Lithium-Ion Battery Health Prediction Using a GRU Model with Protective Characteristic
- Resource Type
- Conference
- Authors
- Ramani, Pooja; Vaghela, Rohan; Popat, Yashvi; Patel, Hirva; Sarda, Jigar; Patel, Arpita
- Source
- 2023 International Conference on Modeling, Simulation & Intelligent Computing (MoSICom) Modeling, Simulation & Intelligent Computing (MoSICom), 2023 International Conference on. :521-525 Dec, 2023
- Subject
- Aerospace
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Fields, Waves and Electromagnetics
Robotics and Control Systems
Signal Processing and Analysis
Lithium-ion batteries
Training
Measurement
Machine learning algorithms
Estimation
Predictive models
Logic gates
Battery
SOH estimation
GRU
Electric Vehicle
Normalization
- Language
An innovative battery management system (BMS) is crucial for verifying the credibility and safety of electric vehicles (EVs) and other electrical devices. The precise state of health (SOH) estimation of the Lithium-ion batteries (LIBs) holds great importance in a BMS. Battery SOH is often committed by the utilization of suitable data-driven approaches and metrics. This study presents an alternative approach for examining the SOH of batteries through the use of a gated recurrent unit (GRU) as an estimating method. Finally, the Oxford battery datasets is subjected to both training and testing procedures. The validity of the proposed method's adaptability is shown by tests managed to estimate the SOH of a battery cell. The performance of our proposed method is validated on the battery dataset of Oxford, where the average RMSE is 0.31%.Graphical Abstract—