Lithium-ion battery is one of the core components of electric vehicles, and the state of charge-state of health estimation results of it is the key to restrict the safe and efficient use of it, which then affects the comprehensive performance of electric vehicles. However, SOC and SOH of lithium-ion batteries have a coupling relationship, and have fast and slow time-varying characteristics respectively, with inconsistent time scales. Hence, it is necessary to carry out SOC-SOH collaborative estimation and select a suitable time scale, which can ensure the accuracy and robustness of SOC-SOH collaborative estimation without consuming too much calculation cost. This article proposed an innovative hybrid optimization network to improve the ability of the analysis and feature extraction capability of the input sequences for precise SOC estimation. This hybrid network fully combines the advantages of convolutional neural network, bidirectional long short-term memory, attention mechanism. Additionally, kepler optimization algorithm is applied for hyperparameter optimization of the hybrid network for the first time according to our knowledge, and SOH is also estimated accurately for more ideal SOC estimation results. The experimental results of lithium-ion batteries indicate that the innovative hybrid optimization network can reach ideal SOC estimation results under different working conditions and ambient temperatures. The mean absolute error and root mean square error are 0.55% and 0.72% respectively, only about a third of the SOC estimation results without considering SOH, which means that SOC-SOH collaborative estimation are very essential. Hence, this article is of great significance for the development of smarter battery management system.
| Published in | Journal of Energy and Natural Resources (Volume 13, Issue 4) |
| DOI | 10.11648/j.jenr.20241304.14 |
| Page(s) | 166-177 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2024. Published by Science Publishing Group |
Lithium-Ion Battery, State of Charge, State of Health, Collaborative Estimation, Innovative Hybrid Optimization Network
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APA Style
Zhang, X., Wang, L., Wu, M. (2024). State of Charge-State of Health Collaborative Estimation of the Lithium-ion Battery Based on an Innovative Hybrid Optimization Network. Journal of Energy and Natural Resources, 13(4), 166-177. https://doi.org/10.11648/j.jenr.20241304.14
ACS Style
Zhang, X.; Wang, L.; Wu, M. State of Charge-State of Health Collaborative Estimation of the Lithium-ion Battery Based on an Innovative Hybrid Optimization Network. J. Energy Nat. Resour. 2024, 13(4), 166-177. doi: 10.11648/j.jenr.20241304.14
AMA Style
Zhang X, Wang L, Wu M. State of Charge-State of Health Collaborative Estimation of the Lithium-ion Battery Based on an Innovative Hybrid Optimization Network. J Energy Nat Resour. 2024;13(4):166-177. doi: 10.11648/j.jenr.20241304.14
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Download@article{10.11648/j.jenr.20241304.14,
author = {Xi Zhang and Li Wang and Muyao Wu},
title = {State of Charge-State of Health Collaborative Estimation of the Lithium-ion Battery Based on an Innovative Hybrid Optimization Network
},
journal = {Journal of Energy and Natural Resources},
volume = {13},
number = {4},
pages = {166-177},
doi = {10.11648/j.jenr.20241304.14},
url = {https://doi.org/10.11648/j.jenr.20241304.14},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.jenr.20241304.14},
abstract = {Lithium-ion battery is one of the core components of electric vehicles, and the state of charge-state of health estimation results of it is the key to restrict the safe and efficient use of it, which then affects the comprehensive performance of electric vehicles. However, SOC and SOH of lithium-ion batteries have a coupling relationship, and have fast and slow time-varying characteristics respectively, with inconsistent time scales. Hence, it is necessary to carry out SOC-SOH collaborative estimation and select a suitable time scale, which can ensure the accuracy and robustness of SOC-SOH collaborative estimation without consuming too much calculation cost. This article proposed an innovative hybrid optimization network to improve the ability of the analysis and feature extraction capability of the input sequences for precise SOC estimation. This hybrid network fully combines the advantages of convolutional neural network, bidirectional long short-term memory, attention mechanism. Additionally, kepler optimization algorithm is applied for hyperparameter optimization of the hybrid network for the first time according to our knowledge, and SOH is also estimated accurately for more ideal SOC estimation results. The experimental results of lithium-ion batteries indicate that the innovative hybrid optimization network can reach ideal SOC estimation results under different working conditions and ambient temperatures. The mean absolute error and root mean square error are 0.55% and 0.72% respectively, only about a third of the SOC estimation results without considering SOH, which means that SOC-SOH collaborative estimation are very essential. Hence, this article is of great significance for the development of smarter battery management system.
},
year = {2024}
}
TY - JOUR T1 - State of Charge-State of Health Collaborative Estimation of the Lithium-ion Battery Based on an Innovative Hybrid Optimization Network AU - Xi Zhang AU - Li Wang AU - Muyao Wu Y1 - 2024/12/07 PY - 2024 N1 - https://doi.org/10.11648/j.jenr.20241304.14 DO - 10.11648/j.jenr.20241304.14 T2 - Journal of Energy and Natural Resources JF - Journal of Energy and Natural Resources JO - Journal of Energy and Natural Resources SP - 166 EP - 177 PB - Science Publishing Group SN - 2330-7404 UR - https://doi.org/10.11648/j.jenr.20241304.14 AB - Lithium-ion battery is one of the core components of electric vehicles, and the state of charge-state of health estimation results of it is the key to restrict the safe and efficient use of it, which then affects the comprehensive performance of electric vehicles. However, SOC and SOH of lithium-ion batteries have a coupling relationship, and have fast and slow time-varying characteristics respectively, with inconsistent time scales. Hence, it is necessary to carry out SOC-SOH collaborative estimation and select a suitable time scale, which can ensure the accuracy and robustness of SOC-SOH collaborative estimation without consuming too much calculation cost. This article proposed an innovative hybrid optimization network to improve the ability of the analysis and feature extraction capability of the input sequences for precise SOC estimation. This hybrid network fully combines the advantages of convolutional neural network, bidirectional long short-term memory, attention mechanism. Additionally, kepler optimization algorithm is applied for hyperparameter optimization of the hybrid network for the first time according to our knowledge, and SOH is also estimated accurately for more ideal SOC estimation results. The experimental results of lithium-ion batteries indicate that the innovative hybrid optimization network can reach ideal SOC estimation results under different working conditions and ambient temperatures. The mean absolute error and root mean square error are 0.55% and 0.72% respectively, only about a third of the SOC estimation results without considering SOH, which means that SOC-SOH collaborative estimation are very essential. Hence, this article is of great significance for the development of smarter battery management system. VL - 13 IS - 4 ER -
School of Automotive and Transportation Engineering, Hefei University of Technology, Hefei, China
School of Automotive and Transportation Engineering, Hefei University of Technology, Hefei, China
School of Automotive and Transportation Engineering, Hefei University of Technology, Hefei, China
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