Assessment of Components Sizing and Energy Management Algorithms Performance for a Parallel PHEV

In Plug in hybrid electric vehicles (PHEVs), the management of the main drivetrain components and the shift between pure electric and hybrid propulsion is decided by the on-board energy management system (EMS). The EMS decisions have a direct impact on CO2 emissions and need to be optimized to achieve as low emissions as possible. This paper presents optimization methods for EMS algorithms of a parallel P2 PHEV. Two different supervisory control algorithms are examined, employing simulations on a validated PHEV platform. An Equivalent Consumption Minimization Strategy (ECMS) algorithm is implemented and compared to a rule-based one, the latter derived by back-engineering of available experimental data. The different EMS algorithms are analyzed and compared on an equal basis in terms of distance, demanded energy and state of charge levels over different driving cycles. A sensitivity analysis on component sizing interaction with algorithm performance is conducted to check robustness of conclusions. The study shows that the ECMS algorithm can adapt the energy management strategy over the component variations, as no fuel consumption (FC) change exceeded 5%. The performance of the rule-based algorithm is affected by the component size variations as they resulted FC changes up to 26%. In that case recalibration would be necessary in order to maintain the fuel economy performance. The outcome of the study could support the selection of the appropriate EMS algorithm considering both FC and optimization robustness, accounting for individual components size.