| Title |
A novel hybrid deep learning model for accurate state of charge estimation of Li-Ion batteries for electric vehicles under high and low temperature / |
| Authors |
Zafar, Muhammad Hamza ; Khan, Noman Mujeeb ; Houran, Mohamad Abou ; Mansoor, Majad ; Akhtar, Naureen ; Sanfilippo, Filippo |
| DOI |
10.1016/j.energy.2024.130584 |
| Full Text |
|
| Is Part of |
Energy.. Kidlington : Elsevier. 2024, vol. 292, art. no. 130584, p. 1-22.. ISSN 0360-5442. eISSN 1873-6785 |
| Keywords [eng] |
deep learning ; electric vehicles ; evolutionary intelligence ; high and low temperatures ; state of charge |
| Abstract [eng] |
This paper presents a novel architecture, termed Fusion-Fission Optimisation (FuFi) based Convolutional Neural Network with Bi-Long Short Term Memory Network (FuFi-CNN-Bi-LSTM), to enhance state of charge (SoC) estimation performance. The proposed FuFi-CNN-Bi-LSTM model leverages the power of both Convolutional Neural Networks (CNN) and Bi-Long Short Term Memory Networks (Bi-LSTM) while utilizing FuFi optimization to effectively tune the hyperparameters of the network. This optimization technique facilitates efficient SoC estimation by finding the optimal configuration of the model. A comparative analysis is conducted against FuFi Algorithm-based models, including FuFi-CNN-LSTM, FuFi-Bi-LSTM, FuFi-LSTM, and FuFi-CNN. The comparison involves assessing performance on SoC estimation tasks and identifying the strengths and limitations of models. Furthermore, the proposed FuFi-CNN-Bi-LSTM model undergoes rigorous testing on various drive cycle tests, including HPPC, HWFET, UDDS, and US06, at different temperatures ranging from -20 to 25 degrees Celsius. The model's robustness and reliability are assessed under different real-world operating conditions using well-established evaluation indexes, including Relative Error (RE),Mean Absolute Error (MAE), R Square (R2), and Granger Causality Test. The results demonstrate that the proposed FuFi-CNN-Bi-LSTM model achieves efficient SoC estimation performance across a wide range of temperatures at higher and lower ranges. This finding signifies the model's efficacy in accurately estimating SoC in various operating conditions. |
| Published |
Kidlington : Elsevier |
| Type |
Journal article |
| Language |
English |
| Publication date |
2024 |
| CC license |
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