Green Energy and Intelligent Transportation最新文献

Accurate simulation of characteristics performance and state of health (SOH) estimation for lithium-ion batteries are critical for battery management systems (BMS) in electric vehicles. Battery simplified electrochemical model (SEM) can achieve accurate estimation of battery terminal voltage with less computing resources. To ensure the applicability of life-cycle usage, degradation physics need to be involved in SEM models. This work conducts deep analysis on battery degradation physics and develops an aging-effect coupling model based on an existing improved single particle (ISP) model. Firstly, three mechanisms of solid electrolyte interface (SEI) film growth throughout life cycle are analyzed, and an SEI film growth model of lithium-ion battery is built coupled with the ISP model. Then, a series of identification conditions for individual cells are designed to non-destructively determine model parameters. Finally, battery aging experiment is designed to validate the battery performance simulation method and SOH estimation method. The validation results under different aging rates indicate that this method can accurately estimate characteristics performance and SOH for lithium-ion batteries during the whole life cycle.

To successfully implement the platoon control of connected and automated vehicles, it is necessary to address motion control issues to achieve longitudinal and lateral collaborative control. However, due to traffic capacity limitations and the complex traffic environment in which autonomous and human-driven vehicles coexist, autonomous platoon faces significant risks and challenges. This paper investigates longitudinal and lateral control issues from the perspective of a single vehicle up to a platoon, simulating the performance and suitability of various controllers. First, a longitudinal controller based on fuzzy logic and PID control is employed for speed tracking control of a single vehicle, followed by the adoption of an MPC controller based on the vehicle kinematics model to realize the lateral motion of a single vehicle. Second, the communication methods of the autonomous platoon are discussed, and the longitudinal controller that considers the platoon's various communication topologies is developed. Thirdly, a framework for robust integrated motion control is established, which combines the robust H-infinity longitudinal controller and the APF-based MPC lateral controller. Simulation results validate the effectiveness of the aforementioned controllers and reveal their limitations.

Faults in a DC aircraft power system typically lead to serious equipment damage, which severely threatens the safety of the whole aircraft system. A fast and accurate real-time fault detection scheme is necessary for aircraft power systems to provide high reliability in the system. In this paper, a new fault detection device (FDD) is proposed based on the comb filter and second derivative of the system voltage to detect both low and high-impedance faults (HIFs) in a fast way. The proposed method utilizes the comb filter in the middle of the two first derivatives to detect both high and low-impedance faults within several microseconds. For demonstrating the efficiency, authenticity, and compatibility of the proposed method, digital time-domain simulations are carried out and verified by real-time simulations using an OPAL-RT simulator under different scenarios such as low- and high-impedance fault, overload, and motor starting to verify distinguishing between non-fault disturbances and faults. The results, which are compared with reported methods, prove the accuracy and speed of the proposed FDD in a DC aircraft.

This study is laser focused on the simulation of fuel consumption and fuel economy label parameters of plug-in hybrid electric vehicles. While fuel economy is a key factor in the design of plug-in hybrid electric vehicles, a fuel economy label can educate customers about the economic advantage of purchasing a particular car. The fuel economy label of a PHEV consists of parameters like driving range, electrical energy consumption, fuel economy for city, highway, and combined use, battery recharge time, and fuel consumption rates. The study used an inverse function model of an artificial neural network to simulate and calculate the parameters of the fuel economy labels of PHEVs. Firstly, the selected parameters of the fuel economy label of plug-in hybrid electric vehicles were used to develop a single output model. The output variable of the single output model was then merged with dummy functions to form input variables for the inverse function model. The output variables simulated were engine size in litres; estimated driving range when the battery is fully charged in km, battery recharged time in hours, city fuel consumption (L/100 ​km), highway fuel consumption (L/100 ​km), combined fuel consumption (L/100 ​km), estimated driving range when the tank is full, carbon dioxide (CO₂) emission in grams/km, electric motor power in kW, number of cylinders, and electrical charges consumed in kWh/100 ​km. Different cases of input variables were considered for the inverse function model. The accuracy of the model was 29.1 times greater than that of the conventional inverse artificial neural network model.

To reduce the complexity of large-scene high-resolution maps while using the dead-end information distributed in the unmanned vehicle driving environment, we propose a novel non-uniform quadtree map-building method including dead-end semantic information extraction. By utilizing quadtree data structures, submaps and a positive-order tree depth organization approach, our proposed map can adapt to the large-scale high-resolution requirement and expand more easily to larger environments. To verify the practicality of our proposed map, we have successfully implemented map matching and path planning in real environments. Additionally, we effectively extract the dead-end semantic information that widely distributes in the environment, which can help unmanned vehicles avoid collisions and improve the search efficiency of the planning procedure. We evaluate our method with KITTI datasets, CARLA Simulator, and our self-collected real-world datasets. The experimental results show that our proposed method significantly reduces the complexity of large-scale high-resolution maps, effectively extracts dead-end semantic information, and has good practicality in real environments. The implementation of our method is released here: https://github.com/biter0088/Non-uniform-quadtree-map.

