State of Health Estimation Based on the Long Short-Term Memory Network Using Incremental Capacity and Transfer Learning Article Swipe
Lei Yao
,
Jishu Wen
,
Shiming Xu
,
Jie Zheng
,
Junjian Hou
,
Zhanpeng Fang
,
Yanqiu Xiao
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3390/s22207835
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3390/s22207835
Battery state of health (SOH) estimating is essential for the safety and preservation of electric vehicles. The degradation mechanism of batteries under different aging conditions has attracted considerable attention in SOH prediction. In this article, the discharge voltage curve early in the cycle is considered to be strongly characteristic during cell aging. Therefore, the battery aging state can be quantitatively characterized by an incremental capacity analysis (ICA) of the voltage distribution. Due to the interference of vibration noise of the test platform, the discrete wavelet transform (DWT) methods are accustomed to soften the premier incremental capacity curves in different hierarchical decompositions. By analyzing the battery aging mechanism, the peak of the curve and its corresponding voltage are used in the characterization of capacity decay by grey relation analysis (GRA) and to optimize the input of the deep learning model, and finally, the double-layer long short-term memory network (LSTM) model is used to train the data. The results demonstrate that the proposed model can predict the SOH of a single battery cycle using only small batch data and the relative error is less than 2%. Further, by freezing the LSTM layer for transfer learning, it can be used for battery health estimation in different loading modes. The results of training and verification show that this method has high accuracy and reliability in SOH estimation.
Related Topics
Concepts
Battery (electricity)
State of health
Computer science
Noise (video)
Voltage
Reliability (semiconductor)
Artificial neural network
Control theory (sociology)
Interference (communication)
Battery capacity
Artificial intelligence
Algorithm
Engineering
Channel (broadcasting)
Power (physics)
Telecommunications
Electrical engineering
Physics
Quantum mechanics
Control (management)
Image (mathematics)
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/s22207835
- https://www.mdpi.com/1424-8220/22/20/7835/pdf?version=1666679761
- OA Status
- gold
- Cited By
- 26
- References
- 42
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4306399440
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4306399440Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/s22207835Digital Object Identifier
- Title
-
State of Health Estimation Based on the Long Short-Term Memory Network Using Incremental Capacity and Transfer LearningWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-10-15Full publication date if available
- Authors
-
Lei Yao, Jishu Wen, Shiming Xu, Jie Zheng, Junjian Hou, Zhanpeng Fang, Yanqiu XiaoList of authors in order
- Landing page
-
https://doi.org/10.3390/s22207835Publisher landing page
- PDF URL
-
https://www.mdpi.com/1424-8220/22/20/7835/pdf?version=1666679761Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.mdpi.com/1424-8220/22/20/7835/pdf?version=1666679761Direct OA link when available
- Concepts
-
Battery (electricity), State of health, Computer science, Noise (video), Voltage, Reliability (semiconductor), Artificial neural network, Control theory (sociology), Interference (communication), Battery capacity, Artificial intelligence, Algorithm, Engineering, Channel (broadcasting), Power (physics), Telecommunications, Electrical engineering, Physics, Quantum mechanics, Control (management), Image (mathematics)Top concepts (fields/topics) attached by OpenAlex
- Cited by
-
26Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 9, 2024: 10, 2023: 7Per-year citation counts (last 5 years)
- References (count)
-
42Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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