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Report on the Cycle Life Prediction of Li-ion Rechargeable Batteries

Report on the Cycle Life Prediction of Li-ion Rechargeable Batteries

Introducing a cycle life prediction using a tool that enables the visualization of workflows!

This document reports on the prediction of the cycle life of Li-ion batteries using machine learning with Pipeline Pilot. We will introduce the sections: "Background," "Flow," "Results," and "Discussion." Pipeline Pilot is a tool that enables large-scale data processing and analysis, automation of tasks, and visualization of workflows. 【Contents】 ■ Background: (1) Li-ion batteries and their cycle life prediction, (2) What is machine learning ■ Flow: (1) Machine learning in Pipeline Pilot ■ Results: (1) A part of the Pipeline Pilot operation screen, (2) Output screen (3) Comparison of output screens ■ Discussion *For more details, please refer to the PDF document or feel free to contact us.

  • Contract Analysis
  • simulator
  • Other analyses

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[Case Study] Machine Learning Example at a Coating Material Manufacturer

[Case Study] Machine Learning Example at a Coating Material Manufacturer

Introducing case studies of machine learning using "Pipeline Pilot"【Case Studies】

This time, the goal was to grasp the trends of optimal experimental conditions by predicting characteristic values. <Background> From traditional to modern times, developing materials with better characteristic values is an important task across all fields and remains a significant challenge. Additionally, since the characteristic values of materials can be influenced by various factors, there is a demand for increased efficiency in material development. For instance, factors such as the materials used during development, reaction time, temperature, and hysteresis may also affect the results. While it is ideal to introduce experimental design methods for all these conditions and discover optimal conditions through actual experiments and measurements, this is not realistic from a cost and time perspective. Therefore, we conducted machine learning using a small dataset that recorded several experimental conditions (for example, explanatory variables X1, X2, X3, X4, ...) and their respective characteristic values (for example, objective variables Y1, Y2). In this case, we were able to easily perform machine learning to create a model and improve prediction accuracy. *For more details, please contact us.*

  • simulator
  • Document and Data Management
  • Business Intelligence and Data Analysis

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