Multivariate Stacked Bidirectional Long Short Term Memory for Lithium-Ion Battery Health Management
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“48 For instance, one study used LSTM networks and Pearson correlation analysis to identify relevant features and model the battery degradation process, achieving high accuracy and low estimation error in SOH estimation by us-ing real-world battery data from electric buses.”
- Authors
- Ardeshiri, R. R., Liu, M., & Ma, C.
- Affiliations
- Shanghai Jiao Tong University (CN)
- Corresponding author
- Liu, Ming
- Venue
- Reliability Engineering & System Safety
- Publisher
- Elsevier BV
- Published
- 2022-08
- Volume
- 224
- Pages
- 108481
- ISSN
- 0951-8320 (ISSN-L)
- OpenAlex
- W4225678079
- Cited by
- 94
- References
- 72
- Citations by year
- 2026: 10 · 2025: 24 · 2024: 24 · 2023: 28 · 2022: 8
- FWCI
- 5.72
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Reliability Engineering & System Safety (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Funders
- Natural Science Foundation of Shanghai (21ZR1431100)
- Topics
- Physical Sciences › Engineering › Automotive Engineering › Advanced Battery Technologies Research
- Physical Sciences › Engineering › Safety, Risk, Reliability and Quality › Reliability and Maintenance Optimization
- Physical Sciences › Engineering › Control and Systems Engineering › Machine Fault Diagnosis Techniques
- Keywords
- remaining useful life prediction, stacked bidirectional LSTM, battery capacity degradation, prognostics and health management, XGBoost feature selection, Bayesian optimization, hyperparameter optimization, multivariate time series
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