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. 2025 Sep 25;15(1):32848.
doi: 10.1038/s41598-025-18042-6.

Advanced battery diagnostics for electric vehicles using CAN based BMS data with EKF and data driven predictive models

Shreeram V Kulkarni  1 , G Arjun  1 , Sandeep Gupta  2 , Raj Sinha  3 , Anand Shukla  4
Affiliations

Advanced battery diagnostics for electric vehicles using CAN based BMS data with EKF and data driven predictive models

Shreeram V Kulkarni et al. Sci Rep. .

Abstract

Accurate evaluations of battery State of Health (SoH) and State of Charge (SoC) are critical for Electric Vehicles (EVs) safety, performance, and durability. This study proposes a novel hybrid diagnostic framework that combines statistical analysis, machine learning, and model-based estimation to improve battery monitoring capabilities. The cell-level voltage, temperature, current, and SoC were measured on 15S2P LiFePO4 battery pack utilizing a CAN interface. By implemting the Extended Kalman Filter (EKF), the impact of sensor noise and model errors was reduced in-comarision with the convetional Coulomb Counting techniques. The Random Forest regression model was used to train for SoH assessment with duty cycles, cycle duration, temperature gradient, and voltage spread, the model performed better than linear regression techniques with projected accuracy. The k-means clustering is used to group the cells with comparable behaviors, and the cells with outlier behaviors were identified using dynamic time warping (DTW) based on temporal deviation. The principal component analysis (PCA) is used for voltage imbalance trends identification and analysis. With the data-driven algorithms, the battery health assessments can be made reliable and interpretable by combining physical estimating techniques. This is a practical and expandable solution for improved EV battery diagnostics and life-cycle management.

Keywords: Charge monitoring; Dynamic time warping; Electric vehicle; Kalman filter; Lifespan assessment; ML Random Forest; Principal component analysis; Real-time BMS.

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Conflict of interest statement

Declarations. Competing interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Figures

Fig. 1
Fig. 1
Experimental hardware setup.
Fig. 2
Fig. 2
EKF vs BMS SoC Estimation.
Fig. 3
Fig. 3
SoC error comparison between Coulomb Counting and EKF, showing improved accuracy with EKF.
Fig. 4
Fig. 4
Actual vs predicted SoH using Random Forest across different cycle durations.
Fig. 5
Fig. 5
Feature Importance in SoH Prediction.
Fig. 6
Fig. 6
Principal Component Analysis (PCA) of Cell Voltages.
Fig. 7
Fig. 7
Clustered voltage profiles using k-means, identifying similar cell behavior groups.
Fig. 8
Fig. 8
DTW distance of each cell vs reference, indicating voltage profile deviations.
Fig. 9
Fig. 9
Time series of voltage (ΔV) and temperature (ΔT) spread, showing imbalance trend.
Fig. 10
Fig. 10
SoH prediction comparison: Random Forest vs Linear Regression model.
See this image and copyright information in PMC

References

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