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نتیجه جستجو - Hybrid electric vehicles

تعداد مقالات یافته شده: 30
ردیف عنوان نوع
1 Rule-interposing deep reinforcement learning based energy management strategy for power-split hybrid electric vehicle
استراتژی مدیریت انرژی مبتنی بر یادگیری تقویتی عمیق قانون برای خودروی الکتریکی هیبریدی تقسیم برق-2020
The optimization and training processes of deep reinforcement learning (DRL) based energy management strategy (EMS) can be very slow and resource-intensive. In this paper, an improved energy management framework that embeds expert knowledge into deep deterministic policy gradient (DDPG) is proposed. Incorporated with the battery characteristics and the optimal brake specific fuel consumption (BSFC) curve of hybrid electric vehicles (HEVs), we are committed to solving the optimization problem of multi-objective energy management with a large space of control variables. By incorporating this prior knowledge, the proposed framework not only accelerates the learning process, but also gets a better fuel economy, thus making the energy management system relatively stable. The experimental results show that the proposed EMS outperforms the one without prior knowledge and the other state-of-art deep reinforcement learning approaches. In addition, the proposed approach can be easily generalized to other types of HEV EMSs.
Keywords: Energy management strategy | Hybrid electric vehicle | Expert knowledge | Deep deterministic policy gradient | Continuous action space
مقاله انگلیسی
2 Cooperative control strategy for plug-in hybrid electric vehicles based on a hierarchical framework with fast calculation
استراتژی کنترل تعاونی برای وسایل نقلیه برقی هیبریدی پلاگین بر اساس یک چارچوب سلسله مراتبی با محاسبه سریع-2020
Developing optimal control strategies with capability of real-time implementation for plug-in hybrid electric vehicles (PHEVs) has drawn explosive attention. In this study, a novel hierarchical control framework is proposed for PHEVs to achieve the instantaneous vehicle-environment cooperative control. The mobile edge computation units (MECUs) and the on-board vehicle control units (VCUs) are included as the distributed controllers, which enable vehicle-environment cooperative control and reduce the computation intensity on the vehicle by transferring partial work from VCUs to MECUs. On this basis, a novel cooperative control strategy is designed to successively achieve the energy management planned by the iterative dynamic programming (IDP) in MECUs and the energy utilization management achieved by the model predictive control (MPC) algorithm in the VCU. The performance of raised control strategy is validated by simulation analysis, highlighting that the cooperative control strategy can achieve superior performance in real-time application that is close to the global optimization results solved offline.
Keywords: Cooperative control strategy | Hierarchical framework | Iterative dynamic programming (IDP) | Model predictive control (MPC) | Plug-in hybrid electric vehicles (PHEVs)
مقاله انگلیسی
3 A real-time blended energy management strategy of plug-in hybrid electric vehicles considering driving conditions
یک استراتژی مدیریت انرژی ترکیبی از زمان واقعی خودروهای برقی پلاگین با توجه به شرایط رانندگی-2020
In this study, a blended energy management strategy considering influences of driving conditions is proposed to improve the fuel economy of plug-in hybrid electric vehicles. To attain it, dynamic programming is firstly applied to solve and quantify influences of different driving conditions and driving distances. Then, the driving condition is identified by the K-means clustering algorithm in real time with the help of Global Positioning System and Geographical Information System. A blended energy management strategy is proposed to achieve the real-time energy allocation of the powertrain with incorporation of the identified driving conditions and the extracted rules, which includes the engine starting scheme, gear shifting schedule and torque distribution strategy. Simulation results reveal that the proposed strategy can effectively adapt to different driving conditions with the dramatic improvement of fuel economy and the decrement of calculation intensity and highlight the feasibility of real-time implementation
Keywords: Plug-in hybrid electric vehicles | Energy management strategy | Global optimization | Driving condition | Equivalent driving distance coefficient
مقاله انگلیسی
4 Online energy management strategy of fuel cell hybrid electric vehicles based on rule learning
استراتژی مدیریت انرژی آنلاین از وسایل نقلیه برقی هیبریدی سلول سوختی بر اساس یادگیری قانون-2020
In this paper, a rule learning based energy management strategy is proposed to achieve preferable energy consumption economy for fuel cell hybrid electric vehicles. Firstly, the optimal control sequence of fuel cell power and the state of charge trajectory of lithium-ion battery pack during driving are derived offline by the Pontryagin’s minimum principle. Next, the K-means algorithm is employed to hierarchically cluster the optimal solution into the simplified data set. Then, the repeated incremental pruning to produce error reduction algorithm, as a propositional rule learning strategy, is leveraged to learn and classify the underlying rules. Finally, the multiple linear regression algorithm is applied to fit the abstracted parameters of generated rule set. Simulation results highlight that the proposed strategy can achieve more than 95% savings of energy consumption economy, solved by Pontryagin’s minimum principle, with less calculation intensity and without dependence on prior driving conditions, thereby manifesting the feasibility of online application.
