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Harnessing big data for estimating the energy consumption and driving range of electric vehicles
Abstract
Analyzing the factors that affect the energy efficiency of vehicles is crucial to the overall improvement of the environmental efficiency of the transport sector, one of the top polluting sectors at the global level. This study analyses the energy consumption rate (ECR) and driving range of battery electric vehicles (BEVs) and provides insight into the factors that affect their energy consumption by harnessing big data from real-world driving. The analysis relied on four data sources: (i) driving patterns collected from 741 drivers over a two-year period; (ii) drivers’ characteristics; (iii) road type; (iv) weather conditions. The results of the analysis measure the mean ECR of BEVs at 0.183 kW h/km, underline a 34% increase in ECR and a 25% decrease in driving range in the winter with respect to the summer, and suggest the electricity tariff for BEVs to be cost efficient with respect to conventional ones. Moreover, the results of the analysis show that driving speed, acceleration and temperature have non-linear effects on the ECR, while season and precipitation level have a strong linear effect. The econometric model of the ECR of BEVs suggests that the optimal driving speed is between 45 and 56 km/h and the ideal temperature from an energy efficiency perspective is 14 °C. Clearly, the performance of BEVs highly depends on the driving environment, the driving patterns, and the weather conditions, and the findings from this study enlighten the consumers to be more informed and manufacturers to be more aware about the actual utilization of BEVs.
... A big data analytics-based research investigation estimated the average ECR of BEVs to be about 0.183 kWh/km, and that there is about a 34% increase in the energy consumption rate during the winter compared with the summer months [58]. ...
... Accordingly, with every additional 100 kg of vehicle mass, energy consumption increases by about 0.20 ± 0.06 kWh/100 km [56]. Driving behavior, speed, and weather conditions such as temperature and precipitation determine energy efficiency [58]. ...
This paper aims to provide small- and medium-sized enterprises (SMEs) owned by families with a simple, achievable technical methodology for the assessment of sustainable mobility alternatives, in particular, the purchase of electric vehicles (EVs) and photovoltaic (PV) systems. By adopting a comprehensive comparative analysis approach, this research aims to empower SMEs to make highly informed decisions concerning the choice of vehicles and energy systems that provide strategic and sustainable value. Based on a quantitative analysis linked to the total costs over ten years, and considering the different types of vehicles (electric, hybrid, and combustion) and the integration of PV systems, practical formulas are used to calculate the total cost of ownership (TCO), energy consumption, and CO2 emissions. The results show that adopting electric vehicles, especially those complemented by photovoltaic systems with storage for night-time charging, can significantly reduce operating costs and carbon emissions, generating economic and environmental value. This study provides an accessible and applicable approach to the context of family SMEs, facilitating the analysis and choice of mobility options based on simple and commercially available data. By focusing on value creation through informed and strategic decisions, this work offers a relevant contribution to the competitiveness and sustainability of SMEs, promoting the adoption of sustainable mobility technologies in an integrated and effective manner.
... microscopic, mesoscopic and macroscopic), at which they were developed for different applications. The macroscopic models [8][9][10][11][12][13], are those which can be used to estimate EVs total energy consumption at the trip level which include number of connecting roads with different traffic conditions. The mesoscopic models [14][15][16][17], are the models used to estimate the EVs energy consumption for each road in the road network so that energy consumption cost can be assigned to each road in the road network and then can be used to plan the optimal route. ...
... The approaches developed so far for EVs energy consumption estimation have either used simulation techniques [23][24][25] or regression based techniques like linear regression [13,20,26], polynomial regression [8,12,14,15,19], logarithmic regression [11]. A few Neural Network (NN) [9,10], Neuro Fuzzy [27,28] and Convolutional Neural Network (CNN) [22] based techniques were also developed. ...
To overcome range anxiety problem of Electric Vehicles (EVs), an accurate real-time energy consumption estimation is necessary, which can be used to provide the EV's driver with information about the remaining range in real-time. A hybrid CNN-BDT approach has been developed, in which Convolutional Neural Network (CNN) is used to provide an energy consumption estimate considering the effect of temperature, wind speed, battery's SOC, auxiliary loads, road elevation, vehicle speed and acceleration. Further, Bagged Decision Tree (BDT) is used to fine tune the estimate. Unlike existing techniques, the proposed approach doesn't require internal vehicle parameters from manufacturer and can easily learn complex patterns even from noisy data. Comparison results with existing techniques show that the developed approach provides better estimates with least mean absolute energy deviation of 0.14.
... However, Bingham et al. did not explore the link between road type and energy consumption in their study [31]. Fetene et al. [32] researched road types, but their findings showed no significant difference in consumption rates between on-and off-motorway driving, though this was based on a single measurement. This highlights a gap in the literature regarding the impact of road type on electric vehicle efficiency. ...
... Accurate knowledge of energy efficiency for various road sections is essential for modelling and planning energy-optimal routes. Electric vehicle drivers often choose routes they believe will lower energy consumption [32], but this behavior needs more scientific validation. Most existing studies rely on simulations, and real traffic condition studies are scarce. ...
Energy consumption in electric vehicles is a key element of their operation, determining energy efficiency and one of its main indicators, i.e., range. Therefore, in this article, mathematical models were developed to evaluate the impact of selected factors on energy consumption in electric vehicles. The phenomenon of energy recuperation was also examined. The study used data from mileage measurements of the electric vehicle (EV) driving on a motorway and in built-up areas. The results obtained showed a strong correlation between acceleration, vehicle speed, battery power, and energy consumption. In urban conditions, engine RPM and vehicle speed had an additional impact on energy consumption. Findings from this study can be used to optimize vehicle acceleration control modules to increase their range, develop eco-driving styles for EV drivers, and better understand the energy efficiency factors of EVs.
... These additional layers significantly increase the model's complexity and make it difficult to adapt for real-time demand predictions. To address this issue, the authors in [37] used EV driving datasets with factors such as environmental and traffic conditions, weather variations, road type and topography, driving behavior, etc. to estimate the variations in the energy consumption rate (ECR) per km in relation to weather variations and driving conditions. A temperature-dependent EV ECR estimation model is also developed in [38] for a single EV in Kuwait using a predetermined driving route. ...
... A temperature-dependent EV ECR estimation model is also developed in [38] for a single EV in Kuwait using a predetermined driving route. However, ECR estimates based on vehicular aerodynamics (as in [37]) or weather conditions (as in [36], [38]) do not account for the potential correlation between the trip lengths and these variables. In addition, the models reported in these works do not incorporate the spatial domain in their energy consumption analysis. ...
