Green Energy and Intelligent Transportation最新文献

Autonomous vehicles (AVs) are becoming a reality and may integrate with existing public transport systems to enable the new generation of autonomous public transport. It is vital to understand what are the alternatives for AV integration from different angles such as costs, emissions, and transport performance. With the aim to support AV integration in public transport, this paper takes a typical European city as a case study for analyzing the impacts of different AV integration alternatives. A transport planning model considering AVs is developed and implemented, with a methodology to estimate the costs of the transport network. Traffic simulations are conducted to derive key variables related to AVs. An optimization process is introduced for identifying the optimal network configuration based on a given AV integration strategy, followed by the design of different AV integration scenarios, simulation, and analyses. With the proposed method, a case study is done for the city of Uppsala with presentation of detailed cost results together with key traffic statistics such as mode share. The results show that integrating AVs into public transport does not necessarily improve the overall cost efficiency. Based on the results and considering the long transition period to fully autonomous vehicles, it is recommended that public transport should consider a gradual introduction of AVs with more detailed analysis on different combination and integration alternatives of bus services and AVs.

Due to manufacturing variation, battery cells often possess heterogeneous characteristics, leading to battery state-of-charge variation in real-time. Since the lowest cell state-of-charge determines the useful life of battery pack, such variation can negatively impact the battery performance and electric vehicles range. Existing research has been focused on control design to mitigate cell imbalance. However, it is yet unclear how much impacts the cell imbalance can have on electric vehicle range. This paper closes this knowledge gap by using a simulation environment consisting of real-world driving speed data, vehicle longitudinal control, propulsion and vehicle dynamics, and cell level battery modeling. In particular, each battery cell is modeled as an equivalent circuit model, and variations among cell parameters are introduced to assess their impact on electric vehicles range and to identify the most influential parameter variations. Simulation results and analysis can be used to assist balancing control design and to benchmark control performance.

Vehicle lateral control is an important subtask of vehicle autonomous driving. There are many external disturbances that will affect the lateral control accuracy of the vehicle, and the inclination of the road is one of the most important ones. The inclined road will lead to additional lateral forces on the vehicle and will also change the magnitude of support force on the vehicle. The change of lateral force and support force will ultimately affect the trajectory tracking performance of the vehicle. Most of the current trajectory tracking methods only consider the trajectory tracking problem on the plane. If the influence of the road surface is considered in the design of the vehicle's trajectory tracking controller, the dynamic response and the tracking accuracy of the vehicle can be improved. This paper proposes a method based on Udwadia–Kalaba equation to calculate the normal and lateral force on a vehicle tracking a desired trajectory on an inclined road. Further, a trajectory tracking controller that considers the road inclination is designed. Finally, the simulation of trajectory tracking performance with an inclination angle is carried out to verify the effectiveness of the proposed controller.

With the rapid development of connected autonomous vehicles (CAVs), both road infrastructure and transport are experiencing a profound transformation. In recent years, the cooperative perception and control supported infrastructure-vehicle system (IVS) attracted increasing attention in the field of intelligent transportation systems (ITS). The perception information of surrounding objects can be obtained by various types of sensors or communication networks. Control commands generated by CAVs or infrastructure can be executed promptly and accurately to improve the overall performance of the transportation system in terms of safety, efficiency, comfort and energy saving. This study presents a comprehensive review of the research progress achieved upon cooperative perception and control supported IVS over the past decade. By focusing on the essential interactions between infrastructure and CAVs and between CAVs, the infrastructure-vehicle cooperative perception and control methods are summarized and analyzed. Furthermore, the mining site as a closed scenario was used to show the current application of IVS. Finally, the existing issues of the cooperative perception and control technology implementation are discussed, and the recommendation for future research directions are proposed.

The loading and unloading operations carried out by transport and logistics operators have a strong impact on city mobility if they are not performed correctly. If loading/unloading bays, i.e., delivery bays (DB), are not available for freight vehicle operations, operators may opt to double park or park on the sidewalk where there is no strong enforcement of these laws, with significant impact on congestion. This paper proposes a methodology for verifying and designing the number of delivery bays needed for freight vehicles for not interfere with cars or pedestrians. The methodology consists of two stages: in the first stage, an initial estimation is made using queueing theory. Subsequently, in the second stage, using such tentative scenario, in order to take into account the system stochasticity involving different entities, a discrete event simulation is performed to more realistically verify and upgrade (if necessary) the number of delivery bays to obtain the expected outcomes. The methodology was applied in the inner area of Santander (Spain). The study area was subdivided into 29 zones where the methodology was applied individually. The results indicated that none of these zones currently have an optimal number of delivery bays to satisfy demand. In some zones, there is an excess of delivery bays, although in most of them, there is a deficit which can cause significant impacts on traffic. The method proposed can be an effective tool to be used by city planners for improving freight operations in urban areas limiting the negative impacts produced in terms of internal and external costs.

