A Novel Real-Time Battery State Estimation Using Data-Driven Prognostics and Health Management
This paper presents a novel data-driven framework for real-time State of Charge (SOC) estimation in lithium-ion battery systems using a data-driven Prognostics and Health Management (PHM) approach. The method leverages an optimized bidirectional Long Short-Term Memory (Bi-LSTM) network, trained with...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-07-01
|
| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/15/15/8538 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849407545673777152 |
|---|---|
| author | Juliano Pimentel Alistair A. McEwan Hong Qing Yu |
| author_facet | Juliano Pimentel Alistair A. McEwan Hong Qing Yu |
| author_sort | Juliano Pimentel |
| collection | DOAJ |
| description | This paper presents a novel data-driven framework for real-time State of Charge (SOC) estimation in lithium-ion battery systems using a data-driven Prognostics and Health Management (PHM) approach. The method leverages an optimized bidirectional Long Short-Term Memory (Bi-LSTM) network, trained with enhanced datasets filtered via exponentially weighted moving averages (EWMAs) and refined through SHAP-based feature attribution. Compared against a Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF) across ten diverse drive cycles, the proposed model consistently achieved superior performance, with mean absolute errors (MAEs) as low as 0.40%, outperforming EKF (0.66%) and UKF (1.36%). The Bi-LSTM model also demonstrated higher R<sup>2</sup> values (up to 0.9999) and narrower 95% confidence intervals, confirming its precision and robustness. Real-time implementation on embedded platforms yielded inference times of 1.3–2.2 s, validating its deployability for edge applications. The framework’s model-free nature makes it adaptable to other nonlinear, time-dependent systems beyond battery SOC estimation. |
| format | Article |
| id | doaj-art-d73e985b54804786aaae048cc7221ea8 |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-d73e985b54804786aaae048cc7221ea82025-08-20T03:36:02ZengMDPI AGApplied Sciences2076-34172025-07-011515853810.3390/app15158538A Novel Real-Time Battery State Estimation Using Data-Driven Prognostics and Health ManagementJuliano Pimentel0Alistair A. McEwan1Hong Qing Yu2College of Science and Engineering, University of Derby, Markeaton St., Derby DE22 3AW, UKCollege of Science and Engineering, University of Derby, Markeaton St., Derby DE22 3AW, UKCollege of Science and Engineering, University of Derby, Markeaton St., Derby DE22 3AW, UKThis paper presents a novel data-driven framework for real-time State of Charge (SOC) estimation in lithium-ion battery systems using a data-driven Prognostics and Health Management (PHM) approach. The method leverages an optimized bidirectional Long Short-Term Memory (Bi-LSTM) network, trained with enhanced datasets filtered via exponentially weighted moving averages (EWMAs) and refined through SHAP-based feature attribution. Compared against a Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF) across ten diverse drive cycles, the proposed model consistently achieved superior performance, with mean absolute errors (MAEs) as low as 0.40%, outperforming EKF (0.66%) and UKF (1.36%). The Bi-LSTM model also demonstrated higher R<sup>2</sup> values (up to 0.9999) and narrower 95% confidence intervals, confirming its precision and robustness. Real-time implementation on embedded platforms yielded inference times of 1.3–2.2 s, validating its deployability for edge applications. The framework’s model-free nature makes it adaptable to other nonlinear, time-dependent systems beyond battery SOC estimation.https://www.mdpi.com/2076-3417/15/15/8538PHMSOC estimationLi-ion batterydata-drivenmachine learningbidirectional LSTM |
| spellingShingle | Juliano Pimentel Alistair A. McEwan Hong Qing Yu A Novel Real-Time Battery State Estimation Using Data-Driven Prognostics and Health Management Applied Sciences PHM SOC estimation Li-ion battery data-driven machine learning bidirectional LSTM |
| title | A Novel Real-Time Battery State Estimation Using Data-Driven Prognostics and Health Management |
| title_full | A Novel Real-Time Battery State Estimation Using Data-Driven Prognostics and Health Management |
| title_fullStr | A Novel Real-Time Battery State Estimation Using Data-Driven Prognostics and Health Management |
| title_full_unstemmed | A Novel Real-Time Battery State Estimation Using Data-Driven Prognostics and Health Management |
| title_short | A Novel Real-Time Battery State Estimation Using Data-Driven Prognostics and Health Management |
| title_sort | novel real time battery state estimation using data driven prognostics and health management |
| topic | PHM SOC estimation Li-ion battery data-driven machine learning bidirectional LSTM |
| url | https://www.mdpi.com/2076-3417/15/15/8538 |
| work_keys_str_mv | AT julianopimentel anovelrealtimebatterystateestimationusingdatadrivenprognosticsandhealthmanagement AT alistairamcewan anovelrealtimebatterystateestimationusingdatadrivenprognosticsandhealthmanagement AT hongqingyu anovelrealtimebatterystateestimationusingdatadrivenprognosticsandhealthmanagement AT julianopimentel novelrealtimebatterystateestimationusingdatadrivenprognosticsandhealthmanagement AT alistairamcewan novelrealtimebatterystateestimationusingdatadrivenprognosticsandhealthmanagement AT hongqingyu novelrealtimebatterystateestimationusingdatadrivenprognosticsandhealthmanagement |