This study addresses the limitations of traditional truck platoon cooperative control methods in optimizing dynamic fuel efficiency. The fixed-order truck platoon has a key flaw: it is unable to dynamically respond to real-time vehicle state changes. To address this, we introduce three innovative methods based on deep reinforcement learning. Compared with fixed-cycle formation transformation strategies, the number of formation transformations is reduced to varying degrees for truck platoons of different sizes. For truck platoons with identical specifications, the impact of different cycle sizes on driving mileage is found to be minimal. This study proves that the dynamic decision mechanism based on deep reinforcement learning can effectively balance formation transformation costs and long-term fuel-saving benefits. The core value lies in establishing an intelligent control paradigm with environmental adaptability. The new algorithm significantly improves fuel economy indicators through real-time state perception and probabilistic decision-making while maintaining formation stability. This method provides a new technical route for energy-saving control in complex transportation scenarios. The core framework can be extended to multi-objective collaborative optimization fields.

Unequal aerodynamic drag across electric vehicles (EVs) in platoons causes imbalanced energy consumption. This reduces overall efficiency and accelerates battery degradation. To address this, we formulate a dynamic reordering task as an Optimal Re-Sequencing (ORS) problem, with the goal of minimizing the final state-of-charge (SOC) variance among vehicles. Departing from conventional fixed-order or heuristic-based strategies, we propose a novel hybrid deep reinforcement learning (DRL) approach that combines NoisyNet-enhanced exploration with the dueling network architecture for value decomposition. Five DRL models, including the proposed Noisy Dueling DQN, are evaluated in a simulation calibrated with real-world highway data. The results show that our method reduces the SOC variance by 34.1% over Dueling DQN, while maintaining low computational cost and fast inference, suitable for deployment in V2X enabled systems. These findings demonstrate the effectiveness and deployability of DRL-based dynamic reordering in enhancing energy-aware EV platoon coordination.

This study addresses the issue of uneven energy consumption in electric vehicle (EV) platoons, arising from the static sequencing of vehicles within the platoon. Such an imbalance can negatively impact the efficiency of individual vehicles and the driving performance of the entire platoon. Our approach proposes dynamically altering the formation of the platoon during transit to balance energy use. The core challenge is to identify the most efficient vehicle sequence at predetermined re-sequencing points during the journey. To address this, we introduce three innovative methods based on deep reinforcement learning, chosen for their ability to handle complex, dynamic optimization problems. Our experimental studies, conducted on actual transportation networks, demonstrate these methods significantly enhance energy management and distribution efficiency in EV platoons, highlighting their potential for practical applications in intelligent transportation systems.

Accurate prediction of sea surface temperature (SST) is crucial in the field of oceanography, as it has a significant impact on various physical, chemical, and biological processes in the marine environment. In this study, we propose a physics-guided attention-based neural network (PANN) to address the spatiotemporal SST prediction problem. The PANN model incorporates data-driven spatiotemporal convolution operations and the underlying physical dynamics of SSTs using a cross-attention mechanism. First, we construct a spatiotemporal convolution module (SCM) using convolutional long short-term memory (ConvLSTM) to capture the spatial and temporal correlations present in the time series of the SST data. We then introduce a physical constraint module (PCM) to mimic the transport dynamics in fluids based on data assimilation techniques used to solve partial differential equations (PDEs). Consequently, we employ an attention fusion module (AFM) to effectively combine the data-driven and PDE-constrained predictions obtained from the SCM and PCM, aiming at enhancing the accuracy of the predictions. To evaluate the performance of the proposed model, we conduct short-term SST forecasts in the East China Sea (ECS) with forecast lead times ranging from one to ten days, by comparing it with several state-of-the-art models, including ConvLSTM, PredRNN, temporal convolutional transformer network (TCTN), convolutional gated recurrent unit (ConvGRU), and SwinLSTM. The experimental results demonstrate that our proposed model outperforms these models in terms of multiple evaluation metrics for short-term predictions.

This paper is concerned with general coopetition networks with signed graphs, based on which both the bipartite consensus and tracking control problems for networked systems subject to nonidentical matching uncertainties are studied. For the case of undirected and connected communication graphs, we propose a distributed discontinuous nonlinear controller which can achieve the bipartite consensus. To cancel the chattering phenomenon of the discontinuous controller, a continuous one is designed by using the boundary layer technique, under which the bipartite consensus error is shown to be uniformly ultimately bounded and can exponentially converge to a small adjustable bounded set. Further, considering the case of a leader having a bounded control action, we present a continuous controller to guarantee the ultimate boundedness of the bipartite tracking error.