Bi-level Energy Management of Plug-in Hybrid Electric Vehicles for Fuel Economy and Battery Lifetime with Intelligent State-of-charge Reference

This paper proposes a bi-level energy management strategy of plug-in hybrid electric vehicles with intelligent state-of-charge (SOC) reference for satisfactory fuel economy and battery lifetime. In the upper layer, Q-learning algorithm is delegated to generate the SOC reference before departure, by taking the model nonlinearities and physical constraints into account while paying less computing labor. In the lower layer, with the short-term drive velocity accurately predicted by the radial basis function neural network, the model predictive control (MPC) controller is designed to online distribute the system power flows and track the SOC reference for the superior fuel economy and battery lifetime extension. Moreover, the terminal SOC constraints are transferred as soft ones by the relaxation operations to guarantee the solving feasibility and smooth tracking effects. Finally, the simulations are carried out to validate the effectiveness of the proposed strategy, which shows the considerable improvements in fuel economy and battery lifetime extension compared with the charge-depleting and charge sustaining method. More importantly, the great robustness of the proposed approach is verified under the cases of inaccurately pre-known drive information, indicating the favorable adaptability for practical application.

Automated summary

This paper proposes a bi-level energy management strategy for plug-in hybrid electric vehicles, achieving improved fuel economy and battery lifetime extension. By utilizing the Q-learning algorithm to generate SOC references and accurately predicting driving speeds with a radial basis function neural network, the proposed approach demonstrates superior adaptability and robustness under practical applications.