A data-driven energy management strategy for plug-in hybrid electric buses considering vehicle mass uncertainty

Conventional energy management strategies (EMSs) for hybrid electric vehicles are devised assuming the vehicle mass remains constant under dynamic driving conditions. However, the EMSs cannot adapt to different load conditions due to the dynamic change of vehicle mass. Investigating the characteristics of vehicle mass change plays significant roles in energy optimization, thereby improving overall efficiency and mobility. To this end, we propose an adaptive EMS for a plug-in hybrid electric bus (PHEB) based on artificial neutral network-Pontryagin minimum principle (ANN-PMP) by considering mass distribution characteristics. Firstly, the skew-normal distribution characteristics of PHEB mass are analyzed, and the distribution characteristic spectrum of vehicle mass is obtained based on the Monte Carlo method. Secondly, the influence of mass uncertainty on the PMP is analyzed, and then the ANN-PMP is devised by updating a dynamic co-state with ANN. Finally, an enhanced ANN-PMP (so-called ANN-PMP-CS) is proposed by combining the ANN-PMP strategy obtained by mass distri-bution training and CS (charge sustaining strategy) to include the no-load or full-load cases. The simulation results demonstrate that the ANN-PMP can adapt to mass change while ensuring that the final state-of-charge (SOC) convergence to the target value. We also observe that the fuel economy of ANN-PMP-CS is similar to that of the widely-used dynamic programming (DP) strategy. Compared with the charge depletion-charge sus-taining (CD-CS) strategy, the fuel economy can be improved by about 46.93 % on average.