The growing global interest in developing environment-friendly and sustainable transportation solutions is motivating mass adoption of Electric Vehicles (EVs). This increasing EV penetration is anticipated to result in a growing electricity demand to address the EV charging requirements. Therefore, precise demand modeling is essential to enable optimal sizing of the electricity generation and distribution networks as well as optimal placement of the EV charging infrastructure. Furthermore, microscopic modeling of EV traffic patterns and trip-wise energy requirements is essential to enable effective charging coordination and demand distribution for on-the-move EVs. However, microscopic EV demand modeling is typically hindered by the scarcity of open-access data that integrates EV charging and driving patterns. Accordingly, this work proposes a methodology for microscopic modeling of the trip-wise electricity demand of mobile EVs in the spatial and temporal domains, using both multiple linear regression and spatial autoregressive models. Secondary open-access data is extracted, wrangled, and pre-processed from a number of data sources to test and validate the proposed methodology on a case study of Dubai – UAE, acknowledging the growing EV adoption rates in the city. The proposed models are benchmarked against baseline models to confirm their superior performance.
... However, existing agent-or activity-based demand prediction models rarely considers variations in either the travel demand or supply side, such as demand variation between weekday and weekend or roadway capacity variation on links with or without incidents. For instance, some literature finds that traffic conditions (e.g., traffic speed, number of stops) can largely affect the energy consumption of EVs (Fetene, 2017;Qi et al. (2018); Zhang et al. (2020)), which thereby influences the timing and location of charging activities. ...
... However, for a large-scale road network with tens of thousands of EVs, a more general assumption is always made to use the average travel speed to indicate the ECR (e.g., KWh/km) for a specific EV trip. This study adopted the empirical equation of the relationship between average speed and ECR derived by Fetene et al (2017) to calculate the electricity consumption of each EV trip, which was based on data collected from GPS data loggers installed on 200 BEVs used by 741 drivers for 276,102 trips and about 2.3 million km traveled. The equation is shown in Equation (12), where v refers to the average travel speed. ...
Transportation system electrification is expected to bring millions of electric vehicles (EVs) on road within decades. Accurately predicting the charging demand is necessary to accommodate the surge in EV deployment. This paper presents a novel modeling framework to predict the public charging demand profile derived from people’s travel trajectories, with the consideration of the demand and supply stochasticity of transportation systems and the charging behavior heterogeneity of EV users. The vehicle charging decision-making process is explicitly modeled, and the charging need of each EV user is estimated associated with their travel trajectories. The methodology enables charging demand prediction with a high spatial–temporal resolution for transportation system electrification planning. A case study was conducted in Los Angeles County to predict the demand for public charging facilities in 2035 and perform corresponding spatial–temporal analysis of EV public charging under various scenarios of future electrification levels and network conditions.
... Previous light-duty EV research has successfully adopted simulation-based models, machine learning models (e.g., regression, PCA, and tree-based models), and neural networks to identify features that most strongly impact vehicle efficiency to guide fleets' actions. Energy efficiency and range were found to be strongly correlated with a vehicle's battery capacity [14,15], speed profile [15][16][17][18], weight [15], acceleration [15], and road profile [17]. While light-duty EV energy efficiency has been widely studied using real-world big data-driven methodologies, there remains a knowledge gap in predicting the energy efficiency and range of MHD EVs. ...
... Veh. J. 2023, 14,330 3 of 17 their conventional diesel internal combustion engine (ICE) counterparts, (2) generated a machine learning model to predict energy efficiency and highlight significantly impactful features, and (3) applied the model to predict operational range for transit buses and HD trucks in both local and regional duty cycles in four U.S. cities. ...
While the market for medium- and heavy-duty battery-electric vehicles (MHD EVs) is still nascent, a growing number of these vehicles are being deployed across the U.S. This study used over 2.3 million miles of operational data from multiple types of MHD EVs across various regions and operating conditions to address knowledge gaps in total cost of ownership and operational range. First, real-world energy cost savings were determined: MHD fleets should experience energy cost savings each year from 2021 to 2035, regardless of vehicle platform, with the greatest savings seen in transit buses (up to USD 4459 annually) and HD trucks (up to USD 3284 annually). Second, to help fleets across various geographies throughout the U.S. assess the suitability of EVs for their year-round operating needs, operational range was modeled using the XGBoost algorithm (R2: 70%) given 22 input features relevant to vehicle efficiency. Finally, this paper recommends (1) that MHD fleets apply energy-saving practices to minimize the impacts of cold temperatures and high congestion levels on vehicle efficiency and range, and (2) that local hauling fleets select trucks with a nominal range nearly double the expected maximum daily range to account for range losses under local, urban driving conditions.
... Except for current and voltage directly related to energy consumption of electric powertrains, descending feature impact trends are velocity, road slope, temperature load, SOC, acceleration, and payload. In the experiment conducted in this study, as well as in previous research (Fetene et al., 2017), it was found that vehicle speed has a significant impact on consumption, particularly in the ranges of approximately 0-30 km/h and above 80 km/h, where energy consumption is particularly high. This finding aligns with the evaluation results. ...
... The temperature load, primarily stemming from air conditioning demand in this study, exhibits a substantial impact on the BEVs employed in tropical terrains, with the highest temperature load up to 20 degrees Celsius. The SOC was also found to have an impact on energy consumption, particularly when the battery is near full charge (Fetene et al., 2017). Therefore, in this study, the SOC was considered in the range of 20-80% to avoid high consumption rates without regenerative braking systems, which most EVs automatically turn off in fully charged states. ...
The transportation industry is undergoing a major shift towards electrification to mitigate carbon emissions and decrease reliance on fossil fuels in response to global climate change and greenhouse gas concerns. Recently, battery electric vehicles (BEVs) have made significant progress and are becoming a more viable option for achieving zero-emission transportation. The aim of this study is to investigate the energy consumption patterns of BEVs operating in real-world driving scenarios encompassing various route conditions. The vehicle sensor data employed in this study was acquired through onboard diagnostic devices that directly gathered raw data and subsequently transmitted it to mobile applications. Various significant factors, such as payload, road slope level, speed range, acceleration, and loads of heating, ventilation, and air conditioning, are considered as variables influencing energy consumption. By utilizing the large amount of data collected, machine learning (ML) techniques were applied to develop a predictive model of energy consumption and identify variables that influence energy consumption. The outcomes of this study hold the potential to offer guidance to transportation policy-makers and furnish valuable insights for prospective buyers considering BEVs. Furthermore, the application of ML in the development of a predictive model demonstrated efficacy and exhibits promising potential for wider-ranging applications.
