Optimization-Based Development of a Causal, Cascaded, Map-Based Energy Management Strategy for Hybrid Electric Vehicles with Multiple Control Variables

The objective of this research is a generally valid and modular methodology for developing a causal, fuel-optimized, model-free energy management strategy (EMS), including a gear shift strategy, for hybrid electric vehicles (HEVs) with multiple electric machines (EMs) and possibly multiple transmissions. Therefore, this paper presents a novel methodology for the optimization-based development of a causal, cascaded, map-based EMS for HEVs with multiple control variables (MCVs), based on related research on Pontryagin's minimum principle (PMP), equivalent consumption minimization strategy (ECMS), and map-based EMS approaches. The proposed methodology is applicable to any hybrid powertrain topology, as well as battery electric vehicles (BEVs) with multiple EMs and possibly multiple transmissions. The EMS is mathematically described as an optimal control problem in order to compute optimal control maps (OCMs) based on multi-criteria optimization considering soft and hard constraints. The EMS is defined as a multi-stage decision-making process and is therefore implemented as a cascaded logic. This enables offline calculated OCMs to be manipulated by downstream additional sub-optimal rules for system and gear state transitions at each decision level. The EMS can be implemented as either a non-predictive or predictive strategy. To demonstrate the functionality of the proposed methodology, it is applied to a P24-HEV as an example, and simulation results are presented and discussed. Finally, recommendations for future work are provided.