Road Grade Research Articles

Clutch Torque Optimization for Heavy-Duty Vehicles Starting Based on Intelligent Identification of Driver’s Starting Intention, Vehicle Mass, and Road Grade

<div>For heavy-duty vehicles equipped with automated mechanical transmission (AMT), the control of automatic clutch torque is crucial during the start-up process. However, the difficulty of controlling clutch torque is exacerbated by differences in driver’s starting intentions, changes in vehicle mass, and road gradient. Therefore, this article proposes the clutch starting torque optimization strategy based on intelligent recognition of driver’s starting intention, vehicle mass, and road gradient. First, an intelligent recognition strategy is proposed based on the combination of data-driven and onboard transmission control unit (TCU) algorithms, which improves the accuracy of recognizing the driver’s intention to start as well as the vehicle mass and road gradient. Based on the vehicle’s historical state data information, the predictive model is trained offline using a long–short-term memory (LSTM) network to obtain predicted parameter identification results, which are then used to calibrate the computed values of the onboard TCU algorithm. Second, the clutch torque optimization strategy is designed based on the driver’s starting intention, while considering the effects of road gradient and vehicle mass on the clutch starting resistance torque. The weight coefficients of the objective performance function are adjusted according to the driver’s starting intention, and the Pontryagin’s minimum principle (PMP) is used to solve the clutch target torque. Finally, offline data training and real-vehicle testing are performed. The results show that the optimization strategy can effectively reduce the friction work and the degree of impact during the starting process, minimize the clutch slipping time, and improve the smoothness of vehicle starting and driving comfort.</div>

Integrated Dynamic Modeling and Simulation of Wheeled Vehicle with Outer-Rotor In-Wheel Motors and Key Units

The purpose of this paper is to accurately establish a model of a special vehicle driven by in-wheel motors (IWMs) and investigate its dynamic characteristics. This study proposes a novel integrated vehicle modeling strategy, focusing on the IWM and other key subsystems. Based on this strategy, the vehicle model is developed and simulated in ADAMS. The natural frequencies of the vehicle and transmission characteristics of key components are compared with MATLAB simulation results to validate the accuracy of the model. In the time-domain analysis, the vehicle system’s time-domain characteristics are obtained by using random road spectra of different road grades as excitations. Our simulation results demonstrate that vibration input can be reduced by between 97% and 99% after multi-stage vibration reduction. This work will provide relevant parameters for the design of special vehicles driven by IWMs.

Electric vehicle eco-driving strategy at signalized intersections based on optimal energy consumption

Electric vehicles (EVs), which are a great substitute for gasoline-powered vehicles, have the potential to achieve the goal of reducing energy consumption and emissions. However, the energy consumption of an EV is highly dependent on road contexts and driving behavior, especially at urban intersections. This paper proposes a novel ecological (eco) driving strategy (EDS) for EVs based on optimal energy consumption at an urban signalized intersection under moderate and dense traffic conditions. Firstly, we develop an energy consumption model for EVs considering several crucial factors such as road grade, curvature, rolling resistance, friction in bearing, aerodynamics resistance, motor ohmic loss, and regenerative braking. For better energy recovery at varying traffic speeds, we employ a sigmoid function to calculate the regenerative braking efficiency rather than a simple constant or linear function considered by many other studies. Secondly, we formulate an eco-driving optimal control problem subject to state constraints that minimize the energy consumption of EVs by finding a closed-form solution for acceleration/deceleration of vehicles over a time and distance horizon using Pontryagin’s minimum principle (PMP). Finally, we evaluate the efficacy of the proposed EDS using microscopic traffic simulations considering real traffic flow behavior at an urban signalized intersection and compare its performance to the (human-based) traditional driving strategy (TDS). The results demonstrate significant performance improvement in energy efficiency and waiting time for various traffic demands while ensuring driving safety and riding comfort. Our proposed strategy has a low computing cost and can be used as an advanced driver-assistance system (ADAS) in real-time.

