State-of-Charge assessment of Li-ion battery using Genetic Algorithm-Neural Network (GANN)
- Resource Type
- Conference
- Authors
- Cardelli, Ermanno; Crescimbini, Fabio; Fulginei, Francesco Riganti; Quercio, Michele; Sabino, Lorenzo
- Source
- 2024 International Conference on Artificial Intelligence, Computer, Data Sciences and Applications (ACDSA) Artificial Intelligence, Computer, Data Sciences and Applications (ACDSA), 2024 International Conference on. :1-5 Feb, 2024
- Subject
- Bioengineering
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Engineered Materials, Dielectrics and Plasmas
Fields, Waves and Electromagnetics
General Topics for Engineers
Photonics and Electrooptics
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
Sociology
Estimation
Computer architecture
Artificial neural networks
Lithium batteries
Robustness
State of charge
Artificial Intelligence
Lithium battery
Neural Network
State of Charge
- Language
The accurate estimation of State-of-Charge (SoC) is crucial for optimal performance and safe operation of lithium batteries. Traditional methods for SoC estimation have limitations in terms of robustness and accuracy, leading to the exploration of alternative techniques such as neural networks (NN). Neural networks are highly effective mathematical models that take inspiration from the organization and operation of the human brain, and their ability to handle complex nonlinear relationships makes them ideal for SoC estimation. The aim of this work is to train a NN with an optimized architecture for SoC predicting. In particular a Genetic Algorithm Neural Network (GANN) was used with three hidden layers to evaluate the state of charge of the lithium battery. The results show that an average error of 2% is riched on the test set. So the GANN method can be considered promising for this kind of evaluation.