Content uploaded by Jiaqi Ma
Author content
All content in this area was uploaded by Jiaqi Ma on Jan 19, 2019
Content may be subject to copyright.
A preview of the PDF is not available
Abstract and Figures
Eco-drive is one of the many techniques that has been developed to increase vehicle fuel efficiency and improve the sustainability of the entire transportation system. Connected and automated vehicle (CAV) data is now being used to allow vehicles to cooperate better with current and future environments, such as traffic conditions, signal timing, and terrain information. This study proposes an eco-drive algorithm for vehicle fuel consumption optimization on rolling terrains, which frequently causes additional fuel waste due to the inefficient transformation between kinetic and potential energy. The algorithm using the Relaxed Pontryagin's Minimum Principle (RPMP) is computationally efficient and applicable in real time. While similar algorithms have proven effective in simulations with many assumptions, it is necessary to test these algorithms in the field to better understand the algorithm performance and thus make enhancements to optimally control vehicles for eco-drive. Therefore, this study further tested and verified the newly developed algorithms on an innovative CAV platform and quantified the fuel saving benefits of eco-drive. The proposed eco-drive system is compared against conventional constant speed cruise control on a total of 7 road segments over 47 miles. Experimental data shows that more than 20 percent of fuel consumption can be saved. Detailed analysis through linear models also reveal the main geometrical contributors to the eco-drive fuel savings. This finding can provide a rough estimate of the fuel saving potential of given roadways and help state departments of transportation to identify roadways where eco-drive should be implemented. The algorithm and the experiment can also support original equipment manufacturers in developing and marketing this technology to reduce fuel consumption and emissions in the future.
Content may be subject to copyright.
... The precise prediction and estimation of the instantaneous fuel consumption rate (FCR) of vehicles is a cornerstone for the adoption of energy-saving technologies like eco-driving and eco-routing (Zhou et al., 2016;Ma et al., 2019;Miotti et al., 2021;Zhang et al., 2022b), as well as for the reduction of carbon dioxide emissions Wang et al., 2022a). Existing fuel consumption models often fail to fully consider the complex effects of external variables, such as vehicle weight, on fuel consumption in real-world driving scenarios, leading to potential uncertainties (Zhang et al., 2017;Wang et al., 2021b;Rosero et al., 2021). ...
Insufficient consideration of vehicle weight dynamics during real-world driving could lead to inaccurate fuel consumption estimates. This study examined the impact of vehicle weight on fuel consumption rate (FCR) by analyzing extensive, high-resolution operating data obtained from 162 heavy-duty trucks (HDTs). An engine output power-based (EOP) model, an artificial neural network (ANN) model, and a long short-term memory-convolutional (LSTM-Conv) model were developed and contrasted with conventional vehicle specific power (VSP) and Virginia Tech Microscopic (VT-Micro) models. The results indicated a significant, non-linear relationship between weight and FCR. Compared to 5-ton trucks, FCR for trucks weighing 15–25 tons and 45–55 tons increased by 290% and 755%, respectively, under low-speed and positive acceleration conditions. The LSTM-Conv model outperformed the VSP, VT-Micro, and EOP models, achieving MAPEs of 9.81% for FCR and 1.49% for trip fuel economy estimation. The deep-learning models exhibited enhanced stability across varying speeds, accelerations, and vehicle weights.
... Consequently, a local speed tracker is required to accurately track and maintain the ecological speed [46]. Ma et al. were one of the first studies on eco-cruising [47]. Their controller is specifically designed to precisely align with the final state of the target time window within the spatial domain. ...
