Electric Vehicle Battery Lifetime Extension through an Intelligent Double-Layer Control Scheme

Omid Rahbari,Marc A Rosen,Maitane Berecibar,Thierry Coosemans,Joeri Van Mierlo,Noshin Omar

doi:10.3390/en12081525

Abstract

Electric vehicles (EVs) are recognized as promising options, not only for the decarbonization of urban areas and greening of the transportation sector, but also for increasing power system flexibility through demand-side management. Large-scale uncoordinated charging of EVs can impose negative impacts on the existing power system infrastructure regarding stability and security of power system operation. One solution to the severe grid overload issues derived from high penetration of EVs is to integrate local renewable power generation units as distributed generation units to the power system or to the charging infrastructure. To reduce the uncertainties associated with renewable power generation and load as well as to improve the process of tracking Pareto front in each time sequence, a predictive double-layer optimal power flow based on support vector regression and one-step prediction is presented in this study. The results demonstrate that, through the proposed control approach, the rate of battery degradation is reduced by lowering the number of cycles in which EVs contribute to the services that can be offered to the grid via EVs. Moreover, vehicle to grid services are found to be profitable for electricity providers but not for plug-in electric vehicle owners, with the existing battery technology and its normal degradation.

Highlights

Despite the Paris Agreement, pursuing efforts to limit the global temperature rise to less than 2 ◦ C through a technological transition from a hydrocarbon-based economy to a post-petroleum era, there is little tangible evidence of the success such efforts
The first layer employs the optimal power flow flow algorithm to maintain the nominal operation of the nano-grid by predicting the PV output and algorithm to maintain the nominal operation of the nano-grid by predicting the PV output and demand one-step ahead; the second layer controls each Electric vehicles (EVs) individually, based on its battery state of charge (SoC) and demand one-step ahead; the second layer controls each EV individually, based on its battery SoC and capacity
To the complexities caused by the stochastic nature of renewable energy sources, a robust method based address the complexities caused by the stochastic nature of renewable energy sources, a robust method based on solar specifications of a given region is required

Summary

Introduction

Despite the Paris Agreement, pursuing efforts to limit the global temperature rise to less than 2 ◦ C through a technological transition from a hydrocarbon-based economy to a post-petroleum era, there is little tangible evidence of the success such efforts. Recent studies indicate that energy demand will increase to 736 from 663 quadrillion Btus between 2015 and 2040, and that annual carbon dioxide (CO2 ) emissions will increase to 45.5 billion metric tons in 2040 [1]. The energy use of the US transportation sector has risen significantly since 1950. Energies 2019, 12, x FOR PEER REVIEW Figure 1. Energy consumption consumption of of industrial, industrial, transportation, transportation, residential residential and and commercial commercial sectors sectors in in the the Figure US since [2].

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