Real-time predictive energy management of plug-in hybrid electric vehicles for coordination of fuel economy and battery degradation

Abstract

Abstract This paper proposes a real-time predictive energy management strategy (PEMS) of plug-in hybrid electric vehicles for coordination control of fuel economy and battery lifetime, including velocity predictor, state-of-charge (SOC) reference generator, and online optimization. In velocity predictor, the radial basis function neural network algorithm is adopted to accurately estimate the future drive velocity. Based on predictive velocity and current driven distance, the SOC reference in predictive horizon can be determined online by reference generator. To coordinate fuel consumption and battery degradation, a model predictive control problem of cost minimization including fuel consumption cost, electricity cost of battery charging/discharging, and equivalent cost of battery degradation, is formulated. To mitigate the huge calculation burden in optimization, the continuation/generalized minimal residual (C/GMRES) algorithm is delegated to find the expected engine power command in real time. Since original C/GMRES algorithm cannot directly handle inequality constraints, the external penalty method is employed to meet physical inequality limits of powertrain. Numerical simulations are carried out and yield the desirable performance of the proposed PEMS in fuel consumption minimization and battery aging restriction. More importantly, the proposed C/GMRES algorithm shows great solving quality and real-time applicability in PEMS by comparing with sequence quadratic programming and genetic algorithms.