Hierarchical control strategies for energy management of connected hybrid electric vehicles in urban roads

This paper presents a fuel efficient control strategy for a group of connected hybrid electric vehicles (HEVs) in urban road conditions. A hierarchical control architecture is proposed in this paper for every HEV, where the higher level and the lower level controller share information with each other and solve two different problems that aim at improving its fuel efficiency. The higher level controller of each HEV is considered to utilize traffic light information, through vehicle to infrastructure (V2I) communication, and state information of the vehicles in its near neighborhood, via vehicle to vehicle (V2V) communication. Apart from that, the higher level controller of each HEV uses the recuperation information from the lower level controller and provides it the optimal velocity profile by solving its problem in a model predictive control framework. Each lower level controller uses adaptive equivalent consumption minimization strategy (ECMS) for following their velocity profiles, obtained from the higher level controller, in a fuel efficient manner. In this paper, the vehicles are modeled in Autonomie software and the simulation results are provided in the paper that shows the effectiveness of the proposed control architecture.     

Optimal speed advisory for connected vehicles in arterial roads and the impact on mixed traffic

Connected Vehicles (CV) equipped with a Speed Advisory System (SAS) can obtain and utilize upcoming traffic signal information to manage their speed in advance, lower fuel consumption, and improve ride comfort by reducing idling at red lights. In this paper, a SAS for pre-timed traffic signals is proposed and the fuel minimal driving strategy is obtained as an analytical solution to a fuel consumption minimization problem. We show that the minimal fuel driving strategy may go against intuition of some people; in that it alternates between periods of maximum acceleration, engine shut down, and sometimes constant speed, known in optimal control as bang-singular-bang control. After presenting this analytical solution to the fuel minimization problem, we employ a sub-optimal solution such that drivability is not sacrificed and show fuel economy still improves significantly. Moreover this paper evaluates the influence of vehicles with SAS on the entire arterial traffic in micro-simulations. The results show that SAS-equipped vehicles not only improve their own fuel economy, but also benefit other conventional vehicles and the fleet fuel consumption decreases with the increment of percentage of SAS-equipped vehicles. We show that this improvement in fuel economy is achieved with a little compromise in average traffic flow and travel time.     

Optimal driving for single-vehicle fuel economy

This study uses fuel consumption simulators for 15 late-model automobiles to determine how one ought to drive to maximize fuel economy. The simulation is based on extensive on-road and dynamometer testing of the 15 cars. Dynamic programming is used to determine the optimal way to accelerate from rest to cruising speed, to drive a block between stop signs, and to cruise on hilly terrain while maintaining a given average speed. The dependence of fuel economy on cruising speed is also characterized for various road grades. Findings include that optimal speeds are generally higher for larger cars and higher on downgrades than on upgrades, and that the relative fuel penalty for exceeding the speed limit is no worse for small cars than large cars. Optimal control for accelerate-and-cruise and for driving between stop signs varies considerably from car to car; in the latter case fuel economy is much improved by achieving a rather low peak speed. Optimal control on hills is consistent from car to car and can achieve fuel economy 7% to 30% better than that of constant-speed driving on 3% to 6% grades. Results that appear generalizable to other cars are reduced to advice for the fuel-conscious driver.     

Controlled time of arrival windows for already initiated energy-neutral continuous descent operations

Continuous descent operations with controlled times of arrival at one or several metering fixes could enable environmentally friendly procedures without compromising terminal airspace capacity. This paper focuses on controlled time of arrival updates once the descent has been already initiated, assessing the feasible time window (and associated fuel consumption) of continuous descent operations requiring neither thrust nor speed-brake usage along the whole descent (i.e. only elevator control is used to achieve different metering times). Based on previous works, an optimal control problem is formulated and numerically solved. The earliest and latest times of arrival at the initial approach fix have been computed for the descent of an Airbus A320 under different scenarios, considering the potential altitudes and distances to go when receiving the controlled time of arrival update. The effects of the aircraft mass, initial speed, longitudinal wind and position of the initial approach fix on the time window have been also investigated. Results show that time windows about three minutes could be achieved for certain conditions, and that there is a trade-off between robustness facing controlled time of arrival updates during the descent and fuel consumption. Interestingly, minimum fuel trajectories almost correspond to those of minimum time.     

