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Nowadays, driving simulation devices represent a continuously evolving and developing area in the world of virtual reality. One of the fundamental elements in the design of driving software is the track model. This work aims to study the use of advanced technologies for the three-dimensional modeling of a racing track in a driving simulator. Specifically, it employs the LiDAR methodology to acquire the three-dimensional coordinates of a 1 km long circuit located on the Fisciano campus of the University of Salerno. The purpose of this work is to explain and present a novel track acquisition and modeling methodology within the realm of simulated driving reality. Following the study, the Virtual Reality Laboratory's driving simulator at the Department of Industrial Engineering conducted tests to validate the proposed circuit model. The test rides analyzed the realism of the driving experience, thereby validating the proposed track model. This phase was complemented by a series of proposals for possible future developments in the field of three-dimensional modeling applied to driving simulation and beyond. In the end, the 3D model obtained demonstrated the high definition of the acquired result and the speed with which multiple data were obtained simultaneously, thanks to the laser scanner used. [ABSTRACT FROM AUTHOR]

Assessment of driver stress, crucial for road safety, can greatly benefit from the analysis of multimodal physiological signals. However, fusing such heterogeneous data poses significant challenges, particularly in intermediate fusion where noise can also be fused. In this study, we address this challenge by exploring a 1D convolutional neural network (CNN) with self-attention mechanisms on multimodal data. Electrocardiogram (ECG) signals (256 Hz) and respiration (RESP) signals (128 Hz) were obtained from ten subjects using textile electrodes while driving in different scenarios, namely normal driving and phone usage (calling). The obtained multimodal data is preprocessed and then applied to a self-attention mechanism (SAM) CNN (SAMcNN) to identify driver stress. Experiments are validated using Leave-one-outsubject cross validation. The proposed approach is capable of classifying driver stress. It is observed that shorter segments yield an accuracy of 64.16% compared to longer segment lengths. Thus, exploring self-attention mechanisms for multimodal signals using wearable shirts facilitates non-intrusive monitoring in real-world driving scenarios.

Numerous studies are currently focused on improving the performance and efficiency of electric vehicles (EVs). This research aims to evaluate the necessity for a practical testing methodology to simulate real-world driving scenarios by comparing the driving range measured on a chassis dynamometer with the ranges observed under various actual driving conditions. Tests were conducted on the chassis dynamometer using the multi-cycle test (MCT) mode, employing the urban dynamometer driving schedule (UDDS) and the highway fuel economy driving test (HWFET). Subsequently, we assessed the energy efficiency of three routes compliant with the real-driving emissions-light duty vehicles (RDE-LDV) regulations under real-world driving conditions. Our findings revealed disparities in energy efficiency ranging from 10.8 to 22.9% when driving on the same route and up to 29.3% when driving on different routes. This study highlights the importance of tailoring information provision, such as certification tests, to each country's environmental context. [ABSTRACT FROM AUTHOR]

As the automotive industry undergoes a phase of rapid transformation driven by technological advancements, the integration of driving simulators stands out as an important tool for research and development. The usage of such simulators offers a controlled environment for studying driver behavior; the alignment of data, however, remains a complex aspect that warrants a thorough investigation. This research investigates driver state classification using a dataset obtained from real-road and simulated conditions, recorded through JINS MEME ES_R smart glasses. The dataset encompasses electrooculography signals, with a focus on standardizing and processing the data for subsequent analysis. For this purpose, we used a recurrent neural network model, which yielded a high accuracy on the testing dataset (86.5%). The findings of this study indicate that the proposed methodology could be used in real scenarios and that it could be used for the development of intelligent transportation systems and driver monitoring technology. [ABSTRACT FROM AUTHOR]

Nowadays, driving simulation devices represent a continuously evolving and developing area in the world of virtual reality. One of the fundamental elements in the design of driving software is the track model. This work aims to study the use of advanced technologies for the three-dimensional modeling of a racing track in a driving simulator. Specifically, it employs the LiDAR methodology to acquire the three-dimensional coordinates of a 1 km long circuit located on the Fisciano campus of the University of Salerno. The purpose of this work is to explain and present a novel track acquisition and modeling methodology within the realm of simulated driving reality. Following the study, the Virtual Reality Laboratory’s driving simulator at the Department of Industrial Engineering conducted tests to validate the proposed circuit model. The test rides analyzed the realism of the driving experience, thereby validating the proposed track model. This phase was complemented by a series of proposals for possible future developments in the field of three-dimensional modeling applied to driving simulation and beyond. In the end, the 3D model obtained demonstrated the high definition of the acquired result and the speed with which multiple data were obtained simultaneously, thanks to the laser scanner used.

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 policymakers 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.

The European Union has intensified efforts to reduce CO2 emissions from the transport sector, with the target of reducing tailpipe CO2 emissions from lightduty vehicle new registrations by 55% by 2030 and achieving zero emissions by 2035 according to the "Fit for 55" package. To promote fuel and energy consumption awareness among users under real-world conditions the MILE21--LIFE project provided tools such as a self-reporting tool and a find-a-car tool that included the official and representative on-road fuel/energy consumption values. In order to produce representative values, an in-house vehicle longitudinal dynamics simulation modelwas developed for use in the background of the on-line platform utilizing only a limited amount of inputs. To achieve this, the applied methodology is based on precalculated efficiency values. These values have been produced using vehicle micro-model simulations covering a wide range of operating conditions. The model was validated using measurements from a dedicated testing campaign and performed well for petrol vehicles with an average divergence of -1.1%. However, the model showed a divergence of 9.7% for diesel vehicles, 10.6% for hybrids and 8.7% for plug-in hybrids. The model was also applied to US vehicles and showed a divergence of 1.2% and 10% for city and highway driving, respectively. The application of the developed model presented in this work showed that it is possible to predict real-world fuel and energy consumption with the desired accuracy using a simplified approach with limited input data. [ABSTRACT FROM AUTHOR]

