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This paper presents a data-driven supervisory energy management strategy(EMS) for plug-in hybrid electric vehicles which leverages Vehicle-to-Cloudconnectivity to increase energy efficiency by learning control policies fromcompleted trips. The proposed EMS consists of two layers, a cloud layer and anon-board layer. The cloud layer has two main tasks: the first task is to learnEMS policy parameters from historical trip data, and the second task is toprovide the policy parameters along a certain route requested from the vehicle.The on-board layer receives the learned policy parameters from the cloud layerand computes a real-time solution to the powertrain energy management problem,using a model predictive control scheme. The proposed EMS is evaluated on morethan 3000 miles (48 independent driving cycles) of real-world trip data,collected along three commuting routes in California. For the routes, theproposed algorithm shows 3.3%, 7.3%, and 6.5% improvement in average MPGe whencompared to a baseline EMS.

Data-driven Energy Management Strategy for Plug-in Hybrid Electric Vehicles with Real-World Trip Information