Engine Activation Planning for Series Hybrid Electric Vehicles

Abstract

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.