Optimizing charge discharge cycles using QPPONet-enabled hybrid learning framework for energy management and safety in electric vehicles

Nature·Mar 18

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Researchers are exploring various frameworks to improve energy management and safety in electric vehicles. These frameworks include hybrid learning approaches that integrate different techniques, such as QPPONet, GRU state-of-health estimation, and transformer-guided multi-modal learning. The goal of these frameworks appears to be optimizing charge discharge cycles, predicting battery degradation, and enhancing overall vehicle performance.

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An intelligent cloud-based battery management framework integrating GRU state-of-health estimation and double DQN charging optimisation

Nature·May 5

A transformer guided multi modal learning framework for predictive and causal assessment of thermal runaway in high energy batteries

Nature·Oct 23

A physics-guided temporal convolutional learning with XGBoost integration for degradation-aware and robust electric vehicle range prediction

Nature·Apr 18

Enhanced extreme gradient boosting based algorithm for mobility management of autonomous vehicles from sub 6 GHz to mmWave networks

Nature·Jul 1