The electrification of vehicles is considered one of the most important strategies for addressing the issues related to energy dependence and climate change. To meet user needs, electric vehicle (EV) management for charging operations is essential. This study uses modelling and simulation of EV user behaviour to forecast possible scenarios for electric charging in cities and to identify potential management problems and opportunities for improvement of EVs and EV charging infrastructures. The conurbation of Turin was selected as a case study to reproduce realistic scenarios by applying discrete choice modelling based on socio-economic and transport system data. One of objectives of the study was to describe user charging behaviour from a geographic perspective to model where users prefer to charge in the area studied according to the variables that may affect decisions. Another objective was to estimate the number of electric vehicles in Turin and the characteristics of their users, both of which are helpful in understanding electric mobility within a city. Analysing these behavioural issues in a modelling framework can provide a set of tools to compare and evaluate a variety of possible modifications, indicating an adequate network of charging infrastructure to facilitate the diffusion of electric vehicles.

With the development of fuel cells, multi-stack fuel cell system (MFCS) for high power application has shown tremendous development potential owing to their obvious advantages including high efficiency, durability, reliability, and pollution-free. Accordingly, the state-of-the-art of MFCS is summarized and analyzed to advance its research. Firstly, the MFCS applications are presented in high-power scenarios, especially in transportation applications. Then, to further investigate the MFCS, MFCS including hydrogen and air subsystem, thermal and water subsystem, multi-stack architecture, and prognostics and health monitoring are reviewed. It is noted that prognostics and health monitoring are investigated rarely in MFCS compared with previous research. In addition, the efficiency and durability of MFCS are not only related to the application field and design principle but also the energy management strategy (EMS). The reason is that the EMS is crucial for lifespan, cost, and efficiency in the multi-stack fuel cell system. Finally, the challenge and development potential of MFCS is proposed to provide insights and guidelines for future research.

Decision-making for autonomous vehicles in the presence of obstacle occlusions is difficult because the lack of accurate information affects the judgment. Existing methods may lead to overly conservative strategies and time-consuming computations that cannot be balanced with efficiency. We propose to use distributional reinforcement learning to hedge the risk of strategies, optimize the worse cases, and improve the efficiency of the algorithm so that the agent learns better actions. A batch of smaller values is used to replace the average value to optimize the worse case, and combined with frame stacking, we call it Efficient-Fully parameterized Quantile Function (E-FQF). This model is used to evaluate signal-free intersection crossing scenarios and makes more efficient moves and reduces the collision rate compared to conventional reinforcement learning algorithms in the presence of perceived occlusion. The model also has robustness in the case of data loss compared to the method with embedded long and short term memory.

To meet the needs of the human-machine co-driving decision problem in the intelligent assisted driving system for real-time comprehensive driving ability evaluation of drivers, this paper proposes a real-time comprehensive driving ability evaluation method that integrates driving skill, driving state, and driving style. Firstly, by analyzing the driving experiment data obtained based on the intelligent driving simulation platform (the experiment can effectively distinguish the driver's driving skills and avoid the interference of driving style), the feature values that significantly represent driving skills and driving state are selected, and the time correlation between driving state and driving skills is pointed out. Furthermore, the concept of relativity in comprehensive driving ability evaluation is further proposed. Under this concept, the natural driving trajectory dataset-HighD is used to establish the distribution map of feature values of the human driver group as the evaluation benchmark to realize the relative evaluation of driving skill and driving state. Similarly, HighD is used to establish a distribution map of human driver style feature values as an evaluation benchmark to achieve relative driving style evaluation. Finally, a comprehensive driving ability evaluation model with a “punishment” and “affirmation” mechanism is proposed. The experimental comparative analysis shows that the evaluation algorithm proposed in this paper can take into account the driver's driving skill, driving state, and driving style in the real-time comprehensive driving ability evaluation, and draw differential evaluation conclusions based on the “punishment” and “affirmation” mechanism model to achieve a comprehensive and objective evaluation of the driver's driving ability. It can meet the needs of human-machine shared driving decisions for driver's driving ability evaluation.

The safety and reliability of battery storage systems are critical to the mass roll-out of electrified transportation and new energy generation. To achieve safe management and optimal control of batteries, the state of charge (SOC) is one of the important parameters. The machine-learning based SOC estimation methods of lithium-ion batteries have attracted substantial interests in recent years. However, a common problem with these models is that their estimation performances are not always stable, which makes them difficult to use in practical applications. To address this problem, an optimized radial basis function neural network (RBF-NN) that combines the concepts of Golden Section Method (GSM) and Sparrow Search Algorithm (SSA) is proposed in this paper. Specifically, GSM is used to determine the optimum number of neurons in hidden layer of the RBF-NN model, and its parameters such as radial base center, connection weights and so on are optimized by SSA, which greatly improve the performance of RBF-NN in SOC estimation. In the experiments, data collected from different working conditions are used to demonstrate the accuracy and generalization ability of the proposed model, and the results of the experiment indicate that the maximum error of the proposed model is less than 2%.