Brake wear particles are generated through frictional contact between the brake disc or brake drum and the brake pads. Some of these particles may be released into the atmosphere, contributing to airborne fine particulate matter (PM2.5). In this study, an onboard system was developed and tested to measure brake wear particles emitted under real-world driving conditions. Brake wear particles were extracted from a fixed volume enclosure surrounding the pad and disc installed on the front wheel of a light-duty vehicle. Real-time data on size distribution, number concentration, PM2.5 mass, and the contribution of semi-volatiles were obtained via a suite of instruments sub-sampling from the constant volume sampler (CVS) dilution tunnel. Repeat measurements of brake particles were obtained from a 42 min bespoke drive cycle on a chassis dynamometer, from on-road tests in an urban area, and from braking events on a test track. The results showed that particle emissions coincided with braking events, with mass emissions around 1 mg/km/brake during on-road driving. Particle number emissions of low volatility particles were between 2 and 5 × 109 particles/km/brake. The highest emissions were observed under more aggressive braking. The project successfully developed a proof-of-principle measurement system for brake wear emissions from transient vehicle operation. The system shows good repeatability for stable particle metrics, such as non-volatile particle number (PN) from the solid particle counting system (SPCS), and allows for progression to a second phase of work where emissions differences between commercially available brake system components will be assessed. [ABSTRACT FROM AUTHOR]

Objective: Parkinson's disease (PD) impairs motor and non-motor functions. Driver strategies to compensate for impairments, like avoiding driving in risky environments, may reduce on-road risk at the cost of decreasing driver mobility, independence, and quality of life (QoL). It is unclear how PD symptoms link to driving risk exposure, strategies, and QoL. We assessed associations between PD symptoms and driving exposure (1) overall, (2) in risky driving environments, and (3) in relationship to QoL. Methods: Twenty-eight drivers with idiopathic PD were assessed using the Movement Disorders Society-Unified Parkinson's Disease Rating Scale (MDS-UPDRS) and RAND 36-Item Short Form Health Survey (SF-36). Real-world driving was monitored for 1 month. Overall driving exposure (miles driven) and risky driving exposure (miles driven in higher risk driving environments) were assessed across PD symptom severity. High traffic, night, and interstate roads were considered risky environments. Results: 18,642 miles (30,001km) driven were collected. Drivers with PD with worse motor symptoms (MDS-UPDRS Part III) drove more overall (b=0.17, P<.001) but less in risky environments (night: b=-0.35, P<.001; interstate roads: b=-0.23, P<.001; high traffic: b=-0.14, P<.001). Worse non-motor daily activities symptoms (MDS-UPDRS Part I) did not affect overall driving exposure (b=-0.05, P=.43) but did affect risky driving exposure. Worse non-motor daily activities increased risk exposure to interstate (b=0.36, P<.001) and high traffic (b=0.09, P=.03) roads while reducing nighttime risk exposure (b=-0.15, P=.01). Daily activity impacts from motor symptoms (MDS-UPDRS Part II) did not affect distance driven. Reduced driving exposure (number of drives per day) was associated with worse physical health-related QoL (b=2.87, P=.04). Conclusions: Results provide pilot data revealing specific PD symptom impacts on driving risk exposure and QoL. Drivers with worse non-motor impairments may have greater risk exposure. In contrast, drivers with worse motor impairments may have reduced driver risk exposure. Reduced driving exposure may worsen physical health-related QoL. Results show promise for using driving to inform clinical care. [ABSTRACT FROM AUTHOR]

The European Union has intensified efforts to reduce CO2 emissions from the transport sector, with the target of reducing tailpipe CO2 emissions from light-duty vehicle new registrations by 55% by 2030 and achieving zero emissions by 2035 according to the “Fit for 55” package. To promote fuel and energy consumption awareness among users under real-world conditions the MILE21—LIFE project provided tools such as a self-reporting tool and a find-a-car tool that included the official and representative on-road fuel/energy consumption values. In order to produce representative values, an in-house vehicle longitudinal dynamics simulation model was developed for use in the background of the on-line platform utilizing only a limited amount of inputs. To achieve this, the applied methodology is based on precalculated efficiency values. These values have been produced using vehicle micro-model simulations covering a wide range of operating conditions. The model was validated using measurements from a dedicated testing campaign and performed well for petrol vehicles with an average divergence of −1.1%. However, the model showed a divergence of 9.7% for diesel vehicles, 10.6% for hybrids and 8.7% for plug-in hybrids. The model was also applied to US vehicles and showed a divergence of 1.2% and 10% for city and highway driving, respectively. The application of the developed model presented in this work showed that it is possible to predict real-world fuel and energy consumption with the desired accuracy using a simplified approach with limited input data.

Global carbon dioxide (CO2) emissions have continuously grown over the past decade. In recent years, nations worldwide have encouraged the use of electric vehicles to reduce the use of fossil fuels in the global transportation sector. To encourage people to transition to electric vehicles, the total cost of ownership (TCO) is the main focus of devising appropriate incentives or subsidies. However, most TCOs emphasize the expenses an owner must incur, regardless of the hidden cost that society must pay. Consequently, the social cost of carbon plays a significant role in the assessment of the losses from the point of view of society. This study reveals the social cost over the lifetime of electric vehicles (EVs), compared to an internal combustion engine vehicle (ICEV). In this study, the energy consumption of the considered vehicles was obtained from a real-world driving test. The CO2 emissions from energy consumption and battery production are evaluated. The social cost model was developed based on the CO2 emissions. A sensitivity analysis validates the social cost model via case scenarios by considering assumptions and conditions suitable for Thailand’s context. The social cost model can be applied with the TCO model for government policymakers and manufacturer planners to estimate the appropriate subsidy to incentivize EV buyers and minimize the social costs.

Currently, studies regarding the factors influencing the real-world electricity consumption of electric motorcycles are lacking. The objective of this study was to examine the factors influencing the real-world electricity consumption of electric motorcycles when driving along an uncongested road network. This study developed an onboard measurement device to collect on-road data, including instant speed data and electricity consumption, from the test electric motorcycle while it was driving on a real-world road. Overall, 105 participants (n = 105) drove the test motorcycle along the uncongested urban road network. Multiple linear regression analysis was applied to explore the effect of influencing variables on the electricity consumption of electric motorcycles. The analysis results revealed that the rider's weight and average running speed positively influenced electricity consumption, whereas decelerating time negatively influenced electricity consumption. Noticeably, the rider's weight affected electricity consumption more than other factors. The lightweighting of electric motorcycles was mainly recommended to lower electricity consumption. Subsequently, CO2 emissions from electricity generation could be reduced. [ABSTRACT FROM AUTHOR]

