Abstract

SWARNKAR, Radhika; HARIKRISHNAN, R; THAKUR, Prabhat  and  SINGH, Ghanshyam. Electric Vehicle Lithium-ion Battery Ageing Analysis Under Dynamic Condition: A Machine Learning Approach. SAIEE ARJ [online]. 2023, vol.114, n.1, pp.4-13. ISSN 1991-1696.

Currently, the smart cities, smart vehicles, and smart gadgets will improve the way of living standard. Cloud connectivity of IoT sensed devices will capture real-time data in the cloud which helps to improve the system performance and quick response to queries. Electric Vehicle battery health diagnosis plays an important role in the proper functioning of the battery management system, guarantees safety, and warranty claim. Society 5.0 develops with the advancement in the road, infrastructure, better connectivity, transportation, and options available to purchase. Battery health cannot be measured directly. There are internal and external factors that affect battery health such as State of Charge, model parameters, charging/discharging method, temperature, Depth of Discharge, C-rate, battery chemistry, form factor, thermal management, and load change effect. Battery degrades due to both calendar ageing and cyclic ageing. Artificial Intelligence plays a significant role in Battery management system due to the nonlinear behavior of lithium-ion battery. Prediction of battery health accurately and in due time will reduce the risk of recklessness. Timely maintenance will reduce the risk of fatal accidents. This paper presents different batteries analysis under different discharge voltage and capacity conditions. Different machine learning algorithms such as Neural Network, Modified Support Vector Machine (M-SVM) and Linear Regression are used to predict state of health. The proposed M-SVM performs well with less error for all four-battery discharge data.

Keywords : Battery ageing; Support Vector Machine; Linear Regression; Neural Network; Capacity fading.

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