Green Energy and Intelligent Transportation最新文献

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%.

Extracting the unknown parameters of proton exchange membrane fuel cell (PEMFC) models accurately is vital to design, control, and simulate the actual PEMFC. In order to extract the unknown parameters of PEMFC models precisely, this work presents an improved version of neural network algorithm (NNA), namely the multiple learning neural network algorithm (MLNNA). In MLNNA, six learning strategies are designed based on the created local elite archive and global elite archive to balance exploration and exploitation of MLNNA. To evaluate the performance of MLNNA, MLNNA is first employed to solve the well-known CEC 2015 test suite. Experimental results demonstrate that MLNNA outperforms NNA on most test functions. Then, MLNNA is used to extract the parameters of two PEMFC models including the BCS 500 ​W PEMFC model and the NedStack SP6 PEMFC model. Experimental results support the superiority of MLNNA in the parameter estimation of PEMFC models by comparing it with 10 powerful optimization algorithms.

Accurate prediction of the motion state of the connected vehicles, especially the preceding vehicle (PV), would effectively improve the decision-making and path planning of intelligent vehicles. The evolution of vehicle-to-vehicle (V2V) communication technology makes it possible to exchange data between vehicles. However, since V2V communication has a transmission interval, which will result in the host vehicle not receiving information from the PV within the time interval. Furthermore, V2V communication is a time-triggered system that may occupy more communication bandwidth than required. On the other hand, traditional estimation methods of the PV state based on individual models are usually not applicable to a wide range of driving conditions. To address these issues, an event-triggered unscented Kalman filter (ETUKF) is first employed to estimate the PV state to strike a balance between estimation accuracy and communication cost. Then, an interactive multi-model (IMM) approach is combined with ETUKF to form IMMETUKF to further improve the estimation accuracy and applicability. Finally, simulation experiments under different driving conditions are implemented to verify the effectiveness of IMMETUKF. The test results indicated that the IMMETUKF has high estimation accuracy even when the communication rate is reduced to 14.84% and the proposed algorithm is highly adaptable to different driving conditions.

The most viable path to alleviate the Global Climate Change is the substitution of fossil fuel power plants for electricity generation with renewable energy units. This substitution requires the development of very large energy storage capacity, with the inherent thermodynamic irreversibility of the storage-recovery process. Currently, the world experiences a significant growth in the numbers of electric vehicles with large batteries. A fleet of electric vehicles is equivalent to an efficient storage capacity system to supplement the energy storage system of the electricity grid. Calculations based on the hourly demand-supply data of ERCOT, a very large electricity grid, show that a fleet of electric vehicles cannot provide all the needed capacity and the remaining capacity must be met by hydrogen. Even though the storage capacity of the batteries is close to 1–2% of the needed storage capacity of the grid, the superior round-trip storage efficiency of batteries reduces the energy dissipation associated with the storage and recovery processes by up to 38% and the total hydrogen storage capacity by up to 50%. The study also shows that anticipated improvements in the round-trip efficiencies of batteries are almost three times more effective than improvements in hydrogen storage systems.

The hybrid electric propulsion system (HEPS) holds clear potential to support the goal of sustainability in the automobile and aviation industry. As an important part of the three-dimensional transportation network, vehicles and aircraft using HEPSs have the advantages of high fuel economy, low emission, and low noise. To fulfill these advantages, the design of their energy management strategies (EMSs) is essential. This paper presents an in-depth review of EMSs for hybrid electric vehicles (HEVs) and hybrid electric aircraft. First, in view of the main challenges of current EMSs of HEVs, the referenced research is reviewed according to the solutions facing real-time implementation problems, variable driving conditions adaptability problems, and multi-objective optimization problems, respectively. Second, the existing research on the EMSs for hybrid electric aircraft is summarized according to the hybrid electric propulsion architectures. In addition, with the advance in propulsion technology and mechanical manufacturing in recent years, flying cars have gradually become a reality, further enriching the composition of the three-dimensional transportation network. And EMSs also play an essential role in the efficient operation of flying cars driven by HEPSs. Therefore, in the last part of this paper, the development status of flying cars and their future prospects are elaborated. By comprehensively summarizing the EMSs of HEPS for vehicles and aircraft, this review aims to provide guidance for the research on the EMSs for flying cars driven by HEPS and serve as the basis for knowledge transfer of relevant researchers.