Development a new power management strategy for power split hybrid electric vehicles

Reduction of greenhouse gas emission and fuel consumption as one of the main goals of automotive industry leading to the development hybrid vehicles. The objective of this paper is to investigate the energy management system and control strategies effect on fuel consumption, air pollution and performance of hybrid vehicles in various driving cycles. In order to simulate the hybrid vehicle, the combined feedback–feedforward architecture of the power-split hybrid electric vehicle based on Toyota Prius configuration is modeled, together with necessary dynamic features of subsystem or components in ADVISOR. Multi input fuzzy logic controller developed for energy management controller to improve the fuel economy of a power-split hybrid electric vehicle with contrast to conventional Toyota Prius Hybrid rule-based controller. Then, effects of battery’s initial state of charge, driving cycles and road grade investigated on hybrid vehicle performance to evaluate fuel consumption and pollution emissions. The simulation results represent the effectiveness and applicability of the proposed control strategy. Also, results indicate that proposed controller is reduced fuel consumption in real and modal driving cycles about 21% and 6% respectively.     

Fuel consumption and emission modelling by power demand and a comparison with other models

Part 1 describes a fuel consumption model based upon the instantaneous power demand experienced by a vehicle, which has been developed from chassis dynamometer experiments on 177 in-use Australian vehicles. When applied to an individual vehicle, the model provides aggregate fuel consumption estimates for on-road driving which are within 2% of the actual measured fuel usage. Emission rate models for hydrocarbons and nitrogen oxides which are of the same form as the fuel consumption model are also presented. The vehicle model can be applied in any traffic situation provided on-road power demand is known. On-road instantaneous power demand is derived from the vehicle's mass, drag, velocity acceleration and road gradient. In the first part 1929 km and 2778 links of traffic driving pattern data for both urban and non-urban trips are presented. Correlations between the link power and traffic parameters are presented and it is shown that vehicle link fuel consumption and emissions can be accurately calculated from vehicle mass, engine capacity, link average velocity, link average positive inertial power, link altitude change and link trip time. In the non-urban case, link power, and hence fuel consumption and emissions, are not dependent upon positive inertial power. In Part 2 the instantaneous vehicle power demand model is used to develop fuel usage input information to evaluate a simple average velocity model and an elemental model. The performance of these two models is compared with that of the on-road power method by “driving” all three models over 2281 links and 956 km of recorded on-road velocity, acceleration and gradient data. It is shown that all three models can be made to perform well for long trips. The elemental model, however, suffers from an inability to adequately describe the fuel usage of different stop-start manoeuvres and requires some calibration in order to account for cruise speed fluctuations. For short trips, 3.5 km in length or less, the on-road power demand method is superior. Under these conditions, both the simple V̄ and elemental models are unable to adequately describe the fuel usage relating to inertial power demands. It is shown that for short trips, inertial power demand does not correlate with average velocity and may range from near zero to up to twice the total trip averaged power.     

Eco-driving advisory strategies for a platoon of mixed gasoline and electric vehicles in a connected vehicle system

As electric vehicles (EVs) have gained an increasing market penetration rate, the traffic on urban roads will tend to be a mix of traditional gasoline vehicles (GVs) and EVs. These two types of vehicles have different energy consumption characteristics, especially the high energy efficiency and energy recuperation system of EVs. When GVs and EVs form a platoon that is recognized as an energy-friendly traffic pattern, it is critical to holistically consider the energy consumption characteristics of all vehicles to maximize the energy efficiency benefit of platooning. To tackle this issue, this paper develops an optimal control model as a foundation to provide eco-driving suggestions to the mixed-traffic platoon. The proposed model leverages the promising connected vehicle technology assuming that the speed advisory system can obtain the information on the characteristics of all platoon vehicles. To enhance the model applicability, the study proposes two eco-driving advisory strategies based on the developed optimal control model. One strategy provides the lead vehicle an acceleration profile, while the other provides a set of targeted cruising speeds. The acceleration-based eco-driving advisory strategy is suitable for platoons with an automated leader, and the speed-based advisory strategy is more friendly for platoons with a human-operated leader. Results of numerical experiments demonstrate the significance when the eco-driving advisory system holistically considers energy consumption characteristics of platoon vehicles.     