Cooperative Driving Automation (CDA) stands at the forefront of the evolving landscape of vehicle automation, elevating driving capabilities within intricate real-world environments. This research aims to navigate the path toward the future of CDA by offering a thorough examination from the perspective of Planning and Control (PnC). It classifies state-of-the-art literature according to the CDA classes defined by the Society of Automotive Engineers (SAE). The strengths, weaknesses, and requirements of PnC for each CDA class are analyzed. This analysis helps identify areas that need improvement and provides insights into potential research directions. The research further discusses the evolution directions for CDA, providing valuable insights into the potential areas for further enhancement and enrichment of CDA research. The suggested areas include: Control robustness against disturbance; Risk-aware planning in a mixed environment of Connected and Automated Vehicles (CAVs) and Human-driven Vehicles (HVs); Vehicle-signal coupled modeling for coordination enhancement; Vehicle grouping to enhance the mobility of platooning.
... A number of eco-driving systems have been proposed based on road contexts. Examples of such works are those that mainly focused on rolling terrains or hilly roads [11][12][13][14]. However, the horizontal curve is another important feature of the road that significantly affects fuel consumption and carbon emissions [15]. ...
Energy consumption and emissions of a vehicle are highly influenced by road contexts and driving behavior. Especially, driving on horizontal curves often necessitates a driver to brake and accelerate, which causes additional fuel consumption and emissions. This paper proposes a novel optimal ecological (eco) driving scheme (EDS) using nonlinear model predictive control (MPC) considering various road contexts, i.e., curvatures and surface conditions. Firstly, a nonlinear optimization problem is formulated considering a suitable prediction horizon and an objective function based on factors affecting fuel consumption, emissions, and driving safety. Secondly, the EDS dynamically computes the optimal velocity trajectory for the host vehicle considering its dynamics model, the state of the preceding vehicle, and information of road contexts that reduce fuel consumption and carbon emissions. Finally, we analyze the effect of different penetration rates of the EDS on overall traffic performance. The effectiveness of the proposed scheme is demonstrated using microscopic traffic simulations under dense and mixed traffic environment, and it is found that the proposed EDS substantially reduces the fuel consumption and carbon emissions of the host vehicle compared to the traditional (human-based) driving system (TDS), while ensuring driving safety. The proposed scheme can be employed as an advanced driver assistance system (ADAS) for semi-autonomous vehicles.
... Searchbased methods include the A* algorithm [21], Markov decision process [22], genetic algorithm [23], [24], Dijkstra algorithm [25], [26], and others. Gradient-based methods involve formulating and solving nonlinear programming problems using point-wise minimization [27], pseudo-spectral algorithm [28], Pontryagin's minimum algorithm [29], [30], receding dynamic programming [31], or model predictive control [5], [32], [33], etc. However, there are certain limitations in the existing methods. ...
Connected and automated vehicles provide enormous opportunities for improving fuel economy, safety and capacity of the transportation system. In this paper, an Eco-driving speed advisory for electric vehicle platoons is proposed by taking regenerative braking and braking torque distribution into account. An efficient Poly-Eco speed planning method is then presented to improve computational efficiency. A control scheme comprising a splitting/merging decision-making and a modified intelligent driver model vehicle-following controller is established to verify the effectiveness of the proposed Poly-Eco speed advisory in mixed traffic scenarios. Comprehensive hardware-in-the-loop tests are conducted to examine the proposed Poly-Eco control scheme in terms of energy consumption, traffic efficiency and ride comfort. The rule-based and sequential programming (SP) methods are used for comparison. The comparison results show that the proposed Poly-Eco method can reduce the energy consumption by 4.71% while guaranteeing lower jerk and less arrival time compared with the commonly-used SP method. The lapse time per period for the proposed Poly-Eco is only 2.3% of that for the SP method. In addition, the proposed Poly-Eco method stages superior performance under typical mix traffic scenarios.
