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Estimation of the Energy Consumption of an Electric Utility Vehicle: A Case Study
Abstract
The development of electric vehicles (EV’s) has been growing in last decade since is a promising technology that will optimize the use of energy. The estimation of energy consumption is a key task to design and select components of power train, control strategies and predict lifecycle. Some factors such as road slope, temperature, type of route, driver’s behavior directly affect the energy consumption. This article provides an easy methodology to estimate energy consumption for an electric utility vehicle (EUV) using Green Race Software, which allows different types of routes, estimate a road slope, energy consumption and percentage of regeneration. From this analysis, is possible to decide the best route for harvesting cocoa and sizing the powertrain of the vehicle before purchasing materials. The most important advantage of the proposal method is that can be used in early stage of design and assembly of EV’s.
... Cacao is the ripe fruit of the cocoa tree, which grows in the tropical regions of Africa and South America. Raw cocoa is one of the most nutritious foods in the world and has been shown to protect the body from free radicals, reduce stress and depression, and protect against heart disease and many types of cancer [1]- [4]. This health benefit of cocoa beans is often granted thanks to the presence of polyphenols. ...
In a country like Colombia, food drying plays an important role in the extension of technology so that farmers improve the quality of their productivity. The objective of this work is to develop a system for acquiring thermodynamic variables, to establish the energy efficiency of a product drying oven, specifically for cocoa beans using Arduino hardware and LabView software. This research begins by establishing the variables to be measured and recorded by the DHT11 sensors, such as temperature and humidity. These sensors are connected to an Arduino Uno board that has an Atmel microcontroller, which captures the information from each of these. An algorithm was also developed that captures the temperature and humidity data and sends it through the serial port to the LabView software, in which the visualization on the front panel and programming in the block diagram have been developed, being viewed from the HMI (Human Machine Interface). For the experimental tests, 3,309 grams of fermented cocoa beans were taken, and dried for 36 hours, removing a total mass of water of 1,650 grams. The results show an energy efficiency of 10.62%, concluding that the drying oven that takes advantage of the residual heat of an 18000 BTU condenser integrated with the proposed variable system is suitable for drying cocoa beans. Despite this efficiency being low, it meets the objective of removing moisture from them.
... It is clear that the distance between the two cities is not the only barrier to electrification. In fact, several studies reports how an high slope can affect the range of autonomy of an EV [18]. The slope profile of the street is reported in Fig. 2. The reason behind the choice of the direction of the travel in this case study, depend on the slope, since the scope of the work is to verify the satisfaction of the service in the worst case condition. ...
Considering the new stringent constraints proposed by public authorities, decarbonization became a key trend in the last years. Transport sector still represents one of the most pollutant fragment. Even if several countries start their process of decarbonization through the introduction of several Electric Vehicles in their public services, still, for several countries, especially the developing ones, transportation represents a hard to abate sector, which generally uses outdated and pollutant vehicles, discouraging the use of public transport and facilitating the creation of traffic congestions. Basing on these considerations, this work want to implement a simulation for a public service in a developing country, evaluating if it is possible to perform a long trip using an electric minibus. Therefore, a case study will be implemented highlighting the barriers of the transport electrification in this area, producing results on consumption and reliability of the service. Finally, an environmentally sustainable solution to power the service will be proposed to highlight the potential of electrification in the area.
The research shows the development process of a 3 m2 flat solar collector, composed of three modules of 1 m2 each, with labyrinth trap geometry, turbulence ridges and thermal energy storage tanks, the latter using different types of phase change materials (PCM), specifically two types of paraffins with melting points in the range of 48 to 58 ℃. Additionally, one of its modules filled with motor oil.During the first phase, a bibliometric exploration methodology is carried out with VOSviewer Software, identifying prominent technologies in the subject, which allows the design and simulation of a prototype using CAD Software. Subsequently, when implementing it, an analysis of the behavior of the vacuum collector with respect to the use of thermal energy storage is carried out, identifying the advantages in the use of PCM. Among the results, a working power close to 450 W and an approximate efficiency of 25% stand out, contrasting with the simulation, the importance of PCMs in increasing the efficiency of these devices used in different applications is concluded.KeywordsFlat solar collectorPCM - Phase Change MaterialsThermal energy storage VOSviewer
Efforts to limit climate change should concern the transportation sector which is responsible for roughly a quarter of greenhouse gas emissions. Aside from vehicle’s technical progress and driver eco-driving awareness, road infrastructure has a role to play in this environmental aim. At the project stage, the design of roads can avoid energy losses linked to marked ramps, but afterwards, during the use phase, road management can be a lever too.
In this use phase framework, our paper is focused on energy saving that can be achieved by managing speed sectioning. The key point is to ensure consistency between vehicle dynamics, road longitudinal profile and speed policy. Indeed, eco-driving could be impeded if a limiting speed sign is encountered on a steep slope or in a sharp turn. In such a situation the speed sign will be qualified as misplaced. Mechanical braking has then to be used instead of simple natural deceleration. In 2018 the French government lowered authorized speed on secondary roads, from 90 to 80 km/h, with road safety as the primary motivation. In order to assess energy impact of speed-sectioning for these two speed limits, experiments have been carried out in four experimental sites. Furthermore criterion and dissipated energy computation have been developed. The developed energy computation yields to determine the expected fuel economy for the entire traffic over a day on a selected route or network.
As a result, over consumption for a misplaced speed sign can reach up to 40 liters of fuel per day with an approaching speed of 80 km/h and 50 liters of fuel per day with an approaching speed of 90 km/h according to traffic data. Significant energy savings could therefore be achieved by sign placement optimization.
