Our Models

Machine Learning Model for SOH and RUL Prediction

Real-Time Temperature Distribution Model

Overview
ThermoBatt's machine learning model innovatively employs ambient temperature, cycle count, and other critical factors to accurately predict the State of Health (SOH) and Remaining Useful Life (RUL) of batteries. This model is designed to provide a more nuanced understanding of battery health, moving beyond traditional indicators to include thermal influences.

Methodology
Utilizing advanced machine learning algorithms, including regression analysis and neural networks, the model analyzes historical and real-time data to identify patterns and correlations. These algorithms are trained on extensive datasets, allowing the model to adapt and improve its predictive accuracy over time. The model's versatility enables it to cater to various battery types and usage scenarios.

Overview
Our Real-Time Temperature Distribution Model offers a dynamic visualization of how heat is generated and dispersed within a battery during charge/discharge cycles. This model is key in understanding the thermal behavior of batteries under operational stresses and contributes significantly to preventive maintenance and safety.

Methodology
The model employs computational techniques to simulate heat transfer and temperature gradients within the battery structure, providing a real-time heat map visualization. This graphically represents temperature changes, offering intuitive insights into thermal hotspots and potential areas of concern. This is instrumental in identifying abnormal heat patterns, which are often precursors to battery degradation or failure.