RESEARCH PAPER
Improving Reliability in Electric Vehicle Battery Management Systems through Deep Learning-Based Cell Balancing Mechanisms
1
Electronics and Communication Engineering, Erode Sengunthar Engineering College, Erode, Tamilnadu, India
2
Electrical and Electronics Engineering, SNS College of Engineering, Coimbatore, Tamilnadu, India
3
Electronics and Communication Engineering, Sri Krishna College of Technology, Coimbatore, Tamilnadu, India
4
Electronics and Instrumentation Engineering, Erode Sengunthar Engineering College, Erode, Tamilnadu, India
Submission date: 2024-11-03
Final revision date: 2024-11-22
Acceptance date: 2025-02-02
Online publication date: 2025-02-09
Publication date: 2025-02-09
Corresponding author
Poorani Shivkumar
Electrical and Electronic Engineering, Erode Sengunthar Engineering College, Perundurai Road, Post, Thuduppathi., 638057, Erode, India
Electrical and Electronic Engineering, Erode Sengunthar Engineering College, Perundurai Road, Post, Thuduppathi., 638057, Erode, India
Eksploatacja i Niezawodność – Maintenance and Reliability 2025;27(3):200714
HIGHLIGHTS
- Improving reliability and efficiency in electric vehicle Battery Management Systems (BMS).
- Applying Deep Learning for enhancement of Cell Balancing (CB) mechanisms.
- MAE, MSE, RMSE, demonstrates a better performance of hybrid model CNN-BiLSTM.
- Charging period, temperature, thermal management and battery chemistry are considered.
KEYWORDS
Deep LearningConvolution Neural NetworkMean Square ErrorBattery Management SystemRoot Mean Square Error
TOPICS
ABSTRACT
Cell Balancing (CB) is a crucial aspect of the Battery Management System (BMS), which is used to increase the batteries operate time as well as operational life. The most widely used method is Passive Cell Balancing (PCB) since it is inexpensive and simple to use. This work proposes an algorithm using Deep Learning (DL) that selects the balancing resistor effectively with respect to increasing temperature, C-rate, level of cell imbalance, and balancing duration. Convolution Neural Network (CNN), hybrid method of Convolution Neural Network (CNN)-Long short-term memory (LSTM) (CNN-LSTM) and Convolution Bidirectional LSTM (CNN-BiLSTM) are used to assess the performance of the suggested system. In order to optimize the balancing parameters, the balancing system's error analysis is carried out, and performance indices like Mean Square Error (MSE), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE) are used to compare the suggested algorithms. The hybrid CNN-BiLSTM obtained MAE of 0.0453, MSE of 0.0062 and RMSE value of 0.0671 which is better than other models.
ACKNOWLEDGEMENTS
The author would like to express his heartfelt gratitude to the supervisor for his guidance and unwavering support during this research for his guidance and support.
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| eISSN: | 2956-3860 |
| ISSN: | 1507-2711 |
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