Decentralized Control of Electric Vehicle Smart Charging for Cost Minimization Considering Temperature and Battery Health

Dahmane, Yassir; Ghanes, Malek; Chenouard, Raphaël; Alvarado-Ruiz, Mario

doi:10.1109/smartgridcomm.2019.8909796

Cited by 9 publications

(8 citation statements)

References 13 publications

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“…Most algorithms include an energy constraint, which ensures that vehicles achieve the appropriate level of charge. However, some include additional constraints -for example on the network operation [27] or the battery temperature [28].…”

Section: Introductionmentioning

confidence: 99%

The opportunity for smart charging to mitigate the impact of electric vehicles on transmission and distribution systems

2020

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A rapid increase in the number of electric vehicles is expected in coming years, driven by government incentives and falling battery prices. Charging these vehicles will add significant load to the electricity network, and it is important to understand the impact this will have on both the transmission and distribution level systems, and how smart charging can alleviate it. Here we analyse the effects that charging a large electric vehicle fleet would have on the power network, taking into account the spatial heterogeneity of vehicle use, electricity demand, and network structure. A conditional probability based method is used to model uncontrolled charging demand, and convex optimisation is used to model smart charging. Stochasticity is captured using Monte Carlo simulations. It is shown that for Great Britain's power system, smart charging can simultaneously eliminate the need for additional generation infrastructure required with 100% electric vehicle adoption, while also reducing the percentage of distribution networks which would require reinforcement from 28% to 9%. Discussion is included as to how far these results can be extended to other power systems.

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Section: Introductionmentioning

confidence: 99%

The opportunity for smart charging to mitigate the impact of electric vehicles on transmission and distribution systems

2020

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“…27 Moreover, most of the research on optimal EV charging is customer oriented, that is, usually the objective function concerns cost minimization. 28,29 The optimal charging of EVs can improve certain operational aspects of the DN, but published research on this field is subject to implementation limitations. For instance, the optimal EV charging in Reference 16 can achieve the goals of valley filling and voltage regulation.…”

Section: Motivation and Contributionmentioning

confidence: 99%

Distribution network energy loss reduction under EV charging schedule

Bouhouras

Kothona

Gkaidatzis

et al. 2022

Intl J of Energy Research

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Summary This article deals with the problem of energy losses in distribution networks (DNs) under electric vehicle (EV) penetration. The problem of charging overlaps causing severe power losses and voltage drops is faced under appropriate EV charging. An optimized EV charging schedule is proposed under EV time‐of‐arrival consideration. The solution algorithm is based on a particle swarm optimization (PSO) variant, and both grid‐to‐vehicle (G2V) and vehicle‐to‐grid (V2G) schemes are included regarding the power interactions of the EVs with the grid. The goal is to optimally allocate the charging and/or discharging time periods of the EVs in order to minimize energy losses while delivering fully charges EVs to the owners. New departures after the first EVs' arrival are also handled. The results indicate that with the proposed scheduling, daily energy loss reduction could be reached up to 25% under an improved voltage profile that indicates more uniform loading for the DN.

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“…On the other hand, subzero temperatures decrease the power acceptance, increasing the internal resistance of the LiBs, causing the raising of the joules power losses, decreasing the efficiency of the charging, and affecting the state of health (SOH) of the LiBs [24,25]. In case of cold weather when the temperature is not considered, the final SOC estimation maybe false and the battery does not reach the SOC target desired by the customer [26]. Thus, charging the battery while considering temperature is a very important issue, to get a best estimation of SOC and to conserve the lifespan of EVs batteries.…”

Section: Related Workmentioning

confidence: 99%

“…This article is an extension of an earlier conference paper [26], which has been significantly improved to take into account the constraint of low computational capacity of the embedded EV' system. The contribution of this paper has been capitalized in a patent [27].…”

Section: Contributionsmentioning

confidence: 99%

Optimized time step for electric vehicle charging optimization considering cost and temperature

Dahmane

Chenouard

Ghanes

et al. 2021

Sustainable Energy, Grids and Networks

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Decentralized Control of Electric Vehicle Smart Charging for Cost Minimization Considering Temperature and Battery Health

Cited by 9 publications

References 13 publications

The opportunity for smart charging to mitigate the impact of electric vehicles on transmission and distribution systems

The opportunity for smart charging to mitigate the impact of electric vehicles on transmission and distribution systems

Distribution network energy loss reduction under EV charging schedule

Optimized time step for electric vehicle charging optimization considering cost and temperature

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