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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Using Map Matching for DeIdentification of Connected Vehicle Locations

In this paper we introduce a location deidentification procedure that uses road network structure to protect against certain types of inference-based attacks. Our target is large databases containing vehicle locations. Previous anonymization approaches are inappropriate because location generalization and perturbation of geopositions could negatively affect development of safety-critical applications that require precise position information. Furthermore, k-anonymity-based clustering approaches would lead to significant data suppression. Our algorithm attempts to balance privacy protection and data utility, while protecting against re-identification attacks. Our data is from the first connected vehicle model deployment in the United States.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

Security vulnerabilities of connected vehicle streams and their impact on cooperative driving

Autonomous vehicles capable of navigating unpredictable real-world environments with little human feedback are a reality today. Such systems rely heavily on onboard sensors such as cameras, radar/LIDAR, and GPS as well as capabilities such as 3G/4G connectivity and V2V/V2I communication to make real-time maneuvering decisions. Autonomous vehicle control imposes very strict requirements on the security of the communication channels used by the vehicle to exchange information as well as the control logic that performs complex driving tasks such as adapting vehicle velocity or changing lanes. This research presents a first look at the effects of security attacks on the communication channel as well as sensor tampering of a connected vehicle stream equipped to achieve CACC. Our simulation results show that an insider attack can cause significant instability in the CACC vehicle stream. We also outline how different countermeasures, such as downgrading to ACC mode, could potentially be used to improve the security and safety of the connected vehicle streams.

42 ENGINEERING↗
DOE OSTI2023

Real-time control of connected vehicles in signalized corridors using pseudospectral convex optimization

Recent advances in Connected and Automated Vehicle (CAV) technologies have opened up new opportunities to enable safe, efficient, and sustainable transportation systems. However, developing reliable and rapid speed control algorithms in highly dynamic environments with complex inter-vehicle interactions and nonlinear vehicle dynamics is still a daunting task. In this paper, we develop a novel speed control method for CAVs to produce optimal speed profiles that minimize the fuel consumption and avoid idling at signalized intersections. To this end, an optimal control problem is formulated using the information of the upcoming traffic signal to adapt vehicles' speeds to avoid frequent stop-and-go driving patterns. Here, by applying the pseudospectral discretization method and the sequential convex programming method, the computational efficiency is greatly improved, enabling potential real-time on-vehicle applications. In addition, the algorithm is implemented under a model predictive control framework to ensure online control with instant response for collision avoidance and robust vehicle coordination. The proposed algorithm is verified through numerical simulations of three different traffic scenarios. The convergence and accuracy of the proposed approach are demonstrated by comparing with a popular nonlinear solver. Furthermore, the benefit of the proposed method in both traffic mobility and fuel efficiency is validated using the speed profile determined from a traffic following model in a simulation software as the baseline.

42 ENGINEERING↗
DOE OSTI2019

Investigating the Impact of Connected Vehicle Market Share on the Performance of Reinforcement-Learning Based Traffic Signal Control

We aim to understand and explore the performance of reinforcement learning based signal control algorithms in a mixed environment with less than 100% market share of connected and automated vehicles (CAVs). Within a simulation environment, we have considered partial connectivity—less than 100% market share of CAVs—in the network and investigated the impact on the performance of the signal control algorithm. Two test networks including a four-intersection arterial in Lankershim Boulevard, CA, and a portion of downtown Springfield, IL, with 20 intersections. The first network is calibrated in the micro-simulator PTV Vissim with the US DOT provided NGSIM datasets. The results provide insights regarding the impact of the connectivity and sensing technologies on the practical implementation of traffic signal control algorithms that leverage the data sharing capability of a connected environment. For scenarios with 40% or more market share of CAVs, we observed improvement in the performance metrics—travel time, queue time, and energy consumption—with the increase in market share. Results from our experiments do not indicate any clear trend when the networks have low (less than 40%) market share of CAVs. The higher standard deviations as obtained from the statistical analyses of the performance metrics at low market share may indicate the instability of the RL controller arising from the partial (if not zero) observability of the traffic states. Further, we have conducted simplified scenario analyses to explore the impact of the market share of battery electric vehicles (BEVs) on energy consumption due to the regenerative braking feature. Our results and findings will be the foundation for the future reinforcement learning based control algorithm development that accounts for partial connectivity—less than 100% CAV market share, and the presence of BEVs in a network of connected and automated signalized intersections.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2023

