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Related Experiment Video

Updated: Jan 16, 2026

Author Spotlight: Optimization of Airflow Velocities in Battery Cooling Systems for Enhanced Thermal Performance and Reduced Energy Consumption

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Published on: November 3, 2023

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

Shreeram V Kulkarni¹, G Arjun¹, Sandeep Gupta²

¹Department of Electrical and Electronics Engineering, Nitte Meenakshi Institute of Technology (NMIT), Nitte (Deemed to Be University), Bengaluru, India.

Scientific Reports

|September 25, 2025

Summary

This summary is machine-generated.

This study introduces a hybrid framework for electric vehicle (EV) battery monitoring, enhancing safety and durability. It combines machine learning and model-based methods for accurate State of Health (SoH) and State of Charge (SoC) evaluations.

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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Published on: April 17, 2018

Area of Science:

Electric Vehicle Battery Technology
Data-Driven Diagnostics
Lithium-ion Battery Management Systems

Background:

Accurate battery State of Health (SoH) and State of Charge (SoC) are crucial for electric vehicle (EV) safety, performance, and longevity.
Conventional monitoring techniques like Coulomb Counting can be susceptible to sensor noise and model inaccuracies.

Purpose of the Study:

To develop and validate a novel hybrid diagnostic framework for improved EV battery monitoring.
To enhance the accuracy and reliability of battery State of Health (SoH) and State of Charge (SoC) estimations.

Main Methods:

Implemented a hybrid framework combining statistical analysis, machine learning, and model-based estimation.
Utilized Extended Kalman Filter (EKF) to mitigate sensor noise and model errors.
Employed Random Forest regression for SoH assessment, k-means clustering and Dynamic Time Warping (DTW) for cell behavior analysis, and Principal Component Analysis (PCA) for voltage imbalance identification.

Main Results:

The EKF demonstrated superior performance over conventional Coulomb Counting techniques.
The Random Forest model achieved higher accuracy for SoH assessment compared to linear regression.
Identified outlier cells and analyzed voltage imbalance trends effectively.

Conclusions:

The proposed hybrid framework offers a practical and expandable solution for reliable and interpretable EV battery diagnostics.
This approach significantly improves battery monitoring capabilities, contributing to enhanced EV safety and life-cycle management.