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With on-board sensors such as camera, radar, and Lidar, connected and automated vehicles (CAVs) can sense the surrounding environment and be driven autonomously and safely by themselves without colliding into other objects on the road. CAVs are also able to communicate with each other and roadside infrastructure via vehicle-to-vehicle and vehicle-to-infrastructure communications, respectively, sharing information on the vehicles’ states, signal phase and timing (SPaT) information, enabling CAVs to make decisions in a collaborative manner. As a typical scenario, ramp control attracts wide attention due to the concerns of safety and mobility in the merging area. In particular, if the line-of-the-sight is blocked (because of grade separation), then neither mainline vehicles nor on-ramp vehicles may well adapt their own dynamics to perform smoothed merging maneuvers. This may lead to speed fluctuations or even shockwave propagating upstream traffic along the corridor, thus potentially increasing the traffic delays and excessive energy consumption. In this project, the research team proposed a hierarchical ramp merging system that not only allowed microscopic cooperative maneuvers for connected and automated electric vehicles on the ramp to merge into mainline traffic flow, but also had controllability of ramp inflow rate, which enabled macroscopic traffic flow control. A centralized optimal control-based approach was proposed to both smooth the merging flow and improve the system-wide mobility of the network. Linear quadratic trackers in both finite horizon and receding horizon forms were developed to solve the optimization problem in terms of path planning and sequence determination, and a microscopic electric vehicle (EV) energy consumption model was applied to estimate the energy consumption. The simulation results confirmed that under the regulated inflow rate, the proposed system was able to avoid potential traffic congestion and improve the mobility (in terms of average speed) as much as 115%, compared to the conventional ramp metering and the ramp without any control approach. Interestingly, for EVs (connected and automated EVs in this study), the improved mobility may not necessarily result in the reduction of energy consumption. The “sweet spot” of average speed ranges from 27–34 mph for the EV models in this study.View the NCST Project Webpage

Eco-driving strategies based on connected and automated vehicles (CAV) technology, such as Eco-Approach and Departure (EAD), have attracted significant worldwide interest due to their potential to save energy and reduce tail-pipe emissions. In this project, the research team developed and tested a deep learning–based trajectory-planning algorithm (DLTPA) for EAD. The DLTPA has two processes: offline (training) and online (implementation), and it is composed of two major modules: 1) a solution feasibility checker that identifies whether there is a feasible trajectory subject to all the system constraints, e.g., maximum acceleration or deceleration; and 2) a regressor to predict the speed of the next time-step. Preliminary simulation with microscopic traffic modeling software PTV VISSIM showed that the proposed DLTPA can achieve the optimal solution in terms of energy savings and a greater balance of energy savings vs. computational efforts when compared to the baseline scenarios where no EAD is implemented and the optimal solution (in terms of energy savings) is provided by a graph-based trajectory planning algorithm.View the NCST Project Webpage

Deploying the dynamic wireless power transfer (DWPT) equipment at intersections can improve the transportation efficiency and decrease the energy consumption. It can easily turn the disadvantages of waiting time for red phase into the advantages of charging benefits for electric vehicles (EVs). This study develops a multiobjective speed planning model with differentiated charging strategy to optimize the EV's trajectory at signalized intersections with DWPT. To mitigate the negative impact of DWPT, the proposed model is divided into charging priority driving (C-eco-driving) mode and time priority driving (T-eco-driving) mode for connected and automated electric vehicles (CAEVs) to apply different scenarios. Meanwhile, a widely applied car-following model, i.e., the Intelligent Driver Model (IDM), has been calibrated with the ground-truth dataset to meet the intersection characteristic for EVs in mixed traffic. The efficiency and robustness of two eco-driving modes were validated in single-vehicle and mixed traffic based on MATLAB simulation. For single vehicle, the electricity benefits of the C-eco-driving mode and T-eco-driving mode increase about 0.0867 kWh and 0.0532 kWh, respectively. T-eco-driving mode reduces 2.5 s of travel time. For mixed traffic, the C-eco-driving mode provides more charging benefits about 51~73% than no-control strategy. In contrast, the T-eco-driving mode provides significant time benefits of 2.6 s and slight electricity benefits of 12~30% than no-control strategy. The increment of market penetration rate and wireless charging length can significantly improve the charging benefits. When charging length has the same value, the closer the DWPT facilities to the intersection, the more obvious the charging benefits. [ABSTRACT FROM AUTHOR]