Achieving sustainable transportation poses a variety of challenges and difficulties, such as economic, social, and environmental dimensions. These challenges slow down the transition towards a transportation system that minimizes environmental impact and fuel consumption. The focus has shifted towards increasing efficiency and promoting greener alternatives by integrating innovative solutions like intelligent transportation systems, communication technologies, and other approaches to address these challenges and pursue sustainable and energy-efficient transportation. This study proposes a novel approach that integrates Python, SUMO (Simulation of Urban MObility), Vehicle-to-Infrastructure (V2I) communication, and Fuzzy Logic (FL) to estimate the optimal speed for vehicles based on vehicle velocity, road speed limits, and ambient temperature. In the simulation scenarios, we considered different temperature changes to evaluate the effectiveness of the proposed approach. By using SUMO to retrieve the road speed limit through V2I communication and incorporating it into fuzzy logic, we could estimate the optimal speed for vehicles in real time. The simulation results showed a significant reduction in energy consumption and pollutant emissions compared to the unequipped vehicles with V2I and Fuzzy Logic Systems. Specifically, the study found that adopting this approach led to an average reduction in fuel consumption and CO2 emissions of around 9%. These findings highlight the potential of integrating V2I communication and Fuzzy Logic to achieve more sustainable and efficient transportation energy use.
Autonomous mining transportation is an intelligent traffic control system that can provide better economics than traditional transportation systems. The velocity trajectory of a manned vehicle depends on the driver’s driving style. Still, it can be optimized utilizing mathematical methods under autonomous driving conditions. This paper takes fuel and electric mining vehicles with a load capacity of 50 tons as the subject. It contributes a multi-objective optimization approach considering time, energy consumption, and battery lifetime. The dynamic programming (DP) algorithm is used to solve the optimal velocity trajectory with different optimization objectives under two types of mining condition simulation. The trajectories optimized by the single objective, energy consumption, usually adopt the pulse-and-gliding (PnG) approach frequently, which causes the battery capacity loss and increases the travel time. Hence, a multi-objective optimization approach is proposed. For electric vehicles, trajectories optimized by the multi-objective approach can decrease the battery capacity loss by 22.01 % and the time consumption by 41.28 %, leading to a 42.12 % increment in energy consumption. For fuel vehicles, it can decrease the time consumption by 32.54 %, leading to a 7.68 % increment in energy consumption. This velocity trajectory is smoother with less fluctuation. It can better meet the requirements of mining transportation and has a particular reference value for optimizing autonomous transportation costs in closed areas.
Introduction: The presence of connected and automated vehicles (CAV) in mixed traffic flows with different market penetration rates (MPRs) in urban road scenarios has a significant effect on fuel consumption and exhaust emissions.
Methods: Therefore, in this study, real-world road networks and traffic data are simulated using SUMO based on actual data from a survey. The fuel consumption and emission benefits of CAVs in mixed traffic flows are well-evaluated, and the energy-saving performance of CAVs under low-speed vehicle interference is tested. In addition, this study explores both the energy consumption and emissions of purely electric vehicles.
Results: The results show that with 100% CAV penetration, fuel vehicles have a maximum reduction in fuel consumption of 18% and a maximum increase in average speed of 31.6%, while the energy consumption of electric vehicles increases due to communication, detection, and collaboration between CAVs.
Discussion: However, the results clearly demonstrate that the carbon emissions of electric vehicles are significantly lower than fuel vehicles. In addition, the increase in low-speed vehicles will result in an increase in energy consumption and emissions. Therefore, increasing the percentage of electric vehicles on the roads and transitioning from manual to autonomous driving systems is crucial to curbing carbon emissions.
