IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v318y2025ics0360544225003640.html
   My bibliography  Save this article

Enhanced state of charge estimation in lithium-ion batteries based on Time-Frequency-Net with time-domain and frequency-domain features

Author

Listed:
  • Wang, Xiaoxuan
  • Yi, Yingmin
  • Yuan, Yiwei
  • Li, Xifei

Abstract

Developing high-accuracy state of charge (SOC) estimation algorithms is essential for efficiently and safely using lithium-ion batteries. However, for data-driven-based models, adaptation, robustness and generalization must be considered to estimate SOC accurately. For this reason, Time-Frequency-Net (TFN) is introduced to enhance the accuracy and stability of SOC estimation from time-domain and frequency-domain features. TFN is essentially the combination of the Mamba Block and the Signal Decomposition Block (SDB). Mamba Block optimizes long-term dependency modeling in time series and reinforces time-domain feature extraction. Additionally, SDB incorporates Fast Fourier Transform with convolutional neural networks, facilitating frequency-domain feature extraction and representation. This approach allows TFN to comprehensively learn the patterns of periodicity, battery dynamics and potential noise. TFN can synthesize time-domain and frequency-domain information and identify data trends to output reliable SOC estimations, improving the robustness of the model and ensuring the battery's dynamic behaviors learning. The experimental results indicate that TFN with an overall average mean absolute error of 0.71 % outperforms other models on self-collected datasets. Furthermore, it performs superiorly in various conditions, including currents with different signal-to-noise ratios, extreme temperatures, and distinct battery types. The effectiveness across varying discharging rates and battery-aging levels confirms the adaptability and generalizability of TFN.

