Estimation of End of Life of Lithium-Ion Battery Based on Artificial Neural Network and Machine Learning Techniques Tata Motors Limited

Format:

Book

Conference/Event

Author/Creator:

Lahari, Kothala, author.

Contributor:

Baidya, Kapil

Sharma, Minakshi

Conference Name:

Symposium on International Automotive Technology (2021-09-29 : Pune, India)

Language:

English

Physical Description:

1 online resource cm

Place of Publication:

Warrendale, PA SAE International 2021

Summary:

Various vehicle manufacturers are launching electric vehicles, which are more sustainable and environmentally friendly. The major component in electric vehicles is the battery, and its performance plays a vital role. Usually, the end of life of a battery in the automobile sector is when the battery capacity reaches 80% of its maximum rated capacity. The capacity of a lithium-ion cell declines with the number of cycles. So, a semi-empirical model is developed for estimating the maximum stored capacity at the end of each cycle. The parameters considered in the model explain the changes in battery internal structure, like capacity losses at different conditions. The capacity estimated using the semi-empirical model is further taken as the inputs for estimating capacity using the Artificial Neural Network (ANN) and Machine Learning (ML) techniques id est, Linear Regression (LR), Gaussian Process Regression (GPR), Support Vector Machine methods (SVM). Artificial Neural Network model has been trained in the neural fitting application in MATLAB and the trained neural network model is exported to Simulink and analysed. From the Simulink model, the capacity is estimated until the end of life of the battery. Similarly, regression learning application in MATLAB is studied to estimate the capacity using different Machine Learning techniques. The results are compared and the maximum error percentage of 0.5-0.6% is observed using the SVM method due to the vector-based analysis and the minimum error of 0.003-0.004% is observed using LR method for estimating capacity. These techniques can be implemented in the battery management system of the electric vehicle for estimating the present capacity of the vehicle

Notes:

Vendor supplied data

Publisher Number:

2021-26-0218

Access Restriction:

Restricted for use by site license