| About this Abstract |
| Meeting |
TMS Specialty Congress 2025
|
| Symposium
|
3rd World Congress on Artificial Intelligence in Materials & Manufacturing (AIM 2025)
|
| Presentation Title |
Estimation of SOC in Electric Vehicle Batteries Using Machine Learning Models |
| Author(s) |
Bhanu Sree Vijayanand, Shrishail C Prabhakar, Jayaganthan R |
| On-Site Speaker (Planned) |
Bhanu Sree Vijayanand |
| Abstract Scope |
The estimation of the State of Charge (SOC) of lithium-ion(LFP) batteries is critical for enhancing the performance of electric vehicles (EVs). Conventional SoC estimation methods utilise coloumb counting and SoC vs OCV measurements. In EV applications, the OCV determination is challenging as the battery pack will be in closed circuit condition during drive cycle. It is important to utilise SOC datasets of LFP batteries obtained under various drive cycles and environmental factors, which could enhance the robustness of predicting its ageing behaviour. In the present work, the SOC data of Li-ion battery (LFP) estimated with the simulated actual drive cycles, were used to train Machine learning models such as Support vector Machine (SVM), XG-Boost, CNN, LSTM for predicting the aging behaviour. The comparative study is made on predictive accuracy of these ML models used in the present work. The mechanisms on aging behaviour of Li ion battery is discussed. |
| Proceedings Inclusion? |
Undecided |