Assessment of hybrid‐drive bus fuel savings for Brazilian urban transit

Buses are the main transit mode in Brazil, transporting more than 55 million passengers per day. Most of these vehicles run on diesel oil causing a dependence on oil, extensive greenhouse gas emissions and increasing air pollution in urban areas. In order to improve this situation, options for Brazilian cities include the use of alternative fuels and new propulsion technologies, such as hybrid vehicles. This paper proposes a procedure for evaluating the performance of a recently developed hybrid‐drive technology. A simple procedure is presented to compare hybrid‐drive buses with conventional diesel buses in urban operations, particularly with respect to fuel economy. Next the potential for reducing diesel oil consumption through the use of hybrid‐drive buses is assessed. Field tests carried out by the authors indicate that fuel consumption improvement through the use of hybrid‐drive buses would certainly exceed 20%, resulting in lower fuel costs and carbon dioxide (CO₂) emissions.     

Fuel economy testing of autonomous vehicles

Environmental pollution and energy use in the light-duty transportation sector are currently regulated through fuel economy and emissions standards, which typically assess quantity of pollutants emitted and volume of fuel used per distance driven. In the United States, fuel economy testing consists of a vehicle on a treadmill, while a trained driver follows a fixed drive cycle. By design, the current standardized fuel economy testing system neglects differences in how individuals drive their vehicles on the road. As autonomous vehicle (AV) technology is introduced, more aspects of driving are shifted into functions of decisions made by the vehicle, rather than the human driver. Yet the current fuel economy testing procedure does not have a mechanism to evaluate the impacts of AV technology on fuel economy ratings, and subsequent regulations such as Corporate Average Fuel Economy targets. This paper develops a method to incorporate the impacts of AV technology within the bounds of current fuel economy test, and simulates a range of automated following drive cycles to estimate changes in fuel economy. The results show that AV following algorithms designed without considering efficiency can degrade fuel economy by up to 3%, while efficiency-focused control strategies may equal or slightly exceed the existing EPA fuel economy test results, by up to 10%. This suggests the need for a new near-term approach in fuel economy testing to account for connected and autonomous vehicles. As AV technology improves and adoption increases in the future, a further reimagining of drive cycles and testing is required.     

Fuel economy improvements for urban driving: Hybrid vs. intelligent vehicles

The quest for more fuel-efficient vehicles is being driven by the increasing price of oil. Hybrid electric powertrains have established a presence in the marketplace primarily based on the promise of fuel savings through the use of an electric motor in place of the internal combustion engine during different stages of driving. However, these fuel savings associated with hybrid vehicle operation come at the tradeoff of a significantly increased initial vehicle cost due to the increased complexity of the powertrain. On the other hand, telematics-enabled vehicles may use a relatively cheap sensor network to develop information about the traffic environment in which they are operating, and subsequently adjust their drive cycle to improve fuel economy based on this information – thereby representing ‘intelligent’ use of existing powertrain technology to reduce fuel consumption. In this paper, hybrid and intelligent technologies using different amounts of traffic flow information are compared in terms of fuel economy over common urban drive cycles. In order to develop a fair comparison between the technologies, an optimal (for urban driving) hybrid vehicle that matches the performance characteristics of the baseline intelligent vehicle is used. The fuel economy of the optimal hybrid is found to have an average of 20% improvement relative to the baseline vehicle across three different urban drive cycles. Feedforward information about traffic flow supplied by telematics capability is then used to develop alternative driving cycles firstly under the assumption there are no constraints on the intelligent vehicle’s path, and then taking into account in the presence of ‘un-intelligent’ vehicles on the road. It is observed that with telematic capability, the fuel economy improvements equal that achievable with a hybrid configuration with as little as 7 s traffic look-ahead capability, and can be as great as 33% improvement relative to the un-intelligent baseline drivetrain. As a final investigation, the two technologies are combined and the potential for using feedforward information from a sensor network with a hybrid drivetrain is discussed.     

