Search Results (22)

Fuel economy has long been the core control objective in the energy management strategies of extended-range electric vehicles (EREVs), but little research has considered real driving emissions. In this paper, the real driving emissions of an EREV are investigated, and the abnormal pollutant emissions caused by engine start–stop events are clarified. Accordingly, an interpolated-startup-corrected method is proposed to construct real driving emission models. Next, an optimization problem is constituted with real driving emissions as the constraints and fuel consumption as the objective. The optimization problem is solved using a dynamic programming (DP) algorithm embodying the interpolated-startup-corrected emission models, and the start–stop reduction strategies and condition migration strategies are derived. Compared to the strategy without the emission constraints, the CO and NO_x emissions under the no-start–stop strategy are cut down by about 70%; the PN emissions are even orders of magnitude lower. Meanwhile, the condition migration strategy can compromise the fuel economy and pollutant emissions by adjusting the engine operating points, thus possibly limiting pollutant emissions beyond the start–stop reduction strategy. Full article

The efficiency of hybrid electric powertrains is heavily dependent on energy and power management strategies, which are sensitive to the dynamics of the powertrain components that they use. In this study, a Modified Particle Swarm Optimization (Modified PSO) methodology, which incorporates novel concepts such as the Vector Particle concept and the Seeded Particle concept, has been developed to minimize the fuel consumption and NOx emissions for an extended-range electric vehicle (EREV). An optimization problem is formulated such that the battery state of charge (SOC) trajectory over the entire driving cycle, a vector of size 50, is to be optimized via a control lever consisting of 50 engine/generator speed points spread over the same 2 h cycle. Thus, the vector particle consisted of the battery SOC trajectory, having 50 elements, and 50 engine/generator speed points, resulting in a 100-D optimization problem. To improve the convergence of the vector particle PSO, the concept of seeding the vector particles was introduced. Additionally, further improvements were accomplished by adapting the Time-Varying Acceleration Coefficients (TVAC) PSO and Frankenstein’s PSO features to the vector particles. The MATLAB/SIMULINK platform was used to validate the developed commercial vehicle hybrid powertrain model against a similar ADVISOR powertrain model using a standard rule-based PMS algorithm. The validated model was then used for the simulation of the developed, modified PSO algorithms through a multi-objective optimization strategy using a weighted sum fitness function. Simulation results show that a fuel consumption reduction of 12% and a NOx emission reduction of 35% were achieved individually by deploying the developed algorithms. When the multi-objective optimization was applied, a simultaneous reduction of 9.4% fuel consumption and 7.9% NOx emission was achieved when compared to the baseline model with the rule-based PMS algorithm. Full article

This paper investigates a cup-rotor permanent-magnet doubly fed machine (CRPM-DFM) for extended-range electric vehicles (EREVs). The topology and operating principle of the powertrain system based on CRPM-DFM are introduced. Then, the mathematical model of CRPM-DFM is established and the feedback linearization control of CRPM-DFM is given to realize the decoupling control of flux and torque. Moreover, the torque characteristic of CRPM-DFM is analyzed and the load torque boundaries with sinusoidal steady-state solution of CRPM-DFM is deduced. In addition, the MTPA control is derived to improve the efficiency of CRPM-DFM, and the efficiency of CRPM-DFM regarding various operating modes is investigated. Furthermore, the speed optimization strategy of ICE is proposed to reduce fuel consumption. Finally, the driving performance and fuel economy of the powertrain system are verified by simulation. Full article

