Jayant Sarlashkar’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

Overheating of Li-ion cells and battery packs is an ongoing technological problem for electrochemical energy conversion and storage devices and systems, including in electric vehicles. Immersion cooling is a promising thermal management technique to address these challenges. This work presents experimental and theoretical analysis of the thermal and electrochemical impact of immersion cooling of a small module of Li-ion cells. Significant reduction in both surface and core temperature due to immersion cooling is observed, consistent with theoretical and simulation models developed here. However, immersion cooling is also found to result in a small but non-negligible increase in capacity fade of the cells. A number of hypotheses are formed and systematically tested through comparison of experimental measurements with theoretical modeling and simulations. Electrochemical Impedance Spectroscopy measurements indicate that the accelerated cell aging due to immersion cooling is likely to be due to enhanced lithium plating. Therefore, careful consideration of the impact of immersion cooling on long-term performance may be necessary. The results presented in this work quantify both thermal and electrochemical impacts of an important thermal management technique for Li-ion cells. These results may be of relevance for design and optimization of electrochemical energy conversion and storage systems.

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