Fuel price elasticities in the U.S. combination trucking sector

This paper estimates fuel price elasticities of combination trucking operations in the United States between 1970 and 2012. We evaluate trucking operations in terms of vehicle miles traveled and fuel consumption for combination trucks. Our explanatory variables include measures of economic activity, energy prices, and indicator variables that account for important regulatory shifts and changes in data collection and reporting in national transportation datasets. Our results suggest that fuel price elasticities in the United States’ trucking sector have shifted from an elastic environment in the 1970s to a relatively inelastic environment today. We discuss the importance of these results for policymakers in light of new policies that aim to limit energy consumption and reduce greenhouse gas emissions from heavy-duty vehicles.     

Development of Malaysian urban drive cycle using vehicle and engine parameters

Vehicles travelling in actual urban areas are mostly in idle, low or medium speeds, which reflects engine part-load condition. These regularly visited engine conditions, in reality affect the fuel economy during actual driving. Thus, understanding the characteristics of the actual driving conditions will enable many other benefits besides legislation. This paper presents the development of a preliminary Malaysian urban drive cycle with the inclusion of the engine parameters and characteristics, acquired through an actual urban driving on numerous urban roads in Malaysia that represents the actual consumer’s daily driving experience. The actual engine parameters and its characteristics are integrated into the assessment measures in an attempt to formulate representable drive cycle and fuel consumption data. The initial drive cycle is composed of 17 sequences selected from the actual on-the-road conditions to represent the Malaysian urban driving. The average fuel economy of the established Malaysian urban drive cycle was then measured on a test bench using the same engine from the vehicle. The recorded fuel economy with Malaysian urban drive cycle is 8.5% below the actual Malaysian urban driving which is closer estimation to the actual driving compared to the current in-practice NEDC which shows to be 43.1% below the actual Malaysian urban driving. Thus, Malaysian urban drive cycle is better in representing the Malaysian urban driving conditions compared to the NEDC in terms of the average fuel economy measurements.     

Eco-driving training of professional bus drivers – Does it work?

The drive to reduce fuel consumption and greenhouse gas emissions is one shared by both businesses and governments. Although many businesses in the European Union undertake interventions, such as driver training, there is relatively little research which has tested the efficacy of this approach and that which does exist has methodological limitations. One emerging technology employed to deliver eco-driving training is driver training using a simulator. The present study investigated whether bus drivers trained in eco-driving techniques were able to implement this learning in a simulator and whether this training would also transfer into the workplace. A total of 29 bus drivers attended an all-day eco-driving course and their driving was tested using a simulator both before and after the course. A further 18 bus drivers comprised the control group, and they attended first aid courses as well as completing the same simulator drives (before-after training). The bus drivers who were given the eco-driving training significantly improved fuel economy figures in the simulator, while there was no change in fuel economy for the control group. Actual fuel economy figures were also provided by the bus companies immediately before the training, immediately after the training and six months after the training. As expected there were no significant changes in fuel economy for the control group. However, fuel economy for the treatment group improved significantly immediately after the eco-driving training (11.6%) and this improvement was even larger six months after the training (16.9%). This study shows that simulator-based training in eco-driving techniques has the potential to significantly reduce fuel consumption and greenhouse gas emissions in the road transport sector.     

Effect of mass on multimodal fuel consumption in moving people and freight in the U.S.

The United States transportation sector consumes 5 billion barrels of petroleum annually to move people and freight around the country by car, truck, train, ship and aircraft, emitting significant greenhouse gases in the process. Making the transportation system more sustainable by reducing these emissions and increasing the efficiency of this multimodal system can be achieved through several vehicle-centric strategies. We focus here on one of these strategies – reducing vehicle mass – and on collecting and developing a set of physics-based expressions to describe the effect of vehicle mass reduction on fuel consumption across transportation modes in the U.S. These expressions allow analysts to estimate fuel savings resulting from vehicle mass reductions (termed fuel reduction value, FRV), across modes, without resorting to specialized software or extensive modeling efforts, and to evaluate greenhouse gas emission and cost implications of these fuel savings. We describe how FRV differs from fuel intensity (FI) and how to properly use both of these metrics, and we provide a method to adjust FI based on mass changes and FRV. Based on this work, we estimate that a 10% vehicle mass reduction (assuming constant payload mass) results in a 2% improvement in fuel consumption for trains and light, medium, and heavy trucks, 4% for buses, and 7% for aircraft. When a 10% vehicle mass reduction is offset by an increase in an equivalent mass of payload, fuel intensity (fuel used per unit mass of payload) increases from 6% to 23%, with the largest increase being for aircraft.     

