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Impact of Automated Lane Change Assist on Energy Consumption

Embry-Riddle Aeronautical University-Casey Troxler, Patrick Currier, Charles Reinholtz
  • Technical Paper
  • 2020-01-0082
To be published on 2020-04-14 by SAE International in United States
Automated lane change assist combined with adaptive cruise control has the potential to reduce energy consumption and improve safety. This paper models adaptive cruise control combined with automated lane change assist to investigate the energy consumption improvements that such a system may provide compared to conventional adaptive cruise control. Automatically executing a lane change may improve efficiency, for example, when following a vehicle that is slowing to make a turn. Changing lanes while maintaining speed should be more efficient than staying in the same lane as the turning vehicle and reducing speed. The differences in such scenarios are simulated in a virtual environment using a cuboid model with idealized sensors. The ego-vehicle will detect scenarios, evaluate if a lane change is feasible, and possibly perform a lane change to reduce or eliminate required speed changes. The results of the simulations compare the energy content of the resulting drive cycle as an idealized method to measure energy consumption for each cruise control strategy. The simulations consider traffic laws, such as turn signal requirements that may dictate…
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Modeling of an Integrated Internal Heat Exchanger and Accumulator in R744 Mobile Air-Conditioning Applications

University of Illinois at Urbana-Champaign-Wenying Zhang, Predrag Hrnjak
  • Technical Paper
  • 2020-01-0153
To be published on 2020-04-14 by SAE International in United States
Carbon dioxide (R744) is one of the most promising next-generation refrigerants for mobile air-conditioning applications (MAC), which has the advantages of good heating performance in cold climates and environmental-friendly properties. In this paper, a simulation model of an integrated internal heat exchanger (IHX) and accumulator (ACC) was developed using the finite volume method via EES. The results were validated by experimental results from a transcritical R744 mobile heat pump, and the error was within ±5%. The impacts of mass flow rate, evaporator outlet quality and temperatures of high- and low-side streams on the heat transfer rate, effectiveness and charge of the integrated IHX/Acc were studied. Results show that the heat transfer rate of the IHX is mostly sensitive to the evaporator outlet quality. When the evaporator quality decreases from 0.9 to 0.6, the heat transfer rate increases from 1.1 to 2.4 kW and the superheat reduces from 25.8 to 9.4 ℃. As a result, the compressor discharge temperature and the heating capacity can be reduced. To obtain the maximized capacity, especially during the startup, an…
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Electronic Control of Brake and Accelerator Pedals for Precise Efficiency Testing of Electrified Vehicles

Southwest Research Institute-Michael C. Gross, Jonathan Hamermesh, Kyle Jonson, Joshua Alden
  • Technical Paper
  • 2020-01-1282
To be published on 2020-04-14 by SAE International in United States
Efficiency testing of hybrid-electric vehicles is challenging, because small run-to-run differences in pedal application can change when the engine fires or the when the friction brakes supplement regenerative braking, dramatically affecting fuel use or energy regeneration. Electronic accelerator control has existed for years, thanks to the popularity of throttle-by-wire (TBW). Electronic braking control is less mature, since most vehicles don’t use brake-by-wire (BBW). Computer braking control typically uses a mechanical actuator (which may suffer backlash or misalignment) or braking the dynamometer rather than the vehicle (which doesn’t yield regeneration). The growth of electrification and autonomy provides the means to implement electronic brake control. Electrified vehicles use BBW to control the split between friction and regenerative braking. Automated features, e.g. adaptive cruise control, require BBW to actuate the brakes without pedal input. We present a system for computer control of brake and accelerator inputs on a TBW- and BBW-equipped vehicle. The system injects signals into the vehicle’s wiring harness, bypassing the pedals and obviating mechanical actuation and brake-by-dyno. The system combines feedforward control based on recorded…
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Effects of training on learning and use of an adaptive cruise control system

