Search Results

Optimization-based (OB) methods used in vehicle energy management strategies (EMSs) have the potential to significantly increase fuel economy and extend the electric-only range of plug-in hybrid electric vehicles (PHEVs). However, OB methods are difficult to apply to current real-world vehicles because accurate detailed and high-resolution information about the future, including second-by-second vehicle velocity trajectory data, are not currently available in the current transportation infrastructure. In this paper, a practical reinforcement learning (RL) algorithm for automatic mode-switching of a multimode PHEV is introduced. The PHEV used in the work was a 2016 Chevrolet Volt driven on a simulated commuter route. The goal is to blend the charge depleting and charge sustaining modes during the trip to reduce gasoline consumption and extend electric-only range.

Extended range electric vehicles (EREVs) are a potential solution for fossil fuel usage mitigation and on-road emissions reduction. The use of EREVs can be shown to yield significant fuel economy improvements when proper energy management strategies (EMSs) are employed. However, many in-use EREVs achieve only moderate fuel reduction compared to conventional vehicles due to the fact that their EMS is far from optimal. This paper focuses on in-use rule-based EMSs to improve the fuel efficiency of EREV last-mile delivery vehicles equipped with two-way Vehicle-to-Could (V2C) connectivity. The method uses previous vehicle data collected on actual delivery routes and machine learning methods to improve the fuel economy of future routes. The paper first introduces the main challenges of the project, such as inherent uncertainty in human driver behavior and in the roadway environment. Then, the framework of our practical physics-model guided data-driven approach is introduced.