Preferences for autonomous and alternative fuel-powered heavy-duty trucks in Germany

Germany is by far the largest contributor of greenhouse gas emissions in the European Union but adopted its own climate action plan to achieve greenhouse gas neutrality by 2050. The country’s third-largest emitter of greenhouse gas emissions is the transportation sector. As of January 2019, 99.7% of heavy-duty trucks registered in Germany run on diesel while the share of alternative fuel-powered passenger cars increases steadily. Apart from rising emissions, the industry faces a growing shortage of qualified truck drivers. A solution to increasing emissions and the shortage of drivers are autonomous and alternative fuel-powered heavy-duty trucks. We employed a choice-based conjoint analysis with employees from freight companies in Germany to find out how they assess the main attributes of innovative trucks. Our results reveal that the maximum driving range is the most important attribute followed by the refueling/recharging time. Tank-to-wheel emissions, on the other hand, was ranked as the least relevant attribute. Moreover, we present customers’ preference shares for future heavy-duty trucks until 2035. According to our results, freight companies are generally open to switching from conventional to low emission and (conditionally-) automated heavy-duty trucks, however, a close collaboration between truck manufacturers, customers, infrastructure companies, and policymakers is essential to spur the penetration of autonomous and alternative fuel-powered heavy-duty trucks.     

Fuel economy,cost, and greenhouse gas results for alternative fuel vehicles in 2011

This paper presents in-service data collected from over 300 alternative fuel vehicles and over 80 fueling stations to help fleets determine what types of applications and alternative fuels may help them reduce their environmental impacts and fuel costs. The data were compiled in 2011 by over 30 organizations in New York State using a wide variety of commercial vehicle types and technologies. Fuel economy, incremental vehicle purchase cost, fueling station purchase cost, greenhouse gas reductions, and fuel cost savings data clarifies the performance of alternative fuel vehicles and fuel stations. Data were collected from a range of vehicle types, including school buses, delivery trucks, utility vans, street sweepers, snow plows, street pavers, bucket trucks, paratransit vans, and sedans. CNG, hybrid, LPG, and electric vehicles were tracked.     

A review of vehicle fuel consumption models to evaluate eco-driving and eco-routing

Fuel consumption models have been widely used to predict fuel consumption and evaluate new vehicle technologies. However, due to the uncertainty and high nonlinearity of fuel systems, it is difficult to develop an accurate fuel consumption model for real-time calculations. Additionally, whether the developed fuel consumption models are suitable for eco-routing and eco-driving systems is unknown. To address these issues, a systematic review of fuel consumption models and the factors that influence fuel economy is presented. First, the primary factors that affect fuel economy, including travel-related, weather-related, vehicle-related, roadway-related, traffic-related, and driver-related factors, are discussed. Then, state-of-the-art fuel consumption models developed after 2000 are summarized and classified into three broad types based on transparency, i.e., white-box, grey-box and black-box models. Consequently, the limitations and potential possibilities of fuel consumption modelling are highlighted in this review.     

Evaluation of fuel cell auxiliary power units for heavy-duty diesel trucks

A large number of heavy-duty trucks idle a significant amount. Heavy-duty line-haul truck engines idle about 20–40% of the time the engine is running, depending on season and operation. Drivers idle engines to power climate control devices (e.g., heaters and air conditioners) and sleeper compartment accessories (e.g., refrigerators, microwave ovens, and televisions) and to avoid start-up problems in cold weather. Idling increases air pollution and energy use, as well as wear and tear on engines. Efforts to reduce truck idling in the US have been sporadic, in part because it is widely viewed in the trucking industry that further idling restrictions would unduly compromise driver comfort and truck operations. The auxiliary power units (APUs) available to replace the idling of the diesel traction engine all have had limited trucking industry acceptance. Fuel cells are a promising APU technology. Fuel cell APUs have the potential to greatly reduce emissions and energy use and save money. In this paper, we estimate costs and benefits of fuel cell APUs. We calculate the payback period for fuel cell APUs to be about 2.6–4.5 years. This estimate is uncertain since future fuel cell costs are unknown and cost savings from idling vary greatly across the truck fleet. The payback period is particularly sensitive to diesel fuel consumption at idle. Given the large potential environmental and economic benefits of fuel cell APUs, the first major commercial application of fuel cells may be as truck APUs.     