The development of novel vehicle control systems that improve Fuel Economy (FE) is of significant interest, owing to the economic, environmental, and social implications of transportation. These control methods can either alter driver behavior, vehicle dynamics, or powertrain operation. This review focuses on the Eco-driving (ED) application for Autonomous Vehicles (AVs) and aims to provide a current understanding of critical research gaps, and novel detailed overview of initial studies addressing these identified research gaps. A systems-level perspective on an ED realization in AVs is used to identify research gaps, which are then studied to determine subsystem Technology Readiness Levels (TRLs), Integration Readiness Levels (IRLs) and overall System Readiness Level (SRL). Major research gaps identified include (1) the influence of real-world AV sensors on the identification of important ED parameters, (2) the effect of sparse or missing sensor data on global derivation of Autonomous Eco-driving (AED), and (3) the implications of implementing AED with a Physical Vehicle. The studies that have attempted to address each research gap are highlighted, along with suggestions for future research. After identifying research gaps, ED capabilities for AVs are asserted to be viable in current vehicles, revealing the possibility for greater transportation sustainability at present.KeywordsBattery electric vehicleConnected and automated vehicleEco-drivingEnergy management systemAutonomous vehicle
While safety is one of the most critical contributions of Cooperative Adaptive Cruise Control (CACC), it is impractical to assess such impacts in a real world. Even with simulation, many factors including vehicle dynamics, sensor errors, automated vehicle control algorithms and crash severity need to be properly modeled. In this paper, a simulation platform is proposed which explicitly features: (i) vehicle dynamics; (ii) sensor errors and communication delays; (iii) compatibility with CACC controllers; (iv) state-of-the-art predecessor leader following (PLF) based cooperative adaptive cruise control (CACC) controller; and (v) ability to quantify crash severity and CACC stability. The proposed simulation platform evaluated the CACC performance under normal and cybersecurity attack scenarios using speed variation, headway ratio, and injury probability. The first two measures of effectiveness (MOEs) represent the stability of CACC platoon while the injury probability quantifies the severity of a crash. The proposed platform can evaluate the safety performance of CACC controllers of interest under various paroxysmal or extreme events. It is particularly useful when traditional empirical driver models are not applicable. Such situations include, but are not limited to, cyber-attacks, sensor failures, and heterogeneous traffic conditions. The proposed platform is validated against data collected from real field tests and tested under various cyber-attack scenarios.
In urban areas, traffic signals cause unnecessary stops and delays to vehicles resulting in excessive energy consumptions and emissions. The eco-approach technology has been introduced to achieve environmentally friendly driving behaviors and to improve energy efficiencies with reduced rapid accelerations or decelerations. However, it is not clear how the behavior of drivers would change while following an eco-approaching vehicle. Especially in the mixed traffic, dynamics behind the interactions between the human-driven vehicle and connected automated vehicle (CAV) that perfectly achieve the ecoapproach, and the impacts on energy consumptions are not transparent. This research designs a driving simulation environment for the mixed traffic to assess the benefits of the eco-approach. In the experiments, a straight roadway with four signalized intersections equipped with communication devices is developed. An eco-guidance algorithm for human drivers and an eco-approach control algorithm of CAV with the I2V communication system are designed and compared, where each scenario is tested 50 times with a human driver using a driving simulator. The results indicate that both eco-guidance scenario and following CAV with the eco-approach scenario could reduce over 6% of fuel consumptions compared to the base-case scenario, which is normal-driving without guidance. This indicates the energy efficiency benefit of the ecoapproach of CAVs and its impact on surrounding vehicles in the mixed traffic.
Most existing studies on connected and automated vehicle (CAV) applications apply simulation to evaluate system effectiveness. Model accuracy, limited data for calibration, and simulation assumptions limit the validity of evaluation results. One alternative approach is to use emerging hardware-in-the-loop (HIL) testing methods. HIL test environments enables physical test vehicles to interact with virtual vehicles from traffic simulation models, providing an evaluation environment that can replicate deployment conditions at early stages of CAV implementation without incurring excessive costs related to large field tests. In this study, a HIL testing system for CAV applications is proposed. The involved software and hardware includes CAVs controlled in real-time, traffic signal controllers, communication devices, and a traffic simulator (VISSIM). Such HIL systems increase validity by considering the physical vehicle’s trajectories, which are constrained by real-world factors such as GPS accuracy, communication delay, and vehicle kinematics, in a simulated traffic environment. The developed HIL system is then applied to test a representative early deployment CAV application: queue-aware signalized intersection approach and departure (Q-SIAD). The Q-SIAD algorithm generates recommended speed profiles based on the vehicle’s status, signal phase and timing (SPaT), downstream queue length, and system constraints and parameters (e.g., maximum acceleration and deceleration). The algorithm also considers the status of other vehicles in designing the speed profiles. The experiment successfully demonstrated this functionality with one test CAV driving through one intersection controlled by a fixed-timing traffic signal under various simulated traffic conditions.
