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State of Charge predictor

Battery Management System and State of Charge Detection with Neural Network.

State of Charge predictor

Battery Management System and State of Charge Detection with Neural Network.

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A Teoresi Group project

Project

The system can predict the State of Charge of a battery pack using a Neural Network. It is integrated within a microcontroller resulting in a real-time IoT system that delivers precise information on battery performance. The system is an advanced solution for battery monitoring and management that can help optimize performance and extend battery life.

Client

Customer in the field of energy management

Industries

Power Utilities, Electronics and Infrastructure

Scenario

As the electric car market continues to expand globally, battery management becomes increasingly important for ensuring optimal performance and longevity. Accessing precise data from batteries is essential for the efficient management of Remaining Useful Life (RUL) and End of Life (EOL).

To address these challenges, developing solutions that can accurately predict battery performance and integrate with IoT systems is crucial.

Challenges

Temperature has a significant impact on battery performance, so the Neural Network had to be trained to handle variations in temperature to provide accurate predictions
Integrating the trained Neural Network into the microcontroller in order to have all the parameters in one single environment
Designing a user-friendly interface that can clearly and accurately convey the battery’s performance data

Our added Values

Teoresi’s expertise in developing embedded systems and AI-based solutions, combined with cutting-edge technologies and an analytical and testing approach, ensures that solutions are innovative, cost- and time-effective, and meet the highest quality standards

We don’t need the crystal ball to predict We prefer Neural Networks.

Results

The system developed is characterized by its high precision, which outperforms many other methods of measurement of the State of Charge of the battery. Additionally, it is easily adjustable and has low implementation costs.

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