Multi-Level Energy Management for Hybrid Electric Vehicles—Part I

Abstract

The fuel economy of a hybrid electric vehicle (HEV) is improved, by taking the energy relevant system states into account in the energy management system (EMS). With an increasing number of states and decision variables, energy optimizing algorithms in the EMS can be prohibitive for real-time implementation. In part I of this work, a model-based, multi-level approach is taken to subdivide the original (large) optimization problem into computational efficient sub-problems, based on optimal control techniques using a preview. The resulting EMS solves the problem of power-split between engine and motor/generator, mode and gear switching including switching costs, with battery energy constraints. The superior energy efficiency of the multi-level EMS is simulated on a representative heavy duty drive cycle, where it saves 7.0% fuel, compared to a conventional vehicle, where the baseline EMS for the HEV saves 5.8%. In part II, real-world validation of the EMS is performed.

Multi-Level Energy Management—Part II: Implementation and Validation

“…The multi-level EMS from [14] is implemented on the HEV. The algorithms on each level are mapped to two control platforms as depicted in Figure 3.…”

“…Using the vehicle position measured by the GPS sensor, the position on the map is determined (i.e., 'mapmatching') and for the known route ahead, slope information is retrieved from the map. With the algorithm as described in [14], Section 6, the power demand is predicted. The validation of the power prediction is performed by testing the repeatability on a repeated test cycle and varying vehicle parameters, i.e., the vehicle mass.…”

“…This work contributes to the actual implementation and validation of the multi-level EMS [14], using preview, on a heavy-duty parallel hybrid electric truck. The EMS optimizes power split with four hybrid modes (including stop-start) and gear selection, taking into account switching costs and battery constraints.…”

“…To solve the mixed integer, multi-state optimal control problem, the EMS is partitioned in three functional levels, each with their own abstraction of the vehicle, calculating control information for the levels below [14]. On the lowest level, the power split, mode, and gear shifting are optimized; on the middle level, the battery energy is optimized; and on the top level, the power demand is predicted.…”

“…These fast dynamics are typically approximated in high-fidelity fuel simulation environments, resulting in a mismatch between simulation and real-life control, whereas the effect of the slower dynamics (e.g., battery and vehicle longitudinal dynamics) are well captured. With the chosen route, specifically the faster dynamics are excited, thus creating a good complement to the high-fidelity simulation results, as presented in [14], Section 7.3.…”