We present a solution for intelligent planning of engine activations for series hybrid electric vehicles (HEVs), Beyond minimizing energy expenditure, other real-world objectives must be incorporated, such as minimizing the perceived engine noise and the frequency of mode transitions between activation and deactivation. We model this problem as a multiobjective stochastic shortest path (MOSSP) problem that takes a vehicle model and navigation map as input and outputs a engine activation policy. The vehicle model and navigation map are learned from GPS traces with metadata, and includes the topological road structure, traversal speeds/times, battery consumption/regeneration, and ambient noise. We analyze our results in simulation on different navigation maps generated from actual GPS traces learned from a real series HEV. Experiments in simulation demonstrate that our approach compared with the baseline system can reduce total energy expenditure (EE), namely on hills, by up to 3%; total additional noise (AN) generated by up to 15%; and total mode transition (MT) frequency by up to 12%. The approach is demonstrated on a real series hybrid vehicle, driving on real public roads.
Engine Activation Planning for Series Hybrid Electric Vehicles
11.07.2021
7985723 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
ENGINE ACTIVATION PLANNING FOR SERIES HYBRID ELECTRIC VEHICLES
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