This work compares the Micro-trips (MT), Markov chains–Monte Carlo (MCMC) and Fuel-based (FB) methods in their ability of constructing driving cycles (DC) that: (i) describe the real driving patterns of a given region and (ii) reproduce the real fuel consumption and emissions exhibited by the vehicles in that region. To that end, we selected four regions and monitored simultaneously the speed, fuel consumption and emissions of CO2, CO and NOx from a fleet of 15 buses of the same technology during eight months of normal operation. The driving patterns exhibited by drivers in each region were described in terms of 23 characteristic parameters (CPs) such as average speed and average positive kinetic energy. Then, for each region, we constructed their DC using the MT method and evaluated how close it describes the observed driving pattern in each region. We repeated the process using the MCMC and FB methods. Given the stochastic nature of MT and MCMC methods, the DCs obtained changed every time the methods were applied. Hence, we repeated the process of constructing the DCs up to 1000 times and reported their average relative differences and dispersion. We observed that the FB method exhibited the best performance producing DCs that describe the observed driving patterns. In all the regions considered in this study, the DCs produced by this method showed average relative differences smaller than 20% for all the CPs considered. A similar performance was observed for the case of fuel consumption and emission of pollutants.
With serious concerns on environmental problems related to CO emissions and energy crisis, special attention has recently been paid to Electric Vehicles (EVs). Specifically, an accurate estimation of energy consumption for EV is still a big issue. Therefore, quantifying the impact of various external factors on EV energy consumption, such as road topology, driver profile and traffic conditions is a necessity. In this paper, we review the energy consumption models for EVs. Then, we analyze the impact of the road gradient on EV energy consumption through a set of real world experimentations. The obtained results, allow us to better understand the impact of upward and downward gradient on both energy consumption and energy recuperation processes.
Accurate estimation of vehicles’ energy consumption is a demanding task. It might not be so critical for conventional vehicles because of their high travel range; however, this is something important for electric vehicles (EVs). On the other hand, EVs with less energy on board need more accurate energy management systems. This study focuses on the development of an energy consumption estimation model to be used in an EV fleet management system (FMS). The proposed estimator consists of a vehicle model, a driver model, and terrain models. It is demonstrated that a combination of these three parts can provide an accurate estimation of EV energy consumption on a particular route. As part of this study, a commercially available passenger car is modeled using MATLAB/Simulink. A number of specific routes are selected for EV road testing to be driven for simulation model verification. In the second part of this study, the impact of energy consumption estimation accuracy is investigated at a larger scale for a fleet of EVs. It is quantitatively demonstrated how much sensitive is the performance of a FMS to the accuracy of the energy estimator. Simulation results have shown that the total energy consumption of an EV fleet is decreased significantly by improving the estimation accuracy. It is also demonstrated how the uncertainties in EV energy consumption estimation limit the overall performance of a FMS.
We propose a novel statistical description of the physical properties of road transport operations by using stochastic models arranged in a hierarchical structure. The description includes speed signs, stops, speed bumps, curvature, topography, road roughness and ground type, with a road type introduced at the top of the hierarchy to group characteristics that are often connected. Methods are described how to generate data on a form (the operating cycle format) that can be used in dynamic simulations to estimate energy usage and CO2 emissions. To showcase the behaviour of the description, two examples are presented using a modular vehicle model for a heavy-duty truck: a sensitivity study on impacts from changes in the environment, and a comparison study on a real goods transport operation with respect to energy usage. It is found that the stop intensity and topography amplitude have the greatest impact in the sensitivity study (8.3% and 9.5% respectively), and the comparison study implies that the statistical description is capable of capturing properties of the road that are significant for vehicular energy usage. Moreover, it is discussed how the statistical description can be used in a vehicle design process, and how the mean CO2 emissions and its variation can be estimated for a vehicle specification.
Vehicle energy economy is affected by different driving styles of individual drivers. To improve energy economy of plug-in hybrid electric vehicles (PHEVs), it is of great importance to develop the driving style adaptive optimal control strategy. In fact, driving styles are often influenced and restricted by different driving cycles. Therefore, to recognize driving style more accurately, this paper decouples driving styles from driving cycles. Based on classification and identification of driving cycles, the accelerator pedal opening and its change rate in different driving cycles are analyzed and the fuzzy-logic recognizer is built to identify driving styles. Afterwards, the driving style adaptive optimal control strategy is realized by combining the recognized driving style with the equivalent consumption minimization strategy (ECMS) and adopting a hybrid particle swarm optimization-genetic algorithm (PSO-GA) to optimize the relationship between the driving style and the equivalence factor (EF). The effectiveness of proposed driving style adaptive control strategy is validated by real vehicle test, which indicates that, compared with the original ECMS, the proposed driving style recognition based adaptive optimal control strategy improves the energy economy by 3.69% in the New European Driving Cycle (NEDC). This adaptive optimal strategy provides guidance for incorporating driving style into PHEV energy management strategy to improve fuel economy.
This paper presents a novel braking torque distribution strategy for electric vehicles with four in-wheel motors equipped with the regenerative braking system. Safety and regeneration efficiency are the two major themes in the design of a regenerative braking system. Based on model predictive control (MPC) theory, the issues of multiple objectives and constraints of the regenerative braking system is well addressed. This proposed torque distribution controller can maximize the regeneration efficiency by determining the hydraulic braking torque and motor braking torque, which subject to the actuator constraints. Total braking torque of the four wheels tracks the braking requirement. Furthermore, in the case of ensuring brake safety, the braking torque of front and rear wheels are optimized to improve the possibility of energy recovery. The simulations have been conducted in Simulink environment based on an electric vehicle model established in AMESim software to verify the advantage of the proposed model predictive controller. In addition, the real-time test also demonstrates the effectiveness of the presented optimal control strategy.