ASEAN-Webinar_Battery Life Prediction_1Aug2024 (2)

Overview

Discover the cutting-edge methodologies for predicting battery life in our comprehensive webinar. Designed for battery engineers, data scientists, and researchers, this session will delve into the integration of predictive maintenance and deep learning techniques using MATLAB and Simscape. We will present a holistic view of battery life prediction, encompassing initial operation data analysis, advanced modeling, and state-of-health estimation. Through practical demonstrations, attendees will learn how to apply these techniques to improve battery management and extend battery lifespan.

Highlights:

  • Predicting Battery Life Using Initial Operation Data and Deep Learning: Learn how to use initial operation data to forecast battery cycle life accurately and explore how deep learning models enhance prediction accuracy. This includes practical implementations, training, and validation processes using MATLAB.
  • Nonlinear State Estimation of Degrading Batteries: Understand advanced nonlinear state estimation techniques and their integration with Simscape models to monitor battery degradation accurately. 
  • State-of-Health Estimation for Second-Life Applications: Gain insights into the reuse of batteries in second-life applications and learn methods for estimating the state-of-health (SoH) of used batteries. 

 

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Our Speaker

Supamith-3

Supamith Sutharojana
Application Engineer Team Lead

He specialized in the field of Model-based design (Simulink with Verification & Validation workflow), Data Analytics, and various areas, especially in the Automotive industry, e.g., V&V workflow, EV Autonomous Driving, and Predictive Maintenance. Before joining Ascendas Systems, he was an Engine Software Designer at a Japanese Automotive manufacturer company.

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