A multiagent‐based microgrid operation method considering charging and discharging strategies of electric vehicles

In this paper, we propose a multiagent‐based microgrid (MG) operation method considering charging and discharging electric vehicles (EVs). The proposed system consists of five types of agents: single microgrid controller agent, several load agents, several gas turbines/engine agents, several photovoltaic generation agents, and several electric vehicle agents. In the proposed method, the load balancing can be realized by suppressing sudden fluctuations in supply and demand balance due to the synchronization of charging and discharging of EVs. From the simulation results, it can be seen that the proposed multiagent system could realize the load equalization in MG.     

Towards the Unified Principles for Level 5 Autonomous Vehicles

The rapid advance of autonomous vehicles (AVs) has motivated new perspectives and potential challenges for existing modes of transportation. Currently, driving assistance systems of Level 3 and below have been widely produced, and several applications of Level 4 systems to specific situations have also been gradually developed. By improving the automation level and vehicle intelligence, these systems can be further advanced towards fully autonomous driving. However, general development concepts for Level 5 AVs remain unclear, and the existing methods employed in the development processes of Levels 0–4 have been mainly based on task-driven function development related to specific scenarios. Therefore, it is difficult to identify the problems encountered by high-level AVs. The essential logical and physical mechanisms of vehicles have hindered further progression towards Level 5 systems. By exploring the physical mechanisms behind high-level autonomous driving systems and analyzing the essence of driving, we put forward a coordinated and balanced framework based on the brain–cerebellum–organ concept through reasoning and deduction. Based on a mixed mode relying on the crow inference and parrot imitation approach, we explore the research paradigm of autonomous learning and prior knowledge to realize the characteristics of self-learning, self-adaptation, and self-transcendence for AVs. From a systematic, unified, and balanced point of view and based on least action principles and unified safety field concepts, we aim to provide a novel research concept and develop an effective approach for the research and development of high-level AVs, specifically at Level 5.     

Traffic video‐based intelligent traffic control system for smart cities using modified ant colony optimizer

Road traffic congestion is a serious problem in today's world and it happens because of urbanization and population growth. The traffic reduces the transport efficiency in the city, increases the waiting time and travel time, and also increases the usage of fuel and air pollution. To overcome these issues this papers propose an intelligent traffic control system using the Internet of Vehicles (IoV). The vehicles or nodes present in the IoV can communicate between themselves. This technique helps in determining the traffic intensity and the best route to reach the destination. The area of study used in this paper is Vellore city in Tamilnadu, India. The city map is separated into many segments of equal size and Ant Colony Algorithm (AOC) is applied to the separated maps to find the optimal route to reach the destination. Further, Support Vector Machine (SVM) is used to calculate the traffic density and to model the heavy traffic. The proposed algorithm performs better in finding the optimal route when compared to that of the existing path selection algorithms. From the results, it is evident that the proposed IoV‐based route selection method provides better performance.     

Integrated and inherently safe design and operation of a mobile power generation: Process intensification through microreactor reformer and HT-PEMFC

In the present study, due to various advantages of process miniaturization, the integrated design and operation of a mobile power generation system consisting of a microreactor reformer and a proton exchange membrane fuel cell (PEMFC) are investigated. The hydrogen fuel is supplied through autothermal steam-reforming of methanol in a microreactor leading to a safer, as well as more efficient, and economical operation. A high-temperature polymer membrane fuel cell with external accessories is applied for power generation. Then, simulation-optimization programming is applied for simultaneously optimizing the process design and operation at the same level. The result shows that the implemented procedure ensures the economic and flexible operation of the process while satisfying the safety constraints. The maximum gross power and net power generation are 109.3 and 91.9, respectively, while the cost of hydrogen production reduces from 13 to 20 $/kg to 7.7 $/kg. The fuel (methanol) consumption can be as low as 0.42 L/kWh.     

Deep reinforcement learning-based cooperative interactions among heterogeneous vehicular networks

Most real-world vehicle nodes can be structured into an interconnected network of vehicles. Through structuring these services and vehicle device interactions into multiple types, such internet of vehicles becomes multidimensional heterogeneous overlay networks. The heterogeneousness of the overlays makes it difficult for the overlay networks to coordinate with each other to improve their performance. Therefore, it poses an interesting but critical challenge to the effective analysis of heterogeneous virtual vehicular networks. A variety of virtual vehicular networks can be easily deployed onto the native network by applying the concept of SDN (Software Defined Networking). These virtual networks reflect their heterogeneousness due to their different performance goals, and they compete for the same physical resources of the underlying network, so that a sub-optimal performance of the virtual networks may be achieved. Therefore, we propose a Deep Reinforcement Learning (DRL) approach to make the virtual networks cooperate with each other through the SDN controller. A cooperative solution based on the asymmetric Nash bargaining is proposed for co-existing virtual networks to improve their performance. Moreover, the Markov Chain model and DRL resolution are introduced to leverage the heterogeneous performance goals of virtual networks. The implementation of the approach is introduced, and simulation results confirm the performance improvement of the latency sensitive, loss-rate sensitive and throughput sensitive heterogeneous vehicular networks using our cooperative solution.     

