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

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. (c) 2020 Elsevier Ltd. All rights reserved.

Automated summary

This paper proposes a real-time predictive energy management strategy for plug-in hybrid electric vehicles, which coordinates fuel economy and battery lifetime through velocity prediction and SOC reference generator, formulates a model predictive control problem for cost minimization, and utilizes the C/GMRES algorithm to handle real-time optimization of engine power command. Numerical simulations demonstrate the effectiveness of this strategy in minimizing fuel consumption and restricting battery aging, showing superior solving quality and real-time applicability compared to other algorithms.