More Like this

1. Intelligent Cruise Control of Diesel Powered Vehicles Addressing the Fuel Consumption versus Emissions Trade-off

   Huang, Chunan; Salehi, Rasoul; Stefanopoulou, Anna G. (January 2018, American Control Conference)

   Intelligent cruise control with traffic preview introduces a potential to adjust the vehicle velocity and improve fuel consumption and emissions. This paper presents trade-offs observed during velocity trajectory optimizations when the objective function varies from fuel-based targets to emissions-based. The scenarios studied consider velocity optimization while following a hypothetical leader executing the federal test procedure (FTP) velocity profile with distance constraint, instead of the classical legislated velocity constraint, to enable the flexibility in optimizing the velocity trajectory. The vehicle model including longitudinal dynamics, fuel consumption and tailpipe NOx emissions is developed for a medium-duty truck with a diesel engine and verified over the FTP. Then, dynamic programming is applied on a reduced-order model to solve the constraint trajectory optimization problem and calculate an optimal vehicle velocity profile over the temperature stabilized phase (Bag 2) of the FTP. Results show 59% less tailpipe NOx emissions with an emission-optimized drive cycle but with 17% more fuel consumption compared to a non-optimized baseline. Whereas, a fuel-optimized cycle improves the fuel efficiency by 18% but with doubled tailpipe NOx emissions. Moreover, it is shown that for a diesel powertrain, including the aftertreatment system efficiency associated with the thermal dynamics is crucial to optimize the tailpipe NOx emissions and can not be ignored for problem simplification. 

   more » « less
2. A Robust Energy and Emissions Conscious Speed Control Framework for Connected Vehicles with Privacy Considerations

   Huang, Chunan; Zhang, Xueru; Salehi, Rasoul; Ersal, Tulga; Stefanopoulou, Anna G. (July 2020, Proceedings of the American Control Conference)

   While perturbation schemes for vehicle-to-vehicle (V2V) communications can address data privacy concerns, they can significantly compromise the performance of the speed controllers of connected automated vehicles (CAVs) if such controllers rely on the preview information available through V2V in car-following scenarios. This paper presents a robust predictive speed controller for a CAV when preview information is provided through a privacy-guaranteed V2V communication network. This is the first such controller that considers energy and emissions concurrently. The impact of privacy assurance in the communication data is studied, while inter-vehicular distance constraint is guaranteed to be satisfied through a robust design of the predictive controller using a robust control invariant set. The robust optimal speed controller is shown to reduce fuel consumption and emissions successfully while satisfying the constraints even in the presence of perturbations in the V2V communication. Results suggest a need for an integrated design procedure to achieve the best performance under a given level of privacy guarantee and emissions requirements. 

   more » « less
3. Dampen the Stop-and-Go Traffic with Connected and Automated Vehicles – A Deep Reinforcement Learning Approach

   https://doi.org/10.1109/MT-ITS49943.2021.9529289  

   Jiang, Liming; Xie, Yuanchang; Wen, Xiao; Chen, Danjue; Li, Tienan; Evans, Nicholas G. (January 2021, 2021 7th International Conference on Models and Technologies for Intelligent Transportation Systems)

   Stop-and-go traffic poses significant challenges to the efficiency and safety of traffic operations, and its impacts and working mechanism have attracted much attention. Recent studies have shown that Connected and Automated Vehicles (CAVs) with carefully designed longitudinal control have the potential to dampen the stop-and-go wave based on simulated vehicle trajectories. In this study, Deep Reinforcement Learning (DRL) is adopted to control the longitudinal behavior of CAVs and real-world vehicle trajectory data is utilized to train the DRL controller. It considers a Human-Driven (HD) vehicle tailed by a CAV, which are then followed by a platoon of HD vehicles. Such an experimental design is to test how the CAV can help to dampen the stop-and-go wave generated by the lead HD vehicle and contribute to smoothing the following HD vehicles’ speed profiles. The DRL control is trained using real-world vehicle trajectories, and eventually evaluated using SUMO simulation. The results show that the DRL control decreases the speed oscillation of the CAV by 54% and 8%-28% for those following HD vehicles. Significant fuel consumption savings are also observed. Additionally, the results suggest that CAVs may act as a traffic stabilizer if they choose to behave slightly altruistically. 

   more » « less
4. Dampen the Stop-and-Go Traffic with Connected and Automated Vehicles – A Deep Reinforcement Learning Approach

   Jiang, L (June 2021, Proceedings of the 7th International IEEE Conference on Models and Technologies for Intelligent Transportation Systems)

   null (Ed.)

   Stop-and-go traffic poses significant challenges to the efficiency and safety of traffic operations, and its impacts and working mechanism have attracted much attention. Recent studies have shown that Connected and Automated Vehicles (CAVs) with carefully designed longitudinal control have the potential to dampen the stop-and-go wave based on simulated vehicle trajectories. In this study, Deep Reinforcement Learning (DRL) is adopted to control the longitudinal behavior of CAVs and real-world vehicle trajectory data is utilized to train the DRL controller. It considers a Human-Driven (HD) vehicle tailed by a CAV, which are then followed by a platoon of HD vehicles. Such an experimental design is to test how the CAV can help to dampen the stop-and-go wave generated by the lead HD vehicle and contribute to smoothing the following HD vehicles’ speed profiles. The DRL control is trained using realworld vehicle trajectories, and eventually evaluated using SUMO simulation. The results show that the DRL control decreases the speed oscillation of the CAV by 54% and 8%-28% for those following HD vehicles. Significant fuel consumption savings are also observed. Additionally, the results suggest that CAVs may act as a traffic stabilizer if they choose to behave slightly altruistically. 

   more » « less
5. Data-Driven Modeling and Predictive Control of Combustion Phasing for RCCI Engines

   Khoshbakht Irdmousa, Behrouz; Rizvi, Syed Z; Mohammadpour Velni, Javad; Naber, Jeffrey; Shahbakhti, Mahdi (July 2019, Proceedings of the ... American Control Conference)

   Reactivity controlled compression ignition (RCCI) engines center on a combustion strategy with higher thermal efficiency, lower particulate matter (PM), and lower oxides of nitrogen (NOx) emissions compared to conventional diesel combustion (CDC) engines. However, real time optimal control of RCCI engines is challenging during transient operation due to the need for high fidelity combustion models. Development of a simple, yet accurate control-oriented RCCI model from physical laws is time consuming and often requires substantial calibrations. To overcome these challenges, data-driven models can be developed. In this paper, a data-driven linear parameter varying (LPV) model for an RCCI engine is developed. An LPV state space model is identified to predict RCCI combustion phasing as a function of multiple RCCI control variables. The results show that the proposed method provides a fast and reliable route to identify an RCCI engine model. The developed model is then used for the design of a model predictive controller (MPC) to control crank angle for 50% fuel burnt (CA50) for varying engine conditions. The experimental results show that the designed MPC with the data-driven LPV model can track desired CA50 with less than 1 crank angle degree (CAD) error against changes in engine load. 

   more » « less