An estimation method for the State of Health (SOH) of lithium-ion battery using machine learning

The main complex problem of Lithium Ion battery is the degradation process based on electrochemical properties and uncertain external environment. Therefore, a battery management system (BMS) is necessary to ensure safe, efficient, reliable and durable battery operations. The main tasks of a battery

Project Title

An estimation method for the State of Health (SOH) of lithium-ion battery using machine learning

Project Area of Specialization

Computer Science

Project Summary

The main complex problem of Lithium Ion battery is the degradation process based on electrochemical properties and uncertain external environment. Therefore, a battery management system (BMS) is necessary to ensure safe, efficient, reliable and durable battery operations. The main tasks of a battery management system are state estimation, safety control. The State of Health (SOH) of a battery is the one main parameter of the battery and would directly affect the battery performance, reliability, and safety. We will predict a reliable State of Health (SOH) estimation using machine learning Algorithms. We will use the experiment dataset for verification of algorithm estimation and We will build a Hardware for collecting our own dataset and use it for estimation.

Project Objectives

• Ensure battery performance.
• Ensure battery safety
• Ensure battery reliability

Project Implementation Method

We are developing a tool for predicting State of Heatlh (SOH) of of Lithum-Ion Batteries which is a Desktop based application using python.The tool works with offline data-set.The predicton is done by using  Machine Learning Algorthm.We will build a Hardware using Arduino for collecting our own dataset and use it for estimation and our hardware will give physical warning of the battery that is end of its life.

Benefits of the Project

Lithium-ion batteries are utilized as the leading energy storage source for many fields such as electric vehicles, micro-grids, and other consumer electronics, Due to their excellent properties in self-discharge rate, lifespan, energy density, and power capability. Therefore, knowledge of the present battery health status (State of Health) is compulsory to avoid battery running risk and to ensure the battery reliability and safety.

Technical Details of Final Deliverable

Hardware:

• Collect Data set.
• Give physical warning of the battery that is end of its life.give physical warnings(Battery Low) of the battery that is end of its life.
• Safe Charging and Discharging.

Software:

• State of Health Prediction using Machine Learning Algorithm.
• Hardware live Feed Reading and collecting data set .

Documentation:

• Pupose of Project.
• Scope of Project.
• Functional and Non-Functional Requirments
• Implementation method.
• Hardware Schmetics.
• Future Work.

Final Deliverable of the Project

HW/SW integrated system

Core Industry

Energy

Other Industries

Core Technology

Artificial Intelligence(AI)

Other Technologies

Sustainable Development Goals

Responsible Consumption and Production

Required Resources

If you need this project, please contact me on contact@adikhanofficial.com