Dynamic Battery Type Detection Using Neural Networks

• Published in: 2023 IEEE 20th International Conference on Smart Communities: Improving Quality of Life using AI, Robotics and IoT (HONET) pp. 1 - 5
• Main Authors: Lopez, Hector K., Zilouchian, Ali, Abtahi, Amir
• Format: Conference Proceeding
• Language: English
• Published: IEEE 04.12.2023
• Subjects: Adaptation models; Artificial neural networks; Chemistry; Discharges (electric); Energy Storage; Lead; Neural Networks; Technological innovation; Training data; Vehicle-to-Grid Battery Modeling

Dynamically detecting battery chemistries, including LiFePO4, Ni-MH, and Lead Acid, is explored through extensive simulations. Utilizing discharge curves as training data, three neural network architectures-Single Hidden Layer, Double Hidden Layer, and Radial Basis Transfer Function-are employed for...