Implement a Kalman filter to predict battery SOC
Budget: $30 – $250 USD
Environment: Raspberry Pi4
Data : Available live on InfluxDB
The aim of this project is to implement a Kalman filter to better predict the State Of Charge of a battery bank. The measurements are done live using sensors which store the data in InfluxDB on the Rasberry.
The system at present is measuring current, voltage and temperature of the battery.
Ah in/out of the battery is calculated and stored together with the other above-mentioned measures.
The outputs needed from the KF are:
• Remaining charge available (%)
• Time to reach the full charge when in charging mode (minutes)
• Time to reach the full discharge when in discharging mode(minutes)
I will be able to support with measurements in CSV format, battery parameters and characteristics. Connecting from remote, I will give access for the final implementation on the device. A bit of training will be then necessary to explain me how it is implemented and how to set the variables to optimize the KF.
Please do not bid for this project if you have no experience with KF.
Data : Available live on InfluxDB
The aim of this project is to implement a Kalman filter to better predict the State Of Charge of a battery bank. The measurements are done live using sensors which store the data in InfluxDB on the Rasberry.
The system at present is measuring current, voltage and temperature of the battery.
Ah in/out of the battery is calculated and stored together with the other above-mentioned measures.
The outputs needed from the KF are:
• Remaining charge available (%)
• Time to reach the full charge when in charging mode (minutes)
• Time to reach the full discharge when in discharging mode(minutes)
I will be able to support with measurements in CSV format, battery parameters and characteristics. Connecting from remote, I will give access for the final implementation on the device. A bit of training will be then necessary to explain me how it is implemented and how to set the variables to optimize the KF.
Please do not bid for this project if you have no experience with KF.