... El-Taweel et al. (2021) investigated the impact of the number of stops and signals encountered during bus travel from a road topology perspective. While these studies primarily focused on one aspect of factors, but as more data can be acquired, different categories were combined to estimate the energy consumption (Fetene et al., 2017). Vepsä lä inen et al. (2019) proposed an energy demand estimation model, highlighting temperature, rolling friction coefficient, and load as the most significant. ...
... The provision of accurate data on energy consumption and driving range in electric vehicles is considered to be of significant importance to alleviate customer concerns and encourage the widespread adoption of electric vehicles. This matches the findings of the aforementioned study, which states that changes in the energy consumption and range of electric vehicles under different weather and driving conditions are considered to be the main obstacle to their adoption by end users (Fetene et al., 2017). Consequently, it is argued that there is a pressing need for a more comprehensive understanding and measurement of the seasonal factors that influence energy consumption and range under real driving conditions. ...
The global automotive industry is currently undergoing a transformation driven by a number of factors, including environmental concerns, sustainability targets, and the advent of innovative technologies. The adoption of electric vehicles represents a pivotal aspect of this transformation, offering individual and corporate users in the car rental sector a significant alternative to traditional internal combustion engine vehicles. The economic and operational advantages of electric vehicles, coupled with the opportunity for car rental companies to fulfil their environmental responsibilities, are accelerating the transformation of the automotive industry. This study presents a case study on the utilization of electric vehicles for long-term car leasing companies for the purpose of providing corporate internal services. The aim is to provide a comprehensive evaluation of the issue from multiple perspectives. The objective of this paper is to provide a comprehensive overview of the concept of electric vehicle leasing, encompassing a range of considerations pertinent to decision-making. These include environmental sustainability, economic advantages, user experience, and operational efficiency.
... The EV model with the best efficiency was revealed with this temperature. Kai et al. [41], conducted studies on 68 different EVs currently in operation in Japan. In these studies, they tried to analyze how the outside temperature affects energy consumption. ...
Calculating the energy consumption of electric vehicles (EVs) is crucial to optimize efficiency and driving range, taking into account the outdoor temperature. Research shows that low temperatures significantly increase motor and battery energy consumption while inhibiting regenerative energy recovery, with optimum efficiency achieved at around 20-30 degrees Celsius. Furthermore, the use of heating and cooling systems in different seasons also affects the overall efficiency by affecting battery energy consumption. Therefore, outdoor temperature and driving conditions must be taken into account to accurately assess and optimize the energy consumption of EVs. In this study, the effects of outdoor temperature on range and energy consumption are analyzed using real-time big data from Electric Buses (EB). The field application of the study is based on the EB route currently in operation in Malatya. The EB route is divided into 4 different regions and the energy consumption and the corresponding outdoor temperature for each region are analyzed using regression analysis techniques. As a result of the calculations, it was calculated that the most efficient consumption for the entire EB route is 3,02 kWh/km and this consumption value can be achieved with a temperature of 21,5 oC.
... Global battery electric vehicle (BEV) sales exceeded 10 million in 2022, representing 14% of new car sales, up from 5% in 2020 (International Energy Agency, 2022). With the highest market shares in northern-latitude countries such as Korea (27%), Norway (25%, Iceland (16%), China (15%), and Sweden (13%), the effect of cold ambient temperatures on the energy utilization and range of BEVs has attracted considerable attention (Bi et al., 2019;Fetene et al., 2017;Heath et al., 2013;Kambly & Bradley, 2015;Sørensen et al., 2023;Tian et al., 2020;Yi et al., 2018;Zahabi et al., 2014;Zhang et al., 2020). Considerably less research has documented the deleterious effects of high temperatures, and these deserve more attention (Al-Wreikat et al., 2021;Hamwi et al., 2022;Hao et al., 2020;Liu et al., 2018;Lohse-Busch et al., 2013;Yu et al., 2020). ...
It is well-known that cold climates impact battery electric vehicle (BEV) performance, but less is known about the effect of extremely high temperatures. This study quantifies the effect of high temperature on energy utilization and driving range using a dataset of 345,622 trips from 300 BEVs, excluding plug-in hybrids, owned by 278 customers of the Salt River Project (SRP) utility in the Phoe-nix, AZ metropolitan region between May 1, 2020 and August 31, 2022. We compare results from two methods to estimate energy utilization per km for auxiliary, propulsion, and total energy and driving range for different ambient temperature bins ranging from-21 °C to 53 °C. The first method is a straightforward average for each bin. The second is a regression model that uses continuous and dummy variables to control for make, model, year, average speed, and trip length. The two methods produce similar U-shaped curves, although the regression that controls for other factors shows a more pronounced effect for extremely high temperatures. For trips at an ambient temperature between 46 °C (115 °F) and 50 °C (122 °F), the energy utilization increases by 28.43% compared with 26-30 °C (79-86 °F), all else being equal. The auxiliary load increases by 50.3%, while energy for propulsion increases by only 23.6%. For driving range, the decrease is 16% for 46-50 °C and 22% for 51-53 °C, compared with the driving range at 26-30 °C. We then estimate that the impact of summer heat on the electrical load, if SRP were to meet its goal of 500,000 BEVs by 2035, would add an additional 4.6 GWh per day (6.5%) during shoulder months and 4.94GWh per day (4.2%) during summer months. On the hottest days, the estimated BEV electricity consumption is 5.3 GWh per day, 3.9% above the average daily demand for the peak days. This research contributes to our understanding of how BEVs perform at very high ambient temperatures. The findings highlight the need to plan for higher summer demand from BEVs in future power investments in hot climates, especially since expected peak demand from BEVs will coincide with existing peak demand.
... Regression analysis was used to decouple the influence of running condition, and a method that could independently evaluate energy consumption characteristics was proposed. Fetene et al. 9 analyzed the energy consumption and driving mileage of electric vehicles based on the big data by taking into account the driving mode, road type, and weather condition under daily driving. Li et al. 10 undertook an analysis on the sensitivity of each factor on the energy consumption based on experimental design, and generated a two dimensional model to estimate the energy consumption of electric vehicles under specific use condition. ...