Keywords: Fuel cell hybrid electric vehicle | Energy management strategy | Hierarchical clustering | Rule learning
مقاله انگلیسی
5 Data-driven reinforcement-learning-based hierarchical energy management strategy for fuel cell/battery/ultracapacitor hybrid electric vehicles
استراتژی مدیریت سلسله مراتبی مبتنی بر یادگیری تقویتی مبتنی بر داده برای وسایل نقلیه الکتریکی ترکیبی سلول سوختی / باتری / فوق خازن-2020
A reinforcement-learning-based energy management strategy is proposed in this paper for managing energy system of Fuel Cell Hybrid Electric Vehicles (FCHEV) equipped with three power sources. A hierarchical power splitting structure is employed to shrink large state-action space based on an adaptive fuzzy filter. Then, the reinforcement-learning-based algorithm using Equivalent Consumption Minimization Strategy (ECMS) is proposed for tackling high-dimensional state-action space, and finding a trade-off between global learning and realtime implementation. The power splitting policy based on experimental data is obtained by using reinforcement learning algorithm, which allows for many different driving cycles and traffic conditions. The proposed energy management strategy can achieve low computation cost, optimal fuel cell efficiency and energy consumption economy. Simulation results confirm that, compared with existing learning algorithms and optimization methods, the proposed reinforcement-learning-based energy management strategy using ECMS can achieve high computation efficiency, lower power fluctuation of fuel cell and optimal fuel economy of FCHEV.
Keywords: Fuel cell hybrid electric vehicle | Energy management strategy | Reinforcement learning | Data driven | Hierarchical power splitting
مقاله انگلیسی
6 A Non-Convex Control Allocation Strategy as Energy-Efficient Torque Distributors for On-Road and Off-Road Vehicles
یک استراتژی تخصیص کنترل غیر محدب به عنوان توزیع کننده گشتاور مقرون به صرفه برای وسایل نقلیه جاده ای و خارج از جاده-2020
A Vehicle with multiple drivetrains, like a hybrid electric one, is an over-actuated system that means there is an infinite number of combinations of torques that individual drivetrains can supply to provide a given total torque demand. Energy efficiency is considered as the secondary objective to determine the optimum solution among these feasible combinations. The resulting optimisation problem, which is nonlinear due to the multimodal operation of electric machines, must be solved quickly to comply with the stability requirements of the vehicle dynamics. A theorem is developed for the first time to formulate and parametrically solve the energyefficient torque distribution problem of a vehicle with multiple different drivetrains. The parametric solution is deployable on an ordinary electronic control unit (ECU) as a small-size lookup table that makes it significantly fast in operation. The fuel-economy of combustion engines, load transformations due to longitudinal and lateral accelerations, and traction efficiency of the off-road conditions are integrated into the developed theorem. Simulation results illustrate the effectiveness of the provided optimal strategy as torque distributors of on-road and off-road electrified vehicles with multiple different drivetrains.
Keywords: Traction efficiency | Control allocation | Energy management strategies | Hybrid electric vehicles | Power loss | Multiple drivetrains
مقاله انگلیسی
7 Thorough state-of-the-art analysis of electric and hybrid vehicle powertrains: Topologies and integrated energy management strategies
تجزیه و تحلیل دقیق و پیشرفته از موتورهای برقی و هیبریدی خودرو:: توپولوژی و استراتژی های مدیریت انرژی یکپارچه-2020
Hybrid and electric vehicles have been demonstrated as auspicious solutions for ensuring improvements in fuel saving and emission reductions. From the system design perspective, there are numerous indicators affecting the performance of such vehicles, in which the powertrain type, component configuration, and energy management strategy (EMS) play a key role. Achieving an energy-efficient powertrain requires tackling several conflicting control objectives such as the drivability, fuel economy, reduced emissions, and battery state of charge preservation, which make the EMS the most crucial aspect of powertrain system design. Accordingly, in the present study, various powertrain systems and topologies of (plug-in) hybrid electric vehicles and full-electric vehicles are assessed. In addition, EMSs as applied in the literature are systematically surveyed for a qualitative investigation, classification, and comparison of existing approaches in terms of the principles, advantages, and drawbacks through a comprehensive review. Furthermore, potential challenges considering the gaps in research are addressed, and directives paving the way toward further development of powertrains and EMSs in all respects are thoroughly provided.
