Abstract
Hybrid propulsion systems allow for a reduction of fuel consumption and pollutant emissions of future off-highway applications. A challenging aspect of a hybridization is the larger number of system components that further increases both the complexity and the diversification of such systems. Hence, beside a standardization on the hardware side for off-highway systems, a high flexibility and modularity of the control schemes is required to employ them in as many different applications as possible. In this paper, a causal optimization-based power management algorithm is introduced to control the power split between engine and electric machine in a hybrid powertrain. The algorithm optimizes the power split to achieve the maximum power supply efficiency and, thereby, considers the energy cost for maintaining the battery charge. Furthermore, the power management provides an optional function to control the battery state of charge in such a way that a target value is attained. In a simulation case study, the potential and the benefits of the proposed power management for the hybrid powertrain—aiming at a reduction of the fuel consumption of a DMU (diesel multiple unit train) operated on a representative track—will be shown.
Highlights
Hybrid propulsion systems allow for a reduction of fuel consumption and pollutant emissions of future off-highway applications
Lowering fuel consumption and CO2 emissions is the major goal in developing future off-highway propulsion systems
For the model validation, measured data from a hardware-in-the-loop (HiL) test rig is used. It represents a realistic prototype of a hybrid propulsion system driving a virtual vehicle model of the diesel multiple unit trains (DMUs)
Summary
Hybrid electric and full electric vehicles play a significant role in efforts to meet future legislated emission targets. Include deterministic rule-based and fuzzy logic control algorithms [14,15,16,17] Those approaches are Fromstate a functional perspective, heuristic, often called rule-based or sub-optimal, and optimization-based, of the art in most prototype and production hybrid vehicles [9]. Thealgorithms optimization-based or optimal PMAs are mainlyHeuristic processedstrategies offline Those strategiesrule-based are normally non-causal and,control process information aboutapproaches the whole drive cycle.of the deterministic and fuzzy logic algorithms [14,15,16,17].
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