State of charge estimation of lithium-ion batteries in an electric vehicle using hybrid metaheuristic - deep neural networks models
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 sy...
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| Format: | Article |
| Language: | English |
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Elsevier B.V.
2025
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| Online Access: | http://umpir.ump.edu.my/id/eprint/44975/ http://umpir.ump.edu.my/id/eprint/44975/1/1-s2.0-S2772683525000068-main.pdf |
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| author | Zuriani, Mustaffa Mohd Herwan, Sulaiman Isuwa, Jeremiah |
| author_facet | Zuriani, Mustaffa Mohd Herwan, Sulaiman Isuwa, Jeremiah |
| author_sort | Zuriani, Mustaffa |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-15T03:57:22Z |
| format | Article |
| id | ump-44975 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T03:57:22Z |
| publishDate | 2025 |
| publisher | Elsevier B.V. |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-449752025-07-09T01:58:20Z http://umpir.ump.edu.my/id/eprint/44975/ State of charge estimation of lithium-ion batteries in an electric vehicle using hybrid metaheuristic - deep neural networks models Zuriani, Mustaffa Mohd Herwan, Sulaiman Isuwa, Jeremiah QA75 Electronic computers. Computer science TK Electrical engineering. Electronics Nuclear engineering 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. Elsevier B.V. 2025-06 Article PeerReviewed pdf en cc_by_nc_nd_4 http://umpir.ump.edu.my/id/eprint/44975/1/1-s2.0-S2772683525000068-main.pdf 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) https://doi.org/10.1016/j.enss.2025.01.002 https://doi.org/10.1016/j.enss.2025.01.002 |
| spellingShingle | QA75 Electronic computers. Computer science TK Electrical engineering. Electronics Nuclear engineering Zuriani, Mustaffa Mohd Herwan, Sulaiman Isuwa, Jeremiah State of charge estimation of lithium-ion batteries in an electric vehicle using hybrid metaheuristic - deep neural networks models |
| title | State of charge estimation of lithium-ion batteries in an electric vehicle using hybrid metaheuristic - deep neural networks models |
| title_full | State of charge estimation of lithium-ion batteries in an electric vehicle using hybrid metaheuristic - deep neural networks models |
| title_fullStr | State of charge estimation of lithium-ion batteries in an electric vehicle using hybrid metaheuristic - deep neural networks models |
| title_full_unstemmed | State of charge estimation of lithium-ion batteries in an electric vehicle using hybrid metaheuristic - deep neural networks models |
| title_short | State of charge estimation of lithium-ion batteries in an electric vehicle using hybrid metaheuristic - deep neural networks models |
| title_sort | state of charge estimation of lithium-ion batteries in an electric vehicle using hybrid metaheuristic - deep neural networks models |
| topic | QA75 Electronic computers. Computer science TK Electrical engineering. Electronics Nuclear engineering |
| url | http://umpir.ump.edu.my/id/eprint/44975/ http://umpir.ump.edu.my/id/eprint/44975/ http://umpir.ump.edu.my/id/eprint/44975/ http://umpir.ump.edu.my/id/eprint/44975/1/1-s2.0-S2772683525000068-main.pdf |