Keywords: Plug-in hybrid electric vehicle | Full-electric vehicle | Energy management strategy optimisation | Online EMS | Offline EMS | Optimal control strategy
مقاله انگلیسی
8 Optimization & validation of Intelligent Energy Management System for pseudo dynamic predictive regulation of plug-in hybrid electric vehicle as donor clients
بهینه سازی و اعتبار سنجی سیستم مدیریت انرژی هوشمند برای تنظیم پیش بینی شبه دینامیکی پلاگین در خودروهای برقی هیبریدی به عنوان مشتری دهنده-2020
In developing countries, policies for discarding the existing Internal Combustion (IC) Engine vehicles for faster adoption of Electric Vehicles’ not only creates burden on the existing power grid but also is impractical. The conversion of Conventional IC Engine based Online Taxis or public transport vehicles into Plug-in Hybrid Electric Vehicles donor clients, to participate in Vehicle to Grid & Vehicle to Vehicle power transfer model, is the solution. These vehicles would not only have emissions within compliance standards but would also reduces the load on the power grid meanwhile making an income through power transfer. The Intelligent Energy Management System (IEMS) developed makes use of a Non Dominated Sorting Genetic Algorithm (NSGA-II) based Pseudo dynamic predictive regulation approach on the powertrain to optimize the emissions, fuel cost and traction battery SoC. If the vehicle intends to participate in power transfer, the IEMS would predetermine the amount of SoC that would be used for an upcoming journey using Global Positioning System(GPS) data interconnected with a server unit which enables the IEMS to optimize the operating conditions of the vehicle. The modelled IEMS performance is tested for a given driving cycle with varying traffic levels on a virtual simulation environment using the IPG CarMaker software. A prototype with a 150 cc, 7.5 kW IC engine integrated to a 3 kW BLDC traction motor is developed and the response to the predictive model is evaluated and found to provide 27.66%, 13.73% and 7.72% equivalent energy to micro grid for low, medium and high criticality conditions for the user.
Keywords: Vehicle to grid | NSGA-II optimization | State of charge | Emission | GPS | Real-time validation
مقاله انگلیسی
9 Internet of energy-based demand response management scheme for smart homes and PHEVs using SVM
اینترنت برنامه پاسخگویی به تقاضای مبتنی بر انرژی برای خانه های هوشمند و PHEV با استفاده از SVM-2020
The usage of information and communication technology (ICT) in the power sector has led to the emergence of smart grid (SG). The connected loads in SG are able to communicate their consumption data to the grid using ICT and thus forming a large Internet of Energy (IoE) network. However, various issues such as–increasing demand–supply gap, grid instability, and deteriorating quality of service persist in this network which degrade its performance. These issues can be handled in an efficient way by managing the demand response (DR) of different types of loads. For this purpose, cloud computing can be leveraged to gather the data generated in IoE network and perform analytics to manage DR. Working in this direction, a novel scheme to handle the DR of smart homes (SHs) and plug-in hybrid electric vehicles (PHEVs) is presented in this paper. The proposed scheme is based on analyzing the demand of these users at the cloud server for flattening the overall load profile of grid. This scheme is divided into two hierarchical stages which work as follows. In the first stage, the residential and PHEV users are identified whose demands can be regulated. This task is achieved with the help of a binary-class support vector machine (SVM) which uses Gaussian kernel function to classify these users. In the next stage, the load in SHs is curtailed on the basis of a pre-defined rule-base after analyzing the consumption data of various devices; whereas PHEVs are managed by controlling their charging rates. The efficacy of proposed scheme has been tested on PJM benchmark data and Open Energy Information dataset. The simulation results prove that the proposed scheme is effective in maintaining the overall load profile of SG by managing the DR of SHs and PHEV users.
Keywords: Data analytics | Demand response | Plug-in hybrid electric vehicles | Smart grid | Smart homes | Support vector machine
مقاله انگلیسی
10 Energy management of hybrid electric vehicles: A review of energy optimization of fuel cell hybrid power system based on genetic algorithm
مدیریت انرژی وسایل نقلیه الکتریکی هیبریدی: مروری بر بهینه سازی انرژی سیستم قدرت هیبریدی سلول سوختی بر اساس الگوریتم ژنتیکی-2020
Under the background of current environmental pollution and serious shortage of fossil energy, the development of electric vehicles driven by clean new energy is the key to solve this problem, especially the hybrid electric vehicle driven by fuel cell is the most effective solution. Many scholars have found that the output performance of hybrid system is an important reason to determine the life of fuel cell. Unreasonable output will affect the control characteristics of the drive system, resulting in a series of serious consequences such as the reduction of the life of fuel cell hybrid power system. Therefore, the energy management strategy and performance optimization of hybrid system is the key to ensure the normal operation of the system. At present, many excellent researchers have carried out relevant research in this field. Genetic algorithm is a heuristic algorithm, which has better optimization performance. It can easily choose satisfactory solutions according to the optimization objectives, and make up for these shortcomings by using its own characteristics. These characteristics make genetic algorithm have outstanding advantages in the iterative optimization of energy management strategy. This paper analyzes and summarizes the optimization effect of genetic algorithm in various energy management strategies, aiming to analyze and select the optimization rules and parameters, optimization objects and optimization objectives. This paper hopes to provide guidance for the optimal control strategy and structural design of the fuel cell hybrid power system, contribute to the research on improving the energy utilization efficiency of the hybrid power system and extending the life of the fuel cell, and provide more ideas for the optimization of energy management in the future.
Keywords: Fuel cell hybrid electric vehicle | Energy management strategy | Hybrid power system | Genetic algorithm |Optimization parameters and objectives
مقاله انگلیسی
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بازدید امروز: 1856 :::::::: بازدید دیروز: 0 :::::::: بازدید کل: 1856 :::::::: افراد آنلاین: 48