Suggested Citation

  • Wang, Xiaoxuan & Yi, Yingmin & Yuan, Yiwei & Li, Xifei, 2025. "Enhanced state of charge estimation in lithium-ion batteries based on Time-Frequency-Net with time-domain and frequency-domain features," Energy, Elsevier, vol. 318(C).
  • Handle: RePEc:eee:energy:v:318:y:2025:i:c:s0360544225003640
    DOI: 10.1016/j.energy.2025.134722
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544225003640
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.energy.2025.134722?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. Ouyang, Tiancheng & Xu, Peihang & Chen, Jingxian & Su, Zixiang & Huang, Guicong & Chen, Nan, 2021. "A novel state of charge estimation method for lithium-ion batteries based on bias compensation," Energy, Elsevier, vol. 226(C).
    2. Zafar, Muhammad Hamza & Khan, Noman Mujeeb & Houran, Mohamad Abou & Mansoor, Majad & Akhtar, Naureen & Sanfilippo, Filippo, 2024. "A novel hybrid deep learning model for accurate state of charge estimation of Li-Ion batteries for electric vehicles under high and low temperature," Energy, Elsevier, vol. 292(C).
    3. Huang, Haichi & Bian, Chong & Wu, Mengdan & An, Dong & Yang, Shunkun, 2024. "A novel integrated SOC–SOH estimation framework for whole-life-cycle lithium-ion batteries," Energy, Elsevier, vol. 288(C).
    4. Shuqing Li & Chuankun Ju & Jianliang Li & Ri Fang & Zhifei Tao & Bo Li & Tingting Zhang, 2021. "State-of-Charge Estimation of Lithium-Ion Batteries in the Battery Degradation Process Based on Recurrent Neural Network," Energies, MDPI, vol. 14(2), pages 1-21, January.
    5. Xiao, Renxin & Hu, Yanwen & Jia, Xianguang & Chen, Guisheng, 2022. "A novel estimation of state of charge for the lithium-ion battery in electric vehicle without open circuit voltage experiment," Energy, Elsevier, vol. 243(C).
    6. Guo, Shanshan & Ma, Liang, 2023. "A comparative study of different deep learning algorithms for lithium-ion batteries on state-of-charge estimation," Energy, Elsevier, vol. 263(PC).
    7. Fujin Wang & Zhi Zhai & Zhibin Zhao & Yi Di & Xuefeng Chen, 2024. "Physics-informed neural network for lithium-ion battery degradation stable modeling and prognosis," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
    8. Li, Feng & Zuo, Wei & Zhou, Kun & Li, Qingqing & Huang, Yuhan & Zhang, Guangde, 2024. "State-of-charge estimation of lithium-ion battery based on second order resistor-capacitance circuit-PSO-TCN model," Energy, Elsevier, vol. 289(C).
    9. Zhang, Kai & Bai, Dongxin & Li, Yong & Song, Ke & Zheng, Bailin & Yang, Fuqian, 2024. "Robust state-of-charge estimator for lithium-ion batteries enabled by a physics-driven dual-stage attention mechanism," Applied Energy, Elsevier, vol. 359(C).
    10. Qian, Wei & Li, Wan & Guo, Xiangwei & Wang, Haoyu, 2024. "A switching gain adaptive sliding mode observer for SoC estimation of lithium-ion battery," Energy, Elsevier, vol. 292(C).
    11. Lian, Gaoqi & Ye, Min & Wang, Qiao & Li, Yan & Xia, Baozhou & Zhang, Jiale & Xu, Xinxin, 2024. "Robust state-of-charge estimation for LiFePO4 batteries under wide varying temperature environments," Energy, Elsevier, vol. 293(C).
    12. Zhang, Chu & Zhang, Yue & Li, Zhengbo & Zhang, Zhao & Nazir, Muhammad Shahzad & Peng, Tian, 2024. "Enhancing state of charge and state of energy estimation in Lithium-ion batteries based on a TimesNet model with Gaussian data augmentation and error correction," Applied Energy, Elsevier, vol. 359(C).
    13. Liu, Zixi & Ruan, Guanqiang & Tian, Yupeng & Hu, Xing & Yan, Rong & Yang, Kuo, 2024. "A real-world battery state of charge prediction method based on a lightweight mixer architecture," Energy, Elsevier, vol. 311(C).
    14. Wan, Sicheng & Yang, Haojing & Lin, Jinwen & Li, Junhui & Wang, Yibo & Chen, Xinman, 2024. "Improved whale optimization algorithm towards precise state-of-charge estimation of lithium-ion batteries via optimizing LSTM," Energy, Elsevier, vol. 310(C).
    15. Li, Hao & Fu, Lijun & Long, Xinlin & Liu, Lang & Zeng, Ziqing, 2024. "A hybrid deep learning model for lithium-ion batteries state of charge estimation based on quantile regression and attention," Energy, Elsevier, vol. 294(C).
    16. Qi, Wei & Qin, Wenhu & Yun, Zhonghua, 2024. "Closed-loop state of charge estimation of Li-ion batteries based on deep learning and robust adaptive Kalman filter," Energy, Elsevier, vol. 307(C).
    17. Liu, Donglei & Wang, Shunli & Fan, Yongcun & Fernandez, Carlos & Blaabjerg, Frede, 2024. "An optimized multi-segment long short-term memory network strategy for power lithium-ion battery state of charge estimation adaptive wide temperatures," Energy, Elsevier, vol. 304(C).
    18. Qian, Cheng & Guan, Hongsheng & Xu, Binghui & Xia, Quan & Sun, Bo & Ren, Yi & Wang, Zili, 2024. "A CNN-SAM-LSTM hybrid neural network for multi-state estimation of lithium-ion batteries under dynamical operating conditions," Energy, Elsevier, vol. 294(C).
    19. Zhang, Jie & Xiao, Bo & Niu, Geng & Xie, Xuanzhi & Wu, Saixiang, 2024. "Joint estimation of state-of-charge and state-of-power for hybrid supercapacitors using fractional-order adaptive unscented Kalman filter," Energy, Elsevier, vol. 294(C).
    20. Feng, Xiong & Chen, Junxiong & Zhang, Zhongwei & Miao, Shuwen & Zhu, Qiao, 2021. "State-of-charge estimation of lithium-ion battery based on clockwork recurrent neural network," Energy, Elsevier, vol. 236(C).
    21. Zhou, Yifei & Wang, Shunli & Feng, Renjun & Xie, Yanxin & Fernandez, Carlos, 2024. "Multi-temperature capable enhanced bidirectional long short term memory-multilayer perceptron hybrid model for lithium-ion battery SOC estimation," Energy, Elsevier, vol. 312(C).
    22. Xu, Zhicheng & Wang, Jun & Lund, Peter D. & Zhang, Yaoming, 2022. "Co-estimating the state of charge and health of lithium batteries through combining a minimalist electrochemical model and an equivalent circuit model," Energy, Elsevier, vol. 240(C).
