Machine Learning-Based Energy Management in a Hybrid Electric Vehicle to Minimize Total Operating Cost

Abstract

This paper investigates the energy management problem in hybrid electric vehicles (HEVs) focusing on the minimization of the operating cost of an HEV, including both fuel and battery replacement cost. More precisely, the paper presents a nested learning framework in which both the optimal actions (which include the gear ratio selection and the use of internal combustion engine versus the electric motor to drive the vehicle) and limits on the range of the state-of-charge of the battery are learned on the fly. The inner-loop learning process is the key to minimization of the fuel usage whereas the outer-loop learning process is critical to minimization of the amortized battery replacement cost. Experimental results demonstrate a maximum of 48% operating cost reduction by the proposed HEV energy management policy.

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Cite this paper

@inproceedings{Lin2015MachineLE, title={Machine Learning-Based Energy Management in a Hybrid Electric Vehicle to Minimize Total Operating Cost}, author={Xue Lin and Paul Bogdan and Naehyuck Chang and Massoud Pedram}, booktitle={ICCAD}, year={2015} }