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