World Electric Vehicle Journal

With the rapid adoption of electric vehicles (EVs), optimizing charging infrastructure and route planning has become increasingly crucial. Traditional methods such as Linear Programming (LP) have been widely used to address these challenges. However, these approaches often struggle with scalability, computational efficiency, and the ability to handle complex logical constraints involving multiple decision factors like distance, time, cost, battery levels, and charging station compatibility. To overcome these limitations, this study proposes a novel Boolean Satisfiability (SAT)-based optimization framework for intelligent EV charging station recommendation. Unlike conventional approaches, the proposed model encodes real-world constraints into Conjunctive Normal Form (CNF) using De Morgan’s Theorem, allowing efficient processing through the CP-SAT solver. This logical transformation enables the systematic representation of intricate relationships between variables, ensuring better compatibility and computational efficiency. The SAT-based framework was applied to intercity EV routing scenarios, where it demonstrated substantial improvements over traditional methods in terms of route optimization, cost reduction, and charging station relevance. Notably, the SAT model was effective in avoiding redundant charging recommendations, selecting only those stations necessary to complete the route while satisfying all energy and infrastructure constraints. Moreover, the solver showed rapid convergence and greater adaptability under varied operational scenarios. In conclusion, this study highlights the effectiveness of SAT-based modeling—particularly its CNF formulation and logical expressiveness—in delivering a scalable, intelligent, and efficient solution for real-time EV route planning and charging station optimization.

In the context of complex scenarios, identifying the posture of individuals is a critical technology in the fields of intelligent surveillance and autonomous driving. However, existing methods face challenges in effectively balancing real-time performance, occlusion, and recognition accuracy. To address this issue, we propose a lightweight hybrid model, referred to as YOLO-SwinTransformer, in this study. This model utilizes YOLOv8’s CSP Darknet as the primary network to achieve efficient multi-scale feature extraction. It integrates the Path Aggregation Network aggregation (PANet) and HRNet with high-resolution multi-scale feature extraction, enhancing cross-level semantic information interaction. The primary innovation of this model is the design of a modified Swin Transformer posture identification module, incorporating the Spatial Locality-Aware Module (SLAM) to enhance local feature extraction, achieving a combined modeling of space attention and time-series continuity. This effectively addresses the challenges posed by occlusion and video distortion in identifying posture. Additionally, we have extended the CIoU Loss and weighted mean square error loss functions to improve posture identification strategies, enhancing the precision of key points. Ultimately, extensive experimentation with both the COCO dataset and the self-built realistic road dataset demonstrated that the YOLO-SwinTransformer model achieved a state-of-the-art Average Precision (AP) of 84.9% on the COCO dataset, representing a significant 12.8% enhancement over the YOLOv8 baseline (72.1% AP). More importantly, on our challenging self-built real-world road dataset, the model achieved 82.3% AP (a 13.7% improvement over the baseline’s 68.6% AP), proving its superior robustness in complex occlusion and low-light scenarios. The model’s size is 27.3 M, and its lightweight design enables 39–41 FPS of real-time processing on edge devices, providing a feasible solution for intelligent monitoring and autonomous driving applications with high precision and efficiency.

The controller of the energy management system must be capable of meeting the rapid and dynamic demands of fuel cell electric vehicles (FCEVs) without compromising its performance and durability. The performance of FCEVs can be enhanced through powertrain hybridization with battery and ultracapacitor systems. The overall dynamic optimization of the energy between the batteries/ultracapacitors and the Proton Exchange Membrane Fuel Cell (PEMFC) output can play an important role in hydrogen fuel economy and the durability of vehicle systems. The present study investigates the system’s efficiency and fuel consumption in European Drive Cycles when employing diverse energy management strategies. This investigation utilizes a novel switch real-time strategy (SWA_RTO), which is founded on an A-factor algorithm that alternates between the most effective Real Time Optimization (RTO) strategies. The objective of this paper is to underscore the significance of algorithmic optimization by presenting the optimal results obtained for the fuel economy of the SWA_RTO strategy. These results are compared with the basic RTO strategy and the static Feed-Forward (sFF) reference strategy. The load demand during driving cycles is primarily determined by the PEMFC system. Minor discrepancies in power balance are addressed by the hybrid battery and ultracapacitor system. Consequently, the lifespan of the subject will increase, and the state of charge (SOC) will no longer be a factor in monitoring.