To support power grid operators to detect and evaluate potential power grid congestions due to the electrification of urban private cars, accurate models are needed to determine the charging energy and power demand of battery electric vehicles (BEVs) with high spatial and temporal resolution. Typically, e-mobility traffic simulations are used for this purpose. In particular, activity-based mobility models are used because they individually model the activity and travel patterns of each person in the considered geographical area. In addition to inaccuracies in determining the spatial distribution of BEV charging demand, one main limitation of the activity-based models proposed in the literature is that they rely on data describing traffic flow in the considered area. However, these data are not available for most places in the world. Therefore, this paper proposes a novel approach to develop an activity-based model that overcomes the spatial limitations and does not require traffic flow data as an input parameter. Instead, a route assignment procedure assigns a destination to each BEV trip based on the evaluation of all possible destinations. The basis of this evaluation is the travel distance and speed between the origin of the trip and the destination, as well as the car-access attractiveness and the availability of parking spots at the destinations.

The applicability of this model is demonstrated for the urban area of Berlin, Germany, and its 448 sub-districts. For each district in Berlin, both the required daily BEV charging energy demand and the power demand are determined. In addition, the load shifting potential is investigated for an exemplary district. The results show that peak power demand can be reduced by up to 31.7% in comparison to uncontrolled charging.

The automotive sector is undergoing a significant transformation to address critical challenges affecting consumers and the climate. One of the most difficult tasks is reducing the weight of vehicles in order to minimize energy consumption. A ten percent decrease in curb weight is predicted to result in a six to eight percent reduction in energy consumption. Composite materials having better strength to weight ratio are one of the finest options for planning, designing and manufacturing of the lightweight components. In automobile sector, employment of composite materials would reduce the weight of electric vehicles as well as influence their aerodynamic properties. Therefore, it would decrease the consumption of fuel as well by cutting down harmful emissions and particulate matter. Numerous developments in such technologies are studied over the last decade by automobile establishments and academic researchers. Fiber-reinforced polymers, particularly those established on glass and carbon fibers, have attracted attention of the automobile sector due to their high performance and lesser weight. This paper reviews the applications of various types of composite materials and the fabrication techniques of such composites in electric vehicles and automobiles. Furthermore, a comprehensive data breakdown of the lightweight materials statistics and figures on market analysis of high performance composite is presented. Finally, a discussion is made on the different applications of these composites. Hence, the details presented in this study should be useful for automobile companies to align with NET ZERO global mission while sustaining their businesses.

Solar powered vehicles are currently being developed towards entirely self-sustaining vehicles that harness their energy directly from the sun. For such vehicles, it is important to optimise their solar exposure while driving, thereby reducing their energy consumption through fossil fuels. Research has emerged to estimate optimised routes for solar vehicles, and this paper builds on this work to expand on the parameters used to calculate the route, thereby improving the energy-harnessing quality of the route together with its overall utility for the driver. The ArcGIS tool and the open weather API are used to predict the solar potential of a vehicle by taking into account shade based on surrounding topography, vehicle type, weather, distance and time of day. The model was implemented as a user mobile application ‘Drive Solar’ that calculates the optimal route for the user based on their preferences for time and energy efficiency. The effectiveness of the prediction model was tested using a solar irradiance sensor in Dublin city. The results show that the model predicts the route with the most energy absorbed with a 51.65% accuracy and chooses the route with the most energy consumed with a 86.65% accuracy. We conclude that Drive Solar can aid in the transition to widespread use of self-sustaining solar vehicles.

To realize the widespread application and continuous functional development of intelligent vehicles, test and evaluation of vehicle's functionality and Safety Performance in complex off-road scenarios are fundamental. Since traditional distance-based road tests cannot meet the evolving test requirements, a method to design the function-based off-road testing scenario library for intelligent vehicles(IV) is proposed in this paper. The testing scenario library is defined as a critical set of scenarios that can be used for IV tests. First, for the complex and diverse off-road scenarios, a hierarchical, structural model of the test scenario is built. Then, the critical test scenarios are selected adaptively according to the vehicle model to be tested. Next, those parameters representing the challenging test scenarios are selected. The selected parameters need to fit the natural distribution probability of scenarios. The critical test-scenario library is built combing these parameters with the structural model. Finally, the test scenarios that are most approximate to the natural driving scenario are determined with importance sampling theory. The test-scenario library built with this method can provide more critical test scenarios, and is widely applicable despite different vehicle models. Verified by simulation in the off-road interaction scenarios, test would be accelerated significantly with this method, about 800 times faster than testing in the natural road environment.