Fixed-time disturbance observer based fixed-time back-stepping control for an air-breathing hypersonic vehicle

This paper proposes a fixed-time backstepping control scheme based on fixed-time disturbance observer for flexible air-breathing hypersonic vehicles. The backstepping control is combined with the fixed-time control technique to achieve fixed-time convergence. A fixed-time super-twisting disturbance observer, which is convergent independently of initial conditions, is employed to estimate and compensate the uncertainties and flexible effects in tracking process. A nonlinear first-order filter is adopted to avoid the “explosion of complexity” problem that arises in traditional backstepping, and to guarantee overall fixed-time stability. The closed-loop system is proven to be semi-globally uniformly ultimately fixed-time bounded via Lyapunov analysis. Simulation results are given to demonstrate the effectiveness of the proposed scheme.     

求解带时间窗车辆路径问题的改进型烟花算法

针对带时间窗车辆路径问题,为寻求组合优化问题最优解,构建总运输成本最小数学模型。由于烟花算法搜索半径不能自适应调整,算法后期易陷入局部最优,故对爆炸算子进行改进,使最优烟花搜索半径自适应调整,增强后期局部搜索能力;同时利用分布式信息共享机制避免算法早熟并引入变异火花增强种群多样性。利用标准测试集进行验证后,结果表明该算法在求解带时间窗车辆路径问题时不仅具有可行性和有效性,并且收敛速度快、搜索质量高。     

基于熵权-灰色关联分析的电动汽车充电基础设施PPP项目风险评价研究

电动汽车充电基础设施PPP项目的建设和实施,不仅可以有效缓解政府财政和管理压力,还可以借助社会资本先进的管理运营经验,提高充电服务供给效率,实现双赢。但是,电动汽车充电基础设施PPP项目具有投资大、特许运营期长等特点,同时还涉及多个项目参与者,关系复杂,潜在的风险因素较多。文章通过文献梳理,首先识别出了电动汽车充电基础设施PPP项目潜在的风险因素,从宏观、中观、微观三个层次建立了基于项目全生命周期的风险评价指标体系,然后建立了基于熵权-灰色关联分析的项目风险评价模型。最后,以T市电动汽车充电基础设施PPP项目为例进行了实证分析,并根据风险评价的结果,从不同项目参与方的角度提出了风险应对的策略。     

Risk-involved stochastic performance of hydrogen storage based intelligent parking lots of electric vehicles using downside risk constraints method

Today, the utilizations of hydrogen storage systems (HSS), renewable generation units (PV and wind generation) and distributed energy units are increased in the intelligent parking lots (IPL) in order to charge the electric vehicles (EVs) with clean energy sources. In this work, the uncertainties of upstream grid price, the demand of IPL, wind speed, solar irradiation and temperature are modeled via scenario approach based on stochastic programing. Furthermore, the downside risk constraints method (DRCM) is applied to consider risk related to uncertainties to get risk-involved stochastic performance of hydrogen storage based intelligent parking lots of electric vehicles. The proposed risk-based formulation is modeled using mixed-integer linear programming (MIP) which is implemented under GAMS software and solved via CPLEX solver. Two cases namely risk-averse and risk-neutral strategies are studied and compared to show the effects of DRCM implementation. The obtained results demonstrate the expected performance cost (EPC) of IPL is slowly raised while risk-in-cost (RIC) is significantly reduced due to model of risk related to uncertainties.     

Heuristic algorithms for a three-dimensional loading capacitated vehicle routing problem in a carrier

In this paper, we present heuristic algorithms for a three-dimensional loading capacitated vehicle routing problem arising in a real-world situation. In this problem, customers make requests of goods, which are packed in a sortment of boxes. The objective is to find minimum cost delivery routes for a set of identical vehicles that, departing from a depot, visit all customers only once and return to the depot. Apart of the usual 3D container loading constraints which ensure that the boxes are packed completely inside the vehicles and that the boxes do not overlap each other in each vehicle, the problem also takes into account constraints related to the vertical stability of the cargo and multi-drop situations. The algorithms are based on the combination of classical heuristics from both vehicle routing and container loading literatures, as well as two metaheuristic strategies, and their use in more elaborate procedures. Although these approaches cannot assure optimal solutions for the respective problems, they are relatively simple, fast enough to solve real instances, flexible enough to include other practical considerations, and normally assure relatively good solutions in acceptable computational times in practice. The approaches are also sufficiently generic to be embedded with algorithms other than those considered in this study, as well as they can be easily adapted to consider other practical constraints, such as the load bearing strength of the boxes, time windows and pickups and deliveries. Computational tests were performed with these methods considering instances based on the vehicle routing literature and actual customers’ orders, as well as instances based on a real-world situation of a Brazilian carrier. The results show that the heuristics are able to produce relatively good solutions for real instances with hundreds of customers and thousands of boxes.