Artificial Intelligence and Cloud Synergy for Scalable Battery Management Systems SRM Institute of Science and Technology

Format:

Book

Conference/Event

Author/Creator:

R, Rajarajeswari, author.

Contributor:

Francis, Elgin Calister

N, Kalaiarasi

Conference Name:

Advances in Design, Materials, Manufacturing, and Surface Engineering (ADMMS'26) (2026-02-06 : Chennai, India)

Language:

English

Physical Description:

1 online resource cm

Place of Publication:

Warrendale, PA SAE International 2026

Summary:

Due to the rapid transformation of EVs and the battery storage system, the battery management system (BMS) is essential to ensure optimal performance of the battery storage piles. A BMS monitors and controls parameters such as SOC, voltage, current, and temperature. A traditional BMS has a minimum support of analytics, and it's limited to local processing. However, when the battery information is uploaded to the internet, it becomes easier to manage maintenance and track the battery's performance from anywhere in the world. This Cloud-based system is easy and made earlier, thereby giving a system alarm before the issue becomes big. Managing many batteries at once saves a significant amount of money in places like EV charging stations and Energy Storage Systems (BESS). Software updates to the system can also be sent remotely. Also, a BMS connected to the cloud can be used to support weaker grids in an instant if it needs the reactive power support. Cloud integration of BMS with the grid network will help in better planning of energy management at load dispatch centers. A BMS managing a pack of batteries at a renewable energy system can help to understand power demand and decide when the best time is to charge or discharge. So, this can monitor all the batteries without being near them. Further, identifying the problems is work that focuses on an ML-RL-based battery management system connected to the cloud to control and monitor the Voltage, temperature, Cell balancing, SOC, SOH, and fault identification. This BMS system has easy scalability to thousands of batteries connected. As the demand for EVs and clean energy soars, this cloud-integrated BMS would play an important role in managing the batteries that are part of that system, making it smarter, efficient, and reliable. The proposed Q-learningbased Cloud BMS achieves 96.5% energy efficiency, 3.2% SOC RMSE, and zero safety violations across 75,000 simulated samples, using an adaptive 6,000-state Q-learning agent validated through real-time cloud integration

Notes:

Vendor supplied data

Publisher Number:

2026-28-0119

Access Restriction:

Restricted for use by site license