Routing systems to extend the driving range of electric vehicles

被引:81
|
作者
Neaimeh, Myriam [1 ]
Hill, Graeme A. [1 ]
Huebner, Yvonne [1 ]
Blythe, Phil T. [1 ]
机构
[1] Newcastle Univ, Sch Civil Engn & Geosci, TORG, Newcastle Upon Tyne NE1 7RU, Tyne & Wear, England
基金
英国工程与自然科学研究理事会;
关键词
electric vehicles; graph theory; road traffic; search problems; vehicle routing; electric vehicle routing systems; EV driving range; traffic conditions; road network topography; high-resolution real-world data; SwitchEV trial; Global Positioning System; battery drain; energy regeneration; time-stamp; linear models; energy expenditure equations; smart navigation; eco-driving assist systems; intelligent transport systems; energy consumption; driver optimisation; range anxiety mitigation; Dijkstra graph search algorithm; BARRIERS;
D O I
10.1049/iet-its.2013.0122
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
This study develops a more accurate range prediction for electric vehicles (EVs) resulting in a routing system that could extend the driving range of EVs through calculating the minimum energy route to a destination, based on topography and traffic conditions of the road network. Energy expenditure of EVs under different conditions is derived using high-resolution real-world data from the SwitchEV trial. The SwitchEV trial has recorded the second-by-second driving events of 44 all-electric vehicles covering a distance of over 400 000 miles across the North East of England, between March 2010 and May 2013. Linear models are used to determine the energy expenditure equations and Dijkstra's graph search algorithm is used to find the route minimising energy consumption. The results from this study are being used to better inform the decisions of the smart navigation and eco-driving assist systems in EVs, thus improving the intelligent transport systems provisions for EV drivers. The outputs of the research are twofold: providing more accurate estimations of available range and supporting drivers' optimisation of energy consumption and as a result extending their driving range. Both outputs could help mitigate range anxiety and make EVs a more attractive proposition to potential customers.
引用
收藏
页码:327 / 336
页数:10
相关论文
共 50 条
  • [31] A novel data-driven framework for driving range prognostics in electric vehicles
    Bustos, Jorge E. Garcia
    Baeza, Cesar
    Schiele, Benjamin Brito
    Rivera, Violeta
    Masserano, Bruno
    Orchard, Marcos E.
    Burgos-Mellado, Claudio
    Perez, Aramis
    ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, 2025, 142
  • [32] Probabilistic Prediction of Energy Demand and Driving Range for Electric Vehicles with Federated Learning
    Thorgeirsson A.T.
    Scheubner S.
    Funfgeld S.
    Gauterin F.
    IEEE Open Journal of Vehicular Technology, 2021, 2 : 151 - 161
  • [33] A Hybrid Method to Calculate the Real Driving Range of Electric Vehicles on Intercity Routes
    Armenta-Deu, Carlos
    Cortes, Hernan
    VEHICLES, 2023, 5 (02): : 482 - 497
  • [34] Analysis of the Driving Range Evaluation Method for Fuel-Cell Electric Vehicles
    Guo, Ting
    Sun, Letian
    Wang, Guozhuo
    Wu, Shiyu
    WORLD ELECTRIC VEHICLE JOURNAL, 2024, 15 (06):
  • [35] A Time and Energy Efficient Routing Algorithm for Electric Vehicles Based on Historical Driving Data
    Bozorgi, Amir Masoud
    Farasat, Mehdi
    Mahmoud, Anas
    IEEE TRANSACTIONS ON INTELLIGENT VEHICLES, 2017, 2 (04): : 308 - 320
  • [36] Driving Without Anxiety: a Route Planner Service with Range Prediction for the Electric Vehicles
    Bedogni, Luca
    Bononi, Luciano
    D'Elia, Alfredo
    Di Felice, Marco
    Di Nicola, Marco
    Cinotti, Tullio Salmon
    2014 INTERNATIONAL CONFERENCE ON CONNECTED VEHICLES AND EXPO (ICCVE), 2014, : 199 - 206
  • [37] Evaluating system architectures for driving range estimation and charge planning for electric vehicles
    Thorgeirsson, Adam Thor
    Vaillant, Moritz
    Scheubner, Stefan
    Gauterin, Frank
    SOFTWARE-PRACTICE & EXPERIENCE, 2021, 51 (01): : 72 - 90
  • [38] Real Driving Range in Electric Vehicles: Influence on Fuel Consumption and Carbon Emissions
    Armenta-Deu, Carlos
    Cattin, Erwan
    WORLD ELECTRIC VEHICLE JOURNAL, 2021, 12 (04):
  • [39] Remaining Driving Range Estimation for Electric Vehicles through Information Fusion method
    Liu, Guangming
    Fu, Hong
    Lu, Languang
    Wang, Yanjing
    Hua, Jianfeng
    Li, Jianqiu
    Ouyang, Minggao
    Feng, Chao
    Xue, Shan
    Chen, Ping
    MECHANICAL COMPONENTS AND CONTROL ENGINEERING III, 2014, 668-669 : 641 - +
  • [40] Use of Inductive Power Transfer Sharing to Increase the Driving Range of Electric Vehicles
    Dutta, Promiti
    2013 IEEE POWER AND ENERGY SOCIETY GENERAL MEETING (PES), 2013,