Prediction of Electric Vehicle Range: A Comprehensive Review of Current Issues and Challenges

Electric vehicles (EV) are the immediate solution to drastically reducing pollutant emissions from the transport sector. There is a continuing increase in the number of EVs in use, but their widespread and massive acceptance by automotive consumers is related to the performance they can deliver. The most important feature here (a hot topic at present in EV research) is related to the possibility of providing a more accurate prediction of range. Range prediction is a complex problem because it depends on a lot of influence factors (internal, external, constant, variables) and the present paper aims to investigate the effect of these factors on the range of EVs. The results and aspects of current worldwide research on this theme are presented through the analysis of the main classes of influence factors: Vehicle design, the driver and the environment. Further, the weight and effect of each potential factor which influences EV range was analyzed by presenting current issues. An exhaustive and comprehensive analysis has made it possible to identify future research and development directions in the EV research field, resulting in massive future and immediate EV penetration in the automotive market.

[1]  Jun Bi,et al.  Residual range estimation for battery electric vehicle based on radial basis function neural network , 2018, Measurement.

[2]  Xinkai Wu,et al.  Energy-Optimal Speed Control for Electric Vehicles on Signalized Arterials , 2015, IEEE Transactions on Intelligent Transportation Systems.

[3]  Thomas H. Bradley,et al.  Geographical and temporal differences in electric vehicle range due to cabin conditioning energy consumption , 2015 .

[4]  Thomas Franke,et al.  Interacting with limited mobility resources: Psychological range levels in electric vehicle use , 2013 .

[5]  Paolo Iora,et al.  Effect of Ambient Temperature on Electric Vehicles’ Energy Consumption and Range: Model Definition and Sensitivity Analysis Based on Nissan Leaf Data , 2019, World Electric Vehicle Journal.

[6]  Karen Sentoff,et al.  Implications of Driving Style and Road Grade for Accurate Vehicle Activity Data and Emissions Estimates , 2015 .

[7]  João L. Afonso,et al.  Data mining approach for range prediction of electric vehicle , 2012 .

[8]  Mark Singer,et al.  Consumer Views on Plug-in Electric Vehicles -- National Benchmark Report , 2016 .

[9]  Ganesan Nagasubramanian,et al.  Effects of additives on thermal stability of Li ion cells , 2005 .

[10]  Elodie Labeye,et al.  The Electric Vehicle: A New Driving Experience Involving Specific Skills and Rules , 2016 .

[11]  Anind K. Dey,et al.  Maximum Entropy Inverse Reinforcement Learning , 2008, AAAI.

[12]  Kang Xu,et al.  The low temperature performance of Li-ion batteries , 2003 .

[13]  Yue Yuan,et al.  Temperature effect on electric vehicle battery cycle life in Vehicle-to-grid applications , 2010, CICED 2010 Proceedings.

[14]  Jeremy J. Michalek,et al.  Effects of regional temperature on electric vehicle efficiency, range, and emissions in the United States. , 2015, Environmental science & technology.

[15]  Jaephil Cho,et al.  Who will drive electric vehicles, olivine or spinel? , 2011 .

[16]  Ren.  Renewables 2019 Global Status Report , 2012 .

[17]  Dorothea Wagner,et al.  Energy-optimal routes for electric vehicles , 2013, SIGSPATIAL/GIS.

[18]  Erik Wilhelm,et al.  Comparing the Mass, Energy, and Cost Effects of Lightweighting in Conventional and Electric Passenger Vehicles , 2014 .

[19]  M. Wohlfahrt‐Mehrens,et al.  Ageing mechanisms in lithium-ion batteries , 2005 .

[20]  Konstantinos N. Genikomsakis,et al.  A computationally efficient simulation model for estimating energy consumption of electric vehicles in the context of route planning applications , 2017 .

[21]  Hesham Rakha,et al.  Power-based electric vehicle energy consumption model: Model development and validation , 2016 .

[22]  Lutz M. Kolbe,et al.  Understanding the influence of in-vehicle information systems on range stress – Insights from an electric vehicle field experiment , 2016 .

[23]  M. Dubarry,et al.  Identifying battery aging mechanisms in large format Li ion cells , 2011 .

[24]  Han Zhiqiang,et al.  Practical state of health estimation of power batteries based on Delphi method and grey relational grade analysis , 2015 .

[25]  Hui Liang,et al.  Energy state of health estimation for battery packs based on the degradation and inconsistency , 2017 .

[26]  Dirk Uwe Sauer,et al.  Advanced mathematical methods of SOC and SOH estimation for lithium-ion batteries , 2013 .

[27]  C.E.I. Byrne,et al.  Aerodynamics of Road Vehicles — 4th edition. Edited by W-H. Hucho. SAE International, Warrendale, PA, USA. Materials Park, OH 44073-0002, USA 1998. 918 pp. Illustrated. £78. , 1999, The Aeronautical Journal (1968).

[28]  Vitor Monteiro,et al.  Dynamic range prediction for an electric vehicle , 2013, 2013 World Electric Vehicle Symposium and Exhibition (EVS27).