Extended-Range Electric Vehicles (EREVs) are intended to improve the range of battery electric vehicles and thus eliminate drivers’ concerns about running out of energy before reaching the desired destination. This paper gives an insight into EREV’s performance operating according to the proposed control strategy over various driving cycles, including the Worldwide Harmonized Light-duty Test Cycle Class 3b (WLTC 3b), Federal Test Procedure (FTP-75), and China Light-Duty Vehicle Test Cycle (CLTC-P). Simulation runs were performed in Matlab-Simulink^® for different cases of drive range, electricity mix, and vehicle mass. The control strategy goal was to aim at a specified value of battery state of charge at the targeted range value. The obtained test results included: pure electric drive range, acceleration times, EREV range tests, control strategy range errors, Range Extender (REX) utilization metric and distribution of its engagement instances, fuel consumption, total equivalent CO₂ emission, powertrain efficiency, and specific energy consumption. The control strategy operated on average with a range error of −1.04% and a range mean square error of 2.13%. Fuel consumption (in range extension mode) varied between 1.37 dm³/100 km (FTP-75) and 6.85 dm³/100 km (WLTC 3b Extra-High 3). CO₂eq emission was 95.3–244.2 g/km for Poland, 31.0–160.5 g/km for EU-27, and 1.2–147.6 g/km for Sweden. This paper is a valuable source of information for scientists and engineers seeking to learn the advantages and shortcomings of EREV drives with a proposed control strategy, based on various sets of results. Full article

New energy vehicles (NEVs), especially electric vehicles (EVs), address the important task of reducing the greenhouse effect. It is particularly important to measure the environmental efficiency of new energy vehicles, and the life cycle analysis (LCA) model provides a comprehensive evaluation method of environmental efficiency. To provide researchers with knowledge regarding the research trends of LCA in NEVs, a total of 282 related studies were counted from the Web of Science database and analyzed regarding their research contents, research preferences, and research trends. The conclusion drawn from this research is that the stages of energy resource extraction and collection, carrier production and energy transportation, maintenance, and replacement are not considered to be research links. The stages of material, equipment, and car transportation and operation equipment settling, and forms of use need to be considered in future research. Hydrogen fuel cell electric vehicles (HFCEVs), vehicle type classification, the water footprint, battery recovery and reuse, and battery aging are the focus of further research, and comprehensive evaluation combined with more evaluation methods is the direction needed for the optimization of LCA. According to the results of this study regarding EV and hybrid power vehicles (including plug-in hybrid electric vehicles (PHEV), fuel-cell electric vehicles (FCEV), hybrid electric vehicles (HEV), and extended range electric vehicles (EREV)), well-to-wheel (WTW) average carbon dioxide (CO₂) emissions have been less than those in the same period of gasoline internal combustion engine vehicles (GICEV). However, EV and hybrid electric vehicle production CO₂ emissions have been greater than those during the same period of GICEV and the total CO₂ emissions of EV have been less than during the same period of GICEV. Full article

For smart cities using clean energy, optimal energy management has made the development of electric vehicles more popular. However, the fear of range anxiety—that a vehicle has insufficient range to reach its destination—is slowing down the adoption of EVs. The integration of an auxiliary power unit (APU) can extend the range of a vehicle, making them more attractive to consumers. The increased interest in optimizing electric vehicles is generating research around range extenders. These days, many systems and configurations of extended-range electric vehicles (EREVs) have been proposed to recover energy. However, it is necessary to summarize all those efforts made by researchers and industry to find the optimal solution regarding range extenders. This paper analyzes the most relevant technologies that recover energy, the current topologies and configurations of EREVs, and the state-of-the-art in control methods used to manage energy. The analysis presented mainly focuses on finding maximum fuel economy, reducing emissions, minimizing the system’s costs, and providing optimal driving performance. Our summary and evaluation of range extenders for electric vehicles seeks to guide researchers and automakers to generate new topologies and configurations for EVs with optimized range, improved functionality, and low emissions. Full article

The U.S. Department of Energy’s Vehicle Technologies Office (DOE-VTO) supports research and development (R&D), as well as deployment of efficient and sustainable transportation technologies, that will improve energy efficiency and fuel economy and enable America to use less petroleum. To accelerate the creation and adoption of new technologies, DOE-VTO has developed specific targets for a wide range of powertrain technologies (e.g., engine, battery, electric machine, lightweighting, etc.). This paper quantifies the impact of VTO R&D on vehicle energy consumption and cost compared to expected historical improvements across vehicle classes, powertrains, component technologies and timeframes. We have implemented a large scale simulation process to develop and simulate tens of thousands of vehicles on U.S. standard driving cycles using Autonomie, a vehicle simulation tool developed by Argonne National Laboratory. Results demonstrate significant additional reductions in both cost and energy consumption due to the existence of VTO R&D targets compared to predicted historical trends. It is observed that, over time, the fuel consumption of different electrified vehicles is expected to decrease by 40–50% and a reduction of 45–55% for vehicle manufacturing costs owing to significant improvements through various VTO R&D targets. Full article

