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HVS-4523. Smart Battery Management System Using Raspberry pi 4 with active cell balancing, SOC, SOH and ML
HVS-4523. Smart Battery Management System Using Raspberry pi 4 with active cell balancing, SOC, SOH and ML
HVS-4523. Smart Battery Management System Using Raspberry pi 4 with active cell balancing, SOC, SOH and ML
HVS-4523. Smart Battery Management System Using Raspberry pi 4 with active cell balancing, SOC, SOH and ML
HVS-4523. Smart Battery Management System Using Raspberry pi 4 with active cell balancing, SOC, SOH and ML

HVS-4523. Smart Battery Management System Using Raspberry pi 4 with active cell balancing, SOC, SOH and ML

₹19,000.00

A battery management system (BMS) is proposed which is used for electronic vehicle that manages a rechargeable battery (cell or battery pack), such as by protecting the battery from operating outside its safe operating area, monitoring its state of charging and state of health using Machine Learning algorithm to improve the performance of the designed project.

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The use of green energy is becoming increasingly more important in today’s world. Therefore, electric vehicles are currently the best choice for the environment in terms of public and personal transportation. Because of its high energy and current density, lithium-ion batteries are widely used in electric vehicles. Unfortunately, lithium-ion batteries can be dangerous if they are not operated within their Safety Operation Area (SOA). Therefore, a battery management system (BMS) must be used in every lithium-ion battery, especially for those used in electric vehicles. A battery management system (BMS) is proposed which is used for electronic vehicle that manages a rechargeable battery (cell or battery pack), such as by protecting the battery from operating outside its safe operating area, monitoring its state of charging and state of health using Machine Learning algorithm to improve the performance of the designed project.   Machine learning (ML) is the study of algorithms and mathematical models that computer systems use to progressively improve their performance on a specific task. Machine learning algorithms build a mathematical model of sample data, known as "training data", in order to make predictions or decisions without being explicitly programmed to perform the task.   The Raspberry Pi4 measure the SOC (State-of-Charge) and (SoH) State-of-Health from voltage sensors and based on that it will switch ON/OFF the relays for battery charging. Here relay works as a switch to ON/OFF the charging connection. And also, it will display the voltage, current, temperature, SOC and SOH values on LCD module. It will activate the buzzer if the sensor data exceed threshold value.  

The major building blocks of this project are:
  • Adapter power supply.
  • Raspberry pi4.
  • Temperature sensor.
  • Voltage sensor.
  • Current sensor.
  • Buzzer
  • Three battery packs
  • Three Relays.
  • Charging Circuit.
  • LCD display.
  • LED Indicators
  Software’s used in the project:
  1. Embedded Linux OS.
  2. Python language.
  3. Express SCH for Circuit design.
  4. Machine learning (ML).
 

video:

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