Reduction of the driving range under either cold temperature or real-world running condition has become the biggest challenge for battery electric vehicle (BEV). In this paper, a simulation platform that combines a kinematics model, a thermal management model, and extracted typical running conditions has been established to estimate the energy flows inside the electric vehicle under cold temperature and real-world running condition. Three vehicles have been selected to validate the accuracy of the simulation platform, giving an accuracy between 90.6% and 96.6% according to different running conditions. Under highway running condition, the driving range could be reduced by 54%. Under urban running condition, when the environment temperature drops down to −20°C, the driving range is only 49.1% of that under 20°C. In addition, there could be a 4.4% increase in driving range if the target cabin temperature could be decreased from 28°C to 20°C. According to simulation, the application of motor waste heat recovery, internal gas recirculation, and heat pump, could increase the driving range at −7°C under urban running condition by 3.5%, 2.9%, and 3.9%, indicating a 10.3% improvement in total. This has been validated via experimental test after implementing these three approaches onto the test vehicle.
... The weather-related factors, which are also of interest to the research included in this study, relate to temperature, humidity, brightness, visibility wind effects, and other environmental characteristics). Studies by Fetene et al. [60], based on big data analysis, revealed how strongly the energy consumption rate (ECR) of EVs varies highly and nonlinearly with driving patterns and weather conditions. The analysis of the impact of temperature on the energy efficiency of EVs was analyzed for many parts of the world, like Kuwait (Hamwi et al. [61]), the United States (Yuksel and Michalek [62]), and Alaska (Wilber et al. [63]), both for variable temperature conditions and for high temperature (Jeffers et al. [64] and Ma et al. [65]) and low-temperature conditions in winter (Hajidavalloo et al. [66], Smith et al. [67], and Aris and Shabani [68]). ...
Citation: Cieśla, M.; Nowakowski, P.; Wala, M. The Impact of Variable Ambient Temperatures on the Energy Efficiency and Performance of Electric Vehicles during Waste Collection. Energies 2024, 17, 4228. https:// Abstract: The market for electric cars (EVs) is growing quickly, which has led to a diversity of models and significant technological advancements, particularly in the areas of energy management, charging, range, and batteries. A thorough analysis of the scientific literature was conducted to determine the operational and technical parameters of EVs' performance and energy efficiency, as well as the factors that influence them. This article addresses the knowledge gap on the analysis of ambient temperature-related parameters' effects on electric garbage trucks operating in particular urban traffic conditions for selective waste collection. To optimize vehicle routes, a computational model based on the Vehicle Routing Problem was used, including the Ant Colony Optimization algorithm, considering not only the load capacity of garbage trucks but also their driving range, depending on the ambient temperature. The results show that the median value of collected bulky waste for electric waste collection vans, depending on the ambient temperature, per route is 7.1 kg/km and 220 kg/h. At a temperature of −10 • C, the number of points served by EVs is 40-64% of the number of points served by conventional vehicles. Waste collection using EVs can be carried out over short distances of up to 150 km, which constitutes 95% of the optimized routes in the analyzed case study. The research contributed to the optimal and energy-efficient use of EVs in variable temperature conditions.
... The assumption is made that EV owners are charging every day and, thus, are only charging what was consumed during one day. The value of = 0.183 kWh/km is chosen since the [37] was carried out on a data set from Denmark, which is assumed to be the most representative of European car fleets. ...
... Topic 5 includes 194 records on "intelligent transportation systems and new technologies". It covers topics such as Internet of Vehicles [48,59,60], connected vehicle-infrastructure environment [61], electric vehicles [62], and the optimization of charging stations location [63], as well as autonomous vehicles (AV) and self-driving vehicle technology [64]. ...
This paper identifies trends in the application of big data in the transport sector and categorizes research work across scientific subfields. The systematic analysis considered literature published between 2012 and 2022. A total of 2671 studies were evaluated from a dataset of 3532 collected papers, and bibliometric techniques were applied to capture the evolution of research interest over the years and identify the most influential studies. The proposed unsupervised classification model defined categories and classified the relevant articles based on their particular scientific interest using representative keywords from the title, abstract, and keywords (referred to as top words). The model’s performance was verified with an accuracy of 91% using Naïve Bayesian and Convolutional Neural Networks approach. The analysis identified eight research topics, with urban transport planning and smart city applications being the dominant categories. This paper contributes to the literature by proposing a methodology for literature analysis, identifying emerging scientific areas, and highlighting potential directions for future research.
... Zou et al. [40] studied BEV taxis in China by analyzing driver behavior and seasonal differences in electricity consumption and cruising range. Fetene et al. [41] employed big data analysis and revealed a 34% increase in winter battery electricity consumption. Wang et al. [42] compared linear and multiple regression models and identified an asymmetric U-shaped relationship between electricity consumption and ambient temperature. ...
The transportation sector is the third-largest global energy consumer and emitter, making it a focal point in the transition toward the net-zero future. To accelerate the decarbonization of passenger cars, this work is the first to propose a bottom-up charging demand model to estimate the operational electricity use and associated carbon emissions of best-selling battery electric vehicles (BEVs) in various climate zones in China during the 2020s. The findings reveal that (1) the operational energy demand of the top-20 selling BEV models in China, such as Tesla, Wuling Hongguang, and BYD, increased from 601 to 3054 giga-watt hours (GWh) during 2020-2022, with BEVs in South China contributing more than half of the total electricity demand; (2) from 2020 to 2022, the energy and carbon intensities of the best-selling models decreased from 1364 to 1095 kilowatt-hour per vehicle and from 797 to 621 kilograms of carbon dioxide (CO 2) per vehicle, respectively, with North China experiencing the highest intensity decline compared to that in other regions; and (3) the operational energy demand of BEV stocks in China increased from 4774 to 12,048 GWh during 2020-2022, while the carbon emissions of BEV stocks rose to 6.8 megatons of CO 2 in 2022, reflecting an annual growth rate of~50%. In summary, this work delves into the examination and contrast of benchmark data on a nation-regional scale, as well as performance metrics related to BEV chargings. The primary aim is to support nationwide efforts in decarbonization, aiming for carbon mitiga-tion and facilitating the swift evolution of passenger cars toward a carbon-neutral future.
The electrification of road transport, as the predominant mode of transportation in Africa, represents a great opportunity to reduce greenhouse gas emissions and dependence on costly fuel imports. However, it introduces major challenges for local energy infrastructures, including the deployment of charging stations and the impact on often fragile electricity grids. Despite its importance, research on electric mobility planning in Africa remains limited, while existing planning tools rely on detailed local mobility data that is often unavailable, especially for privately owned passenger vehicles. In this study, we introduce a novel framework designed to support private vehicle electrification in data-scarce regions and apply it to Addis Ababa, simulating the mobility patterns and charging needs of 100,000 electric vehicles. Our analysis indicate that these vehicles generate a daily charging demand of approximately 350 MWh and emphasize the significant influence of the charging location on the spatial and temporal distribution of this demand. Notably, charging at public places can help smooth the charging demand throughout the day, mitigating peak charging loads on the electricity grid. We also estimate charging station requirements, finding that workplace charging requires approximately one charging point per three electric vehicles, while public charging requires only one per thirty. Finally, we demonstrate that photovoltaic energy can cover a substantial share of the charging needs, emphasizing the potential for renewable energy integration. This study lays the groundwork for electric mobility planning in Addis Ababa while offering a transferable framework for other African cities.