Preference-based evolutionary algorithm for airport surface operations

In addition to time efficiency, minimisation of fuel consumption and related emissions has started to be considered by research on optimisation of airport surface operations as more airports face severe congestion and tightening environmental regulations. Objectives are related to economic cost which can be used as preferences to search for a region of cost efficient and Pareto optimal solutions. A multi-objective evolutionary optimisation framework with preferences is proposed in this paper to solve a complex optimisation problem integrating runway scheduling and airport ground movement problem. The evolutionary search algorithm uses modified crowding distance in the replacement procedure to take into account cost of delay and fuel price. Furthermore, uncertainty inherent in prices is reflected by expressing preferences as an interval. Preference information is used to control the extent of region of interest, which has a beneficial effect on algorithm performance. As a result, the search algorithm can achieve faster convergence and potentially better solutions. A filtering procedure is further proposed to select an evenly distributed subset of Pareto optimal solutions in order to reduce its size and help the decision maker. The computational results with data from major international hub airports show the efficiency of the proposed approach.     

Estimating fuel burn impacts of taxi-out delay with implications for gate-hold benefits

The aviation community is actively investigating initiatives to reduce aircraft fuel consumption from surface operations, as surface management strategies may face fewer implementation barriers compared with en route strategies. One fuel-saving initiative for the air transportation system is the possibility of holding aircraft at the gate, or the spot, until the point at which they can taxi unimpeded to the departure runway. The extent to which gate holding strategies have financial and environmental benefits hinges on the quantity of fuel that is consumed during surface operations. A pilot of an aircraft may execute the taxi procedure on a single engine or utilize different engine thrust rates during taxi because of a delay. In the following study, we use airline fuel consumption data to estimate aircraft taxi fuel consumption rates during the “unimpeded” and “delayed” portions of taxi time. We find that the fuel consumption attributed to a minute of taxi-out delay is less than that attributed to minute of unimpeded taxi time; for some aircraft types, the fuel consumption rate for a minute of taxi delay is half of that for unimpeded taxi. It is therefore not appropriate, even for rough calculations, to apply nominal taxi fuel consumption rates to convert delayed taxi-out time into fuel burn. On average we find that eliminating taxi delay would reduce overall flight fuel consumption by about 1%. When we consider the savings on an airport-by-airport basis, we find that for some airports the potential reduction from reducing taxi delay is as much as 2%.     

Optimal vehicle speed trajectory on a signalized arterial with consideration of queue

Vehicle speed trajectory significantly impacts fuel consumption and greenhouse gas emissions, especially for trips on signalized arterials. Although a large amount of research has been conducted aiming at providing optimal speed advisory to drivers, impacts from queues at intersections are not considered. Ignoring the constraints induced by queues could result in suboptimal or infeasible solutions. In this study, a multi-stage optimal control formulation is proposed to obtain the optimal vehicle trajectory on signalized arterials, where both vehicle queue and traffic light status are considered. To facilitate the real-time update of the optimal speed trajectory, a constrained optimization model is proposed as an approximation approach, which can be solved much quicker. Numerical examples demonstrate the effectiveness of the proposed optimal control model and the solution efficiency of the proposed approach.     

Eco-driving: An economic or ecologic driving style?

In this work the trade-off between economic, therefore fuel saving, and ecologic, pollutant emission reducing, driving is discussed. The term eco-driving is often used to refer to a vehicle operation that minimizes energy consumption. However, for eco-driving to be environmentally friendly not only fuel consumption but also pollutant emissions should be considered. In contrast to previous studies, this paper will discuss the advantages of eco-driving with respect to improvements in fuel consumption as well as pollutant gas emissions. Simulating a conventional passenger vehicle and applying numerical trajectory optimization methods best vehicle operation for a given trip is identified. With hardware-in-the-loop testing on an engine test bench the fuel and emissions are measured. An approach to integrate pollutant emission and dynamically choose the ecologically optimal gear is proposed.     