Connectivity-based optimization of vehicle route and speed for improved fuel economy

Traditionally, vehicle route planning problem focuses on route optimization based on traffic data and surrounding environment. This paper proposes a novel extended vehicle route planning problem, called vehicle macroscopic motion planning (VMMP) problem, to optimize vehicle route and speed simultaneously using both traffic data and vehicle characteristics to improve fuel economy for a given expected trip time. The required traffic data and neighbouring vehicle dynamic parameters can be collected through the vehicle connectivity (e.g. vehicle-to-vehicle, vehicle-to-infrastructure, vehicle-to-cloud, etc.) developed rapidly in recent years. A genetic algorithm based co-optimization method, along with an adaptive real-time optimization strategy, is proposed to solve the proposed VMMP problem. It is able to provide the fuel economic route and reference speed for drivers or automated vehicles to improve the vehicle fuel economy. A co-simulation model, combining a traffic model based on SUMO (Simulation of Urban MObility) with a Simulink powertrain model, is developed to validate the proposed VMMP method. Four simulation studies, based on a real traffic network, are conducted for validating the proposed VMMP: (1) ideal traffic environment without traffic light and jam for studying the fuel economy improvement, (2) traffic environment with traffic light for validating the proposed traffic light penalty model, (3) traffic environment with traffic light and jam for validating the proposed adaptive real-time optimization strategy, and (4) investigating the effect of different powertrain platforms to fuel economy using two different vehicle platforms. Simulation results show that the proposed VMMP method is able to improve vehicle fuel economy significantly. For instance, comparing with the fastest route, the fuel economy using the proposed VMMP method is improved by up to 15%.     

Vehicle attribute trade-offs to meet the 2025 CAFE fuel economy target

The literature analyzes changes in vehicle attributes that can improve fuel economy to meet Corporate Average Fuel Economy (CAFE) standards. However, these analyses exclude either vehicle price, size, acceleration or technology advancement. A more comprehensive examination of the trade-offs among these attributes is needed, this case study focuses on technically feasible modifications to a reference 2012 vehicle to meet the 2025 fuel economy target. Scenarios developed to examine uncertainty in technology advancement indicate that expected technology cost reductions over time will be insufficient to offset the costs of additional fuel efficiency technologies that could be used to meet the 2025 fuel economy target while maintaining other vehicle attributes. The mid-price scenario results show the targeted 66% increase in fuel economy from 2012 to 2025 can be achieved with (i) a 10% ($2070) vehicle price increase (lightweight hybrid electric vehicle), (ii) a 31% (2.9 second) increase in the 0–97 km/h (60 mph) acceleration time (smaller engine), or (iii) a 17% (700 L) decrease in interior volume (smaller body) while maintaining other vehicle attributes. These results are consistent with those obtained using methods that generalize the US light-duty vehicle fleet, but are not a forecast of future vehicle attributes because combinations of less perceptible changes to vehicle price, acceleration and size would also be feasible. This study shows there are numerous ways that 2025 fuel economy targets can be met; therefore, the trade-offs quantified provide important insights on the implications of future CAFE standards.     

