Real-time sliding mode observer estimator integration in hybrid electric vehicles battery management systems

Abstract

In this paper we develop and implement a real-time sliding mode observer estimator (SMOE) for state-of-charge (SOC) and for current fault in Li-Ion batteries packs integrated in the battery management systems (BMS) structure of hybrid electric vehicles (HEVs). The estimation of SOC is critical in automotive industry for successful marketing of both electric vehicles (EVs) and hybrid electric vehicles (HEVs). Gradual capacity reduction and performance decay can be evaluated rigorously based on the current knowledge of rechargeable battery technology, and consequently is required a rigorous monitoring and a tight control of the SOC level, necessary for increasing the operating batteries lifetime. The novelty of this paper is that the proposed estimator structure can be also tailored to estimate the SOC and the possible faults that could occur inside of the batteries of different chemistry by augmenting the dimension of the model states, according to the number of estimated battery faults. The preliminary results obtained in this research are encouraging and reveal the effectiveness of the real-time implementation of the proposed estimator in a MATLAB/SIMULINK programming simulation environment.