Battery Temperature Prediction Using an Adaptive Neuro-Fuzzy Inference System

• Published in: Batteries (Basel) Vol. 10; no. 3; p. 85
• Main Authors: Zhang, Hanwen, Fotouhi, Abbas, Auger, Daniel J., Lowe, Matt
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-03-2024
• Subjects: Adaptive systems; Ambient temperature; Analysis; ANFIS; Artificial neural networks; Batteries; battery thermal management; Cooling; Dynamic loads; Fuzzy logic; Heat; Hybrid systems; Inference; Kalman filters; Li-ion battery; Lithium; Lithium ions; Machine learning; Management systems; Mathematical models; Model accuracy; neural network; Neural networks; Open circuit voltage; Parameter identification; Prediction models; Product safety; R&D; Real time; Research & development; Safety management; system identification; Temperature; temperature prediction; Thermal management; Weather forecasting
• ISSN: 2313-0105; 2313-0105
• DOI: 10.3390/batteries10030085

Maintaining batteries within a specific temperature range is vital for safety and efficiency, as extreme temperatures can degrade a battery’s performance and lifespan. In addition, battery temperature is the key parameter in battery safety regulations. Battery thermal management systems (BTMSs) are pivotal in regulating battery temperature. While current BTMSs offer real-time temperature monitoring, their lack of predictive capability poses a limitation. This study introduces a novel hybrid system that combines a machine learning-based battery temperature prediction model with an online battery parameter identification unit. The identification unit continuously updates the battery’s electrical parameters in real time, enhancing the prediction model’s accuracy. The prediction model employs an Adaptive Neuro-Fuzzy Inference System (ANFIS) and considers various input parameters, such as ambient temperature, the battery’s current temperature, internal resistance, and open-circuit voltage. The model accurately predicts the battery’s future temperature in a finite time horizon by dynamically adjusting thermal and electrical parameters based on real-time data. Experimental tests are conducted on Li-ion (NCA and LFP) cylindrical cells across a range of ambient temperatures to validate the system’s accuracy under varying conditions, including state of charge and a dynamic load current. The proposed models prioritise simplicity to ensure real-time industrial applicability.