Advanced Machine Learning Approach for Lithium-Ion Battery State Estimation in Electric Vehicles
Abstract
To fulfill reliable battery management in electric vehicles (EVs), an advanced State-of-Charge (SOC) estimator is developed via machine learning methodology. A novel genetic algorithm-based fuzzy C-means (FCM) clustering technique is first used to partition the training data sampled in the driving cycle-based test of a lithium-ion battery. The clustering result is applied to learn the topology and antecedent parameters of the model. Recursive least-squares algorithm is then employed to extract its consequent parameters. To ensure good accuracy and resilience, the backpropagation learning algorithm is finally adopted to simultaneously optimize both the antecedent and consequent parts. Experimental results verify that the proposed estimator exhibits sufficient accuracy and outperforms those built by conventional fuzzy modeling methods.
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Work in battery ML has focused on predicting battery state, degradation, and remaining lifetime using supervised learning, fuzzy logic, and clustering.
“Work in this area has focused on predicting battery state, degradation, and remaining lifetime using supervised learning techniques, fuzzy logic, and clustering [42,104,218,358,414,725,735,819,854].”
From Tackling climate change with machine learning (2023) · cites this paper as [358]
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- Authors
- Hu, X., Li, S. E., & Yang, Y.
- Affiliations
- Chongqing University (CN)
- Tsinghua University (CN)
- Venue
- IEEE Transactions on Transportation Electrification
- Published
- 2016-06
- Volume
- 2
- Issue
- 2
- Pages
- 140-149
- ISSN
- 2332-7782 (ISSN-L)
- 2372-2088
- OpenAlex
- W2344174278
- Cited by
- 375
- References
- 37
- Citations by year
- 2026: 19 · 2025: 30 · 2024: 47 · 2023: 52 · 2022: 42 · 2021: 41 · 2020: 43 · 2019: 39 · 2018: 35 · 2017: 21 · 2016: 6
- FWCI
- 21.37
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Transactions on Transportation Electrification (publishedVersion)
- Funders
- National Natural Science Foundation of China (51575064)
- 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 › Electric Vehicles and Infrastructure
- Keywords
- lithium-ion batteries, state of charge estimation, electric vehicles, battery management systems, genetic algorithm, fuzzy c-means clustering, recursive least squares, backpropagation, fuzzy modeling, driving cycles
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