Integrated data-driven modeling to estimate PM2.5 pollution from heavy-duty truck transportation activity over metropolitan area

Based on the national emission inventory data from different countries, heavy-duty trucks are the highest on-road PM_2.5 emitters and their representation is estimated disproportionately using current modeling methods. This study expands current understanding of the impact of heavy-duty truck movement on the overall PM_2.5 pollution in urban areas through an integrated data-driven modeling methodology that could more closely represent the truck transportation activities. A detailed integrated modeling methodology is presented in the paper to estimate urban truck related PM_2.5 pollution by using a robust spatial regression-based truck activity model, the mobile source emission and Gaussian dispersion models. In this research, finely resolved spatial–temporal emissions were calculated using bottom-up approach, where hourly truck activity and detailed truck-class specific emissions rates are used as inputs. To validate the proposed methodology, the Cincinnati urban area was selected as a case study site and the proposed truck model was used with U.S. EPA’s MOVES and AERMOD models. The heavy-duty truck released PM_2.5 pollution is estimated using observed concentrations at the urban air quality monitoring stations. The monthly air quality trend estimated using our methodology matches very well with the observed trend at two different continuous monitoring stations with Spearman’s rank correlation coefficient of 0.885. Based on emission model results, it is found that 71 percent of the urban mobile-source PM_2.5 emissions are caused by trucks and also 21 percent of the urban overall ambient PM_2.5 concentrations can be attributed to trucks in Cincinnati urban area.     

Fuel price elasticities for single-unit truck operations in the United States

This paper provides fuel price elasticity estimates for single-unit truck activity, where single-unit trucks are defined as vehicles on a single frame with either (1) at least two axles and six tires; or (2) a gross vehicle weight greater than 10,000 lb. Using data from 1980 to 2012, this paper applies first-difference and error correction models and finds that single-unit truck activity is sensitive to certain macroeconomic and infrastructure factors (gross domestic product, lane miles expansion, and housing construction), but is not sensitive to diesel fuel prices. These results suggest that fuel price elasticities of single unit truck activity are inelastic. These results may be used by policymakers in considering policies that have a direct impact on fuel prices, or policies whose effects may be equivalent to fuel price adjustments.     

The economy of alternative fuels when including the cost of air pollution

In this study, the costs involved in the use of petrol, diesel, natural gas, biogas, and methanol (produced from natural gas and biomass) in cars and heavy trucks are compared. The cost includes fuel cost, extra capital cost for vehicles using alternative fuels, and the environmental cost of VOC, NO_x, particulate and CO₂ emission based on actual 1996 and estimated 2015 emission factors. The costs have been calculated separately for rural, urban and city-centre traffic. A complete macroeconomic assessment of the effect of introducing alternative fuels is not, however, included in the study. The study shows that no alternative fuel can compete with petrol and diesel in rural traffic when the economic valuation of CO₂ emission is taken as current Swedish CO₂ taxes ($200/tonne C). In cities with a natural gas network, natural gas is the fuel with the lowest cost for both cars and heavy trucks, based on 1996 emission factors. Methanol from natural gas and biogas from waste products can also compete with diesel in urban traffic. With predicted improvements in technology and subsequent emission reductions, no alternative fuel can compete with petrol in any of the traffic situations studied by 2015, and only in city-centre traffic will alternative fuels be less costly than diesel in heavy vehicles. Of the biomass-based fuels studied, low-cost biogas from waste products is the most competitive one and is, already at current CO₂ taxes, the fuel with lowest cost for heavy trucks in urban traffic in areas where natural gas networks do not exist. To enable the more widespread use of biomass-based fuels, i.e. using feedstocks such as energy crops or logging residues that are available in larger amounts, the economic valuation of CO₂ emission has to be 2–2.5 times higher than current Swedish CO₂ tax level.     

Elasticities of fuel and traffic demand and the direct rebound effects: An econometric estimation in the case of Norway

We estimate the elasticities of fuel and travel demand with respect to fuel prices and income in the case of Norway. Furthermore, we derive the direct rebound effects that explain the degree to which a fuel price increase is “offset” in the form of greater fuel use and/or travel due to improvements in vehicle fuel efficiency. For this purpose, we use and compare two alternative econometric approaches: the error correction model (ECM) and the dynamic model. Our initial assumption is that one should not be indifferent with respect to the approach used to derive elasticities. The data used are for the period 1980–2011. Our results indicate the following: (1) the dynamic model fits the data better than the ECM model does; (2) the estimated elasticities of fuel demand with respect to price and income are −0.26 and 0.06 in the short run and −0.36 and 0.09 in the long run. For travel demand, the respective elasticities are −0.11 and 0.06 in the short run and −0.24 and 0.13 in the long run, implying inelastic demands for fuel and travel demand; and (3) rebound effects indicate that 0.26% and 0.06% of fuel savings as a result of fuel price increase will be offset in the form of more fuel use in the short run and in the long run, respectively, if fuel efficiency increases by 1%. Our policy recommendations are that policies should not be indifferent to the methods used to derive elasticities. We contend that it is crucial to seriously consider rebound effects in policy making because basic elasticity estimates exaggerate the impact of fuel price increases.     