    23. Chen, Junxiong & Zhang, Yu & Wu, Ji & Cheng, Weisong & Zhu, Qiao, 2023. "SOC estimation for lithium-ion battery using the LSTM-RNN with extended input and constrained output," Energy, Elsevier, vol. 262(PA).
    24. Ng, Kong Soon & Moo, Chin-Sien & Chen, Yi-Ping & Hsieh, Yao-Ching, 2009. "Enhanced coulomb counting method for estimating state-of-charge and state-of-health of lithium-ion batteries," Applied Energy, Elsevier, vol. 86(9), pages 1506-1511, September.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Liu, Zixi & Ruan, Guanqiang & Tian, Yupeng & Hu, Xing & Yan, Rong & Yang, Kuo, 2024. "A real-world battery state of charge prediction method based on a lightweight mixer architecture," Energy, Elsevier, vol. 311(C).
    2. Semeraro, Concetta & Caggiano, Mariateresa & Olabi, Abdul-Ghani & Dassisti, Michele, 2022. "Battery monitoring and prognostics optimization techniques: Challenges and opportunities," Energy, Elsevier, vol. 255(C).
    3. Wu, Jiang & Lei, Dong & Liu, Zelong & Zhang, Yan, 2024. "A fusion algorithm of multidimensional element space mapping architecture for SOC estimation of lithium-ion batteries under dynamic operating conditions," Energy, Elsevier, vol. 311(C).
    4. Ahn, Junyoung & Lee, Yoonseok & Han, Byeongjik & Lee, Sohyeon & Kim, Yunsun & Chung, Daewon & Jeon, Joonhyeon, 2025. "A highly effective and robust structure-based LSTM with feature-vector tuning framework for high-accuracy SOC estimation in EV," Energy, Elsevier, vol. 325(C).
    5. Li, Feng & Zuo, Wei & Zhou, Kun & Li, Qingqing & Huang, Yuhan & Zhang, Guangde, 2024. "State-of-charge estimation of lithium-ion battery based on second order resistor-capacitance circuit-PSO-TCN model," Energy, Elsevier, vol. 289(C).
    6. Duan, Linchao & Zhang, Xugang & Jiang, Zhigang & Gong, Qingshan & Wang, Yan & Ao, Xiuyi, 2023. "State of charge estimation of lithium-ion batteries based on second-order adaptive extended Kalman filter with correspondence analysis," Energy, Elsevier, vol. 280(C).
    7. Chen, Yuan & Duan, Wenxian & Huang, Xiaohe & Wang, Shunli, 2024. "Multi-output fusion SOC and SOE estimation algorithm based on deep network migration," Energy, Elsevier, vol. 308(C).
    8. Hu, Lipeng & Tang, Jinjun & Xu, Fuqiao & Liang, Xiao, 2025. "SOC prediction for electric buses based on interpretable transformer model: Impact of traffic conditions and feature importance," Energy, Elsevier, vol. 324(C).
    9. Xie, Yanxin & Wang, Shunli & Zhang, Gexiang & Fan, Yongcun & Fernandez, Carlos & Blaabjerg, Frede, 2023. "Optimized multi-hidden layer long short-term memory modeling and suboptimal fading extended Kalman filtering strategies for the synthetic state of charge estimation of lithium-ion batteries," Applied Energy, Elsevier, vol. 336(C).
    10. Zhang, Chengzhong & Zhao, Hongyu & Wang, Liye & Liao, Chenglin & Wang, Lifang, 2024. "A comparative study on state-of-charge estimation for lithium-rich manganese-based battery based on Bayesian filtering and machine learning methods," Energy, Elsevier, vol. 306(C).
    11. Siyi Tao & Bo Jiang & Xuezhe Wei & Haifeng Dai, 2023. "A Systematic and Comparative Study of Distinct Recurrent Neural Networks for Lithium-Ion Battery State-of-Charge Estimation in Electric Vehicles," Energies, MDPI, vol. 16(4), pages 1-17, February.
    12. Zeng, Xiaoyong & Sun, Yaoke & Xia, Xiangyang & Chen, Laien, 2025. "A framework for joint SOC and SOH estimation of lithium-ion battery: Eliminating the dependency on initial states," Applied Energy, Elsevier, vol. 377(PD).
    13. Chen, Junxiong & Zhang, Yu & Wu, Ji & Cheng, Weisong & Zhu, Qiao, 2023. "SOC estimation for lithium-ion battery using the LSTM-RNN with extended input and constrained output," Energy, Elsevier, vol. 262(PA).
    14. Cui, Zhenhua & Kang, Le & Li, Liwei & Wang, Licheng & Wang, Kai, 2022. "A combined state-of-charge estimation method for lithium-ion battery using an improved BGRU network and UKF," Energy, Elsevier, vol. 259(C).
    15. Takyi-Aninakwa, Paul & Wang, Shunli & Liu, Guangchen & Fernandez, Carlos & Kang, Wenbin & Song, Yingze, 2025. "Deep learning framework designed for high-performance lithium-ion batteries state monitoring," Renewable and Sustainable Energy Reviews, Elsevier, vol. 218(C).
    16. Takyi-Aninakwa, Paul & Wang, Shunli & Zhang, Hongying & Yang, Xiao & Fernandez, Carlos, 2023. "A hybrid probabilistic correction model for the state of charge estimation of lithium-ion batteries considering dynamic currents and temperatures," Energy, Elsevier, vol. 273(C).
    17. Son, Donghee & Song, Youngbin & Park, Shina & Oh, Junseok & Kim, Sang Woo, 2025. "Online state-of-charge and capacity co-estimation for lithium-ion batteries under aging and varying temperatures," Energy, Elsevier, vol. 316(C).
    18. Wan, Sicheng & Yang, Haojing & Lin, Jinwen & Li, Junhui & Wang, Yibo & Chen, Xinman, 2024. "Improved whale optimization algorithm towards precise state-of-charge estimation of lithium-ion batteries via optimizing LSTM," Energy, Elsevier, vol. 310(C).
    19. Wang, Ya-Xiong & Chen, Zhenhang & Zhang, Wei, 2022. "Lithium-ion battery state-of-charge estimation for small target sample sets using the improved GRU-based transfer learning," Energy, Elsevier, vol. 244(PB).
    20. Shu, Xiong & Li, Yongjing & Wei, Kexiang & Yang, Wenxian & Yang, Bowen & Zhang, Ming, 2025. "Research on the output characteristics and SOC estimation method of lithium-ion batteries over a wide range of operating temperature conditions," Energy, Elsevier, vol. 317(C).

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:energy:v:318:y:2025:i:c:s0360544225003640. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.