Zuriani, Mustaffa and Mohd Herwan, Sulaiman and Isuwa, Jeremiah (2025) State of charge estimation of lithium-ion batteries in an electric vehicle using hybrid metaheuristic - deep neural networks models. Energy Storage and Saving, 4 (2). pp. 111-122. ISSN 2772-6835. (Published)
|
Pdf
1-s2.0-S2772683525000068-main.pdf Available under License Creative Commons Attribution Non-commercial No Derivatives. Download (8MB) | Preview |
Abstract
Accurate estimation of the state of charge (SoC) of lithium-ion batteries (LIBs) in electric vehicles (EVs) is crucial for optimizing performance, ensuring safety, and extending battery life. However, traditional estimation methods often struggle with the nonlinear and dynamic behavior of battery systems, leading to inaccuracies that compromise the efficiency and reliability of electric vehicles. This study proposes a novel approach for SoC estimation in BMW EVs by integrating a metaheuristic algorithm with deep neural networks. Specifically, teaching-learning based optimization (TLBO) is employed to optimize the weights and biases of the deep neural networks model, enhancing estimation accuracy. The proposed TLBO-deep neural networks (TLBO-DNNs) method was evaluated on a dataset of 1,064,000 samples, with performance assessed using mean absolute error (MAE), root mean square error (RMSE), and convergence value. The TLBO-DNNs model achieved an MAE of 3.4480, an RMSE of 4.6487, and a convergence value of 0.0328, outperforming other hybrid approaches. These include the barnacle mating optimizer-deep neural networks (BMO-DNNs) with an MAE of 5.3848, an RMSE of 7.0395, and a convergence value of 0.0492; the evolutionary mating algorithm-deep neural networks (EMA-DNNs) with an MAE of 7.6127, an RMSE of 11.2287, and a convergence value of 0.0536; and the particle swarm optimization-deep neural networks (PSO-DNNs) with an MAE of 4.3089, an RMSE of 5.9672, and a convergence value of 0.0345. Additionally, the TLBO-DNNs approach outperformed standalone models, including the autoregressive integrated moving average (ARIMA) model (MAE: 14.3301, RMSE: 7.0697) and support vector machines (SVMs) (MAE: 6.0065, RMSE: 8.0360). This hybrid TLBO-DNNs technique demonstrates significant potential for enhancing battery management systems (BMS) in electric vehicles, contributing to improved efficiency and reliability in electric vehicle operations.
| Item Type: | Article |
|---|---|
| Additional Information: | Indexed by Scopus |
| Uncontrolled Keywords: | Deep learning, Deep neural networks, Electric vehicle, Machine learning, Optimization, State of charge estimation, Teaching-learning based optimization |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science T Technology > TK Electrical engineering. Electronics Nuclear engineering |
| Faculty/Division: | Faculty of Computing Faculty of Electrical and Electronic Engineering Technology |
| Depositing User: | Dr. Mohd Herwan Sulaiman |
| Date Deposited: | 09 Jul 2025 01:58 |
| Last Modified: | 09 Jul 2025 01:58 |
| URI: | http://umpir.ump.edu.my/id/eprint/44975 |
| Download Statistic: | View Download Statistics |
Actions (login required)
 |
View Item |