Real-time state-of-health estimation for electric vehicle batteries: a data-driven approach
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Various supervised ML techniques have been utilized for battery SOH estimation, including ANN, SVM, RVM, kNN, GPR, RFR.
“Various supervised ML techniques have been utilized for battery SOH estimation, including Artificial Neural Networks (ANN) [81], Support Vector Machine (SVM) [85], relevance vector machine (RVM) [86], k-Nearest Neighbours (kNN) [87], Gaussian Process Regression (GPR) [82] or random forest regression (RFR) [83].”
From Data-driven health estimation and lifetime prediction of lithium-ion batteries: A review (2019) · cites this paper as [81]
For FNN with 2 hidden layers and 80 hidden neurons, evaluated on 10 EV drive cycles at temperatures -10 °C, 60 °C, 45 °C, and 25 °C, SOC estimation achieved MAE ≤ 3.81%.
“| [78] FNN | • HLs 2 • HNs 80 | • Drive cycles 10 EV • Temperatures-10 ◦ C, 60 ◦ C, 45 ◦ C and 25 ◦ C. | • RMSE ≤ 1.06 % • MAE ≤ 3.81 %”
From Recent developments and challenges in state-of-charge estimation techniques for electric vehicle batteries: A review (2024) · cites this paper as [78]
For FNN with 2 hidden layers and 80 hidden neurons, evaluated on 10 EV drive cycles at temperatures -10 °C, 60 °C, 45 °C, and 25 °C, SOC estimation achieved RMSE ≤ 1.06%.
“| [78] FNN | • HLs 2 • HNs 80 | • Drive cycles 10 EV • Temperatures-10 ◦ C, 60 ◦ C, 45 ◦ C and 25 ◦ C. | • RMSE ≤ 1.06 % • MAE ≤ 3.81 %”
From Recent developments and challenges in state-of-charge estimation techniques for electric vehicle batteries: A review (2024) · cites this paper as [78]
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Data-driven health estimation and lifetime prediction of lithium-ion batteries: A review
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- Authors
- You, G.-W., Park, S., & Oh, D.
- Affiliations
- Samsung (South Korea) (KR)
- Corresponding author
- You, Gae-won
- ORCID
- Park, Sangdo
- Venue
- Applied Energy
- Publisher
- Elsevier BV
- Published
- 2016-08
- Volume
- 176
- Pages
- 92-103
- ISSN
- 0306-2619 (ISSN-L)
- OpenAlex
- W2351263822
- Cited by
- 314
- References
- 29
- Citations by year
- 2026: 16 · 2025: 46 · 2024: 40 · 2023: 47 · 2022: 34 · 2021: 44 · 2020: 31 · 2019: 25 · 2018: 19 · 2017: 9 · 2016: 3
- FWCI
- 16.25
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Applied Energy (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- 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 › Advancements in Battery Materials
- Keywords
- state of health estimation, electric vehicle batteries, battery management systems, real-time estimation, data-driven methods, driving patterns, battery degradation, capacity fade, historical distribution
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