Artificial Intelligence in Electric Vehicle Battery Management System: A Technique for Better Energy Storage MRIIRS Faridabad

Format:

Book

Conference/Event

Author/Creator:

Vashist, Devendra, author.

Contributor:

Raj, Rishi

Sharma, Deepanshu

Conference Name:

SAENIS TTTMS Thermal Management Systems Conference (2024-09-19 : Delhi, India)

Language:

English

Physical Description:

1 online resource cm

Place of Publication:

Warrendale, PA SAE International 2024

Summary:

The automobile industry is currently undergoing a huge transition from IC Engine based systems to electric based mobility systems. Battery technology based on Li ion has made interesting move towards popularization of electric vehicles (EVs) in world market. battery management system (BMS) forms one of the major constituents of this technology. Battery pack as a whole is the most sought-after component of EVs which needs intensive monitoring and control. Battery parameters such as State of Health (SOH) and State of Charge (SOC) needs precise measurement and calculation. Monitoring them directly is a difficult task. In the present work methodologies and approaches for estimating the batteries parameters using Artificial Intelligent methods were investigated. Six machine learning algorithms used for state estimation were critically reviewed. The employed methods are linear, random forest, gradient boost, light gradient boosting (light-GBM), extreme gradient boosting (XGB), and support vector machine (SVM) regressors. A comparation between these reviewed methods were made. It is found that AI combined with a battery management system can improve energy usage with further electric vehicle performance improvement

Notes:

Vendor supplied data

Publisher Number:

2024-28-0089

Access Restriction:

Restricted for use by site license