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

Project Title

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

Project Area of Specialization Computer ScienceProject 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 systemCore Industry Energy Other IndustriesCore Technology Artificial Intelligence(AI)Other TechnologiesSustainable Development Goals Responsible Consumption and ProductionRequired Resources

Item Name	Type	No. of Units	Per Unit Cost (in Rs)	Total (in Rs)
			Total in (Rs)	41630
Arduino Uno	Equipment	3	4000	12000
Constant Current Constant Voltage Power Supply	Equipment	2	1500	3000
Relay Module	Equipment	2	1500	3000
Current Sensor	Equipment	2	1200	2400
Digital Multi Meter	Equipment	1	1800	1800
OLED	Equipment	3	800	2400
Battery Cell 18650	Equipment	5	400	2000
Soldering Iron	Equipment	1	1000	1000
Soldering Wire	Equipment	2	500	1000
Soldering Paste	Equipment	2	350	700
Wire Roll	Equipment	1	500	500
Battery Terminal Header	Equipment	4	100	400
Buzzer	Equipment	4	80	320
DC Jack	Equipment	2	80	160
Electrical Toolkit	Equipment	1	4500	4500
Male To Male Connector	Equipment	4	100	400
Soldering Wipes	Equipment	6	25	150
Vero Board Large	Equipment	4	400	1600
Outer Acrylic Case	Equipment	1	2500	2500
Battery Holder	Equipment	4	200	800
Mosfets	Equipment	4	100	400
Resistors pack	Equipment	2	100	200
Load Resistor	Equipment	4	100	400