Intelligent Battery Management in Electric Vehicles: A Comprehensive Review of AI Techniques | Chapter 02 | Engineering Research: Perspectives on Recent Advances Vol. 12

 

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

Determining the Hazards Analysis of Nickel-Rich Lithium-Ion Battery Thermal Runaway under Different States of Charge | Chapter 04 | Novel Perspectives of Engineering Research Vol. 5

 The extended volume plus acceleration calorimeter (EV+-ARC) is used to explore the TR properties of nickel-rich 18650 lithium-ion batteries with the Li[Ni0.8Co0.1Mn0.1]O2 cathode in varied SOC settings. The lithium-ion battery business has been rapidly evolving, with increasing energy density and capacity. However, the subsequent lithium-ion power battery safety incidents have posed a major threat to passengers' personal safety. As a result, greater attention is being paid to lithium-ion battery thermal safety research, such as thermal runaway (TR) mechanism analysis and preventive strategies, and so on. The TR properties of nickel-rich 18650 lithium-ion batteries with Li[Ni0.8Co0.1Mn0.1]O2 cathode in various states of charge (SOC) are investigated using an extended volume plus acceleration calorimeter (EV+-ARC) in this research. Some distinctive parameters from the experiment are selected to examine the influence of SOC on the critical state of TR in order to evaluate the TR features. These parameters include battery voltage, surface temperature, temperature increase rate, and so on. The maximum temperature of the battery surface drops as SOC decreases, however the self-generated heat temperature and TR trigger temperature increase as SOC decreases, as shown in the experiment findings.

Author(S) Details

Kun Jiang
School of Control Science and Engineering, Shandong University, Jinan 250061, China.

Pingwei Gu
School of Control Science and Engineering, Shandong University, Jinan 250061, China.

Peng Huang
School of Control Science and Engineering, Shandong University, Jinan 250061, China.

Ying Zhang
School of Control Science and Engineering, Shandong University, Jinan 250061, China.

Bin Duan
School of Control Science and Engineering, Shandong University, Jinan 250061, China.

Chenghui Zhang
School of Control Science and Engineering, Shandong University, Jinan 250061, China.

View Book:- https://stm.bookpi.org/NPER-V5/article/view/5320