TY - JOUR
T1 - Remaining driving range prediction for electric vehicles
T2 - Key challenges and outlook
AU - Mei, Peng
AU - Karimi, Hamid Reza
AU - Huang, Cong
AU - Chen, Fei
AU - Yang, Shichun
N1 - Publisher Copyright:
© 2023 The Authors. IET Control Theory & Applications published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology.
PY - 2023/9
Y1 - 2023/9
N2 - Remaining driving range (RDR) research has continued to consistently evolve with the development of electric vehicles (EVs). Accurate RDR prediction is a promising approach to alleviate distance anxiety when power battery technology is not yet fully matured. This paper first introduces the research motivation of RDR prediction, summarizes the previous research progress, and classifies the influencing factors of RDR. Second, conduct research and analysis on the physical model of EVs, mainly including battery and vehicle models. Based on the physical model, the energy flow problem of EVs is analyzed and discussed. Third, four key challenges of RDR prediction are summarized: battery state estimation, driving behavior classification and recognition, driving condition prediction and speed prediction, and RDR calculation method. Finally, given the four challenges faced by RDR, a driving range prediction method based on vehicle-cloud collaboration is proposed, which combines the advantages of cloud computing and machine learning to provide further research trends.
AB - Remaining driving range (RDR) research has continued to consistently evolve with the development of electric vehicles (EVs). Accurate RDR prediction is a promising approach to alleviate distance anxiety when power battery technology is not yet fully matured. This paper first introduces the research motivation of RDR prediction, summarizes the previous research progress, and classifies the influencing factors of RDR. Second, conduct research and analysis on the physical model of EVs, mainly including battery and vehicle models. Based on the physical model, the energy flow problem of EVs is analyzed and discussed. Third, four key challenges of RDR prediction are summarized: battery state estimation, driving behavior classification and recognition, driving condition prediction and speed prediction, and RDR calculation method. Finally, given the four challenges faced by RDR, a driving range prediction method based on vehicle-cloud collaboration is proposed, which combines the advantages of cloud computing and machine learning to provide further research trends.
KW - electric vehicles
KW - remaining driving range prediction
KW - vehicle-cloud collaboration
UR - https://www.scopus.com/pages/publications/85157995617
U2 - 10.1049/cth2.12486
DO - 10.1049/cth2.12486
M3 - 文献综述
AN - SCOPUS:85157995617
SN - 1751-8644
VL - 17
SP - 1875
EP - 1893
JO - IET Control Theory and Applications
JF - IET Control Theory and Applications
IS - 14
ER -