SOC prediction method based on battery pack aging and consistency deviation of thermoelectric characteristics
Open accessAbstract
In view of the influence of different cell state parameters on the estimation of power battery packs’ state of charge (SOC), based on the travel data of electric vehicles in Beijing, random forest is used to reduce dimensionality, and the aging and thermoelectric characteristic parameters which have high correlation are selected as the input features of long short-term memory (LSTM). Then, using grid search to optimize the LSTM structure. Finally construct a data-driven method for high robustness prediction of battery SOC. The results show that the maximum absolute error (MaxAE) of the proposed SOC prediction method is only 1.539% under different temperatures, battery aging degrees and operating conditions. Compared with the two SOC prediction methods of gate recurrent unit (GRU) and recurrent neural network (RNN) , the MaxAE is reduced by 71.8% and 26.1% respectively. The research results provide a method basis for improving the robustness of power battery SOC estimation.
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Wu et al. applied random forest methods to reduce dimensionality of battery aging and thermoelectric characteristics before feeding data into LSTM and GRU networks for SOC estimation.
“Wu et al. applied random forest methods to reduce the dimensionality of battery aging and thermoelectric characteristics before feeding the data into LSTM and GRU networks for SOC estimation[103].”
From Advancements in the estimation of the state of charge of lithium-ion battery: a comprehensive review of traditional and deep learning approaches (2025) · cites this paper as [103]
The Abstract and §4.2 say Wu et al. used random forest to select aging and thermoelectric features as inputs to an LSTM SOC model. Section 5 describes a GRU network used for comparison, but does not establish that the random-forest-selected features were fed into the GRU as the claim states. Page numbers are unavailable in our copy. (LLM, conf=0.86)
Experimental — accuracy not yet measuredML methods for feature extraction from raw input data are highly effective, with some methods even surpassing Transformer models and top implementations such as PatchTST in time series prediction.
“First, ML methods for feature extraction from raw input data are highly effective[90,91,103], with some methods even surpassing[91] Transformer models and top implementations such as PatchTST in time series prediction.”
From Advancements in the estimation of the state of charge of lithium-ion battery: a comprehensive review of traditional and deep learning approaches (2025) · cites this paper as [103]
Sections 3.1–3.2 describe using random forest to select input features, and Section 5.1 reports improved battery state-of-charge prediction with the selected features. This supports the feature-selection point in this setting, but the paper does not report a comparison with Transformer models or PatchTST. Page numbers were not supplied in our copy. (LLM, conf=0.86)
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References 37 · Cited by 53
Research works printed but not linked: 3
- Montavon, G., Orr, G., Müller, K.-R., 2012. Neural Networks: Tricks of the Trade. Vol. 7700, springer.
- Wei, B., Jianjun, G., 2020. Study on battery SOC prediction method for electric bus based on sparsely sampled data. Autom. Eng. 42 (3), 367-374.
- Yin, G., Jiang, C., Yang, Y., Xiao, W., 2021. SOC prediction of lithium battery based on SA-PSO-BP neural network fusion. In: J. Phys.: Conf. Ser.. 1738, (1), IOP Publishing, 012070.
- Authors
- Wu, X., Li, M., Du, J., & Hu, F.
- Affiliations
- Harbin University of Science and Technology (CN)
- Tsinghua University (CN)
- Corresponding author
- Wu, Xiaogang
- Venue
- Energy Reports
- Publisher
- Elsevier BV
- Published
- 2022-11
- Volume
- 8
- Pages
- 2262-2272
- ISSN
- 2352-4847 (ISSN-L)
- OpenAlex
- W4220961898
- Cited by
- 53
- References
- 37
- Citations by year
- 2026: 7 · 2025: 15 · 2024: 10 · 2023: 15 · 2022: 6
- FWCI
- 3.14
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Energy Reports (publishedVersion, cc-by-nc-nd)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Natural Science Foundation of Heilongjiang Province (ZD2021E004)
- National Natural Science Foundation of China (51877057, 51877121)
- Topics
- Physical Sciences › Engineering › Automotive Engineering › Advanced Battery Technologies Research
- Physical Sciences › Engineering › Automotive Engineering › Electric and Hybrid Vehicle Technologies
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Advancements in Battery Materials
- Keywords
- state of charge estimation, electric vehicle batteries, LSTM, random forest feature selection
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