In this research, by taking advantage of dynamic fuel consumption – speed data from Internet of Vehicles, we develop two novel computational approaches to more accurately estimate truck fuel consumption. The first approach is on the basis of a novel index, named energy consumption index (ECI), which is to explicitly reflect the dynamic relationship between truck fuel consumption and truck drivers’ driving behaviors obtained from Internet of Vehicles. The second approach is based on a Generalized Regression Neural Network (GRNN) model to implicitly establish the same relationship. We further compare the two proposed models with three well-recognized existing models: vehicle specific power (VSP) model, Virginia Tech microscopic (VT-Micro) model, and Comprehensive Modal Emission Model (CMEM). According to our validations at both microscopic and macroscopic levels, the two proposed models have stronger performances in predicting fuel consumption in new routes. The models can be used to design more energy-efficient driving behaviors in the soon-to-come era of connected and automated vehicles.
In the upcoming decades, connected vehicles will join regular vehicles to appear on roads, and the characteristics of traffic flow will be changed accordingly. To model the heterogeneous traffic mixing regular and connected vehicles, a generic car-following framework is first proposed in this paper. A linear stability condition is theoretically derived, which indicates that the stability of the heterogeneous traffic is closely related to the penetration rate and the spatial distribution of connected vehicles. The generic car-following framework is applied by taking the Intelligent Driver Model as an example, and it is shown that connected vehicles can obviously enhance the stability of traffic flow and improve traffic efficiency in particular when traffic is in congestion. Moreover, a driver assistance strategy based on distributed feedback control is developed for connected vehicles, and the simulation results show that the proposed driver assistance strategy performs satisfactorily in stabilizing traffic as well as improving traffic efficiency. Index Terms-Car-following model, advanced driver assistance systems, feedback control, traffic efficiency, stability analysis.
This research conducted a comprehensive study to better estimate the impact of production Adaptive Cruise Control (ACC) on uninterrupted flow facilities using the highly cited ACC car-following models in the literature. Four different car-following models were investigated, including the Autonomous Adaptive Cruise Control (AACC) model, Intelligent Driver Model (IDM), California Partners for Advanced Transit and Highways (PATH) empirical ACC model, and the Technical University of Delft empirical ACC model. Each of the four models were recalibrated using data collected by a 2013 Cadillac SRX with production ACC engaged while following a human-driven 2013 Cadillac SRX in northern Virginia. Off-the-shelf commercial microscopic simulation software VISSIM was utilized to simulate the ACC traffic flow. Sensitivity analysis was conducted on ACC market penetration and following headway. Capacity and fundamental diagrams were used to quantify the impact of the ACC on traffic flow. Results indicate that freeway capacity highly depends on the market penetration of ACC vehicles. Small number of ACC vehicles (<25%) is beneficial to roadway capacity, while wider deployment of ACC vehicles (>75%) significantly harms capacity. The results strongly suggest the urgent need for vehicle connectivity.
In this paper, the problem of eco-driving for a conventional vehicle equipped with an internal combustion engine is studied. The associated optimal control problem is formulated and solved using Dynamic Programming (DP). The impact of the mesh choice on the optimality of the DP solution is investigated in order to find a trade-off between the optimality of the DP solution and its computation time. The eco-driving speed trajectories obtained were tested on a high-frequency HIL (Hardware-In-the-Loop) engine test bench to quantify the real reductions in fuel consumption. Simulations and experiments are compared.