Transport electrification, which entails replacing fossil fuel-powered engines with electric drivetrains through the use of electric vehicles (EVs), has been identified as a potential strategy for reducing emissions in the transportation sector. As the adoption of EVs increases, there is a growing need to understand their performance and characteristics, particularly the factors that influence energy consumption under actual driving conditions. This study sought to investigate the actual energy consumption of commercial battery electric vehicles (BEVs) in Thailand by conducting real-world driving tests under various route conditions, including urban and rural route modes. Data collection was performed through the use of onboard diagnostics and global positioning system devices. The result shows that the average energy consumption of the BEVs in this study was 148.03 Wh/km. Moreover, several machine learning (ML) techniques were utilized to analyze the collected dataset to predict energy consumption and identify the key factors influencing energy consumption. A comprehensive investigation of factor significance was carried out by employing a specific algorithm in conjunction with the SHapley Additive exPlanations (SHAP) approach. This investigation provided insights into the influence of battery current and vehicle speed on the energy consumption of BEVs, particularly in the context of urban route conditions. The results of this study provide valuable insights into the energy consumption of BEVs and the factors affecting it, which can aid in improving energy efficiency and informing policy decisions related to transport electrification. [ABSTRACT FROM AUTHOR]

Introduction: This study examined the contribution of psychological resilience on self-reported driving comfort, abilities, and restrictions, and on naturalistic driving (ND) behavior of older adults at two time points, five years apart (N = 111; Male: 65.8%, Mean age = 86.1 years). Method: Participants from the Ozcandrive older driver cohort study completed a demographic questionnaire, functional assessments, psychosocial driving questionnaires, and a resilience scale. Participants' vehicles were equipped with a recording device to monitor driving behavior throughout the study. Over 1.7 million kilometers of ND data were analyzed. Results: There was a significant increase in resilience over time, and both self-reported and ND measures revealed reduced driving across five years. Hierarchical regression analyses using age, sex, driving exposure, functional measures, and resilience showed that adding resilience into the models at the final step resulted in statistically significant increases in the amount of variance explained for driving comfort during the day and night, perceived driving abilities, number of trips, trip distance, and proportion of night trips. Conclusions: This research leveraged the longitudinal nature of the Ozcandrive study to provide the first insights into the role of resilience and ND. The observed patterns of reduced driving, captured by both subjective and objective measures, are suggestive of increased levels of self-regulation. As resilience is associated with adaptive coping skills, older adults with higher resilience may be able to more effectively engage in appropriate coping behaviors with regard to driving behavior, safety, and mobility. Practical Applications: Effective methods of increasing resilience in the context of driving is worthy of future research as it will provide valuable information about how older drivers navigate the process of aging as it relates to driving and may assist stakeholders in developing suitable measures to support older driver safety. [ABSTRACT FROM AUTHOR]

As vehicles provide various services to drivers, research on driver emotion recognition has been expanding. However, current driver emotion datasets are limited by inconsistencies in collected data and inferred emotional state annotations by others. To overcome this limitation, we propose a data collection system that collects multimodal datasets during real-world driving. The proposed system includes a self-reportable HMI application into which a driver directly inputs their current emotion state. Data collection was completed without any accidents for over 122 h of real-world driving using the system, which also considers the minimization of behavioral and cognitive disturbances. To demonstrate the validity of our collected dataset, we also provide case studies for statistical analysis, driver face detection, and personalized driver emotion recognition. The proposed data collection system enables the construction of reliable large-scale datasets on real-world driving and facilitates research on driver emotion recognition. The proposed system is avaliable on GitHub. [ABSTRACT FROM AUTHOR]

Road traffic accidents are increasing worldwide and cause a high number of fatalities and injuries. Mental Work Load (MWL) is a contributing factor in road safety. The primary aim of this work was to study important MWL factors and then compare conventional and BRT (Bus Rapid Transit) drivers' MWL. This study evaluated bus drivers' MWL using the Driving Activity Load Index (DALI) questionnaire conducted with 123 bus drivers in Tehran. The results revealed significant differences between conventional and BRT drivers' mental workload. Moreover, data modelling showed that some organisational and environmental factors such as bus type, working hours per day, road maze, and route traffic volume contribute to drivers' mental workload. These findings suggest some essential customised factors that may help measure and offer practical solutions for decreasing the level of bus drivers' MWL in real-world road driving. Practitioner summary Mental workload is affected by several contributing factors. Depending on the working context, some of these contributing factors have a more significant influence on the level of the experienced MWL. Therefore, the main factors influencing the MWL of BRT and conventional bus drivers were assessed in their real-life environment. Abbreviations: MWL: mental work load; BRT: bus rapid transit; CB: conventional bus; DALI: driving activity load index; NASA-TLX: NASA task load index; SWAT: subjective workload assessment technique; EEG: electroencephalography electrocardiogram; fNIRS: functional magnetic resonance imaging; ITS: intelligent transportation systems; AVL: automated vehicle location [ABSTRACT FROM AUTHOR]

Plug-in hybrid electric vehicles (PHEVs) show a high pollutant emission variability that strongly depends on the operating conditions of the internal combustion engine. Additionally, studies indicate that driving situations outside of the real driving emissions boundary conditions can lead to substantial pollutant emission increases. The objective of this study is to measure and analyze the particulate number (PN) and nitrogen oxides (NOx) emissions of a Euro 6 PHEV for a selected real-world driving test route in the Stuttgart metropolitan area. For this purpose, the vehicle is set out with multiple measurement devices to monitor vehicle internal and external parameters. Particle distribution results show an overall uniform pattern, which allows a comparative analysis of the different test scenarios on the basis of the PN concentration. While the trip-average PN emissions are in good agreement, transient effects during highway driving can substantially increase emissions, whereas the fuel consumption does not necessarily increase in such situations. PN measurements including ultrafine particles (UFP) show a significant increase in urban emissions due to higher cold start emission peaks. Additionally, low ambient temperatures raise the uncertainty of NOx and PN cold start emissions. With regard to future emission regulations, which claim that vehicles need to be as clean as possible in all driving situations, PHEV emission investigations for further situations outside of the current legislations are required. [ABSTRACT FROM AUTHOR]

This paper presents a study on simulation of the on-road fuel consumption for motorcycles using the Neural Networks. Ten representative streets in the inner city of Hanoi were selected to collect the on-road operation characteristics of the test motorcycle, consisting of instantaneous speed and fuel consumption rate. The collected data, including 14,000 data points, was divided into 70% for the training process and 30% for the validating and testing process. An ANN architecture consisting of one layer of suitable input variables, two hidden layers with 20 neurons, and one layer of output fuel consumption rate was proposed. The Levenberg-Marquardt ANN fitting tool and the sigmoid activation function were used in developing model architecture. Three powerful input variables were identified, consisting of instantaneous speed, acceleration of motorcycle, and engine revolution speed. In the model development, the mean absolute percentage errors were 5.10% and 11.10%, and the correlation coefficient R values were greater than 0.8 for the training and testing dataset. The developed model performance was post-evaluated using on-road and laboratory datasets. More than 75.40% of the predicted instantaneous fuel consumption values had a relative deviation less than 20% compared to the on-road measured values. The difference in average fuel consumption (L/100 km) between predicted and measured values was less than 10%. Following the datasets from the laboratory test on the motorcycle chassis dynamometer AVL CD20", including the World motorcycle test cycle and Hanoi motorcycle driving cycle, the fuel consumption rate determined using the developed model correlated well with the measured ones, with the R2 values were 0.67 and 0.72. [ABSTRACT FROM AUTHOR]

