Robust state of charge estimation based on a sequence-to-sequence mapping model with process information
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Experimental results suggested that, even under complex unknown dynamic load profiles and varying sampling frequency rates, the SSMPI model achieved precise SOC estimation with certain robustness.
“Experimental results suggested that, even under complex unknown dynamic load profiles and varying sampling frequency rates, the SSMPI model achieved precise SOC estimation with certain robustness.”
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 [116]
The SSMPI model leverages empirical process data between input sequence steps, beyond just the relationship between multi-step measurable parameters and SOC.
“Using this approach, the sequence-to-sequence mapping with process information (SSMPI) model was developed, which leverages empirical process data between input sequence steps, beyond just the relationship between multi-step measurable parameters and SOC.”
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 [116]
Ma et al. enhanced the LSTM unit, introducing the PLSTM, which integrates state information and processes it together.
“Ma et al. enhanced the LSTM unit, introducing the PLSTM, which integrates state information and processes it together[116].”
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 [116]
The initial phase of research encompassed a wide range of training methods, including hyperparameter optimization through advanced intelligent optimization algorithms, rapid multi-battery and multi-scenario model generation based on transfer learning, and the "unsupervised pre-training" along with "supervised fine-tuning" approach.
“The initial phase of research encompassed a wide range of training methods, including hyperparameter optimization through advanced intelligent optimization algorithms, rapid multi-battery and multi-scenario model generation based on transfer learning, and the “unsupervised pre-training” along with “supervised fine-tuning”[116] approach.”
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 [116]
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- Authors
- Ma, L., Wang, Z., Yang, F., Cheng, Y., Lu, C., Tao, L., & Zhou, T.
- Affiliations
- National Key Laboratory of Science and Technology on Reliability and Environmental Engineering (CN)
- Wuhan Ship Development & Design Institute (CN)
- Corresponding author
- Tao, Laifa
- Venue
- Journal of Power Sources
- Publisher
- Elsevier BV
- Published
- 2020-10
- Volume
- 474
- Pages
- 228691
- ISSN
- 0378-7753 (ISSN-L)
- OpenAlex
- W3046282643
- Cited by
- 28
- References
- 35
- Citations by year
- 2026: 1 · 2025: 6 · 2024: 4 · 2023: 7 · 2022: 8 · 2021: 2
- FWCI
- 1.57
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Power Sources (publishedVersion)
- Funders
- National key Laboratory of Science and Technology on Reliability and Environmental Engineering
- Fundamental Research Funds for the Central Universities
- National Natural Science Foundation of China
- Topics
- Physical Sciences › Engineering › Automotive Engineering › Advanced Battery Technologies Research
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Advancements in Battery Materials
- Physical Sciences › Engineering › Control and Systems Engineering › Control Systems and Identification
- Keywords
- state of charge estimation, lithium-ion batteries, sequence-to-sequence models, long short-term memory, process information, variable sampling frequency, battery management systems
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