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.
Highlights
Hybrid electric vehicles (HEVs) have emerged as a promising solution to reduce operational cost in commercial road transportation, while complying to increasingly stringent emission legislation.Since hybrid electric vehicle (HEV) have more than one power converter, they offer additional control freedom, compared to conventional vehicles, which give opportunities for the energy management system (EMS) to decrease fuel consumption and emissions
Since HEVs have more than one power converter, they offer additional control freedom, compared to conventional vehicles, which give opportunities for the energy management system (EMS) to decrease fuel consumption and emissions
T0 refers to the vehicle’s current time and t f is relative to t0, with a fixed horizon length, each sample time, only one iteration is applied, i.e., step 5 in the iteration scheme is skipped, the horizon length decreases on each level: t(s f ) > t f 3 > t f 2 > t f 1, each level runs in its own regular schedule, where the higher levels run slower than the lower levels
Summary
Hybrid electric vehicles (HEVs) have emerged as a promising solution to reduce operational cost in commercial road transportation, while complying to increasingly stringent emission legislation. The multi-level EMS solves this control problem with preview, for real-time implementation, using optimal control techniques (PMP and DP), thereby eliminating calibration of parameters. The power-split is explicitly solved using the Pontryagin Minimum Principle (PMP), starting from [15] This method is extended with costs on mode and gear switching, thereby eliminating unacceptable switching behaviour. The algorithms used on each of the three levels are explained in Section 4 (power-split, mode and gear switch optimization), Section 5 (battery energy optimization) and Section 6 (velocity prediction). The real-world validation of the multi-level EMS is described in [28]
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