Estimation of aircraft taxi fuel burn using flight data recorder archives

This paper builds a model for estimating the fuel consumption of a taxiing aircraft using flight data recorder information from operational aircraft. The taxi fuel burn is modeled as a linear function of several potential explanatory variables including the taxi time, number of stops, number of turns and number of acceleration events, and the coefficients are estimated using least-squares regression. The statistical significance of each potential factor is investigated. Our analysis shows that in addition to the taxi time, the number of acceleration events is a significant factor in determining taxi fuel consumption. Since the model parameters are estimated using data from operational aircraft, they provide more accurate estimates of fuel burn than methods that use idealized physical models of fuel consumption based on aircraft velocity profiles, or the baseline fuel consumption estimates provided by the International Civil Aviation Organization.     

Optimally combined headway and timetable reliable public transport system

This paper presents a model-based multiobjective control strategy to reduce bus bunching and hence improve public transport reliability. Our goal is twofold. First, we define a proper model, consisting of multiple static and dynamic components. Bus-following model captures the longitudinal dynamics taking into account the interaction with the surrounding traffic. Furthermore, bus stop operations are modeled to estimate dwell time. Second, a shrinking horizon model predictive controller (MPC) is proposed for solving bus bunching problems. The model is able to predict short time-space behavior of public transport buses enabling constrained, finite horizon, optimal control solution to ensure homogeneity of service both in time and space. In this line, the goal with the selected rolling horizon control scheme is to choose a proper velocity profile for the public transport bus such that it keeps both timetable schedule and a desired headway from the bus in front of it (leading bus). The control strategy predicts the arrival time at a bus stop using a passenger arrival and dwell time model. In this vein, the receding horizon model predictive controller calculates an optimal velocity profile based on its current position and desired arrival time. Four different weighting strategies are proposed to test (i) timetable only, (ii) headway only, (iii) balanced timetable - headway tracking and (iv) adaptive control with varying weights. The controller is tested in a high fidelity traffic simulator with realistic scenarios. The behavior of the system is analyzed by considering extreme disturbances. Finally, the existence of a Pareto front between these two objectives is also demonstrated.     

A stochastic model for the integrated optimization on metro timetable and speed profile with uncertain train mass

The integrated timetable and speed profile optimization model has recently attracted more attention because of its good achievements on energy conservation in metro systems. However, most previous studies often ignore the spatial and temporal uncertainties of train mass, and the variabilities of tractive force, braking force and basic running resistance on energy consumption in order to simplify the model formulation and solution algorithm. In this paper, we develop an integrated metro timetable and speed profile optimization model to minimize the total tractive energy consumption, where these real-world operating conditions are explicitly considered in the model formulation and solution algorithm. Firstly, we formulate a two-phase stochastic programming model to determine the timetable and speed profile. Given the speed profile, the first phase determines the timetable by scheduling the arrival and departure times for each station, and the second phase determines the speed profile for each inter-station with the scheduled arrival and departure times. Secondly, we design a simulation-based genetic algorithm procedure incorporated with the optimal train control algorithm to find the optimal solution. Finally, we present a simple example and a real-world example based on the operation data from the Beijing Metro Yizhuang Line in Beijing, China. The results of the real-world example show that, during peak hours, off-peak hours and night hours, the total tractive energy consumptions can be reduced by: (1) 10.66%, 9.94% and 9.13% in comparison with the current timetable and speed profile; and (2) 3.35%, 3.12% and 3.04% in comparison with the deterministic model.     

基于最佳碳排放与油耗最优化的行驶车速区间研究

在碳排放实验的基础上,文章研究了CO2排放比例与行车速度之间的关系,并提出最佳碳排放行驶车速区间这一概念。文章结合前人提出的油耗与车速模型,建立了高速公路最优车速区间的计算模型,车辆在此区间上行驶时,油耗低并且燃油充分,即CO2排放比例高。利用面积法确定了最优行驶车速区间的计算方法,求得了这一车速区间,从而达到经济效益与社会环境效益的最大化,同时也能够为高速公路的运营管理提供理论支撑。     

Fuel consumption model for bus rapid transit

Transportation planners and policy analysts require, for scenario testing, a detailed fuel consumption model of public transit operating in multimodal corridors. Although much effort has been devoted to the development of detailed methodology for estimating fuel consumption of automobile travel on freeways and arterials, the same is not the case for public transit. This paper presents methodology for the estimation of fuel consumption for bus operation on transitways/busways serving major travel corridors. Bus fuel consumption model is reported for standard and articulated buses. This model was adapted from an existing heavy vehicle fuel use model by incorporating the transitway design and bus operational characteristics.     