Excessive Daytime Sleepiness is a core symptom of narcolepsy and idiopathic hypersomnia, which impairs driving performance. Adequate treatment improves daytime alertness, but it is unclear whether driving performance completely normalizes. This study compares driving performance of patients with narcolepsy and idiopathic hypersomnia receiving treatment to that of healthy controls. Patients diagnosed with narcolepsy type 1 (NT1, n = 33), narcolepsy type 2 (NT2, n = 7), or idiopathic hypersomnia (IH, n = 6) performed a standardized one-hour on-the-road driving test, measuring standard deviation of lateral position (SDLP). Results showed that mean SDLP in patients did not differ significantly from controls, but the 95%CI of the mean difference (+1.02 cm) was wide (-0.72 to +2.76 cm). Analysis of subgroups, however, showed that mean SDLP in NT1 patients was significantly increased by 1.90 cm as compared to controls, indicating impairment. Moreover, four NT1 patients requested to stop the test prematurely due to self-reported somnolence, and two NT1 patients were stopped by the driving instructor for similar complaints. Driving performance of NT1 patients may still be impaired, despite receiving treatment. No conclusions can be drawn for NT2 and IH patients due to the low sample sizes of these subgroups. In clinical practice, determination of fitness to drive for these patients should be based on an individual assessment in which also coping strategies are taken into account. [ABSTRACT FROM AUTHOR]

• During real-world driving, driver's glance behaviors were studied. • Participants either drove with or without images displayed on 2 different screens. • Displaying images did not lead to longer glance durations towards screens. • Results are applicable only for album covers and contact pictures. Intelligent vehicle technologies like driver assistance systems and in-vehicle information systems, enhance convenience of the driving experience for drivers and passengers. At the same time, these systems may increase driver distraction and workload. Guidelines developed for this purpose include principles, methods, and assessments which are widely agreed upon, with some being singled out for a particular recommendation or requirement. Especially the display of graphical or photographic images are generally assumed to distract the driver from safely operating the vehicle and should be blocked during driving under all circumstances (so called per se lock outs). This study investigates the effect of displaying graphical and photographical images during driving on driveŕs glance behavior during real-world driving. Findings presented in this paper provide empirical evidence for the unobtrusiveness of these stimuli: Participants didn't exhibit longer glance durations towards in-vehicle information systems, nor a deterioration of driver distraction parameters such as total eyes off road time and long glance proportion when being compared to driving without displaying any photographic images. [ABSTRACT FROM AUTHOR]

• Hybrid electric regional-haul truck designs with a micro-pilot natural gas (NG) engine. • Combined effects of hybridization and low-carbon fuels on class 8 trucks. • Pre-transmission parallel and series plug-in hybrid electric class 8 truck configurations. • Fuel conversion efficiency and CO2 GHG emissions of hybridized class 8 trucks. • Creating a real-world regional-haul highway drive cycle including road grade. • Possibility of engine downsizing in hybrid class 8 regional-haul truck configurations. Diesel-fueled heavy-duty (HD) trucks are a key component of global freight transport, but consume 25% of the transportation industry's energy and emitting substantial greenhouse gas (GHG) emissions. Utilizing low-carbon fuels like natural gas (NG), combined with hybridization, presents an excellent opportunity for reducing GHG emissions and fuel consumption. Micro-pilot dual fuel diesel/NG engines offer high engine brake thermal efficiency (BTE) with an efficient three-way catalyst (TWC) to limit pollutant emissions like C O 2 without the need for costly exhaust aftertreatment systems. This engine is particularly well-suited for hybridization, as it features high-efficiency under stable, mid-, and high-load conditions, but with reduced performance at low loads. This paper validates two conventional baseline models (X15 Cummins diesel engine and micro-pilot NG engine) based on a tested class 8 regional-haul truck on highway. The study investigates the combined effect of low-carbon fuels and hybridization on improving the fuel economy and GHG emissions of class 8 regional-haul trucks. This involves designing pre-transmission parallel and plug-in hybrid configurations with downsized 6.7-liter micro-pilot NG engines. The cargo load and performance requirements are maintained equivalent to the conventional diesel baseline. Results show comparable fuel economy and engine BTE between conventional diesel baseline and NG models on highways with full cargo load. The parallel and series plug-in hybrid NG designs exhibit fuel economy improvements of 2.9% and 26%, respectively, compared to the micro-pilot NG over highway with full cargo load. In the same conditions, the C O 2 emissions reductions for parallel and series plug-in hybrid NG designs are 15% and 51%, respectively, relative to the conventional diesel baseline. In addition, this article provides a detailed comparison and analysis of non-plug-in and plug-in hybrids based on component energy losses and internal combustion engine (ICE) operating points. [ABSTRACT FROM AUTHOR]

[Display omitted] • The impact of ambient temperature was analyzed using the chassis dynamometer and real-world driving. • It shows good performance at an ambient temperature approximately (20 to 30) ℃. • Ambient temperature affects energy recovery, with battery consumption increasing by 35.4 % when compared to −15 ℃ as opposed to 24 ℃. • Real-world driving shows higher efficiency than certified range. Electric vehicles are affected by ambient temperature, which is closely related to the driving range. This study conducted a comprehensive evaluation of the energy efficiency using both a chassis dynamometer and actual driving environments. Under various temperature conditions, the motor energy, battery energy, regenerative energy, and energy efficiency were analyzed using a Combination US06 mode of the chassis dynamometer and the Konkuk University route to realize real-world driving scenario. The results revealed that low temperatures increased the motor and battery energy consumptions (compared to 24 ℃ ambient temperature, −15 ℃ required 35.4 % more.) while hindering regenerative energy recovery during driving. This study confirmed that the optimal energy efficiency for electric vehicles is attained at approximately (20 to 30) ℃. Notably, the energy efficiencies were real- higher than the certified values under real-world driving conditions. Moreover, when the HVAC system is turned on, it increases battery energy consumption by 5.4 % in the summer and by 12.0 % in the winter, influencing the overall energy efficiency with higher battery energy consumption during its operation in both seasons. Therefore, the study findings highlight the importance of considering the ambient temperature and HVAC system usage when evaluating vehicle energy efficiency and driving range. Furthermore, this study emphasizes the need for future vehicle designs and energy management systems to optimize the performance under various ambient conditions, thereby enhancing the overall energy efficiency and extending the driving range. [ABSTRACT FROM AUTHOR]

