Sankar Rengarajan’s research while affiliated with Southwest Research Institute and other places

div class="section abstract"> Onboard sensing and Vehicle-to-Everything (V2X) connectivity enhance a vehicle's situational awareness beyond direct line-of-sight scenarios. A team led by Southwest Research Institute (SwRI) demonstrated 20% energy savings by leveraging these information streams on a 2017 Prius Prime as part of the first phase of the ARPA-E-funded NEXTCAR program. Combining this technology with automation can improve vehicle safety and enhance energy efficiency further. In the second phase, SwRI demonstrated 30% energy savings over the baseline. This paper summarizes the efforts to achieve 30% savings on a 2021 Honda Clarity PHEV. The vehicle was outfitted with the SwRI Ranger automated driving suite for perception and localization. Model-based control schemes with selective interrupt and control (SIC) were used to override stock vehicle controls and actuate the accelerator, brake, and electric power steering system, enabling drive-by-wire and steer-by-wire functionalities. Key algorithms contributing to the 30% savings include Eco-driving, Eco-routing, Plugin Hybrid Electric Vehicle (PHEV) Powertrain mode selection, and cooperative maneuvers such as Eco-merge, and Platooning. These algorithms were tested through large-scale simulations using a high-fidelity forward-looking powertrain model, dynamic and stochastic traffic simulations (calibrated based on real-world corridor data), and real-world trip data. Statistical significance was established for simulation results, and a clustering and downlselection routine was used to select representative scenarios for dynamometer evaluation. This paper presents an overview of the contributing algorithms, the development of the simulation framework, the experiments designed to test the effectiveness of algorithms in simulations, an overview of the scenario downselection routine, and results from simulations and dynamometer tests. </div

title>ABSTRACT Connected and automated vehicles (CAVs) leverage onboard sensing and external connectivity using Vehicle-to-Vehicle (V2V), Vehicle-to-Infrastructure (V2I) and Vehicle-to-Everything (V2X) technologies to "know" the upcoming operating environment with some degree of certainty, significantly narrowing prior information gaps. These technologies have been traditionally developed and used for driver assistance and safety but are now being used to operate the vehicle more efficiently [ 1 – 5 ]. The eco-driving algorithm, which leverages the data available through these streams, performs two key functions: (1) acceleration smoothing and (2) eco-approach and departure (Eco-AND) at signalized intersections. The algorithm uses information from neighboring vehicles and signalized intersections to calculate an energy-efficient speed trajectory. This paper presents the development of an Android-based driver advisory application that leverages cellular Internet connectivity and Traffic Technology Services (TTS) data [ 6 ], to perform Eco-AND at signalized intersections and discusses the potential for saving energy and time with the eco-driving algorithm. Citation: P. Bhagdikar, S. Gankov, S. Rengarajan, J. Sarlashkar, S. Hotz, “Eco-Driving Driver Advisory Application for Connected Vehicles”, In Proceedings of the Ground Vehicle Systems Engineering and Technology Symposium (GVSETS), NDIA, Novi, MI, Aug. 13-15, 2019.</p

div class="section abstract"> Vehicle-to-everything (V2X) communication, primarily designed for communication between vehicles and other entities for safety applications, is now being studied for its potential to improve vehicle energy efficiency. In previous work, a 20% reduction in energy consumption was demonstrated on a 2017 Prius Prime using V2X-enabled algorithms. A subsequent phase of the work is targeting an ambitious 30% reduction in energy consumption compared to a baseline. In this paper, we present the Eco-routing algorithm, which is key to achieving these savings. The algorithm identifies the most energy-efficient route between an Origin-Destination (O-D) pair by leveraging information accessible through commercially available Application Programming Interfaces (APIs). This algorithm is evaluated both virtually and experimentally through simulations and dynamometer tests, respectively, and is shown to reduce vehicle energy consumption by 10-15% compared to the baseline over real-world routes. This paper describes the development, implementation, and validation of the algorithm. </div

div class="section abstract"> Eco-Driving with connected and automated vehicles has shown potential to reduce energy consumption of an individual (i.e., ego) vehicle by up to 15%. In a project funded by ARPA-E, a team led by Southwest Research Institute demonstrated an 8-12% reduction in energy consumption on a 2017 Prius Prime. This was demonstrated in simulation as well as chassis dynamometer testing. The authors presented a simulation study that demonstrated corridor-level energy consumption improvements by about 15%. This study was performed by modeling a six-kilometer-long urban corridor in Columbus, Ohio for traffic simulations. Five powertrain models consisting of two battery electric vehicles (BEVs), a hybrid electric vehicle (HEV), and two internal combustion engine (ICE) powered vehicles were developed. The design of experiment consisted of sweeps for various levels of traffic, penetration of smart vehicles, penetration of technology, and powertrain electrification. The large-scale simulation study consisted of doing approximately 96,000 powertrain simulations. A sophisticated clustering scheme was built and utilized to down select representative traces for each scenario from the simulation study for vehicle testing on a chassis dynamometer. This paper provides a summary of individual ego vehicle testing as well as a comprehensive overview of the method utilized for down selecting representative traces from large scale simulation studies that can be used to quantify corridor level benefits. Vehicle test results along with corresponding analyses are presented. </div

