Li-ion battery aging test & data analytics

• Main Author: Sim, Xiao Hui
• Other Authors: Xu Yan
• Format: Final Year Project
• Language: English
• Published: Nanyang Technological University 2022
• Subjects: Engineering::Electrical and electronic engineering::Electronic circuits
• Online Access: https://hdl.handle.net/10356/157723
• Institution: Nanyang Technological University

With the rising popularity of Li-ion batteries, the ability to attain accurate estimation of the battery core and surface temperature is of crucial importance to ensure the operational performance, safety, and reliability usage of Li-ion batteries. Impedance-based and data-driven based temperature estimation gaining substantial popularity in recent years because of their sensorless estimation characteristic and lower modeling complexity, this study proposes to study a hybrid model of impedance-based temperature estimation method with Stochastic Configuration Network (SCN) approach. To further explore on the accuracy performance of SCN, the model will be compared against Artificial Neural Network (ANN) and Support Vector Regression (SVR). The performance of the models will then be assessed in terms of root mean square error (RMSE) and mean absolute error (MAE).