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Improve in-situ life prediction and classification performance by capturing both the present state and evolution rate of battery aging
And the degradation rate is captured by two features extracted from the differences between the voltage relaxation curves within a moving window (for life prediction), or the differences between the capacity vs. voltage curves at different cycles (for life classification).
Comprehensive performance comparison among different types of features in data-driven battery state of health estimation
In this study, a physics-informed Gaussian process regression (GPR) model is developed for battery SOH estimation, with the performance being systematically compared with that of different types of features and machine learning (ML) methods.
Optimal Eco-driving Control of Autonomous and Electric Trucks in Adaptation to Highway Topography: Energy Minimization and Battery Life Extension
In this real time control model, a novel state-space model is first developed to capture vehicle speed, acceleration, and state of charge.
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