div class="section abstract"> Trained human raters have been used by organizations such as the Coordinating Research Council (CRC) to assess the vehicle driveability performance effect of fuel volatility. CRC conducts workshops to test fuel effects and their impact on vehicle driveability. CRC commissioned Southwest Research Institute (SwRI) to develop a “Trick Car” vehicle that could trigger malfunctions on-demand that mimic driveability events. This vehicle has been used to train novice personnel on the CRC Driveability Procedure E-28-94. While largely effective, even well-trained human raters can be inconsistent with other raters. Further, CRC rater workshop programs used to train and calibrate raters are infrequent, and there are a limited number of available trained raters. The goal of this program was to augment or substitute human raters with an electronic driveability sensing system. The Automated Driveability Rating System (ADRS) was developed for Light Duty (LD) vehicles and can identify and rate fuel-related driveability events including hesitation, stumble, surge, stall, and idle quality at trace, moderate, and heavy severities. The portable system uses sensors such as accelerometers, and interfaces with a vehicle to gather and process an array of information. Overall, ADRS performance ranged from somewhat less accurate to significantly better than trained human raters depending on the event type and severity. For light and moderate vehicle throttle tests, detection of stumble, surge, and hesitation events by the ADRS was close to or better than 90%, while idle quality accuracy was 80%. These results are better when compared to the performance of trained raters. Additional effort in refining the calibration and improving event identification could enhance performance even further, and the system could be applied more broadly in rating ride quality and vehicle behavior. </div

div class="section abstract"> Eco-driving algorithms enabled by Vehicle to Everything (V2X) communications in Connected and Automated Vehicles (CAVs) can improve fuel economy by generating an energy-efficient velocity trajectory for vehicles to follow in real time. Southwest Research Institute (SwRI) demonstrated a 7% reduction in energy consumption for fully loaded class 8 trucks using SwRI’s eco-driving algorithms. However, the impact of these schemes on vehicle emissions is not well understood. This paper details the effort of using data from SwRI’s on-road vehicle tests to measure and evaluate how eco-driving could impact emissions. Two engine and aftertreatment configurations were evaluated: a production system that meets current NO_X standards and a system with advanced aftertreatment and engine technologies designed to meet low NO_X 2031+ emissions standards. For the production system, eco-driving on an urban cycle resulted in a CO₂ reduction of 8.4% but an increase of 18% in brake specific NO_X over the baseline cycle. With the low NO_X system, eco-driving achieved a similar reduction in CO₂. NO_X emissions increased 108% over the baseline but remained below the low NO_X standard. The eco-driving cycles generated lower exhaust temperatures than the baseline cycles, which inhibited SCR catalyst performance and increased tailpipe NO_X. Conversely, a port drayage cycle with eco-driving showed improvements in both CO₂ and NO_X emissions over the baseline. The results demonstrate that eco-driving algorithms can be a technological enabler to meet current and potential future emissions targets for heavy-duty applications. </div

div class="section abstract"> This work is a part of medium-duty Low NO _x technology development project with a focus on evaluating a combination of engine and advanced aftertreatment for 0.02 g/bhp-hr NO _x regulation proposed by CARB (California air resource board). In this project, a control oriented chemical kinetics model of SCR (Selective catalytic reduction) was used in the aftertreatment controller that is susceptible to performance degradation due to hydrothermal and chemical aging. This paper focuses on modeling the NO _x conversion and NH3 storage characteristics using a controls oriented SCR plant model which is further used for a model-based urea dosing scheme. A set of steady state reactor tests were used to calibrate the SCR performance at degreened, hydrothermal only and hydrothermal + chemical aging conditions and also to determine inhibition factors related to aging. The resultant model is capable of simulating SCR performance deterioration such as a reduction in NO _x conversion and NH3 storage. A non-linear aging profile was observed for Lo-SCR and downstream SCR showing a change in the NO _x conversion in the aged system when compared to a degreened system. Upon chemical aging further deterioration of low temperature performance was observed. This aging phenomenon impacts the dosing control strategy of the system. The results on controller performance for a set of Heavy Duty Federal Test Protocol(FTP), Ramp Modal Cycle (RMC), and Low Load Cycle (LLC) are presented. </div

div class="section abstract"> Typical two-site storage-based SCR plant models in literature consider NH₃ stored in the first site to participate in NH₃ storage, NO _x conversion and second site to only participate in NH₃ storage passively. This paper focuses on quantifying the impact of stored NH₃ in the second site on the overall NO _x conversion for an ultra-low NO _x system due to intra site NH₃ mass transfer. Accounting for this intra site mass transfer leads to better prediction of SCR out NH₃ thus ensuring compliance with NH₃ coverage targets and improved dosing characteristics of the controller that is critical to achieving ultra-low NO _x standard. The stored NH₃ in the second site undergoes mass transfer to the first site during temperature ramps encountered in a transient cycle that leads to increased NO _x conversion in conditions where the dosing is switched off. The resultant NH₃ coverage fraction prediction is critical in dosing control of SCR. This phenomenon is evaluated and quantified with different aging conditions, where the increased second site storage and reduced standard SCR activity due to hydrothermal aging leads to further increase in the reported phenomena. Although this phenomenon was observed for both light-off SCR (Lo-SCR) and downstream SCR based on analysis of the data, the impact on Lo-SCR performance was found to be higher compared to the downstream system due to the transient thermal conditions and higher temperatures experienced by the Lo-SCR system. This mass transfer mechanism also plays a role in determining NH₃ slip characteristics of Lo-SCR for real world conditions where the gradual transfer of NH3 in the axial direction leads to NH3 slip. This phenomenon is demonstrated using experimental data collected on a production engine for a set of HFTP, CFTP, RMC and LLC cycles </div