As a core part of an autonomous driving system, motion planning plays an important role in safe driving. However, traditional model- and rule-based methods lack the ability to learn interactively with the environment, and learning-based methods still have problems in terms of reliability. To overcome these problems, a hybrid motion planning framework (HMPF) is proposed to improve the performance of motion planning, which is composed of learning-based behavior planning and optimization-based trajectory planning. The behavior planning module adopts a deep reinforcement learning (DRL) algorithm, which can learn from the interaction between the ego vehicle (EV) and other human-driven vehicles (HDVs), and generate behavior decision commands based on environmental perception information. In particular, the intelligent driver model (IDM) calibrated based on real driving data is used to drive HDVs to imitate human driving behavior and interactive response, so as to simulate the bidirectional interaction between EV and HDVs. Meanwhile, trajectory planning module adopts the optimization method based on road Frenet coordinates, which can generate safe and comfortable desired trajectory while reducing the solution dimension of the problem. In addition, trajectory planning also exists as a safety hard constraint of behavior planning to ensure the feasibility of decision instruction. The experimental results demonstrate the effectiveness and feasibility of the proposed HMPF for autonomous driving motion planning in urban mixed traffic flow scenarios.

This paper proposes a novel power management strategy for plug-in hybrid electric vehicles based on deep reinforcement learning algorithm. Three parallel soft actor-critic (SAC) networks are trained for high speed, medium speed, and low-speed conditions respectively; the reward function is designed as minimizing the cost of energy cost and battery aging. During operation, the driving condition is recognized at each moment for the algorithm invoking based on the learning vector quantization (LVQ) neural network. On top of that, a driving cycle reconstruction algorithm is proposed. The historical speed segments that were recorded during the operation are reconstructed into the three categories of high speed, medium speed, and low speed, based on which the algorithms are online updated. The SAC-based control strategy is evaluated based on the standard driving cycles and Shenyang practical data. The results indicate the presented method can obtain the effect close to dynamic programming and can be further improved by up to 6.38% after the online update for uncertain driving conditions.

To promote the intelligent vehicle safety and reduce the driver steering workload, stackelberg game theory is adopted to design the shared steering control strategy that takes the driver neuromuscular delay characteristics into account. First, a shared steering control framework with adjustable driving weight is proposed, and a coupling interaction model considering the driver neuromuscular delay characteristics is constructed by using the stackelberg game theory. Moreover, the driver-automation optimal control strategy is deduced theoretically when the game equilibrium is reached. Finally, simulation and virtual driving tests are carried out to verify the superiority of the proposed method. The results illustrate that the raised method can enhance the vehicle safety with low driving weight intervention, and it can achieve better auxiliary effect with less control cost. In addition, the driver-in-the-loop test results show that the proposed strategy can achieve better performance in assisting drivers with low driving skills.

Due to the high mileage and heavy load capabilities of hybrid electric vehicles (HEVs), energy management becomes crucial in improving energy efficiency. To avoid the over-dependence on the hard-crafted models, deep reinforcement learning (DRL) is utilized to learn more precise energy management strategies (EMSs), but cannot generalize well to different driving situations in most cases. When driving cycles are changed, the neural network needs to be retrained, which is a time-consuming and laborious task. A more efficient transferable way is to combine DRL algorithms with transfer learning, which can utilize the knowledge of the driving cycles in other new driving situations, leading to better initial performance and a faster training process to convergence. In this paper, we propose a novel transferable EMS by incorporating the DRL method and dueling network architecture for HEVs. Simulation results indicate that the proposed method can generalize well to new driving cycles, with comparably initial performance and faster convergence in the training process.

In the actual use of a parallel battery pack in electric vehicles (EVs), current distribution in each branch will be different due to inconsistence characteristics of each battery cell. If the branch current is approximately calculated by the total current of the battery pack divided by the number of the parallel branches, there will be a large error between the calculated branch current and the real branch current. Adding current sensors to measure each branch current is not practical because of the high cost. Accurate estimation of branch currents can give a safety warning in time when the parallel batteries of EVs are seriously inconsistent. This paper puts forward a method to estimate and correct branch currents based on dual back propagation (BP) neural networks. In the proposed method, one BP neural network is used to estimate branch currents, the other BP neural network is used to reduce the estimation error cause by current pulse excitations. Furthermore, this paper makes discussions on the selection of the best inputs for the dual BP neural networks and the adaptability of the method for different battery capacity and resistence differences. The effectiveness of the proposed method is verified by multiple dynamic conditions of two cells connected in parallel.