Exploring high-emission driving behaviors of heavy-duty diesel vehicles based on engine principles under different road grade levels

To reveal the outstanding high-emission problems that occur when heavy-duty diesel vehicles (HDDV) pass uphill and downhill, this study proposes a method to depict the nitrogen oxides (NOx) and carbon dioxide (CO2) high-emission driving behaviors caused by slopes from the perspective of engine principles. By calculating emission and grade data of HDDV based on on-board diagnostic (OBD) data and digital elevation model (DEM) data, the 262 short trips including uphill, flat-road and downhill are firstly obtained through the rule-based short trip segmentation method, and the significant correlation between the road grade and emissions of the short trips is verified by Kendall's Tau and K-means clustering. Secondly, by comparing the distribution changes of three speed categories (acceleration state, constant speed state and deceleration state), the differences in HDDV operating states under different grade levels are discussed. Finally, the machine learning models (Random Forest, XGBoost and Elastic Net), are used to develop the NOx and CO2 emission estimation model, identifying high-emission driving behaviors, particularly during uphill driving, which showed the highest proportion of high-emission. Explained by the feature importance and SHapley Additive exPlanations (SHAP) model that large accelerator pedal opening, frequent aggressive acceleration, and high engine load have positive effects both on NOx and CO2 emissions. The difference is in the air-fuel ratio that the engine in the rich or slightly lean burning state will increase CO2 emissions and the lean burning state will increase NOx emissions. In addition, due to the uncertainty of the actual uphill, drivers often undergo a rapid “deceleration-uniform-acceleration” process, which significantly contributes to high NOx and CO2 emissions from the engine perspective. The findings provide insights for designing driving strategies in slope scenarios and offer a novel perspective on depicting driving behaviors.

A hierarchical estimation of road grade based on tire force observation

Road grade is important for autonomous vehicles, but it is difficult to measure directly. To address this issue, a hierarchical estimation of road grade is suggested based on the observation of tire forces. First, a 7-degree-of-freedom (DOF) dynamics model, including vehicle longitudinal, lateral, and yaw motions together with wheel rotations, is developed while considering the road grade. Subsequently, a dual-layer road grade estimation strategy is proposed based on an unscented Kalman filter (UKF). The lower-layer UKF estimates the longitudinal and lateral tire forces for road grade observation, and the upper-layer UKF is employed to estimate the road grade by considering the vehicle’s lateral acceleration and yaw rate. Finally, CarSim and MATLAB joint simulations and road tests are performed under different conditions to validate the correctness and effectiveness of the proposed estimation method. The results show that the proposed tire force observation-based estimator exhibits a lower mean absolute error and root mean square error on sloping roads and combined curved and sloping roads, and presents a better overall estimation performance on road grade compared with the widely used kinematics and dynamics model-based estimators.

Electrified fleet and infrastructure aware energy efficient routing

This paper presents an optimization framework to improve the energy efficiency and cost-effectiveness of fleets of commercial trucks operating pickups and deliveries in urban areas. As the electrification of transportation is moving from passenger cars to medium- and heavy-duty vehicles, the proposed analysis considers a fleet of pickup and delivery trucks that includes conventional internal combustion engine vehicles (ICEV), as well as battery electric vehicles (BEV), and plug-in hybrid electric vehicles (PHEV). Given a set of pickups and deliveries, and a fleet including different types of vehicles, the goal is twofold: assign the best vehicle to each task, and solve the vehicle routing problem, i.e., find the optimal route to navigate the vehicle from the origin to the destination(s). To estimate the energy consumption of the different vehicles, vehicle dynamics are considered, together with actual charging infrastructure and road data, including speed limits, road grade, and stop signs. Moreover, the total cost of ownership (TCO) is evaluated to estimate the cost-effectiveness of different fleet compositions and operations. To solve this problem, a hybrid simulated annealing (HSA) heuristic algorithm is proposed. The algorithm is validated against a benchmark exact solver based on mixed integer linear programming (MILP). The proposed methodology achieves optimal results with a 1.2% optimality gap compared to the benchmark, surpassing MILP in computational efficiency. The research findings highlight how fleet composition and operational strategies can vary significantly based on whether the focus is on energy efficiency, total cost of ownership, or a combination of the two, also depending on the number of years of operation. Simulation case studies in the Columbus, OH area demonstrate that integrating fleet and recharging infrastructure information alongside energy savings in vehicle routing problem solutions can achieve 20% to 50% savings in fleet operation costs compared to solely optimizing for minimum energy consumption.

