Adaptive Neural Network Optimization of Vehicle Start-Stop Systems Using Digital Twin Framework

Building upon digital twin frameworks for battery monitoring, this paper introduces an adaptive neural network approach for optimizing vehicle start-stop systems. The proposed ML-enhanced framework integrates Long Short-Term Memory (LSTM) networks for State of Health (SOH) prediction with XGBoost-based optimization for start-stop decision making. By leveraging continuous feedback from the digital twin model, the system achieves adaptive learning capabilities that respond to individual driving patterns. Experimental results demonstrate significant improvements: SOH prediction accuracy improved to 1.82 % mean absolute error, while battery life extension reached$\mathbf{2 4. 2} \boldsymbol{\%}$. The system maintains emission reduction benefits above 92.3 % while adapting optimization thresholds based on realworld usage patterns. The edge-deployable solution requires less than 50 ms inference time, making it suitable for real-time automotive applications.