The excessively high temperature poses a significant risk to battery health, accelerating degradation and causing damage. Despite the recognized importance of battery thermal management (BTM), numerous studies in this domain often overlook the distinct timescales associated with vehicle and battery thermal dynamics. This oversight can compromise the efficacy and cost-effectiveness of BTM strategies in efficiently controlling battery temperature. This study proposes a novel predictive battery thermal and energy management ( $p$ -BTEM) strategy for connected and automated electric vehicles. The $p$ -BTEM leverages a cloud-enabled predictive control framework to synthesize the look-ahead constant and time-varying factors, e.g., vehicle, road, and traffic information. This synthesis aims to achieve global optimization of battery temperature in the Cloud while enabling local adaptations for vehicle acceleration and compressor power on the Vehicle End. This approach ensures proactive and economical regulation of battery temperature, especially in high temperature conditions, thereby maintaining the battery within optimal temperature ranges and reducing energy consumption in dynamic traffic scenarios. To assess the effectiveness of the $p$ -BTEM, representative route simulations are conducted utilizing real-world data. The results reveal the exceptional performance of the $p$ -BTEM in reducing battery cooling energy when compared to two benchmark strategies, with a minimum improvement of 8.58% and 10.31%, respectively. Moreover, the sensitivity analysis is performed to elaborate on the $p$ -BTEM under the influence of traffic, communication, and algorithmic factors.

Data-driven cooperative control of connected and automated vehicles (CAVs) has gained extensive research interest as it can utilize collected data to generate control actions without relying on parametric system models that are generally challenging to obtain. Existing methods mainly focused on improving traffic safety and stability, while less emphasis has been placed on energy efficiency in the presence of uncertainties and diversities of human-driven vehicles (HDVs). In this brief, we employ a Data-EnablEd Predictive Control (DeePC) scheme to address the eco-driving of mixed traffic flows with diverse behaviors of human drivers. Specifically, by incorporating first-order physics, we propose the physics-augmented DeePC (PA-DeePC) approach to handle substantial system variations and uncertainties. Then, a novel optimization framework, avoiding conventional reference trajectories during the receding optimization process, is proposed to further reduce the holistic energy consumption of mixed traffic flows. Simulation results demonstrate the effectiveness of our approach in accurately capturing random human driver behaviors and reducing holistic energy consumption, while ensuring driving safety and traffic efficiency. Furthermore, the proposed optimization framework achieves substantial reductions in energy consumption, i.e., average reductions of 4.14%, 4.97%, and 9.29% when compared to the benchmark algorithms.