Research on particle size distribution and composition of road deposit dust in Xi'an: From the perspective of non-exhaust emissions

Road dust, containing tire-road wear particles, atmospheric dust, or construction-related particles, contaminates road environments and poses a health hazard when inhaled by humans. To further explore the particle size and composition of road deposit dust, this study collects these particles in Xi'an, considering various road grades, materials, service conditions, sections, and locations. The collected particles undergo electron microscopy, laser particle size, thermal, mass spectrometry, infrared spectroscopy, and inductively coupled plasma analyses. Qualitative and quantitative analysis of the particles has been conducted, leading to the following conclusions: Asphalt & tire contribute 8.5% of the road deposit dust, while wear from calcium carbonate accounts for approximately 30%. The average particle size on urban roads (392 μm) is 1.25 times that of rural roads (312 μm). The smaller particles on rural roads are more easily resuspended and inhaled, posing health risks and requiring more attention. Brake zones, which often have 1.4 times the metal content compared to mid-sections, including potentially carcinogenic chromium (Cr), should receive focused watering and adsorption due to brake zone where is also at intersections and pedestrian crossing. New roads, which generate 3-6 times more organic matter than old roads, requires continuous cleaning after opening to traffic. Surface-blocked asphalt concrete (AC) pavements, as their lack of pores can lead to easy re-suspension of deposited particles due to tire-road interaction should also be attention. This study aims to contribute to the field of traffic-related dust pollution cleaning strategies and non-exhaust emissions mitigation measures in the future.

Characteristics of life-cycle carbon dioxide emissions of arterial highway maintenance and the influencing factors

With the focus of highway development transitioning from construction to maintenance, a comprehensive understanding of the characteristics and influencing factors of carbon dioxide (CO2) emissions from highway maintenance activities is crucial for formulating effective strategies to promote the low-carbon development of road infrastructure. However, the quantitative relationships between CO2 emissions from highway maintenance schemes and factors such as pavement deterioration, traffic volume, and road grade remain unclear owing to a lack of comprehensive, multi-category, and real data. Using real maintenance data from 340 arterial highway segments in China, this study conducts the life cycle assessment (LCA) to estimate CO2 emissions from maintenance activities and examines the primary emission sources among various structural layers and materials. Furthermore, multiple linear regression (MLR) analysis is conducted to investigate the impact of traffic volume, road grade, and pavement deterioration on CO2 emissions from maintenance projects, and factors influencing the early-stage degradation of pavement performance. The results demonstrate that average CO2 emissions from heavy rehabilitation projects are 6.97 times higher than those from medium rehabilitation projects. Emissions from heavy rehabilitation projects exhibit a significantly negative linear relationship with the riding quality index (RQI) before maintenance (p < 0.05), and emissions from medium rehabilitation projects show a significant negative linear relationship with the pavement condition index (PCI) before maintenance (p < 0.05). Emissions from heavy and medium rehabilitation projects are significantly positively correlated with heavy vehicle traffic volume before maintenance (p < 0.05). Moreover, the early-stage degradation of PCI after heavy rehabilitation and RQI after medium rehabilitation exhibit significantly negative linear relationships with their respective indicators before maintenance (p < 0.05). The early-stage degradation of RQI after heavy rehabilitation is significantly positively correlated with CO2 emissions from the base course and cushion layers (p < 0.05). The findings emphasize that timely maintenance and reduction of CO2 emissions from asphalt mixing equipment are essential for mitigating emissions from road maintenance. This study offers valuable insights for advancing the low-carbon development of highways in temperate regions.

How Do We Calibrate a Battery Electric Vehicle Model Based on Controller Area Network Bus Data?