The environmental impact of the road transport sector, together with urban freight transport growth, has a notable repercussions in global warming, health and economy. The need to reduce emissions caused by fossil fuel dependence and to foster the use of renewable energy sources has driven the development of zero-emissions powertrains. These clean transportation technologies are not only necessary to move people but to transport the increasing demand for goods and services that is currently taking place in the larger cities. Full electric battery-powered vans seem to be the best-placed solution to the problem. However, despite the progress in driving range and recharge options, those and other market barriers remain unsolved and the current market share of battery electric vehicles (BEVs) is not significant. Based on the development of hydrogen fuel cell stacks, this work explains an emerging powertrain architecture concept for N1 class type vans, that combines a battery-electric configuration with a fuel cell stack powered by hydrogen that works as a range extender (FC-EREV). A literature review is conducted, with the aim to shed light on the possibilities of this hybrid light-duty commercial van for metropolitan delivery tasks, providing insights into the key factors and issues for sizing the powertrain components and fuel management strategies to meet metropolitan freight fleet needs. Full article

The extended-range electric vehicle (E-REV) can solve the problems of short driving range and long charging time of pure electric vehicles, but it is necessary to control the engine working points and allocate the power of the energy sources reasonably. In order to improve the fuel economy of the vehicle, an energy management strategy (EMS) that can adapt to the daily driving characteristics of the driver and adjust the control parameters online is proposed in this paper. Firstly, through principal component analysis (PCA) and iterative self-organizing data analysis techniques algorithm (ISODATA) of historical driving data, a typical driving cycle which can describe driving characteristics of the driver is constructed. Then offline optimization of control parameters by adaptive simulated annealing under each typical driving cycle and online recognition of driving cycles by extreme learning machine (ELM) are applied to the adaptive multi-workpoints energy management strategy (A-MEMS) of E-REV. In the end, compared with traditional rule-based control strategies, A-MEMS achieves good fuel-saving and emission-reduction result by simulation verification, and it explores a new and feasible solution for the continuous upgrade of the EMS. Full article

Extended range electric vehicles (EREVs) operate both as an electric vehicle (EV) and as a hybrid electric vehicle (HEV). As a hybrid, the on-board range extender (REx) system provides additional energy to increase the feasible driving range. In this paper, we evaluate an experimental research EREV based on the 2016 Chevrolet Camaro platform for optimal energy management control. We use model-in-loop and software-in-loop environments to validate the data-driven power loss model of the research vehicle. A discussion on the limitations of conventional energy management control algorithms is presented. We then propose our algorithm derived from adaptive real-time dynamic programming (ARTDP) with a distance constraint for energy consumption optimization. To achieve a near real-time functionality, the algorithm recomputes optimal parameters by monitoring the energy storage system’s (ESS) state of charge deviations from the previously computed optimal trajectory. The proposed algorithm is adaptable to variability resulting from driving behavior or system limitations while maintaining the target driving range. The net energy consumption evaluation shows a maximum improvement of 9.8% over the conventional charge depleting/charge sustaining (CD/CS) algorithm used in EREVs. Thus, our proposed algorithm shows adaptability and fault tolerance while being close to the global optimal solution. Full article

