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arXiv · 2006.03704

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

Yongkeun Choi·Jacopo Guanetti·Scott Moura·Francesco Borrelli
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Abstract

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.

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Yongkeun Choi, Jacopo Guanetti, Scott Moura, Francesco Borrelli. 2020-06-05. Data-driven Energy Management Strategy for Plug-in Hybrid Electric Vehicles with Real-World Trip Information. https://arxiv.org/abs/2006.03704

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