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 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,...
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KeAi Communications Co., Ltd.
2025-06-01
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| Series: | Energy Storage and Saving |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772683525000068 |
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| author | Zuriani Mustaffa Mohd Herwan Sulaiman Jeremiah Isuwa |
| author_facet | Zuriani Mustaffa Mohd Herwan Sulaiman Jeremiah Isuwa |
| author_sort | Zuriani Mustaffa |
| collection | DOAJ |
| description | Accurate estimation of the state of charge (SoC) of lithium-ion batteries 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 (MAE: 6.0065, RMSE: 8.0360). This hybrid TLBO-DNNs technique demonstrates significant potential for enhancing battery management systems in electric vehicles, contributing to improved efficiency and reliability in electric vehicle operations. |
| format | Article |
| id | doaj-art-e036212203a5489f87aa1bcab90d3695 |
| institution | Kabale University |
| issn | 2772-6835 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | Energy Storage and Saving |
| spelling | doaj-art-e036212203a5489f87aa1bcab90d36952025-08-20T03:50:22ZengKeAi Communications Co., Ltd.Energy Storage and Saving2772-68352025-06-014211112210.1016/j.enss.2025.01.002State of charge estimation of lithium-ion batteries in an electric vehicle using hybrid metaheuristic - deep neural networks modelsZuriani Mustaffa0Mohd Herwan Sulaiman1Jeremiah Isuwa2Faculty of Computing, Universiti Malaysia Pahang Al-Sultan Abdullah (UMPSA), Pekan Pahang, 26600, Malaysia; Corresponding author.Faculty of Electrical & Electronics Engineering Technology, Universiti Malaysia Pahang Al-Sultan Abdullah (UMPSA), Pekan Pahang, 26600, MalaysiaDepartment of Computer Science, Federal University of Kashere, Kashere, Gombe, 771103, NigeriaAccurate estimation of the state of charge (SoC) of lithium-ion batteries 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 (MAE: 6.0065, RMSE: 8.0360). This hybrid TLBO-DNNs technique demonstrates significant potential for enhancing battery management systems in electric vehicles, contributing to improved efficiency and reliability in electric vehicle operations.http://www.sciencedirect.com/science/article/pii/S2772683525000068Deep learningDeep neural networksElectric vehicleMachine learningOptimizationState of charge estimation |
| spellingShingle | Zuriani Mustaffa Mohd Herwan Sulaiman Jeremiah Isuwa State of charge estimation of lithium-ion batteries in an electric vehicle using hybrid metaheuristic - deep neural networks models Energy Storage and Saving Deep learning Deep neural networks Electric vehicle Machine learning Optimization State of charge estimation |
| 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 | Deep learning Deep neural networks Electric vehicle Machine learning Optimization State of charge estimation |
| url | http://www.sciencedirect.com/science/article/pii/S2772683525000068 |
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