Plug-in hybrid electric vehicles (PHEVs) show a high pollutant emission variability that strongly depends on the operating conditions of the internal combustion engine. Additionally, studies indicate that driving situations outside of the real driving emissions boundary conditions can lead to substantial pollutant emission increases. The objective of this study is to measure and analyze the particulate number (PN) and nitrogen oxides (NOx) emissions of a Euro 6 PHEV for a selected real-world driving test route in the Stuttgart metropolitan area. For this purpose, the vehicle is set out with multiple measurement devices to monitor vehicle internal and external parameters. Particle distribution results show an overall uniform pattern, which allows a comparative analysis of the different test scenarios on the basis of the PN concentration. While the trip-average PN emissions are in good agreement, transient effects during highway driving can substantially increase emissions, whereas the fuel consumption does not necessarily increase in such situations. PN measurements including ultrafine particles (UFP) show a significant increase in urban emissions due to higher cold start emission peaks. Additionally, low ambient temperatures raise the uncertainty of NOx and PN cold start emissions. With regard to future emission regulations, which claim that vehicles need to be as clean as possible in all driving situations, PHEV emission investigations for further situations outside of the current legislations are required.

Improving the degree of assistance given by in-car navigation systems is an important issue for the safety of both drivers and passengers. There is a vast body of research that assesses the usability and interfaces of the existing navigation systems but very few investigations study the impact on the brain activity based on navigation-based driving. In this paper, a real-world experiment is designed to acquire the electroencephalography (EEG) and in-car information to analyze the dynamic brain activity while the driver is performing the lane-changing task based on the auditory instructions from an in-car navigation system. The results show that auditory cues can influence the speed and increase the frontal EEG delta and beta power, which is related to motor preparation and decision making during a lane change. However, there were no significant results on the alpha power. A better lane-change assessment can be obtained using specific vehicle information (lateral acceleration and heading angle) with EEG features for future naturalized driving study.

• There is little evidence supporting driving simulator validity despite frequent use. • This review included 44 studies directly comparing simulator and real-world driving. • Simulators in around half of the studies achieved absolute or relative validity. • The relationship between fidelity and validity is not straightforward. • The reporting of driving simulator studies requires improvement. Driving simulators are a common tool for researching driver behaviour, providing practical, safe, and controlled environments. Despite their frequent use in research, there is relatively little evidence confirming their validity (i.e., how accurately they represent or reproduce real-world driving). Moreover, there is inconsistency in both the types of simulators used, and the operationalisation of "real-world" driving in validations. This systematic review was undertaken to evaluate the evidence regarding driving simulator accuracy when compared with real-world driving. The review included 44 studies reporting a direct comparison between simulated driving and on-road driving in a vehicle. Measures reported for comparison varied but included mean speed, speed variability, lateral position, overall driving performance, and number of driving errors. Simulators in approximately half of the studies achieved absolute or relative validity, whereas one third produced non-valid results. To understand this further, the fidelity of simulators was considered, however this further clouded our understanding as the relationship between simulator fidelity and validity was not straightforward. The findings suggest that the reporting of driving simulator studies requires improvement, particularly around the validation evidence associated with the simulator, the specific details of the simulated driving environment, and the outputs of statistical analyses. Guidelines are proposed for future research to ensure consistency in the conduct, and reporting, of simulator-based research. [ABSTRACT FROM AUTHOR]

• Real-world hot-stabilized fuel use and exhaust emission rates were measured. • E0, regular and premium E10 with different octane ratings, and E27 were tested. • All five vehicles, including four non-flexible fuel vehicles, adapted to each fuel. • E27 can increase engine efficiency and reduce CO and PM emissions. Differences in fuel use and emission rates of carbon dioxide (CO 2), carbon monoxide (CO), hydrocarbons (HC), nitrogen oxide (NO x), and particulate matter (PM) were quantified for three gasoline-ethanol blends and neat gasoline measured for one flexible-fuel vehicle (FFV) and four non-FFVs using a portable emission measurement system (PEMS). The purpose was to determine if non-FFVs can adapt to a mid-level blend and to compare the fuel use and emission rates among the fuels. Each vehicle was measured on neat gasoline (E0), 10% ethanol by volume (E10) "regular" (E10R) and "premium" (E10P), and 27% ethanol by volume (E27). Four real-world cycles were repeated for each vehicle with each fuel. Second-by-second fuel use and emission rates were binned into Vehicle Specific Power (VSP) modes. The modes were weighted according to real-world standard driving cycles. All vehicles, including the non-FFVs, were able to adapt to E27. Octane-induced efficiency gain was observed for higher octane fuels (E10P and E27) versus lower octane fuels (E0 and E10R). E27 tends to lower PM emission rates compared to E10R and E10P and CO emission rates compared to the other three fuels. HC emission rates for E27 were comparable to those of E10R and E10P. No significant difference was found in NO x emission rates for E27 versus the other fuels. Intervehicle variability in fuel use and emission rates was observed. Lessons learned regarding study design, vehicle selection, and sample size, and their implications are discussed. [ABSTRACT FROM AUTHOR]

This paper characterizes stress levels via a self‐similarity analysis of the electrodermal activity (EDA) collected in a real‐world driving context. To characterize the EDA richness over scales, the fractional Brownian motion (FBM) process and its corresponding exponent H, estimated via a wavelet‐based approach, are used. Specifically, an automatic scale range selection is proposed in order to detect the linearity in a log scale diagram. The procedure is applied to the EDA signals, from the open database drivedb, originally captured on the foot and the hand of the drivers during a real‐world driving experiment, designed to evoke different levels of arousal and stress. The estimated Hurst exponent H offers a distinction in stress levels when driving in highway versus city, with a reference to restful state of minimal stress level. Specifically, the estimated H values tend to decrease when the driving environmental complexity increases. In addition, the estimated H values on the foot EDA signals allow a better characterization of the driving task than that of hand EDA. The self‐similarity analysis was applied to various physiological signals in literature but not to the EDA so far, a signal which was found to correlate most with human affect. The proposed analysis could be useful in real‐time monitoring of stress levels in urban driving spaces, among other applications. [ABSTRACT FROM AUTHOR]

