Cell Temperature Prediction Using Machine Learning for Accurate Estimation of Range of the Electric Vehicle Daimler Trucks Innovation Center India P.

Format:

Book

Conference/Event

Author/Creator:

Śāha, Saurabha, author.

Contributor:

Bhat, Adithya

Gehring, Ottmar

Munirajappa, Chandrashekara

Conference Name:

SAENIS TTTMS Thermal Management Systems Conference (2025-11-06 : Guwahati, India)

Language:

English

Physical Description:

1 online resource cm

Place of Publication:

Warrendale, PA SAE International 2025

Summary:

Accurate prediction of cell temperature is essential for precise energy estimation in battery systems. The cell temperature profile significantly impacts auxiliary energy consumption, particularly for battery heating and cooling. Additionally, cell temperature influences peak charge and discharge currents and powers, which are critical for the overall performance and efficiency of the battery. Without an accurate prediction of cell temperature, estimations of auxiliary energy and total battery energy consumption can be significantly flawed. This paper presents a comprehensive analysis of the effects of cell temperature on battery energy consumption. We propose a model using machine learning to enhance the accuracy of cell temperature and battery heating power predictions. Our model is validated through extensive simulations and vehicle data, demonstrating its effectiveness in improving prediction of auxiliary energy consumption. The findings also underscore the importance of precise cell temperature prediction in predicting battery energy consumption, offering valuable insights for the development of more accurate range prediction algorithm

Notes:

Vendor supplied data

Publisher Number:

2025-28-0395

Access Restriction:

Restricted for use by site license