Deep learning of lithium-ion battery SOH using the DeTransformer model learns the aging characteristics of the battery and then makes predictions about the battery SOH in order to monitor the health of batteries in electric vehicles.
TFDSUNet: Time-Frequency Dual-Stream Uncertainty Network for Battery SOH/SOC Prediction
Degradation mode analysis tool in MATLAB; including optional blend anode OCP calculation
Code repository for the paper SOC-agnostic rapid state-of-health estimation of lithium-ion batteries
Experimental lithium-ion battery pack testing and degradation validation for lifecycle reliability assessment.
Deep Learning for Battery State of Health Prediction using Pyramid TCN-Transformer
Data-driven lithium-ion battery health prognostics and lifecycle performance modelling for EV systems.
Physics-informed shuffle reservoir spiking neural network for lithium-ion battery SOH estimation
Repository containing the final presentation for the courses of Learning for Autonomous Systems and Simulation Intelligence at UniTs 2025/2026.
Machine Learning based EV Battery Health Prediction Dashboard using FastAPI, Streamlit, and Random Forest to estimate battery SOH, risk level, and maintenance recommendations.
Bridging the lab-to-field gap in BESS health diagnostics. Python case studies exploring data quality, baseline-relative degradation trends, and uncertainty-aware SOH assessment to inform asset valuation and insurance risk assessment.
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