Objective: Parkinson's disease (PD) impairs motor and non-motor functions. Driver strategies to compensate for impairments, like avoiding driving in risky environments, may reduce on-road risk at the cost of decreasing driver mobility, independence, and quality of life (QoL). It is unclear how PD symptoms link to driving risk exposure, strategies, and QoL. We assessed associations between PD symptoms and driving exposure (1) overall, (2) in risky driving environments, and (3) in relationship to QoL., Methods: Twenty-eight drivers with idiopathic PD were assessed using the Movement Disorders Society-Unified Parkinson's Disease Rating Scale (MDS-UPDRS) and RAND 36-Item Short Form Health Survey (SF-36). Real-world driving was monitored for 1 month. Overall driving exposure (miles driven) and risky driving exposure (miles driven in higher risk driving environments) were assessed across PD symptom severity. High traffic, night, and interstate roads were considered risky environments., Results: 18,642 miles (30,001 km) driven were collected. Drivers with PD with worse motor symptoms (MDS-UPDRS Part III) drove more overall ( b  = 0.17, P  < .001) but less in risky environments (night: b  = -0.35, P  < .001; interstate roads: b  = -0.23, P  < .001; high traffic: b  = -0.14, P  < .001). Worse non-motor daily activities symptoms (MDS-UPDRS Part I) did not affect overall driving exposure ( b  = -0.05, P  = .43) but did affect risky driving exposure. Worse non-motor daily activities increased risk exposure to interstate ( b  = 0.36, P  < .001) and high traffic ( b  = 0.09, P  = .03) roads while reducing nighttime risk exposure ( b  = -0.15, P  = .01). Daily activity impacts from motor symptoms (MDS-UPDRS Part II) did not affect distance driven. Reduced driving exposure (number of drives per day) was associated with worse physical health-related QoL ( b  = 2.87, P  = .04)., Conclusions: Results provide pilot data revealing specific PD symptom impacts on driving risk exposure and QoL. Drivers with worse non-motor impairments may have greater risk exposure. In contrast, drivers with worse motor impairments may have reduced driver risk exposure. Reduced driving exposure may worsen physical health-related QoL. Results show promise for using driving to inform clinical care.

Highlights • Distraction and drowsiness alerts warning professional drivers were analysed. • Two negative binomial models were developed using variables describing the journey. • In general, different effects were found depending on the type of alert analysed. • Nevertheless, increasing continuous driving time, both type of alerts increase too. • A relationship was found between the drivers' company and the alerts' frequency. Abstract Professional drivers are particularly exposed to drowsiness and distraction inasmuch as they drive for long periods of time and as a daily routine. Therefore, several studies have been conducted to investigate driverś behavior, supported by controlled experiments (e.g. naturalistic and driving simulator studies). However, due to emerging technologies, new study methods can be developed to complement existing studies. In this study, retrospective data gathered from a driver monitoring system (DMS), which monitored 70 professional drivers from different companies, was used to investigate the effect of journey characteristics on the number of alerts due to either distraction or drowsiness. Two separate negative binomial models were developed, including explanatory variables describing the continuous driving time (sub-journey time), the journey time (a set of sub-journeys), the number of breaks and the breaking duration time. Dummy variables were also included. Interesting results were observed such as increasing continuous driving time, the number of distraction and drowsiness alerts increase too. In contrast, the journey time has the opposite effect decreasing the number of alerts. In the case of distraction alerts, stopping the vehicle during the journey (break) was not statistically significant and the increase in the breaking duration time showed an unexpected effect as the number of alerts increased. This was not the case of drowsiness alerts in which the frequency of breaks and the breaking duration time decreases the alerts. The companies (for which the drivers work) affect the alert frequency differently. The study shows that there is potential in terms of using the data obtained by the new technologies to complement other type of studies based on controlled experiments but also to enhance the development of technologies taking into account the driver profile and the type of journey. [ABSTRACT FROM AUTHOR]

Highlights • Comparison of numerical vs. symbolic eco-driving feedback in a large randomized control field trial over 10 weeks. • Only the symbolic eco-driving feedback led to significant fuel reduction of 2–3%. • Effect is stable when controlling for the influence of road attributes and other covariates. Abstract Despite the fact that more and more car dashboards are being equipped with powerful, high-resolution displays, allowing for radically new ways to design driving feedback, the question of what impact different design types and features have on real-world eco-driving remains largely unclear. To address this research gap, we conducted a randomized control field trial in Switzerland with 62 road assistance drivers over a period of 10 weeks, covering over 245,000 km. We evaluate the effect of eco-driving feedback on fuel consumption for two types of feedback: numerical feedback (which uses numbers and gauges to present numerical values) and symbolic feedback (which translates numerical values into symbolic representations). Both, numeric and symbolic eco-driving feedback were tested against a control group. Data analyses are performed on the level of 265,939 dynamic road segments with constant road characteristics to account for the significant effect of road attributes on fuel consumption. Results of a fixed-effects regression models reveal that only the symbolic feedback design led to significant reductions of 2–3% in fuel consumption. The effect is robust across different model specifications that control for the influence of road attributes and other covariates. We conclude that the design of eco-driving feedback can have a significant impact on its effectiveness for promoting a less fuel-consuming driving style. We conjecture that there is a large untapped potential for manufacturers to use modern digitalized dashboards that can improve the impact of driver feedback systems. [ABSTRACT FROM AUTHOR]

Electric Vehicle (EV) is becoming a viable and popular option, but the acceptance of the technology can be challenging and lead to an elevated driving stress. The existing studies on stress of vehicle driving has been mainly limited to the non-EVs or survey analysis. In this research, EV driving data of 40 subjects is analyzed, where each subject was asked to drive an EV over a 53 km course in a suburban city of South Korea. Physiological data including electroencephalogram (EEG) and eye-gazing were obtained along with vehicle operational data such as state of charge, altitude, and speed. The dataset was rich in information, but individual difference and nonlinear patterns made it extremely difficult to draw meaningful insights. As a solution, an information-theoretic framework is proposed to evaluate mutual information between physiological and operational data as well as the entropy of physiological data itself. The result shows two groups of subjects, one not showing much evidence of stress and the other exhibiting sufficient stress. Among the subjects who showed sufficient driving stress, 9 out of the top 10 high EEG-entropy drivers were female, one driver showed a strong pattern of range anxiety, and several showed patterns of uphill climbing anxiety. [ABSTRACT FROM AUTHOR]