Transforming an up-to-date vehicle into a measurement system is a rewarding task due to the large number of different sensors in the onboard control and diagnostic systems. These procedures are not performed by a single control unit; it is necessary to share the signal values over a communication network, to which an external device can be connected to record the real traffic. The paper aims to use these recorded data for 1 DOF longitudinal vehicle and powertrain model validation. For repeatability, three city routes are selected: plain road, smaller road grade, and higher road grade in both directions. Therefore, the drivetrain system is tested in a high load range, even with long-term recuperation. The altitude changes are recorded with a DGPS system. By the recorded measurements, the vehicle and the drivetrain model can be calibrated, such as the air drag parameters, the rolling resistances, and the efficiencies of the drivetrain. The validation criteria are defined for speed tracking, and the relative tolerance of the cumulated energy should be below 10%. At the end of the day, a developed model is ready for energetic analysis or control strategy design. The energy balance of the applied cycles is also presented to prove that.

Revealed Preferences for Utilitarian Cycling Energy Expenditure versus Travel Time

This paper quantifies the perceived cost of utilitarian cycling energy expenditure as the marginal rate of substitution between energy expenditure and travel time (MRSet). This trade-off manifests in, and can be inferred from cyclist cruising speeds. Median MRSet values for observed cruising events in a naturalistic cycling dataset range from 0.05 to 0.95 min/km per kcal/min. The revealed cost of energy expenditure (relative to travel time) increases significantly with road grade, traffic controls, and certain facility types, and is also significantly higher for women, less-dedicated cyclists, and people riding on weekends and for non-commute purposes.

Determination of Passenger Car Unit and Capacity at Grades of Two Lane Undivided Highway: A Case Study of Khurkot–Dhulikhel Section of BP Highway

The majority of Nepal’s National Highway and feeder road system comprises two-lane highways with heterogeneous traffic conditions. Factors such as lane width, gradient, lateral clearance, and shoulder conditions can influence the passenger car unit (PCU) and roadway capacity. Recognizing that grade magnitude (%) is an important yet unexplored roadway factor in Nepal, and the present study primarily aims to estimate the passenger car unit on two-lane undivided hilly roads with gradients under prevailing heterogonous traffic conditions. This study aims to determine the PCU values and roadway capacity at grade under mixed nature traffic flow for a two-lane undivided highway. In this research, the necessary data were collected at five sections of BP highways using a digital video recorder, which was then analyzed to assess traffic characteristics and calculate PCU values. Using the PCU values, the road capacity was estimated using Greenshield’s model. For capacity estimation, flow and density were addressed using dynamic PCU values. This study reveals that the PCU of all categorized vehicles increases linearly with the increase in gradient while roadway capacity decreases with an increase in grade. It was observed that PCU values obtained for two-wheelers in all sections are smaller than the PCU values specified in NRS 2013, whereas for buses, trucks, and LCVs, they are higher than the PCUs given in NRS 2013. The study highlights the impact of road grade on the PCU for different vehicle categories on a highway. Additionally, the study demonstrates that the developed capacity values are realistic and consistent with the values presented in the Highway Capacity Manual (HCM) of developing countries such as Indonesia and China. These results are expected to be beneficial for practitioners and the ongoing effort to develop a highway capacity manual.

Urban network noise control based on road grade optimization considering comprehensive traffic environment benefit

A double-decision optimization model based on the road grade optimization strategy and considered comprehensive traffic environment benefit is proposed to control the traffic noise. The upper-level model maximizes the comprehensive traffic environment benefit, including network noise emission and traffic efficiency. Adjusting the emphasis on noise optimization benefits and traffic efficiency in road network planning through setting weights. The lower-level resolves the question of network traffic flow assignment using a stochastic user-equilibrium model. The increase of traffic environment demand, network noise emissions decrease and travel time rises. In the case, with a low environmental requirement (weighting with 1.1), the sound pressure emission of the network decreases by 9.23% with only a 4.01% increase in travel time. Under the high environmental requirement (weighting with 0.2), the sound pressure decreases by 26.8%, but the travel time rises by as high as 30.9%. The network is optimized towards road grade degradation and is the first to optimize the arterial roads. In addition, it is found that the influence of speed on traffic noise is greater than that of traffic volume through case validation. This method proposing traffic noise optimization strategies at the road network planning level provides technical support for the proactive governance of traffic noise pollution and the improvement of traffic sound environment quality.

