Stanislav Gankov’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

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"> The US EPA and the California Air Resources Board (CARB) require electric vehicle range to be determined according to the Society of Automotive Engineers (SAE) surface vehicle recommended practice J1634 - Battery Electric Vehicle Energy Consumption and Range Test Procedure. In the 2021 revision of the SAE J1634, the Short Multi-Cycle Test (SMCT) was introduced. The proposed testing protocol eases the chassis dynamometer test burden by performing a 2.1-hour drive cycle on the dynamometer, followed by discharging the remaining battery energy into a battery cycler to determine the Useable Battery Energy (UBE). Opting for a cycler-based discharge is financially advantageous due to the extended operating time required to fully deplete a 70-100kWh battery commonly found in Battery Electric Vehicles (BEVs). This paper provides a review of the communication protocols enabling V2X (Vehicle to X, where X can be grid, vehicle, building, etc.) power transfer and the tools required to initiate, control, and terminate the vehicle testing procedure as per SAE J1634. The primary focus is on the series of ISO 15118 standards for road vehicles - vehicle to grid communication interface and SAE J2847/2 (Surface vehicle standard for communication between plug-in vehicles and off-board DC chargers). </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