With the surge of electric vehicles, accurate estimation of their energy consumption becomes increasingly critical. Data-driven models have been widely used for estimating the energy consumption of electric vehicles; however, their applications often face limitations due to inadequate training data, resulting in over-fitting and poor generalizability. In this paper, a physically rational data augmentation approach is proposed to expand the driving trip dataset. By incorporating physical coherence into the augmentation process, new driving trips are synthesized with rational physical context. The effectiveness of the proposed approach is validated by applying it to three data-driven models for estimating the energy consumption of electric vehicles across different validation scenarios. Compared with two baseline data augmentation approaches, our proposed approach demonstrates superior model training performance with less data synthesized. In the best case, the proposed approach achieved a 34% accuracy improvement over the raw data and an 11% improvement over the best-performing baseline. This proposed approach shows considerable promise in facilitating the effective adoption of advanced machine learning algorithms in industrial applications by significantly reducing the data collection requirements.
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Analyzing the influencing factors of fuel consumption helps to formulate reasonable energy-saving measures to reduce the fuel consumption. This paper examines the influence of multi-dimensional features on fuel consumption. Then, fuel consumption under different road characteristics is modeled based on correlation analysis and multiple linear regression methods. Finally, the heterogeneous effects of road characteristics on fuel consumption are analyzed by comparing different models. The result shows significant differences in the influencing factors of fuel consumption under different road characteristics. In addition, the same factor has different effects on fuel consumption under different road characteristics. Driving experience positively affects fuel consumption at intersections and two-way four-lane roads; in contrast, there is a negative effect in two-way six-lane roads. Drivers who agree that “we will experience a major ecological catastrophe if things continue on their present course” have lower fuel consumption at intersections and higher on two-way four-lane and six-lane roads. Higher acceleration leads to higher fuel consumption at intersections and lower fuel consumption at two-way four-lane and six-lane roads. Traffic congestion leads to reduced fuel consumption at intersections and two-way four-lane roads and increased fuel consumption at two-way six-lane roads. These results reveal the heterogeneous effects of road characteristics on fuel consumption, which are helpful in analyzing the influence mechanism of fuel consumption and provide a reference for formulating reasonable measures of energy conservation and emission reduction.
This study addresses the challenge of accurately forecasting the energy consumption of electric vehicles (EVs), which is crucial for reducing range anxiety and advancing strategies for charging and energy optimization. Despite the limitations of current forecasting methods, including empirical, physics-based, and data-driven models, this paper presents a novel machine learning-based prediction framework. It integrates physics-informed features and combines offline global models with vehicle-specific online adaptation to enhance prediction accuracy and assess uncertainties. Our framework is tested extensively on data from a real-world fleet of EVs. While the leading global model, quantile regression neural network (QRNN), demonstrates an average error of 6.30%, the online adaptation further achieves a notable reduction to 5.04%, with both surpassing the performance of existing models significantly. Moreover, for a 95% prediction interval, the online adapted QRNN improves coverage probability to 91.27% and reduces the average width of prediction intervals to 0.51. These results demonstrate the effectiveness and efficiency of utilizing physics-based features and vehicle-based online adaptation for predicting EV energy consumption.
Truck stop behavior plays a critical role in supporting powertrain electrification, which is key to reducing emissions in road freight. However, it is still unclear whether these stops meet the charging needs for electric trucks during intercity trips. This study aims to address this issue by analyzing intercity truck travel behavior based on trajectory data to inform battery electric truck (BET) technology design and charging infrastructure planning. The study proposes a data-driven truck trip identification framework based on GPS data to obtain truck stops and truck trip ends. Empirical analysis based on gasoline-enabled truck drivers’ travel behaviors in China shows that a 400 km battery range will meet almost 90% of the continuous driving needs of intercity truck trips. It is recommended that en-route charging stations be designed with a charging power of more than 564 kW, in line with the parking time characteristics of 80% of truck stops. The study also identifies potential power supply shortages on the intercity road power grid between 11:00-13:00, 18:00-19:00, and 21:00-1:00. By quantifying the number of facilities needed to provide BET drivers an energy-viable path to complete intercity trips without excessive detour costs, the study found that the travel behavior-based optimization model can reduce the level of truck charging detour to 5% by selecting just a small subset of alternative charging facility locations (55/302). Our study demonstrates that analyzing intercity truck travel behavior can provide critical insights for designing BET technology and charging infrastructure to support the transition to low-emission road freight.
Abstract—With the rapid growth of new energy vehicle
ownership, the energy consumption of electric vehicles in low
temperature environments has begun to become a hotspot of
consumer concern as well as a pain point for enterprise research
and development. In order to explore the reasons for the growth
of energy consumption of electric vehicles under low
temperature , this paper determines the low temperature energy
consumption test cycle as CLTC-P(Chinese Light Duty Driving
Test Cycle) based on the actual road travelling characteristics of
a typical city in the north of China as well as the ambient
temperature data in winter with a low-temperature test
temperature is -10℃. Then a pure electric vehicle was selected to
carry out the normal temperature and low temperature energy
consumption test in the laboratory, and the energy flow of key
components such as the electric drive system, high-voltage
battery system, accessories, and air conditioning was collected in
the test process, and the calculation results showed that the whole
vehicle energy consumption rate at -10℃ was 109.3% higher
than that at normal temperature, among which, the increase of
accessory energy consumption, the increase of drive energy
consumption, and the decrease of braking recycling energy had a
significant impact on the overall energy consumption rate.
Among them, the impacts of higher accessory energy
consumption, higher drive energy consumption, and lower brake
recovery energy on the increase in overall vehicle energy
consumption rate are 66.4%, 27.6%, and 15.3%, respectively.
Range anxiety is a major barrier for the mass adoption of electric vehicles (EVs), a contributing factor to this is the variability of the predicted range remaining presented to the driver in the vehicle. This study aims to better understand the causes of potential inaccuracies and how ITS can help resolve these issues. Eleven participants completed 141 logged journeys, with results showing that range (as predicted by the EV and presented to the driver) was overestimated by approximately 50% in comparison to journey distance. Driving style had the most significant impact on range prediction accuracy, where a more aggressive driving style led to greater inaccuracies. However, journey distance and type of road driven, which can be calculated from Satnav systems, were factors which were correlated with having a significant effect on range accuracy. Therefore incorporating these into future range prediction algorithms has the potential to increase the accuracy of information and subsequently increase driver trust.