The bird swarm algorithm (BSA) is a bio-inspired evolution approach to solving optimization problems. It is derived from the foraging, defense, and flying behavior of bird swarm. This paper proposed a novel version of BSA, named as BSAII. In this version, the spatial distance from the center of the bird swarm instead of fitness function value is used to stand for their intimacy of relationship. We examined the performance of two different representations of defense behavior for BSA algorithms, and compared their experimental results with those of other bio-inspired algorithms. It is evident from the statistical and graphical results highlighted that the BSAII outperforms other algorithms on most of instances, in terms of convergence rate and accuracy of optimal solution. Besides the BSAII was applied to the energy management of extended-range electric vehicles (E-REV). The problem is modified as a constrained global optimal control problem, so as to reduce engine burden and exhaust emissions. According to the experimental results of two cases for the new European driving cycle (NEDC), it is found that turning off the engine ahead of time can effectively reduce its uptime on the premise of completing target distance. It also indicates that the BSAII is suitable for solving such constrained optimization problem. Full article

The extended range electric vehicle (EREV) can store much clean energy from the electric grid when it arrives at the charging station with lower battery energy. Consuming minimum gasoline during the trip is a common goal for most energy management controllers. To achieve these objectives, an intelligent energy management controller for EREV based on dynamic programming and neural networks (IEMC_NN) is proposed. The power demand split ratio between the extender and battery are optimized by DP, and the control objectives are presented as a cost function. The online controller is trained by neural networks. Three trained controllers, constructing the controller library in IEMC_NN, are obtained from training three typical lengths of the driving cycle. To determine an appropriate NN controller for different driving distance purposes, the selection module in IEMC_NN is developed based on the remaining battery energy and the driving distance to the charging station. Three simulation conditions are adopted to validate the performance of IEMC_NN. They are target driving distance information, known and unknown, changing the destination during the trip. Simulation results using these simulation conditions show that the IEMC_NN had better fuel economy than the charging deplete/charging sustain (CD/CS) algorithm. More significantly, with known driving distance information, the battery SOC controlled by IEMC_NN can just reach the lower bound as the EREV arrives at the charging station, which was also feasible when the driver changed the destination during the trip. Full article

Vehicle manufacturers offer incentives in an attempt to encourage consumers to purchase or lease new vehicles. Similarly federal and local governments offer incentives to help build and maintain a market for plug-in electric vehicles (1). This paper analyzes manufacturer cash rebates and special lease offers and presents a comparison of plug-in electric vehicle incentives by manufacturer, technology, geographic area, and time period. How these manufacturer plug-in electric vehicle incentives relate to vehicle sales as well as state and federal government incentives is also investigated. Full article

Meeting the worldwide energy demand for the present and future transportation systems with the least impact on the environment is a big challenge. In the i-NEXT (Innovation for greeN Energy and eXchange in Transportation) project a Fuel Cell Hybrid Electric Vehicle (FCHEV) minibus for people transportation has been implemented. This paper reports some preliminary test drive. The vehicle architecture has been developed considering that, both recharge time and autonomy of a purely electric vehicle are operational limits, and the fuel cell technology is able to enhance these parameters. An electric engine with lithium ion batteries and a 20 kW Fuel Cell System characterize the vehicle. The test drive has been carried out in Capo d’Orlando municipality (Sicily) allowing the acquisition of key data. Full article

Emission regulations are strict globally and oil price goes up continuously. There are many researches for eco-friendly vehicle to solve these problems. Among them, extended-range electric vehicle (E-REV) utilizes electric energy directly and can drive extended range by generating additional energy. It has characteristics of both an electric vehicle (EV) and hybrid electric vehicle (HEV). According to state of charge (SOC) for battery, E-REV can drive either EV mode or HEV mode. In this study, effect of regenerative braking is analysed to improve fuel economy for the E-REV bus when vehicle drives as EV mode. In advance, sizing of components is conducted to develop forward simulator for calculating fuel economy. The forward simulator is developed using Matlab/Simulink. Considering performance for battery, limited regenerative braking is applied in the forward simulator and the effect of regenerative braking is analysed when driving cycles are determined. And then effect of coast driving is analysed by comparing to constant speed driving. Effect of regenerative braking when vehicle is coasting is verified and this result can be utilized to develop control logic for regenerative braking in further research. Full article