To reduce carbon emissions and tackle global environmental
problems, the automotive sector has focused heavily on electric vehicles (EVs).
However, the eventual deterioration of battery health and performance may
adversely affect the efficiency of EVs. Due to their ability to accurately
assess battery health, analyse faults, and control temperature for enhanced
safety, reliability, and effective optimisation of EV performance, artificial
intelligence (AI) techniques have garnered significant interest. This review
investigates and evaluates the effects of AI techniques to improve the battery
management system (BMS) of electric vehicles (EVs). A variety of methodologies
are employed to perform a statistical analysis of relevant BMS papers. The
statistical analysis assesses essential characteristics such as current
research trends, keyword analysis, nation analysis, authorship, collaboration,
publishers, and research classification. Moreover, a thorough examination of
the goals, contributions, advantages, and disadvantages of advanced AI methods
is provided. In addition, several key guidelines and recommendations are
presented, along with a number of significant concerns and challenges, for
potential future enhancement. Future researchers could utilise the statistical
analysis as a guide to develop innovative BMS technologies for EVs that
function and are managed sustainably.
Author(s)
Details :-
Ashok Kumar Bandla
Department of CSE(AI&ML), Ramachandra College of Engineering, Eluru,
A.P, India.
Y. Lavanya
Department of ECE, Ramachandra College of Engineering, Eluru, A.P, India.
D. Sai Prasanthi
Department of EEE, Ramachandra College of Engineering, Eluru, A.P, India.
G. Kaladhar
Department of EEE, St. Ann’s college of Engineering & Technology,
Chirala, A.P, India.
Please see the book
here :- https://doi.org/10.9734/bpi/erpra/v12/6573