Development of a driving simulator based eco-driving support system

This research developed an eco-driving feedback system based on a driving simulator to support eco-driving training. This support system could provide both dynamic and static feedback to improve drivers’ eco-driving behavior. In the process of driving, drivers could get voice prompts (e.g., please avoid accelerating rapidly) once non-eco-driving behavior appeared, and also could see the real-time CO₂ emissions curves. After driving, drivers could receive an eco-driving evaluation report including their fuel consumption rank, potential of fuel saving and driving advice corresponding to their driving behavior. In this support system, five items of non-eco-driving behavior (i.e., quick accelerate, rapid decelerate, engine revolutions at a high level, too fast or unstable speed on freeways and idling for a longer time) were defined and could be detected. To validate this support system’s effectiveness in reducing fuel consumption and emissions, 22 participants were recruited and three driving tests were conducted, first without using the support system, then static feedback and then dynamic feedback utilized respectively. A reduction of 5.37% for CO₂ emissions and 5.45% for fuel consumption was obtained. The results indicated that the developed eco-driving support system was an effective training tool to improve drivers’ eco-driving behavior in reducing emissions and fuel consumption.     

Integrated sequencing and merging aircraft to parallel runways with automated conflict resolution and advanced avionics capabilities

Congestion in Terminal Maneuvering Area (TMA) in hub airports is the main problem in Chinese air transportation. In this paper we propose a new system to integrated sequence and merge aircraft to parallel runways at Beijing Capital International Airport (BCIA). This system is based on the advanced avionics capabilities. Our methodology integrates a Multi-Level Point Merge (ML-PM) system, an economical descent approaches procedure, and a tailored heuristic algorithm to find a good, systematic, operationally-acceptable solution. First, Receding Horizontal Control (RHC) technique is applied to divide the entire 24 h of traffic into several sub-problems. Then in each sub-problem, it is optimized on given objectives (conflict, deviation from Estimated Time of Arrival (ETA) on the runway and makespan of the arrival flow). Four decision variables are designed to control the trajectory: the entry time, the entry speed, the turning time on the sequencing leg, and the landing runway allocation. Based on these variables, the real time trajectories are generated by the simulation module. Simulated Annealing (SA) algorithm is used to search the best solution for aircraft to execute. Finally, the conflict-free, least-delay, and user-preferred trajectories from the entry point of TMA to the landing runway are defined. Numerical results show that our optimization system has very stable de-conflict performance to handle continuously dense arrivals in transition airspace. It can also provide the decision support to assist flow controllers to handle the asymmetric arrival flows on different runways with less fuel consumption, and to assist tactical controllers to easily re-sequence aircraft with more relaxed position shifting. Moreover, our system can provide the fuel consumption prediction, and runway assignment information to assist airport and airlines managers for optimal decision making. Theoretically, it realizes an automated, cooperative and green control of routine arrival flows. Although the methodology defined here is applied to the airport BCIA, it could also be applied to other airports in the world.     

Comparing real-world fuel consumption for diesel- and hydrogen-fueled transit buses and implication for emissions

This paper explores the influence of key factors such as speed, acceleration, and road grade on fuel consumption for diesel and hydrogen fuel cell buses under real-world operating conditions. A Vehicle Specific Power-based approach is used for modeling fuel consumption for both types of buses. To evaluate the robustness of the modeling approach, Vehicle Specific Power-based modal average fuel consumption rates are compared for diesel buses in the US and Portugal, and for the Portuguese diesel and hydrogen fuel cell buses that operate on the same route. For diesel buses there is similar intra-vehicle variability in fuel consumption using Vehicle Specific Power modes. For the fuel cell bus, the hydrogen fuel consumption rate was found to be less sensitive to Vehicle Specific Power variations and had smaller variability compared to diesel buses. Relative errors between trip fuel consumption estimates and actual fuel use, based upon predictions for a portion of real-world activity data that were not used to calibrate the models, were generally under 10% for all observations. The Vehicle Specific Power-based modeling approach is recommended for further applications as additional data become available. Emission changes based upon substituting hydrogen versus diesel buses are evaluated.