Voltage Anomaly Detection in Real-World Cloud-Based BMS Data via Context-Enhanced Multiscale LOF and Battery-Model Residual Fusion
- 1Department of Control Science and Engineering, Harbin Institute of Technology, Harbin 150001, China
- 2State Key Laboratory of Smart Farm Technologies and Systems, Harbin 150001, China
Abstract
The actual voltage data collected by the cloud-based battery management system (BMS) are susceptible to disturbances caused by sensor failures, electromagnetic interference, and communication anomalies, which may lead to four types of anomalies: random point anomalies, continuous segment anomalies, fixed-bias anomalies, and data-freezing anomalies. These anomalies reduce the reliability of battery state estimation and delay early fault warning. In order to improve the reliability of voltage anomaly detection, this paper proposes a method that combines a context-enhanced two-stage ensemble local outlier factor (CE-TELOF) with voltage prediction residuals generated by a composite battery model. CE-TELOF characterizes local abrupt changes and persistent anomaly patterns through multidimensional slope statistics, Gaussian context fusion, multiscale local outlier factor analysis, and overlapping-window coverage consistency. Meanwhile, the composite battery model provides physically interpretable evidence of anomalies by evaluating the difference between the measured voltage and the voltage predicted by the battery current–voltage dynamic model. Its parameters are identified through a two-stage optimization strategy combining a genetic algorithm and nonlinear least squares optimization. The two branches are robustly standardized and adaptively weighted to obtain the final anomaly score. The identified composite battery model achieves R2 values exceeding 0.998 across the three datasets, with consistently lower estimation errors than the conventional first-order RC model. Meanwhile, sensitivity analysis of the neighborhood-scale set shows that multiscale configurations provide more stable CE-TELOF performance than single-scale configurations. Across three real-world datasets and four anomaly types, the proposed detection method achieves F1-scores ranging from 0.8140 to 0.9836 and effectively reduces false positives associated with normal operating-condition transitions. In addition, validation using Vehicle No. 9 from a publicly available real-world on-road EV dataset and the NASA B0005 battery dataset further demonstrates the applicability of the proposed method across different data sources and battery operating conditions.
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BibTeX
@article{yan2026voltage,
title = {{Voltage Anomaly Detection in Real-World Cloud-Based BMS Data via Context-Enhanced Multiscale LOF and Battery-Model Residual Fusion}},
author = {Huiyi Yan and Linhui Zhao and Kai Jia},
journal = {Batteries},
year = {2026},
month = oct,
doi = {10.3390/batteries12100393},
url = {https://www.mdpi.com/2313-0105/12/10/393},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Voltage Anomaly Detection in Real-World Cloud-Based BMS Data via Context-Enhanced Multiscale LOF and Battery-Model Residual Fusion
AU - Huiyi Yan
AU - Linhui Zhao
AU - Kai Jia
PY - 2026
DA - 2026/10/03
JO - Batteries
DO - 10.3390/batteries12100393
UR - https://www.mdpi.com/2313-0105/12/10/393
AB - The actual voltage data collected by the cloud-based battery management system (BMS) are susceptible to disturbances caused by sensor failures, electromagnetic interference, and communication anomalies, which may lead to four types of anomalies: random point anomalies, continuous segment anomalies, fixed-bias anomalies, and data-freezing anomalies. These anomalies reduce the reliability of battery state estimation and delay early fault warning. In order to improve the reliability of voltage anomaly detection, this paper proposes a method that combines a context-enhanced two-stage ensemble local outlier factor (CE-TELOF) with voltage prediction residuals generated by a composite battery model. CE-TELOF characterizes local abrupt changes and persistent anomaly patterns through multidimensional slope statistics, Gaussian context fusion, multiscale local outlier factor analysis, and overlapping-window coverage consistency. Meanwhile, the composite battery model provides physically interpretable evidence of anomalies by evaluating the difference between the measured voltage and the voltage predicted by the battery current–voltage dynamic model. Its parameters are identified through a two-stage optimization strategy combining a genetic algorithm and nonlinear least squares optimization. The two branches are robustly standardized and adaptively weighted to obtain the final anomaly score. The identified composite battery model achieves R2 values exceeding 0.998 across the three datasets, with consistently lower estimation errors than the conventional first-order RC model. Meanwhile, sensitivity analysis of the neighborhood-scale set shows that multiscale configurations provide more stable CE-TELOF performance than single-scale configurations. Across three real-world datasets and four anomaly types, the proposed detection method achieves F1-scores ranging from 0.8140 to 0.9836 and effectively reduces false positives associated with normal operating-condition transitions. In addition, validation using Vehicle No. 9 from a publicly available real-world on-road EV dataset and the NASA B0005 battery dataset further demonstrates the applicability of the proposed method across different data sources and battery operating conditions.
SN - 2313-0105
ER -