Battery Second-Life for Dedicated and Shared Energy Storage Systems Supporting EV Charging Stations

Giuseppe Graber,Vincenzo Galdi,Antonio Piccolo,Vito Calderaro

doi:10.3390/electronics9060939

Abstract

Power systems are facing increasing strain due to the worldwide diffusion of electric vehicles (EVs). The need for charging stations (CSs) for battery electric vehicles (BEVs) in urban and private parking areas (PAs) is becoming a relevant issue. In this scenario, the use of energy storage systems (ESSs) could be an effective solution to reduce the peak power request by CSs in PAs to the grid. Moreover, II-Life battery modules are a potential approach for cutting costs and implementing sustainable solutions. We propose a method to size ESSs coupled to CSs by using II-Life battery modules. Our methodology is based on the estimation of the residual cycles and the decrease in the supplied power due to the battery aging for defining the number of EV battery packs required for an ESS use case. Then, economic evaluations are presented to compare II-Life with the equivalent I-Life storage system.

Highlights

According to recent sales data, the global stock of electric vehicles (EVs) reached the nine million-unit milestone at the end of 2019, and this trend is growing in early 2020 [1]
Input data for the energy storage systems (ESSs) sizing procedure (Section 4.1) for single and clustered charging stations (CSs) are the number of CS in the parking areas (PAs) and its PDF parameters
By applying Monte Carlo simulations with 500 iterations, we obtain the ESS power and energy rated values for clustered e single CS aimed to keep the maximum power PG max supplied from the external grid to the University of Salerno (UniSA) microgrid lower than 2.75 MW

Summary

Introduction

According to recent sales data, the global stock of electric vehicles (EVs) (plug-in electric passenger cars and light utility vehicles) reached the nine million-unit milestone at the end of 2019, and this trend is growing in early 2020 [1]. On the other hand, charging of EV battery packs causes additional load on distribution network (DNs) to be taken into account [6,7,8], and grid operators and planners are exploring different approaches to properly deploy battery electric vehicles (BEVs) and mitigate their impact on grids. The use of optimized EVs charging strategies is a widely explored approach in technical literature. Scheduling of charging sessions allows to support a deeper penetration of EVs within DNs, [2]. In this respect, several papers deal with optimized algorithms able to reduce the peak load due to EV charging sessions by using peak shaving and valley-filling strategies or to minimize different objective functions such as charging costs, power losses, etc. Several papers deal with optimized algorithms able to reduce the peak load due to EV charging sessions by using peak shaving and valley-filling strategies or to minimize different objective functions such as charging costs, power losses, etc. [9,10,11]

Methods

Results

Conclusion