An analytical method of evaluating vehicle fuel consumption under standard operating conditions is presented. In the proposed model, vehicle fuel consumption is separated into two different operating modes: cruising at constant speed and acceleration. In each of these modes fuel consumption is calculated based on the instantaneous engine efficiency, approximated using an analytical function rather than typically considered consumption map. The approximation is based on speed-power decoupling, employing two single dimension polynomials instead of a two-dimensional lookup table. The adequacy and accuracy of the model is verified using experimental calculations. Moreover, it is shown that the effect of various design parameters on vehicle fuel consumption can be studied utilizing the proposed model.
An electric vehicle (EV) data collection system has been developed in this paper.•The data collection system has been installed in an EV conversion vehicle.•A comprehensive data analysis on EV and driver behaviors has been conducted.•An analytical EV power estimation model has been proposed in this paper.•The results show the model can estimate EV’s power and trip energy consumption.
With increase in traffic volume and change in travel related characteristics, vehicular emissions and energy consumption have increased significantly since two decades in India. Current models are not capable of estimating vehicular emissions accurately due to inadequate representation of real-world driving. The focus of this paper is to understand the level of Indian Driving Cycle (IDC) in representing the real-world driving and to assess the impact of real-world driving on vehicular emissions. The study has revealed that IDC does not represent the real-world driving. Irrespective of road classes, about 30%of time is spent below 20km/h and the speed too exceeds IDC's maximum limit of 42km/h. Emissions are estimated for different driving patterns using International Vehicle Emission (IVE) model. Emission rates vary significantly from one class of road to another and the largest effect is on local streets.
Energy consumption and exhaust emissions of hybrid electric vehicles (HEVs) strongly depend on the HEV topology, power ratios of their components and applied control strategy. Combined analytical and simulation approach was applied to analyze energy conversion efficiency of different HEV topologies. Analytical approach is based on the energy balance equations and considers all energy paths in the HEVs from the energy sources to the wheels and to other energy sinks. Simulation approach is based on a fast forward-facing simulation model for simulating parallel and series HEVs as well as for conventional internal combustion engine vehicles, and considers all components relevant for modeling energy conversion phenomena. Combined approach enables evaluation of energy losses on different energy paths and provides their impact on the fuel economy. It therefore enables identification of most suitable HEV topology and of most suitable power ratios of the components for targeted vehicle application, since it reveals and quantifies the mechanisms that could lead to improved energy conversion efficiency of particular HEV. The paper exposes characteristics of the test cycles that lead to improved energy conversion efficiency of HEVs. Mechanisms leading to improved fuel economy of parallel HEVs through drive-away and vehicle propulsion at low powertrain loads by electric motor are also analyzed. It was also shown that control strategies managing energy flow through electric storage devices significantly influence energy conversion efficiency of series HEVs.
The relationship between speed and income is established in a micro- economic model focusing on the trade-off between travel time and the risk of receiving a penalty for exceeding the speed limit. This is used to determine when a rational driver will choose to exceed the speed limit. The relationship between speed and income is found again in the empirical analysis of a cross-sectional dataset comprising 60.000 observations of car trips. This is utilised to perform regressions of speed on income, distance travelled and a number of controls. The results are clearly significant and indicate an average income elasticity of speed of 0.03; it is smaller at short distances and about twice as large at the longest distance investigated of 200 km.
In this investigation, it is shown that when a highway vehicle is driven at a constant speed on a level road, what actually determines its fuel economy is the airspeed of the vehicle rather than its ground speed or its speedometer reading. A theoretical model is then developed for the first time that describes the fuel economy of a highway vehicle as a function of its airspeed. The functional form of this model turns out to be in good correlation with the experimental data that have been collected by the authors over the last 2 years. Finally, the concept of parallel corridors is introduced and a design for highway construction is suggested that can significantly improve fuel economy of vehicles where traffic is heavy, by establishing a controlled driving environment. The results of this investigation apply to any vehicle using any source of energy.
This paper investigates the fuel efficiency of commercial hybrid electric vehicles (HEVs) and compares their performance with respect to standard gasoline vehicles in the context of cold Canadian urban environments. The effect of different factors on fuel efficiency is studied including road driving conditions (link type, city size), temperature, speed, cold-starts and eco-driving training. For this study, fuel consumption data at the link level in real-world conditions was used from a sample of 74 instrumented vehicles. From the study fleet, 21 vehicles were HEVs. Among other results, the beneficial fuel efficiency merits of hybrid vehicles were demonstrated with respect to gasoline cars, in particular at low speeds and in urban (city) environments. After controlling for other factors, sedan HEVs were 28% more efficient than sedan gasoline vehicles. However, the low temperatures (below 0 °C) observed regularly during winter season in the study cities were identified as a detrimental factor to fuel economy. In winter, the fuel efficiency of HEVs decrease about 20% with respect to summer. Other factors such as eco-driving training, city size, cold start and vehicle type were also found to be statistically significant.
A large portion of energy consumption in the world is related to transportation. In recent decades, a variety of technologies have been innovated and applied in order to decrease vehicles energy consumption. In this paper, a comprehensive review on the use of driving data and traffic information for vehicles energy conservation is done. The main aim of this paper is the development of a framework for classification and comparative assessment of various methods and technologies, in which driving data or traffic information are utilized for vehicles energy conservation. The applications are classified into three main categories including (1) traffic monitoring and management systems, (2) intelligent energy management systems in vehicles and (3) intelligent management of charging issues. Research topics in each category are explained and their respective effectiveness in vehicles energy consumption reduction is discussed. The review concludes that the use of the driving data and traffic information leads to remarkable improvements in vehicles energy consumption reduction.
Electric vehicles are expected to significantly reduce road transport emissions, given an increasingly renewable power generation. While technological issues are more and more being overcome, the economic viability and thus possible adoption is still constrained, mainly by higher prices than for conventional vehicles.
In our work we analyze possible market developments for electric vehicles with an application to Germany. We develop a drivetrain choice model with economical, technical and social constraints on the current vehicle registrations and inventory. It estimates the demand for electric vehicles until 2030 for private and commercially registered cars as well as light commercial vehicles.
The results show a replacement potential of almost one third of the total German annual mileage for these vehicles. The result has a high granularity to allow for detailed emission calculation along different spatial areas as well as vehicle and engine types. Besides a baseline forecast, our method allows for calculating different scenarios regarding policy actions or the future development of important parameters such as energy prices. The results provide insights for policy measures as well as for transport and environmental modeling.
