Impact study on continuous overcharging of precycled lithium batteries and control algorithm development using machine learning approach

ABSTRACT

This research aims to understand the impact of overcharging on precycled Lithium-Nickel Manganese Cobalt (Li-NMC) batteries through continuous charge and discharge cycles. These Li-NMC cells underwent standard charging and discharging, maintaining their state of health (SOH) above 90% even after 200 cycles. Continuous overcharging of these precycled Li-NMC cells caused their SOH to degrade below 20% within the next 180 cycles. To investigate the underlying reasons for such significant differences in SOH degradation, scanning electron microscopy (SEM) and Energy Dispersive Spectroscopy (EDS) analyses were performed on both pristine and overcharged batteries. The structural changes in the batteries due to overcharging provide valuable insights into the degradation mechanisms. In addition, to predict the health degradation of precycled batteries by utilizing a gradient boost (GB) and support vector machine (SVM) regressor with hyperparameters optimized through Bayesian optimization, tuning of these hyperparameters increases accuracy, where mean squared error (MSE) and root mean squared error (RMSE) decrease more than 65% of the conventional algorithm. The GB and SVM regressors were trained using data acquired during the charge and discharge cycles of the batteries. The trained model demonstrated promising performance in predicting SOH degradation, providing valuable guidance for developing control algorithms for battery management strategies.

KEYWORDS:

• Li-NMC batteries
• machine learning
• overcharging
• support vector machine (SVM) algorithm
• gradient boosting (GB) algorithm

Abbreviations

Lithium -Nickel, Manganese, Cobalt oxide (Li-NMC), State of Health (SOH), Scanning electron microscopy (SEM), Battery Management System (BMS), Support vector machine (SVM), Gradient Boost (GB), RME (Root Mean Error), RMSE (Root Mean Square Error), Constant charge (CC), Constant voltage (CV).

Acknowledgments

The authors are thankful to the Department of Chemistry, Electrochemical Energy Storage and Conversion Laboratory (EESCL), SRM Institute of Science and Technology, Kattankulathur for providing the technical support for experimentation.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Notes on contributors

Naresh G

Naresh G is an Assistant Professor with a distinguished 5-year tenure in the Department of Automobile Engineering at SRM Institute of Science and Technology, Chennai. With a bachelor’s degree in mechanical engineering and a master’s in engineering design from Anna University, Mr. Naresh’s Ph.D. focused on Battery Fault Diagnosis using ML approaches reflects his expertise in this critical aspect of automotive technology. Publications and current initiatives attest to his expertise in this area, which he studies as part of his broader interest in Battery Thermal Management. A veteran in the field, he has made major contributions to the study of mechanical abuse on Li-ion batteries, effective engine cooling, the characterization and optimization of new materials, and the elimination of circularity mistakes in additive manufacturing.

Praveenkumar Thangavelu

Praveenkumar Thangavelu is an Assistant professor at SRM Institute of Science and Technology’s Department of Automobile Engineering. Holding his doctorate from Amrita University, Coimbatore, India, he has made significant strides through research papers on Electric mobility, ML, and Fault Diagnosis of Automotive Components. He uses cutting-edge methods, including pattern recognition, discrete wavelet features, and ML, to create novel defect diagnostic systems for vehicle gearboxes. Dr. Praveenkumar has made several contributions to high-quality publications and academic gatherings. With an immense 5+ years of experience. His academic background is complemented by his expertise as a Team Captain in a Formula racing car for a Society of Automobile Engineers contest and currently heading Electric vehicle technology and testing laboratory.