Six light-duty vehicles, both gasoline- and diesel-fueled, were driven a prescribed 13.8 km route in a real-world low-traffic environment under Finnish subfreezing winter conditions (−28... −10 °C). Cold starts, hot starts, and starts with different preheating strategies were used. Fuel consumption and emissions of particles and nitrogen oxides (NO x) were examined by a chasing method with a mobile laboratory. Both electric preheaters (0.3–1.2 kW) and fuel-operated auxiliary heaters (5 kW) were used in the experiments where a cold engine was preheated before starting. While most vehicles showed potential for reducing fuel consumption and emissions of particles (PM), black carbon (BC), and NO x during hot starts compared to subfreezing-cold starts, the benefits of preheating were relatively small and limited to only a few vehicles. The fuel consumption for the 13.8 km drive decreased less than 4% with one gasoline vehicle and one diesel vehicle by preheating. These two vehicles are both equipped with a fuel-operated auxiliary heater, and taking the fuel consumption of the heater during preheating into account leads to about 30% higher total fuel consumption, canceling the preheating benefit out. These two vehicles also showed the largest reductions in PM, BC, and NO x emissions achieved with preheating, e.g., the PM emission reductions being 72% (the gasoline vehicle) and 24% (the diesel vehicle). Whereas the NO x emission reduction for this gasoline vehicle was 41% when considering only the drive, it decreases to 15% when the NO x emissions from the auxiliary heater during preheating are also taken into account. High particle number (PN) emissions from all vehicles and NO x emissions from the diesel vehicles were detected. The PN emissions of particles larger than 23 nm were up to 2 orders of magnitude higher and the NO x emissions up to a factor of 21 higher than the corresponding limits in the European regulations for type-approval of new vehicles. The PN emissions did not depend on the start types; thus, no benefits to reduce them with preheating were detected. The limit-exceeding PN emissions are partially explained with the used measurement method for PN taking both nonvolatile and semivolatile particles into account, whereas the regulations take only the nonvolatile particles into account. The PM emissions were also observed to consist mostly of semivolatile material in most of the cases, organics being the main component of the semivolatile material. • Fuel consumption and emissions from real-world driving in winter were studied. • Engines were started and vehicles driven in temperatures of −28... −10 °C. • Different engine preheating methods prior to engine starts were examined. • Engine preheating did not lead to energy savings. • Fuel-operated preheaters showed some reductions in PM, BC, and NO x emissions. [ABSTRACT FROM AUTHOR]

Electric vehicles are affected by various factors such as the ambient temperature, traffic conditions, driver behavior, vehicle weight, and route characteristics. This study evaluated the energy efficiency of an electric SUV with regenerative braking system under real-world driving conditions. Data were collected using a controller area network while driving on the same route at each regenerative braking stage. Chassis dynamometer tests were performed to verify battery consumption during acceleration and regenerative braking. From the real-world driving test, it was determined that as the regenerative braking stage increased, the battery consumption (excluding regenerative braking) and energy recovered. However, the net battery consumption decreased. In addition, as the speed increased, the energy consumption increased in the order of urban, rural, and motorway sections owing to the air resistance and rolling resistance. Although the energy efficiency tended to increase with the regenerative braking stage, we observed that the real-world driving environment also had an impact. Therefore, in energy efficiency evaluation research, it is essential to analyze the results that reflect the various influencing factors in real-world driving environments and to verify the characteristics of each regenerative braking stage through chassis dynamometer tests. • Real-world testing of regenerative braking stages shows improved energy efficiency in electric vehicles. • Higher regenerative braking stages lead to greater energy recovery and reduced net energy consumption. • Energy efficiency varies with driving conditions, such as speed and traffic volume. • Further research is needed to quantify the effects of various parameters on EV energy efficiency. [ABSTRACT FROM AUTHOR]

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Official laboratory-measured monitoring data indicate a progressive decline in the average fuel consumption and CO 2 emissions of the European passenger car fleet. There is increasing evidence to suggest that officially reported CO 2 values do not reflect the actual performance of the vehicles on the road. A reported difference of 30–40% between official values and real-world estimates was found which has been continuously increasing. This paper reviews the influence of different factors that affect fuel consumption and CO 2 emissions on the road and in the laboratory. Factors such as driving behaviour, vehicle configuration and traffic conditions are reconfirmed as highly influential. Neglected factors (e.g. side winds, rain, road grade), which may have significant contributions in fuel consumption in real world driving are identified. The margins of the present certification procedure contribute between 10 and 20% in the gap between the reported values and reality. The latter was estimated to be of the order of 40%, or 47.5 gCO 2 /km for 2015 average fleet emissions, but could range up to 60% or down to 19% depending on prevailing traffic conditions. The introduction of a new test protocol is expected to bridge about half of the present divergence between laboratory and real world. Finally, substantial literature was found on the topic; however, the lack of common test procedures, analysis tools, and coordinated activity across different countries point out the need for additional research in order to support targeted actions for real world CO 2 reduction. Quality checks of the CO 2 certification procedure, and the reported values, combined with in-use consumption monitoring could be used to assess the gap on a continuous basis. [ABSTRACT FROM AUTHOR]

We exhaustively review the published research on eye movements during real-world night driving, which is an important field of research as fatal road traffic accidents at night outnumber fatal accidents during the daytime. Eye tracking provides a unique window into the underlying cognitive processes. The studies were interpreted and evaluated against the background of two descriptions of the driving task: Gibson and Crooks' (1938) description of driving as the visually guided selection of a driving path through the unobstructed field of safe travel; and Endsley's (1995) situation awareness model, highlighting the influence of drivers' interpretations and mental capacities (e.g., cognitive load, memory capacity, etc.) for successful task performance. Our review unveiled that drivers show expedient looking behavior, directed to the boundaries of the field of safe travel and other road users. Thus, the results indicated that controlled (intended) eye movements supervened, but some results could have also reflected automatic gaze attraction by salient but task-irrelevant distractors. Also, it is not entirely certain whether a wider dispersion of eye fixations during daytime driving (compared to night driving) reflected controlled and beneficial strategies, or whether it was (partly) due to distraction by stimuli unrelated to driving. We concluded by proposing a more fine-grained description of the driving task, in which the contribution of eye movements to three different subtasks is detailed. This model could help filling an existing gap in the reviewed research: Most studies did not relate eye movements to other driving performance measurements for the evaluation of real-world night driving performance. [ABSTRACT FROM AUTHOR]