In this study, we investigate the extent to which experience affects individual preferences for specific electric vehicle characteristics, individual attitudes toward the environment, and the impact of the attitudes on the choice between an electric and a conventional vehicle. We use a two-wave stated preference experiment where data was collected before and after the respondents experienced an electric vehicle for three months. We estimate a hybrid choice model using jointly the stated choices before and after the test period. The results show that individual preferences change significantly after a real experience with an electric vehicle in the household. In particular, there are major changes in the preference for driving range, top speed, fuel cost, battery life and charging in city centres and train stations. In line with other studies, we find that environmental concern has a positive effect on the preference for EVs both before and after the test period, but the attitude itself and its effect on the choice of vehicle does not change.
Electric Vehicles (EVs) are promoted as a viable near-term vehicle technology to reduce dependence on fossil fuels and resulting greenhouse gas (GHG) emissions associated with conventional vehicles (CVs). In spite of the benefits of EVs, several obstacles need to be overcome before EVs will be widely adopted. A major barrier is that consumers tend to resist new technologies that are considered alien or unproved, thus, policy decisions that consider their critical concerns will have a higher level of success. This research identifies potential socio-technical barriers to consumer adoption of EVs and determines if sustainability issues influence consumer decision to purchase an EV. This study provides valuable insights into preferences and perceptions of technology enthusiasts; individuals highly connected to technology development and better equipped to sort out the many differences between EVs and CVs. This group of individuals will likely be early adopters of EVs only if they perceive them to be superior in performance compared to CVs. These results can guide policymakers in crafting energy and transportation policy. It can also provide guidance to EV engineers' decision in incorporating consumer preference into EV engineering design.
Recently, China has implemented many policy measures to control the oil demand of on-road vehicles. In 2010, China started to report the fuel consumption rates of light-duty vehicles tested in laboratory and to require new vehicles to show the rates on window labels. In this study, we examined the differences between the test and real-world fuel consumption of Chinese passenger cars by using the data reported by real-world drivers on the internet voluntarily. The sales-weighted average fuel consumption of new cars in China in 2009 was 7.80 L/100 km in laboratory and 9.02 L/100 km in real-world, representing a difference of 15.5%. For the 153 individual car models examined, the real-world fuel consumption rates were −8 to 60% different from the test values. The simulation results of the International Vehicle Emission model show that the real-world driving cycles in 22 selected Chinese cities could result in −8 to 34% of changes in fuel consumption compared to the laboratory driving cycle. Further government effort on fuel consumption estimates adjustment, local driving cycle development, and real-world data accumulation through communication with the public is needed to improve the accuracy of the labeling policy.
Hybrid electric vehicles (HEVs) can potentially reduce vehicle fuel consumption and CO2 emissions by using recuperated kinetic vehicle energy stored as electric energy in a hybrid system battery (HSB). Low ambient temperatures can affect the overall HEV powertrain operation under warm-up and hot driving conditions and, consequently, affect fuel consumption and emission performance. The present study investigates the influence of low ambient temperatures on HEV fuel consumption and pollutant and CO2 emissions for five in-use HEV models. Chassis dynamometer measurements have been conducted at different set ambient temperatures using a real-world driving cycle suitable for investigating vehicle cold-start emissions. The main observation is that the amount of HEV cold-start extra emissions (CSEEs) of regulated pollutants are reduced by 30% to 85% on average in comparison to sample CSEEs of conventional gasoline vehicles. The results for HEV CSEEs of CO2 and fuel consumption are mainly similar than those of conventional gasoline vehicles except for CSEEs of some HEVs at the ambient temperature of 23 °C. There, increased CSEEs are observed that exceed maximum sample CSEEs of conventional gasoline vehicles, reaching values for CO2 between 155 [g/start] and 300 [g/start] even though the test runs were initiated with maximum initial state of charge (SOC) of the HSB. Because SOC of the HSB considerably influences the fuel consumption of HEVs, this aspect should be further investigated in regard to the effect of low ambient temperatures on HEV fuel consumption and CO2 emissions. Moreover, no particular influence of low ambient temperatures on HSB performance was observed during hot-phase operation.
Road transportation is a major consumer of fuel in China. This paper explores the influence of driving patterns on fuel consumption using a portable emissions measurement system on ten passenger cars. It shows that vehicle fuel consumption per unit distance is optimum at speeds between 50 and 70km/h, fuel consumption increasing significantly with acceleration. A VSP-based model was developed to calculate vehicle fuel consumption in this study, and produced good results compared to the measured data.
The real-world fuel efficiency and exhaust emission profiles of CO, HC and NOx for light-duty diesel vehicles were investigated. Using a portable emissions measurement system, 16 diesel taxies were tested on different roads in Macao and the data were normalized with the vehicle specific power bin method. The 11 Toyota Corolla diesel taxies have very good fuel economy of (5.9 +/- 0.6) L/100 km, while other five diesel taxies showed relatively high values at (8.5 +/- 1.7) L/100 km due to the variation in transmission systems and emission control strategies. Compared to similar Corolla gasoline models, the diesel cars confirmed an advantage of ca. 20% higher fuel efficiency. HC and CO emissions of all the 16 taxies are quite low, with the average at (0.05 +/- 0.02) g/km and (0.38 +/- 0.15) g/km, respectively. The average NOx emission factor of the 11 Corolla taxies is (0.56 +/- 0.17) g/km, about three times higher than their gasoline counterparts. Two of the three Hyundai Sonata taxies, configured with exhaust gas recirculation (EGR) + diesel oxidation catalyst (DOC) emission control strategies, indicated significantly higher NO2 emissions and NO2/NOx ratios than other diesel taxies and consequently trigger a concern of possibly adverse impacts on ozone pollution in urban areas with this technology combination. A clear and similar pattern for fuel consumption and for each of the three gaseous pollutant emissions with various road conditions was identified. To save energy and mitigate CO2 emissions as well as other gaseous pollutant emissions in urban area, traffic planning also needs improvement.
