estimation with unknown input is able to meet the challenges of SOC estimation in EVs by decoupling the plant disturbance inputs from the state estimation process. The optimally designed FOUIO using a linear matrix inequality (LMI) based formulation has been analytically proved for convergence. The proposed FOUIO has been validated and compared with reported SOC estimation schemes for different drive profiles in terms of accuracy and convergence speed. The numerical simulations and hardware in loop (HIL) based experimental results confirm the effectiveness of the proposed FOUIO in estimating the SOC robustly under different scenarios of battery operation.