Optimal Charging of an Electric Vehicle using a Markov Decision Process

TL;DR

This paper presents a stochastic dynamic programming model using Markov processes to optimize electric vehicle charging strategies by accounting for the randomness in driving patterns, aiming for efficient energy management.

Contribution

It introduces a novel Markov decision process model that incorporates stochastic driving behaviors for optimal EV charging, filling a gap in existing deterministic models.

Findings

Driving pattern randomness significantly affects charging strategies.

The Markov model accurately captures real-world driving variability.

Optimized policies improve energy efficiency and user satisfaction.

Abstract

The combination of electric vehicles (EVs) and renewable energy is taking shape as a potential driver for a future free of fossil fuels. However, the efficient management of the EV fleet is not exempt from challenges. It calls for the involvement of all actors directly or indirectly related to the energy and transportation sectors, ranging from governments, automakers and transmission system operators, to the ultimate beneficiary of the change: the end-user. An EV is primarily to be used to satisfy driving needs, and accordingly charging policies must be designed primarily for this purpose. The charging models presented in the technical literature, however, overlook the stochastic nature of driving patterns. Here we introduce an efficient stochastic dynamic programming model to optimally charge an EV while accounting for the uncertainty inherent to its use. With this aim in mind, driving patterns are described by an inhomogeneous Markov model that is fitted using data collected from the utilization of an EV. We show that the randomness intrinsic to driving needs has a substantial impact on the charging strategy to be implemented.