Evaluating The Impact Of Connected Vehicle Technology On Heavy Duty Vehicle Emissions

Vehicles with advanced driver assistance systems (ADAS) like adaptive cruise control (ACC) could use connected (V2X) data to improve fuel economy  Eco driving generates an energy efficient speed profile by minimizing acceleration events  SwRI demonstrated a 7% reduction in energy consumption for fully loaded class 8 trucks by using nearby vehicle and traffic signal information  How does eco driving impact vehicle emissions and aftertreatment (AT) systems?

Gankov, Stanislav↗
DOE OSTI2018

Optimal velocity prediction for fuel economy improvement of connected vehicles

With the advancement of vehicle-to-vehicle and vehicle-to-infrastructure technologies, more and more real-time information regarding traffic and transportation system will be available to vehicles. This paper presents the development of a novel algorithm that uses available velocity bounds and powertrain information to generate an optimal velocity trajectory over a prediction horizon. When utilised by a vehicle, this optimal velocity trajectory reduces fuel consumption. The objective of this optimisation problem is to reduce dynamic losses, required tractive force, and completing trip distance with a given travel time. Sequential quadratic programming method is employed for this nonlinearly constrained optimisation problem. When applied to a GM Volt-2, the generated velocity trajectory saves fuel compared to a real-world drive cycle. The simulation results confirm the fuel consumption reduction with the rule-based mode selection and the energy management strategy of a GM Volt 2 model in Autonomie.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2018

A Cooperative Freeway Merge Assistance System Using Connected Vehicles

This paper describes a novel freeway merge assistance system utilizing V2X technology with the help of the DSRC protocol. The freeway merge assistance system uses an innovative three-way handshaking protocol and provides advisories to drivers to guide the merging sequence.

Ahmed, Md Salman↗
DOE OSTI2020

Long-Term Vehicle Speed Prediction via Historical Traffic Data Analysis for Improved Energy Efficiency of Connected Electric Vehicles

Connected and automated vehicles (CAVs) are expected to provide enhanced safety, mobility, and energy efficiency. While abundant evidence has been accumulated showing substantial energy saving potentials of CAVs through eco-driving, traffic condition prediction has remained to be the main challenge in capitalizing the gains. The coupled power and thermal subsystems of CAVs necessitate the use of different speed preview windows for effective and integrated power and thermal management. Real-time vehicle-to-infrastructure (V2I) communications can provide an accurate speed prediction over a short prediction horizon (e.g., 30 s to 60 s), but not for a long range (e.g., over 180 s). Therefore, advanced approaches are required to develop detailed speed prediction for robust optimization-based energy management of CAVs. This paper presents an integrated speed prediction framework based on historical traffic data classification and real-time V2I communications for efficient energy management of electrified CAVs. The proposed framework provides multi-range speed predictions with different fidelity over short and long horizons. The proposed multi-range speed prediction is integrated with an economic model predictive control (MPC) strategy for the battery thermal management (BTM) of connected and automated electric vehicles (EVs). The simulation results over real-world urban driving cycles confirm the enhanced prediction performance of the proposed data classification strategy over a long prediction horizon. Despite the uncertainty in long-range CAVs’ speed predictions, the vehicle-level simulation results show that 14% and 19% energy savings can be accumulated sequentially through eco-driving and BTM optimization (eco-cooling), respectively, when compared with normal driving (i.e., human driver) and conventional BTM strategy.

Engineering↗