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AI-Based GEVs Mobility Estimation and Battery Aging Quantification Method

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Automated and Electric Vehicle: Design, Informatics and Sustainability

Part of the book series: Recent Advancements in Connected Autonomous Vehicle Technologies ((RACAVT,volume 3))

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Abstract

The bi-directional link between the electrical system and electric vehicles allows vehicle batteries to provide balancing services for the system in a flexible, low-cost, and quick-response manner. However, two critical issues should be solved in realising the benefits of vehicles to grid (V2G) services. Firstly, grid-connected electric vehicle (GEV) mobility may cause uncertainties in the grid’s energy storage capacity, which may further impact the power quality and stability of the power grid. Thus, in V2G scheduling, it is necessary to access electric vehicle (EV) mobility and estimate its schedulable capacity and charging requirements (SC&CR) information in advance. Furthermore, the key factor that keeps GEVs owners from becoming the prosumers of the grid is the battery life loss caused by additional operating cycles in V2G service, as well as the concern about expensive battery deterioration costs. To promote the adoption of V2G services, battery life loss should be evaluated and mitigated through a behaviour management algorithm. This chapter investigates and compares the performance of existing V2G capacity prediction methods, including statistical model, learning-based model, and rolling prediction model. Thereafter, it introduces a life loss quantification model to analyse battery aging characteristics when providing V2G services. With the predicted GEVs mobility information and battery aging cost analysis model, V2G resources can be better utilized by producing more efficient strategies.

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Abbreviations

V2G:

Vehicle to grid

EVs:

Electric vehicles

GEV:

Grid-connected electric vehicle

SC&CR:

Schedulable capacity and charging requirements

SVR:

Support vector regression

LSTM:

Long short term memory

RNN:

Recurrent neural networks

MC:

Monte carlo

RMSE:

Root-mean-square error

DoD:

Depth of discharge

Crate:

Charging and discharging rate

NoC:

Number of cycles

CTF:

Cycles to failure

RCC:

Rain-flow cycle counting

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Authors and Affiliations

  1. Department of Electronic and Electrical Engineering, University of Bath, Bath, BA2 7AY, UK

    Shuangqi Li & Chenghong Gu

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  1. Shuangqi Li
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Correspondence to Chenghong Gu .

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Editors and Affiliations

  1. School of Cyber Science and Engineering, Wuhan University, Wuhan, Hubei, China

    Yue Cao

  2. Department of Aeronautical and Automotive Engineering, Loughborough University, Loughborough, Leicestershire, UK

    Yuanjian Zhang

  3. Department of Electronic & Electrical Engineering, University of Bath, Bath, UK

    Chenghong Gu

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Li, S., Gu, C. (2023). AI-Based GEVs Mobility Estimation and Battery Aging Quantification Method. In: Cao, Y., Zhang, Y., Gu, C. (eds) Automated and Electric Vehicle: Design, Informatics and Sustainability. Recent Advancements in Connected Autonomous Vehicle Technologies, vol 3. Springer, Singapore. https://doi.org/10.1007/978-981-19-5751-2_7

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