Indirect State-of-Health Estimation for Lithium-Ion Batteries under Randomized Use Article Swipe
Jinsong Yu
,
Baohua Mo
,
Diyin Tang
,
Jie Yang
,
Jiuqing Wan
,
Jingjing Liu
·
YOU?
·
· 2017
· Open Access
·
· DOI: https://doi.org/10.3390/en10122012
YOU?
·
· 2017
· Open Access
·
· DOI: https://doi.org/10.3390/en10122012
Lithium-ion batteries are widely used in many systems. Because they provide a power source to the whole system, their state-of-health (SOH) is very important for a system’s proper operation. A direct way to estimate the SOH is through the measurement of the battery’s capacity; however, this measurement during the battery’s operation is not that easy in practice. Moreover, the battery is always running under randomized loading conditions, which makes the SOH estimation even more difficult. Therefore, this paper proposes an indirect SOH estimation method that relies on indirect health indicators (HIs) that can be measured easily during the battery’s operation. These indicators are extracted from the battery’s voltage and current and the number of cycles the battery has been through, which are far easier to measure than the battery’s capacity. An empirical model based on an elastic net is developed to build the quantitative relationship between the SOH and these indirect HIs, considering the possible multi-collinearity between these HIs. To further improve the accuracy of SOH estimation, we introduce a particle filter to automatically update the model when capacity data are obtained occasionally. We use a real dataset to demonstrate our proposed method, showing quite a good performance of the SOH estimation. The results of the SOH estimation in the experiment are quite satisfactory, which indicates that the method is effective and accurate enough to be used in real practice.
Related Topics
Concepts
State of health
Battery (electricity)
Computer science
Collinearity
Lithium-ion battery
Reliability engineering
Particle filter
Voltage
Control theory (sociology)
Power (physics)
Engineering
Control (management)
Kalman filter
Artificial intelligence
Electrical engineering
Statistics
Quantum mechanics
Mathematics
Physics
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/en10122012
- https://www.mdpi.com/1996-1073/10/12/2012/pdf?version=1512130454
- OA Status
- gold
- Cited By
- 29
- References
- 36
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2773517512
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W2773517512Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/en10122012Digital Object Identifier
- Title
-
Indirect State-of-Health Estimation for Lithium-Ion Batteries under Randomized UseWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
-
2017Year of publication
- Publication date
-
2017-12-01Full publication date if available
- Authors
-
Jinsong Yu, Baohua Mo, Diyin Tang, Jie Yang, Jiuqing Wan, Jingjing LiuList of authors in order
- Landing page
-
https://doi.org/10.3390/en10122012Publisher landing page
- PDF URL
-
https://www.mdpi.com/1996-1073/10/12/2012/pdf?version=1512130454Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
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https://www.mdpi.com/1996-1073/10/12/2012/pdf?version=1512130454Direct OA link when available
- Concepts
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State of health, Battery (electricity), Computer science, Collinearity, Lithium-ion battery, Reliability engineering, Particle filter, Voltage, Control theory (sociology), Power (physics), Engineering, Control (management), Kalman filter, Artificial intelligence, Electrical engineering, Statistics, Quantum mechanics, Mathematics, PhysicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
29Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 2, 2024: 2, 2023: 6, 2022: 3, 2021: 6Per-year citation counts (last 5 years)
- References (count)
-
36Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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