Level of Service Criteria of Rail Road Grade Crossing Based on the Perceptions of Motorist and Non-motorist

Level of Service Criteria of Rail Road Grade Crossing Based on the Perceptions of Motorist and Non-motorist

Strength Evaluation on Stabilized Sub Grade of MMGSY Road

Strength Evaluation on Stabilized Sub Grade of MMGSY Road

Vehicle–Bridge Interaction Dynamic Analysis of Continuous Rigid Frame Composite Box Girder Bridge with Corrugated Steel Webs under Seismic Excitation

To study the vehicle–bridge interaction (VBI) of highway bridges under seismic excitation, a vehicle–bridge couple analysis method based on Ansys is proposed. The 1/2 vehicle model and space beam element model were established to analyze the VBI response of Lanzhou Xiaoshagou bridge. The self-excited excitation of the system is represented by road surface irregularity randomness, while the external excitation is represented by an earthquake. The impact of seismic types, seismic direction, seismic intensity, vehicle speed, and road surface irregularity on the bridge vibration under the vehicle–bridge coupling during an earthquake is thoroughly analyzed. The results reveal that the type of earthquake significantly influences the dynamic response of the bridge, showing a minimum difference of 31.4%. The intensity of the earthquake is positively correlated with the dynamic response of the bridge. Longitudinal and vertical earthquakes have a more noticeable effect on the bridge’s vertical vibration compared to lateral earthquakes. The ratio of the bridge response under vertical or longitudinal seismic excitation to the response of lateral earthquakes ranges from 1.50 to 26.61. Vehicle speed, road irregularity grade, and randomness have a negligible impact on the dynamic response of vehicle–bridge interaction under an earthquake, accounting for less than 3%. These findings indicate that the analysis of earthquake-bridge vibration can simplify the VBI analysis for continuous rigid frame composite box girder bridges with corrugated steel webs under seismic conditions.

Verification of the Applicability of Obstacle Recognition Distance as a Measure of Effectiveness of Road Lighting on Rainy and Foggy Roads

Adverse weather conditions at night are very fatal to drivers, causing serious traffic accidents. Road lighting is a facility that can alleviate these dangerous situations. Nevertheless, road lighting has only rarely been studied during adverse weather. The reason is that the current road lighting performance evaluation method is presented based on normal weather. The current road lighting performance evaluation method uses a luminance meter to measure the road surface, which is not suitable due to scattering during adverse weather such as rain and fog. Therefore, this study proposes obstacle recognition distance as a measure of effectiveness to evaluate the performance of road lighting during adverse weather. There is a lack of actual research on whether obstacle recognition distance can be used as a measure of effectiveness for road lighting during adverse weather. Therefore, in this study, 30 subjects were used to measure the subjects’ obstacle recognition distance according to changes in weather conditions, road lighting grade, and road lighting color temperature. As a result, it was analyzed that there was a clear trend of change in obstacle recognition distance depending on the change in each condition. It was found that, under the same road lighting performance conditions, there was a difference of up to 72.86% by weather condition; under the same weather conditions, there was a difference of up to 22.75% by road lighting grade; and by color temperature, there was a difference of up to 21.87%. In addition, a statistical significance test was performed to support the existence of a difference, and the results were synthesized to suggest that obstacle recognition distance can be used as a performance measure of effectiveness of road lighting in adverse weather.

Impact of different grade roads on ecological networks: A case study of Fuzhou City, China.