[29]  Lin Gan,et al.  Fuzzy Prediction of Power Lithium Ion Battery State of Function Based on the Fuzzy c-Means Clustering Algorithm , 2019, World Electric Vehicle Journal.

[30]  Azah Mohamed,et al.  A review of lithium-ion battery state of charge estimation and management system in electric vehicle applications: Challenges and recommendations , 2017 .

[31]  Federico Alvarez,et al.  Modelling the Effect of Driving Events on Electrical Vehicle Energy Consumption Using Inertial Sensors in Smartphones , 2018 .

[32]  Jay Lee,et al.  Review and recent advances in battery health monitoring and prognostics technologies for electric vehicle (EV) safety and mobility , 2014 .

[33]  Marcelle C. McManus,et al.  Environmental consequences of the use of batteries in low carbon systems: The impact of battery production , 2012 .

[34]  Minoru Inaba,et al.  Temperature effects on SEI formation and cyclability of Si nanoflake powder anode in the presence of SEI-forming additives , 2017 .

[35]  B. Nykvist,et al.  Rapidly falling costs of battery packs for electric vehicles , 2015 .

[36]  Xu Tao,et al.  An Optimized Energy Management Strategy for Preheating Vehicle-Mounted Li-Ion Batteries at Subzero Temperatures , 2017 .

[37]  Bizhong Xia,et al.  A Study on the Open Circuit Voltage and State of Charge Characterization of High Capacity Lithium-Ion Battery Under Different Temperature , 2018, Energies.

[38]  H. Buqaa,et al.  SEI film formation on highly crystalline graphitic materials in lithium-ion batteries , 2022 .

[39]  M. J. Weighall.  Test requirements for 42 V battery systems , 2003 .

[40]  B. Varga,et al.  Indirect environment-related effects of electric car vehicles use , 2018 .

[41]  Kang Xu,et al.  Electrochemical impedance study on the low temperature of Li-ion batteries , 2004 .

[42]  Suzanna Long,et al.  Barriers to widespread adoption of electric vehicles: An analysis of consumer attitudes and perceptions , 2012 .

[43]  Jiangping Chen,et al.  Climate control loads prediction of electric vehicles , 2017 .

[44]  Rochdi Trigui,et al.  Driving Style Modelling for Eco-driving Applications , 2017 .

[45]  Jiyun Zhao,et al.  Thermal issues about Li-ion batteries and recent progress in battery thermal management systems: A review , 2017 .

[46]  Joeri Van Mierlo,et al.  A Data-Driven Method for Energy Consumption Prediction and Energy-Efficient Routing of Electric Vehicles in Real-World Conditions , 2017 .

[47]  Nadine Rauh,et al.  Understanding the Impact of Electric Vehicle Driving Experience on Range Anxiety , 2015, Hum. Factors.

[48]  Tobias Bär,et al.  Probabilistic driving style determination by means of a situation based analysis of the vehicle data , 2011, 2011 14th International IEEE Conference on Intelligent Transportation Systems (ITSC).

[49]  John F. Thomas,et al.  Fuel Economy and Emissions Effects of Low Tire Pressure, Open Windows, Roof Top and Hitch-Mounted Cargo, and Trailer , 2014 .

[50]  Wei Li,et al.  A review of safety-focused mechanical modeling of commercial lithium-ion batteries , 2018 .

[51]  Andrew V. Goldberg,et al.  Customizable Route Planning in Road Networks , 2017, Transp. Sci..

[52]  Matthew B. Pinson,et al.  Theory of SEI Formation in Rechargeable Batteries: Capacity Fade, Accelerated Aging and Lifetime Prediction , 2012, 1210.3672.

[53]  Dalibor Barta,et al.  Analysis of Parameters Influencing Electric Vehicle Range , 2016 .

[54]  Geng Wu,et al.  Total cost of ownership of electric vehicles compared to conventional vehicles: A probabilistic analysis and projection across market segments , 2015 .

[55]  Madlen Günther,et al.  Does this range suit me? Range satisfaction of battery electric vehicle users. , 2017, Applied ergonomics.

[56]  Eunil Park,et al.  Understanding the Emergence and Social Acceptance of Electric Vehicles as Next-Generation Models for the Automobile Industry , 2018 .

[57]  Torsten Bertram,et al.  Model-based remaining driving range prediction in electric vehicles by using particle filtering and Markov chains , 2013, 2013 World Electric Vehicle Symposium and Exhibition (EVS27).

[58]  Helmut Ehrenberg,et al.  Fundamental degradation mechanisms of layered oxide Li-ion battery cathode materials: Methodology, insights and novel approaches , 2015 .

[59]  Pulickel M. Ajayan,et al.  A materials perspective on Li-ion batteries at extreme temperatures , 2017, Nature Energy.

[60]  Y. Lai,et al.  Limiting factors for low-temperature performance of electrolytes in LiFePO4/Li and graphite/Li half cells , 2012 .

[61]  Srikkanth Ramachandran,et al.  Well to wheel analysis of low carbon alternatives for road traffic , 2015 .