The main objectives of this paper are two fold. First, the paper evaluates the impact of vehicle cruise speed and acceleration levels on vehicle fuel-consumption and emission rates using field data gathered under real-world driving conditions. Second, it validates the VT-Micro model for the modeling of real-world conditions. Specifically, an on-board emission-measurement device was used to collect emissions of oxides of nitrogen, hydrocarbons, carbon monoxide, and carbon dioxide using a light-duty test vehicle. The analysis demonstrates that vehicle fuel-consumption and emission rates per-unit distance are optimum in the range of 60–90 km/h, with considerable increase outside this optimum range. The study demonstrates that as the level of aggressiveness for acceleration maneuvers increases, the fuel-consumption and emission rates per maneuver decrease because the vehicle spends less time accelerating. However, when emissions are gathered over a sufficiently long fixed distance, fuel-consumption and mobile-source emission rates per-unit distance increase as the level of acceleration increases because of the history effects that accompany rich-mode engine operations. In addition, the paper demonstrates the validity of the VT-Micro framework for modeling steady-state vehicle fuel-consumption and emission behavior. Finally, the research demonstrates that the VT-Micro framework requires further refinement to capture non-steady-state history behavior when the engine operates in rich mode.
This study is aimed at finding independent measures to describe the dimensions of urban driving patterns and to investigate which properties have main effect on emissions and fuel-use. 62 driving pattern parameters were calculated for each of 19 230 driving patterns collected in real traffic. These included traditional driving pattern parameters of speed and acceleration and new parameters of engine speed and gear-changing behaviour. By using factorial analysis the initial 62 parameters were reduced to 16 independent driving pattern factors. Fuel-use and emission factors were estimated for a subset of 5217 cases using two different mechanistic instantaneous emission models. Regression analysis on the relation between driving pattern factors and fuel-use and emission factors showed that nine of the driving pattern factors had considerable environmental effects. Four of these are associated with different aspects of power demand and acceleration, three describe aspects of gear-changing behaviour and two factors describe the effect of certain speed intervals.
The reduction of transport-generated CO2 emissions is currently a problem of global interest. Hybrid electric vehicles (HEVs) are considered as one promising technological solution for limiting transport-generated greenhouse gas emissions. Currently, the number of HEVs in the market remains limited, but this picture will change in the years to come as HEVs are expected to pave the way for cleaner technologies in transport. In this paper, results are presented regarding fuel economy and pollutant emissions measurements of two hybrid electric production vehicles. The measurements were conducted on a Prius II and a Honda Civic IMA using both the European legislated driving cycle (New European Driving Cycle, NEDC) and real-world simulation driving cycles (Artemis). In addition to the emissions measurements, other vehicle-operating parameters were studied in an effort to better quantify the maximum CO2 reduction potential. Data from real-world operation of a Prius II vehicle were also used in the evaluation. Results indicate that in most cases both vehicles present improved energy efficiency and pollutant emissions compared to conventional cars. The fuel economy benefit of the two HEVs peaked under urban driving conditions where reductions of 60% and 40% were observed, respectively. Over higher speeds the difference in fuel economy was lower, reaching that of conventional diesel at 95 km h−1. The effect of ambient temperature on fuel consumption was also quantified. It is concluded that urban operation benefits the most of hybrid technology, leading to important fuel savings and urban air quality improvement.
Driving patterns (i.e., speed, acceleration and choice of gears) influence exhaust emissions and fuel consumption. The aim here is to obtain a better understanding of the variables that affect driving patterns, by determining the extent they are influenced by street characteristics and/or driver-car categories. A data set of over 14,000 driving patterns registered in actual traffic is used. The relationship between driving patterns and recorded variables is analysed. The most complete effect is found for the variables describing the street environment: occurrence and density of junctions controlled by traffic lights, speed limit, street function and type of neighbourhood. A fairly large effect is found for car performance, expressed in terms of the power-to-mass ratio. For elderly drivers, the average speed systematically decreases for all street types and stop time systematically increases on arterials. The results have implications for the assessment of environmental effects through appropriate street categorisation in emission models, as well as the possible reduction of environmental effects through better traffic planning and management, driver education and car design.
New Zealand transport accounts for over 40% of the carbon emissions with private cars accounting for 25%. In the Ministry of Economic Development's recently released "New Zealand Energy Strategy to 2050", it proposed the wide scale deployment of electric vehicles as a means of reducing carbon emissions from transport. However, New Zealand's lack of public transport infrastructure and its subsequent reliance on private car use for longer journeys could mean that many existing battery electric vehicles (BEVs) will not have the performance to replace conventionally fuelled cars. As such, this paper discusses the potential for BEVs in New Zealand, with particular reference to the development of the University of Waikato's long-range UltraCommuter BEV. It is shown that to achieve a long range at higher speeds, BEVs should be designed specifically rather than retrofitting existing vehicles to electric. Furthermore, the electrical energy supply for a mixed fleet of 2 million BEVs is discussed and conservatively calculated, along with the number of wind turbines to achieve this. The results show that approximately 1350Â MW of wind turbines would be needed to supply the mixed fleet of 2 million BEVs, or 54% of the energy produced from NZ's planned and installed wind farms.
On-board emission measurements were performed on 49 light-duty gasoline vehicles in seven cities of China. Vehicle-specific power mode distribution and emission characteristics were analyzed based on the data collected. The results of our study show that there were significant differences in different types of roads. The emission factors and fuel consumption rates on arterial roads and residential roads were approximately 1.4-2 times those on freeways. The carbon monoxide, hydrocarbon, and nitrogen oxides emission factors of Euro II vehicles were on average 86.2, 88.2, and 64.5% lower than those of carburetor vehicles, respectively. The new vehicle emission standards implemented in China had played an important role in reducing individual vehicle emissions. More comprehensive measures need to be considered to reduce the total amount of emissions from vehicles.
Fuel consumption analysis and prediction model for eco route search
- T Kono
- T Fushiki
- K Asada
- K Nakano
Kono, T., Fushiki, T., Asada, K., Nakano, K., 2008. Fuel consumption analysis and prediction
model for eco route search. Proceedings of the 15th World Congress on Intelligent
Transport Systems, New York, NY.
Parameterisation of Fuel Consumption and CO2 Emissions of Passenger Cars and Light Commercial Vehicles for Modelling Purposes
- G Mellios
- S Hausberger
- M Keller
- C Samaras
- L Ntziachristos
Mellios, G., Hausberger, S., Keller, M., Samaras, C., Ntziachristos, L., 2011. Parameterisation
of Fuel Consumption and CO2 Emissions of Passenger Cars and Light Commercial
Vehicles for Modelling Purposes. European Commission Joint Research Centre, Institute
for Energy and Transport, Ispra, Italy.
Fuel Consumption Modeling of Conventional and Advanced Technology Vehicles in the Physical Emission Rate Estimator (PERE)
- E K Nam
- R Giannelli
Nam, E.K., Giannelli, R., 2005. Fuel Consumption Modeling of Conventional and Advanced
Technology Vehicles in the Physical Emission Rate Estimator (PERE). U.S.
Environmental Protection Agency, Washington, D.C.