Title DeepTimeNet: a novel architecture for precise surface temperature estimation of lithium-ion batteries across diverse ambient conditions /
Authors Zafar, Muhammad Hamza ; Bukhari, Syed Muhammad Salman ; Houran, Mohamad Abou ; Mansoor, Majad ; Khan, Noman Mujeeb ; Sanfilippo, Filippo
DOI 10.1016/j.csite.2024.105002
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Is Part of Case studies in thermal engineering.. Amsterdam : Elsevier. 2024, vol. 61, art. no. 105002, p. 1-22.. ISSN 2214-157X
Keywords [eng] battery management systems ; deep neural networks ; lithium-ion batteries ; predictive modelling ; surface temperature estimation ; temperature-dependent performance ; time-series analysis
Abstract [eng] With the growing demand for battery-powered devices and electric vehicles, the need for improved battery performance and safety is paramount. A key determinant of battery health is the accurate monitoring of surface temperature (ST). Conventional ST estimation often depends on direct sensor measurements, which may not be cost-effective and can impact system reliability. This paper presents DeepTimeNet, a novel approach leveraging deep learning (DL) architectures for sensorless ST prediction in lithium-ion batteries. DeepTimeNet combines Convolutional Neural Networks (CNN), ResNet blocks, Inception modules, Bidirectional LSTM, and GRU layers to precisely model the time-dependent behaviour of batteries. A comprehensive evaluation against traditional models, across temperatures ranging from -20 °C to 25 °C and under various driving profiles, including US06 and Urban Dynamometer Driving Schedule (UDDS), is conducted. DeepTimeNet's performance is quantified by metrics such as mean absolute error (MAE), surpassing that of models like Gated Recurrent Unit-Recurrent Neural Network (GRU-RNN), Convolutional Neural Network-Long Short Term Memory Network (CNN-LSTM), and Long Short Term Memory Network (LSTM). The results demonstrate DeepTimeNet's superior performance, with an RMSE of 0.0971, MSE of 0.0099, MAE of 0.0912, and MAXE of 0.3963, validating it as an advanced tool for enhancing the efficacy of battery management systems and underscoring its potential as a benchmark for future innovations.
Published Amsterdam : Elsevier
Type Journal article
Language English
Publication date 2024
CC license CC license description