Exponent Failure Analysis-Audra Krake, Rachel Jonas, Christian Hoyos, Caroline Crump, Benjamin Lester, David Cades, Ryan Harrington
  • Technical Paper
  • 2020-01-1033
To be published on 2020-04-14 by SAE International in United States
This study examined the effects of formalized training on driver behavior and understanding of an adaptive cruise control (ACC) system with drivers experienced with ACC. Sixteen participants drove an ACC-equipped vehicle while following a lead vehicle around a test track. Participants completed three laps, each featuring different lead vehicle behaviors, such as making a lane change or stopping at a red light, that test the limitations and capabilities of ACC. Immediately before driving, half of the participants watched a training video describing how the ACC system would respond to these lead vehicle behaviors. Braking behavior and use of ACC was recorded by cameras, and participants’ knowledge of the ACC system limitations was assessed by a pre- and post-test questionnaire. Surprisingly, compared to the participants who did not receive training, those who did receive training showed significantly more use of the brake versus allowing the ACC to slow or stop the vehicle for them during certain conditions. We did not observe significant differences between the two groups in time spent using ACC, though participants who did…
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Scalable Simulation Environment for Adaptive Cruise Controller Development

The University Of Alabama-David Barnes, Jared Folden, Hwan-Sik Yoon, Paulius Puzinauskas
  • Technical Paper
  • 2020-01-1359
To be published on 2020-04-14 by SAE International in United States
In the development of an Adaptive Cruise Control (ACC) system, a model-based design process uses a simulation environment with models for sensor data, sensor fusion, ACC, and vehicle dynamics. Previous work has sought to control the dynamics between two vehicles both in simulation and in empirical testing environments. This paper outlines a new modular simulation framework for full model-based design integration, to iteratively design ACC systems. The simulation framework uses physics-based vehicle models to test ACC systems in three ways. The first two are Model-in-the-Loop (MIL) testing, using scripted scenarios or Driver-in-the-Loop (DIL) control of a target vehicle. The third testing method uses collected test data replayed as inputs to the simulation to additionally test sensor fusion algorithms. The simulation framework uses 3D visualization of the vehicles and implements mathematical driver comfortability models to better understand the perspectives of the driver or passenger. The addition of a high-fidelity vehicle plant model provides energy consumption and emissions predictions for autonomous, conventional vehicles or hybrid electric vehicles (HEV) in realistic driving scenarios. Finally, the simulations are run…
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A Connected Controls and Optimization System for Vehicle Dynamics and Powertrain Operation on a Light-Duty Plug-in Multi-Mode Hybrid Electric Vehicle

Michigan Technological University-Joseph Oncken, Joshua Orlando, Pradeep K. Bhat, Brandon Narodzonek, Christopher Morgan, Darrell Robinette, Bo Chen, Jeffrey Naber
  • Technical Paper
  • 2020-01-0591
To be published on 2020-04-14 by SAE International in United States
This paper presents an overview of the connected controls and optimization system for vehicle dynamics and powertrain operation on a light-duty plug-in multi-mode hybrid electric vehicle developed as part of the DOE ARPA-E NEXTCAR program by Michigan Technological University in partnership with General Motors Co. The objective is to enable a 20% reduction in overall energy consumption and a 6% increase in electric vehicle range of a plug-in hybrid electric vehicle through the utilization of connected and automated vehicle technologies. Technologies developed to achieve this goal were developed in two categories, the vehicle control level and the powertrain control level. Tools at the vehicle control level include Eco Routing, Coordinated Adaptive Cruise Control (CACC), Eco Approach and Departure (EcoAND) and in-situ vehicle parameter characterization. Tools at the powertrain level include PHEV mode blending, predictive drive-unit state control, and non-linear model predictive control powertrain torque split management. These tools were developed with the capability of being implemented in a real-time vehicle control system. As a result, many of the developed technologies have been demonstrated in real-time…
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Iterative Dynamic Programming Based Model Predictive Control of Energy Efficient Cruising for Electric Vehicle with Terrain Preview

Nanjing University of Science and Technology-Fei Ju, Liangmo Wang, Qun Wang
Southeast University-Weichao Zhuang
  • Technical Paper
  • 2020-01-0132
To be published on 2020-04-14 by SAE International in United States
Energy-oriented cruising control strategies for conventional vehicles have been studied for several years and tend to use model predictive control (MPC) to optimize the vehicle velocity with terrain profile preview. For electric vehicle (EV) with regenerative braking, the velocity profile should be different from conventional vehicles. As a global optimization method, dynamic programming (DP) can be implemented to calculate the optimal velocity for EV on given driving cycles. Due to its terrible computational burden, conventional DP is not suitable for real-time implementation especially with higher dimensions. In this paper, we propose an iterative dynamic programming (IDP) approach to reduce computing time firstly. The IDP can obtain the optimal control laws alike the conventional DP by converging the optimal control strategy iteratively within an adaptive multidimensional search space. Second, combined with MPC framework, we introduce an IDP based MPC (IDP-MPC) to optimize velocity for an EV with terrain preview. In addition to energy efficiency, battery aging is also considered for charging and discharging rates may accelerate battery decay. Then, to test the energy optimization performance of…
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Identifying Cut-In Vehicles by Fusing Radar and Vision Data for Truck Platooning Safety