The vehicle platooning problem: Computational complexity and heuristics

We create a mathematical framework for modeling trucks traveling in road networks, and we define a routing problem called the platooning problem. We prove that this problem is NP-hard, even when the graph used to represent the road network is planar. We present integer linear programming formulations for instances of the platooning problem where deadlines are discarded, which we call the unlimited platooning problem. These allow us to calculate fuel-optimal solutions to the platooning problem for large-scale, real-world examples. The problems solved are orders of magnitude larger than problems previously solved exactly in the literature. We present several heuristics and compare their performance with the optimal solutions on the German Autobahn road network. The proposed heuristics find optimal or near-optimal solutions in most of the problem instances considered, especially when a final local search is applied. Assuming a fuel reduction factor of 10% from platooning, we find fuel savings from platooning of 1–2% for as few as 10 trucks in the road network; the percentage of savings increases with the number of trucks. If all trucks start at the same point, savings of up to 9% are obtained for only 200 trucks.     

Real-time route diversion control in a model predictive control framework with multiple objectives: Traffic efficiency,emission reduction and fuel economy

In this paper, the route recommendation provided by the traffic management authority, rather than the uncontrollable bifurcation splitting rate, is directly considered as the control variable in the route guidance system; a real-time en-route diversion control strategy with multiple objectives is designed in a Model Predictive Control (MPC) framework with regard to system uncertainties and disturbances. The objectives include not only traffic efficiency, but also emission reduction and fuel economy, which respectively correspond to minimizing the total time spent (TTS), total amount of emissions and fuel consumption for all vehicles moving through a network. In the MPC framework, the routing control problem is transformed to be a constrained combinational optimization, which is solved by the parallel Tabu Search algorithm. Two representative traffic scenarios are tested, and the simulation results show: (1) The room for improvement in each objective by means of route diversion control is not consistent with each other and varies with the utilized traffic scenario. In the peak hour, the routing control can lead to significant improvements in TTS and fuel economy, while a relatively small improvement in emission reduction is achieved; in the off-peak hour, however, it is opposite, which indicates that routing is possibly dispensable from the aspect of improving traffic efficiency, but is required from the aspect of emission reduction. (2) The conflict among the multiple objectives varies with the utilized traffic scenario in route diversion control. Improving traffic efficiency often conflicts with emission reduction in both scenarios. For the objectives of traffic efficiency and fuel economy, they are not conflicting in peak hour, while in the off-peak hour, the two objectives are likely conflicting, and the improvement in one objective can lead to the degradation in the other objective. (3) Regardless of the scenarios of peak hour or off-peak hour, the proposed control strategy can result in a proper trade-off among the three chosen objectives.     

Comparing rail fuel efficiency with truck and waterway

This paper presents a railroad energy efficiency model used to estimate the fuel economies for classes of trains transporting various commodities. Comparable procedures are used to estimate truck and waterway fuel consumption. The results show that coal unit trains are 4.5–5.0 times more energy efficient than movements in the largest trucks allowed in the eastern and western regions of the US, unit grain train movements in the central US are 4.6 times more fuel efficient, soda ash unit train and non-unit train shipments are 4.9 and 3.2 times more efficient, and ethanol unit train and non-unit train movements are 4.8 and 3.0 times more efficient. In terms of barge traffic, coal unit train and non-unit train are 1.3 and 0.9 times as energy efficient in the eastern US, grain unit train and non-unit train movements are 1.7 and 1.0 times more efficient from Minneapolis to the Gulf of Mexico, and grain unit train and non-unit train movements are 1.0 and 0.7 times more fuel efficient from the Upper Ohio River to the Gulf of Mexico.