Events

Name of the Speaker: Mr. Sushant Mutagekar (EE15D206)
Guide: Dr. Ashok Jhunjhunwala
Co-Guide: Dr. Prabhjot Kaur
Date/Time: 15th September 2023 (Friday), 8:30 AM

Abstract
In the realm of Li-ion battery (LIB) research, the existing body of literature primarily addresses the study of Li-ion cells, neglecting to cover the degradation and performance study at the battery pack level extensively. Furthermore, much of the existing research has been conducted under controlled laboratory conditions, limiting its applicability to the real-world operation of battery packs. There exists a compelling necessity to investigate the real-life performance of LIB packs.

In this research, our primary objective is to comprehensively understand the behavior and performance of LIB in field conditions characterized by rough terrains, varying charge-discharge rates, different temperatures (especially hot and humid environments), variable depth of discharge (DoD), and diverse storage conditions. However, attaining this objective necessitates a thorough understanding of cell behavior in controlled environments. In order to accomplish this, we developed battery testers specifically designed to characterize Li-ion cells under different charge-discharge rates, temperatures, DoDs, and storage conditions. These cells are then integrated into battery packs equipped with a battery management system (BMS), designed and developed at the Centre for Battery Engineering and Electric Vehicles (CBEEV). These battery packs are used in e-rickshaws, operating daily under real-life on-road conditions. We analyze the lab-level cell degradation data and real-time on-field data of battery packs collected by BMS to develop a novel and robust first-order health estimation model. The model is validated against actual measurements and demonstrates high accuracy.

Based on the field trials, which were conducted exclusively in demanding environments with daily operations subject to harsh field conditions, we propose a unique battery prognostics method that effectively identifies faults and issues encountered in the field, offering corresponding solutions. The efficacy of this model is demonstrated through various field situations, where it successfully predicts failures in advance, thereby mitigating potential catastrophic events.

In the final segment of this research, we propose an adaptive slow-charging algorithm to extend the life of LIB. Results show a slight change in DoD and charge rates can significantly impact battery life. Additionally, based on our comprehensive study, we present a set of best practices to guide the design and manufacturing of efficient, high-performing, and long-lasting LIB packs.

We have successfully demonstrated the effectiveness of our recommendations for battery pack-design, health estimations, prognostics, and charging techniques for small batteries used at considerable discharge rates without active cooling with extensive real-life field trials.

Online meeting link :
https://teams.microsoft.com/l/meetup-join/19%3ameeting_ZWM3ZjM5YmQtM2ZiNS00MTM5LWJjZGEtNjg4MGUwMjQzYTZm%40thread.v2/0?context=%7b%22Tid%22%3a%22941c76d7-64df-4eb9-bfad-a68969948000%22%2c%22Oid%22%3a%220767c039-2959-4bf1-8099-5491e2b2f48e%22%7d