Highlights • NKE is defined and used as a variable for capturing the regenerative braking effect in EV energy consumption estimation. • A systematic data-driven EV energy consumption decomposition analysis is conducted. • A novel link-level EV energy consumption estimation model is built upon the decomposition analysis. • A "W"-shaped relationship between link-level EV energy consumption rate and average speed is discovered and explained. Abstract Electric vehicles (EVs) have great potential to reduce transportation-related fossil fuel consumption as well as pollutant and greenhouse gas (GHG) emissions, due to their use of renewable electricity as the sole energy source. Therefore, the wide-spread deployment of EVs is regarded as an attractive means to mitigate the environmental problems (e.g., air pollution and climate change) resulting from transportation activities. Government agencies are trying to promote EV deployment by allocating considerable funding as well as promulgating supportive policies. However, the mass adoption of EVs is still impeded by the limited charging infrastructure and all-electric-range (AER). All these lead to a critical research topic: the EV energy consumption analysis and estimation under real-world traffic conditions, which is fundamental to various types of EV-centred applications that aim at improving the EV energy efficiency and extending the AER. For example, eco-routing systems for EVs rely on accurate link-level energy consumption estimation to calculate the EV energy consumption costs of the different route options. In this work, to obtain an accurate link-level energy consumption estimation model for EVs, the energy consumption under real-world traffic congestion is decomposed based on two proposed impact factors: positive kinetic energy (PKE) and negative kinetic energy (NKE). Upon this decomposition, a data-driven model is built to estimate EV energy consumption on each roadway link considering real-world traffic conditions. Finally, the model performance is evaluated by comparing with the performance of baseline model adapted from existing models. The results show that the proposed EV link-level energy consumption estimation model outperforms the existing models in terms of accuracy, implying that it is quite promising in various on-board EV applications. [ABSTRACT FROM AUTHOR]

In recent years, eco-friendly driving or ecodriving technologies are being developed to assist human drivers to achieve maximum fuel/energy efficiency in different driving conditions. Enhanced by V2X wireless communications, connected ecodriving is expected to be very promising in reducing transportation-related fossil fuel consumption as well as pollutant emissions. Besides, the deployment of electric vehicles (EVs) also has great potential in reducing greenhouse gas emissions due to the use of batteries as the sole energy source. Although recent research shows that significant energy savings can be achieved with the aid of ecodriving systems in real-world driving, there have been very few research efforts that consider human driver error, especially for electric vehicle (EV) driving. In this paper, a connected cooperative ecodriving system for energy-efficient driving that considers human driver error is designed and evaluated with an EV energy consumption model. Real-world driving data were collected and used to evaluate system performance in terms of energy consumption. The simulation and numerical analysis shows that an average of 12% energy savings can be achieved by the proposed system that considers human driver error comparing with the conventional ecodriving system without considering driver error. [ABSTRACT FROM AUTHOR]

Abstract Battery electric vehicles (BEVs) are a critical pathway towards achieving energy independence and meeting greenhouse and criteria pollutant gas reduction goals in the current and future transportation sector. Emerging connected and automated vehicle (CAV) technologies further open the door for developing innovative applications and systems to leverage vehicle efficiency and substantially transform transportation systems. Therefore, we present a simulation study of various BEV types and compare the performance when driving on real-road drive cycles to highly optimized eco-driving cycles using advanced CAV technologies. The results demonstrate that eco-driving has a high potential to reduce energy consumption for all types of BEVs considered. The investigated BEVs include a compact vehicle, a transit city bus, and a Class 7 delivery truck. The impact of eco-driving on conventional vehicles was also compared to comparable BEVs. Compared to the BEVs, eco-driving provides a larger reduction in the conventional vehicle's braking energy loss, and also provides conventional vehicles with greater reductions in the engine mechanical energy output but the fuel savings did not show a consistent trend among all the conventional vehicle types. As part of the study, a comprehensive EV powertrain model was developed to account for key EV components and powertrain configurations. Highlights • The developed BEV model enables comparisons between BEV technology options. • BEV key losses are aero drag, rolling resist, motor loss, drivetrain loss, and bus accessory. • Eco-driving boosts efficiency by 27%, 22% and 8% for the passenger BEV, E-bus and E-truck, respectively. • Low rolling resistance tires have a significant impact on the BEVs' efficiency under eco-driving. • The impact of eco-driving on comparable conventional vehicles was compared to the BEVs. [ABSTRACT FROM AUTHOR]