Data-driven Energy Management Strategy for Plug-in Hybrid Electric Vehicles with Real-World Trip Information. Issue 2 (2020)

• Record Type: Journal Article
• Authors: Choi, Yongkeun; Guanetti, Jacopo; Moura, Scott; Borrelli, Francesco
• Is Part Of: IFAC-PapersOnLine. Volume 53:Issue 2(2020)
• Page Start: 14224
• Page End: 14229
• Publication Date: 2020
• Subjects: Data-based control -- Nonlinear predictive control -- Real-time control -- Engine modelling -- control -- Hybrid -- alternative drive vehicles -- Nonlinear -- optimal automotive control; Automatic control -- Periodicals; 629.805
• DOI: 10.1016/j.ifacol.2020.12.1070
• Languages: English
• ISSNs: 2405-8963

Abstract

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 onboard 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.