The expansion of roads exacerbates the fragmentation of ecological networks and obstructs landscape connectivity. Scientific analysis of the impacts of different grades of roads on landscape connectivity and ecological networks is crucial for guiding road planning and ecological conservation. Based on the data of 2020 road network, land cover types, and digital elevation models, we used morphological spatial pattern analysis and circuit theory to construct ecological networks within different species dispersal distances (1, 3, 5, 10 km) in Fuzhou. We analyzed the impacts of roads of different grades (motorway, urban expressway, primary and secondary highway) on landscape connectivity at the landscape-patch-corridor scale. The results showed that at the landscape scale, overall landscape connectivity was significantly positively correlated with species dispersal distance. The motorway, urban expressway, primary and secondary highway had the lowest decline rate of overall landscape connectivity within a 10 km species dispersal range, being reduced by 15.6%, 5.3%, 1.5% and 5.2%, respectively. At the patch scale, in the comparison of roads of different grades, motorway led to the highest decline rate of patch connectivity within 1 and 5 km species dispersal range, while primary highway led to the highest decline rate of patch connectivity within 3 and 10 km species dispersal range. At the corridor scale, urban expressway led the highest increase rate of indices. The cost-weighted distance of the overall least-cost path, the ratio of cost-weighted distance to length, ove-rall effective resistance, and total corridor length within 5 km species dispersal range were increased by 43.4%, 33.2%, 57.3%, and 7.3%, respectively. As the distance of species dispersal increased, the patches with high importance were reduced from the northern, central, and northwestern regions to the northern regions, leading to a decrease in the living space of species, and the key corridors were gradually extending from the northwestern and southern regions to the central regions. Our results can guide the construction and optimization of Fuzhou's ecological network from an overall perspective, and provide a scientific basis for biodiversity conservation, ecological restoration, and road network planning under the context of limited land resource utilization.

A linear programming formulation for eco-driving over road slopes

This research proposes a linear program (LP) formulation for energy-minimizing vehicle velocity optimization in hilly terrain and demonstrates comparable performance to its traditional formulation as a dynamic program (DP). The LP captures the kinetic energy dynamics and road grade preview exactly and employs a novel piecewise linear approximation to travel time. The LP runs faster than its DP counterpart and can run in real-time and in closed-loop control applications. To demonstrate its effectiveness, the LP velocity planner is integrated with a receding horizon motion planner and simulated in a Class 8 tractor–trailer application over a real road slope profile, with and without traffic. In these closed-loop simulations, energy savings are observed relative to an explicit cruise control law when maintaining constant speed requires excessive braking.

Novel method for ecosystem services assessment and analysis of road-effect zones

To explore the cumulative ecological effects of roads in ecologically fragile areas, the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) tool was applied to assess the spatiotemporal changes in habitat quality, water yield, and soil erosion in road-effect zones of the Western Sichuan Plateau, China. Then, generalized estimating equations were formulated to analyze the impact of synergies among road attributes, climate, topography, land cover, and other factors on ecosystem service changes. The results showed that the habitat quality within the road-effect zones was mostly affected by road grade and structure, and water yield and soil erosion were attributed to the factors of road structure, rainfall, and topography. Roadbed sections had the greatest negative impact on ecosystem services, followed by bridge sections and tunnel sections. Overall, the results of this study address habitat encroachment and soil and water loss in ecologically fragile areas, contributing to knowledge on green infrastructure planning.

Realistic U.S. Long-Haul Drive Cycle for Vehicle Simulations, Costing, and Emissions Analysis

Although heavy-duty trucks constitute the backbone of freight transportation in the United States, they also contribute significantly to greenhouse gas emissions. Various alternative powertrains to reduce emissions have been assessed, but few specific to U.S. long-haul applications with a consistent basis of assumptions. To enable a more accurate assessment for all stakeholders, a representative drive cycle for long-haul truck operations in the United States is introduced (USLHC8) for modeling and simulation purposes. This was generated from 58,000 mi of real driving data through a unique random microtrip selection algorithm. USLHC8 covers a total driving time of 10 h 47 min, an average vehicle speed of 55.58 mph, and road grade ranging from −6% to +6%. To establish a benchmark for further powertrain comparisons, a vehicle-level simulation of a conventional diesel powertrain was paired with USLHC8. Benchmarks are presented for fuel consumption, well-to-wheel emissions, and total cost to society under different scenarios (present-day, mid-term, and long-term).