Texas A & M University, College Station-Mengke Liu, Sivakumar Rathinam, Mike Lukuc, Swaminathan Gopalswamy
  • Technical Paper
  • 2020-01-0102
To be published on 2020-04-14 by SAE International in United States
Truck platooning systems (Level 2) extend the radar, camera and vehicle-to-vehicle communications based, cooperative adaptive cruise control to provide precise automated lateral and longitudinal vehicle control in order to maintain a tight formation of vehicles with short following distances. A manually driven truck leads a platoon, while the steering, acceleration and braking of following truck(s) are automatically controlled with the driver(s) engaged, monitoring system performance and the driving environment at all times. Level 1 truck platooning has already demonstrated the potential for significant fuel savings, enhanced mobility, and associated emissions reductions from platooning vehicles. Level 2 automation may increase these benefits while reducing driver workload and increasing safety. The objective of this work is to address fundamental problems that arise when vehicles present in the adjacent lanes of the platooning vehicles maneuver to change lanes and cut into the gap between the platooning trucks. The sensing systems in the platooning trucks must be able to identify and estimate the state of these "cut-in" vehicles in real-time so that suitable control actions can be taken. The…
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Experimental Evaluation of Longitudinal Control for Connected and Automated Vehicles through Vehicle-in-the-Loop Testing

Argonne National Laboratory-Miriam Di Russo, Simeon Iliev, Kevin M. Stutenberg, Eric Rask
Wayne State University-Jerry Ku
  • Technical Paper
  • 2020-01-0714
To be published on 2020-04-14 by SAE International in United States
Automated driving functionalities delivered through Advanced Driver Assistance System (ADAS) have been adopted more and more frequently in consumer vehicles. The development and implementation of such functionalities pose new challenges in safety and functional testing and the associated validations, due primarily to their high demands on facility and infrastructure. This paper presents a rather unique Vehicle-in-the-Loop (VIL) test setup and methodology compared those previously reported, by combining the advantages of the hardware-in-the-loop (HIL) and traditional chassis dynamometer test cell in place of on-road testing, with a multi-agent real-time simulator for the rest of test environment. Details associated with applying the proposed VIL for testing adaptive cruise control (ACC), in conjunction with approaches for creating a virtual lead vehicle, as well as results of energy consumption analysis for a 2017 Toyota Prime with stock and improved longitudinal control algorithm, are reported to illustrate the effectiveness of low-infrastructure-demand test setup and the potential in applying the setup and methodology to other ADAS functionalities
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Obstacle Avoidance using Model Predictive Control: A Detailed Analysis using Scaled Vehicles

Clemson University-Ardashir Bulsara, Adhiti Raman, Srivatsav Kamarajugadda, Matthias Schmid, Venkat N Krovi
  • Technical Paper
  • 2020-01-0109
To be published on 2020-04-14 by SAE International in United States
Over the last decade, tremendous amount of research and progress has been made towards developing smart technologies for autonomous vehicles such as adaptive cruise control, lane keeping assist, lane following algorithms, decision making algorithms for lane changing, adaptive control etc. One of the fundamental objectives for the development of such technologies is to enable autonomous vehicles with the capability to avoid obstacles and maintain safety. Automobiles are intricate systems and increasing autonomy in vehicles increases their complexity by several folds; especially since the dynamics of the vehicle needs to be considered. Model predictive control is a powerful tool that is used extensively to control the behavior of complex, dynamic systems. As a model-based approach, the fidelity of the model and selection of model-parameters plays a role in ultimate performance. In this paper, we use model predictive control to comparatively study controller performance for obstacle avoidance strategy using scaled-vehicles (1/10th scale). The assessment is conducted initially in simulation and planned to be evaluated in a hardware-in-loop framework.