Purpose: While almost all life cycle assessment (LCA) studies published so far are based on generic vehicles, type approval energy consumption as well as emission data, and application scenarios related to standardized laboratory-based driving cycles, this projects aims at quantifying the LCA based on a real-world vehicle composition and energy consumption data measured before and after the electric conversion of a mini class car. Furthermore, consequences of a second life of a vehicle's glider on the environmental impact were investigated. Methods: After having driven 100,000 km, a Smart was converted from combustion to electric in a laboratory project. The inventory was developed grounded upon materials data from laboratory measurements during the conversion process as well as on real-world energy consumption data prior and after the conversion. Three base models are compared in this life cycle impact assessment: a conventional new Smart (combustion engine), a new electric Smart, and a Smart converted from combustion engine to electric. Together with two sensitivity analyses (four different electricity mixes as well as urban vs. mixed driving conditions) and two EOL treatments, 36 scenarios have been quantified. The inventory is based on Ecoinvent database v 2.2 as a background system and includes raw material extraction. Results and discussion: In urban use, the modeled battery electric vehicle has a favorable environmental impact compared to the ICEV even when charged with the German electricity mix of the year 2013. The advantage in summed up endpoints of the converted Smart is 23 % vs. the new electric Smart on average for the mixed driving conditions and 26 % for the urban driving conditions, respectively. Over a variety of impact categories, electricity consumption during battery cell production in China as well as impacts due to microelectronic components dominated the life cycle. Results for 18 midpoint categories, endpoints for damages to human health, to resource quality and to ecosystem quality as well as the Single score endpoints are reported. Conclusions: This investigation points out that real-world treatments in inventory development can more specifically outline the environmental advantages of the electric car. The electric conversion of a used combustion engine vehicle can save an additional 16 % (CO-eq) and 19 % (single score endpoints) of the environmental impact over a lifetime, respectively, when compared with the new BEV. [ABSTRACT FROM AUTHOR]

This paper proposes a blended rule-based energy management system (EMS) for a plug-in hybrid electric vehicle (PHEV). The proposed EMS is formulated over driving information and vehicle trip energy and not over vehicle speed profiles, as usually seen. The proposed EMS design structure and strategy is described followed by its evaluation. This is the first time a platform for a rule-based acausal EMS has been designed. Performance metrics such as the fuel economy and the number of engine stop–starts are compared with conventional rule-based EMS over real-world destinations with uncertain trip demand. Its performance metrics compared with a conventional EMS for a full parallel PHEV was found to be superior in this paper. [ABSTRACT FROM PUBLISHER]

Objective: The current study investigated whether older drivers' driving patterns during a customized on-road driving task were representative of their real-world driving patterns.Methods: Two hundred and eight participants (male: 68.80%; mean age = 81.52 years, SD = 3.37 years, range = 76.00-96.00 years) completed a customized on-road driving task that commenced from their home and was conducted in their own vehicle. Participants' real-world driving patterns for the preceding 4-month period were also collected via an in-car recording device (ICRD) that was installed in each participant's vehicle.Results: During the 4-month period prior to completing the on-road driving task, participants' median real-world driving trip distance was 2.66 km (interquartile range [IQR] = 1.14-5.79 km) and their median on-road driving task trip distance was 4.41 km (IQR = 2.83-6.35 km). Most participants' on-road driving task trip distances were classified as representative of their real-world driving trip distances (95.2%, n = 198).Conclusions: These findings suggest that most older drivers were able to devise a driving route that was representative of their real-world driving trip distance. Future research will examine whether additional aspects of the on-road driving task (e.g., average speed, proportion of trips in different speed zones) are representative of participants' real-world driving patterns. [ABSTRACT FROM AUTHOR]

The effects of basic engine operating parameters on particle emissions from a hybrid city bus were examined under urban driving conditions involving both constant speed and frequent acceleration. To perform the examinations, real-time data for the parameters and solid Particle Number (PN) emissions were synchronously collected. The driving conditions were specifically chosen because, depending on the driving conditions, PN emissions from a city bus may change by an order of magnitude. A PN measurement system highly sensitive to engine transients and background emissions was specifically chosen, because the particle emission levels typically found in modern diesel engines are relatively low. The measurements indicated that while operating over a flat route, the PN emissions of the bus were maximized at full-load accelerations and minimized at a constant speed of 70 km/h, which is the maximum speed. Because the combustion process in a diesel engine is relatively stable under constant speed driving conditions, the particle formation was minimized. During acceleration, however, the particle formation is accelerated due to well-known turbo-lag phenomena that limit the intake air flow rate. For these reasons, there is no simple linear correlation between the PN concentration and the engine operating parameters for driving conditions that involve both acceleration and constant speed driving. However, it was observed that there is a strong correlation between the cumulative PN emissions and cumulative brake engine energy (BEE). The R 2 values of the linear regression lines were almost 1.0 when the acceleration and constant speed operating conditions of the bus were separately evaluated. Because a city bus operates under real-world conditions involving both accelerating and constant speed driving conditions, a real world PN emission factor was suggested as a function of the maximum, minimum PN emissions (#/kW h) of the bus and a traffic intensity factor. [ABSTRACT FROM AUTHOR]

The basic operating characteristics of a conventional bus (CB) and a hybrid electric bus (HEB) were examined under urban driving conditions. To perform this examination, real-time operating data from the buses were collected on the Campus-Return route of the Sakarya Municipality. The main characteristics examined were the traction, braking, engine, engine generator unit (EGU), motor/generator (M/G), and ultracapacitor (Ucap) energies and efficiencies of the buses. The route elevation profile and the frequency of stop-and-go operations of the buses were found to have dramatic impacts on the braking and traction energies of the buses. The declining profile of the Campus-Return route provided an excellent opportunity for energy recovery by the regenerative braking system of the HEB. However, owing to the limits on the capacities and efficiencies of the hybrid drive train components and the Ucap, the bus braking energies were not recovered completely. Braking energies as high as 2.2 kW h per micro-trip were observed, but less than 1 kW h of braking energy per micro-trip was converted to electricity by the M/G; the rest of the braking energy was wasted in frictional braking. The maximum energy recovered and stored in the Ucap per micro-trip was 0.5 kW h, but the amount of energy recovered and stored per micro-trip was typically less than 0.2 kW h for the entire route. The cumulative braking energy recovered and stored in the Ucap for the Campus-Return route was 52% of the available brake energy, which was 13.02 kW h. Consequently, the round-trip efficiency of the regenerative braking system, between the wheels and Ucap, was determined to be 27%. Finally, although the brake engine energy (BEE) of the CB was 1.18 times higher than its positive traction energy (PTE), the BEE of the HEB was only 1.07 times higher than its PTE. In fact, it is normal to expect the BEE to be higher than the PTE owing to power train losses, but the energy recovered by the regenerative braking system was found to cover most of the power train losses and even improve the energy efficiency of the HEB. [ABSTRACT FROM AUTHOR]