Mayuresh Savargaonkar

Mayuresh Savargaonkar

Ph.D.

Idaho National Lab

Biography

As an industrial engineer, I have embarked on a remarkable journey spanning think tanks, academia, and industry. With an unwavering passion for the automotive domain, my expertise shines in the captivating realm of electric, automated, and connected mobility. Harnessing the power of cutting-edge technologies like machine learning, artificial intelligence, Bayesian methods, simulation tools, and the bedrock of engineering and physics, I take great delight in crafting ingenious solutions that propel the boundaries of modern transportation to new heights.


Interests
  • Electric and Connected Vehicles
  • EV Charging Infrastructure
  • Lithium-ion Batteries
  • Explainable Artificial Intelligence
  • Spatio-Temporal Modeling
  • Customized Deep Learning
  • Active Learning and Bayesian Design
  • Autonomous Vehicle Simulation
Education
  • Ph.D. in Industrial and Systems Engineering, 2019-2023

    University of Michigan - Dearborn, USA

  • M.S. Industrial Engineering, 2018

    University of Michigan - Dearborn, USA

  • B.E. Mechanical Engineering, 2015

    University of Pune, India

Experience Highlights

 
 
 
 
 
Electric Vehicle Infrastructure Reliability Engineer
May 2023 – Present Idaho Falls, Idaho

My top research projects include:

  • Electric Vehicle (EV) charging infrastructure reliability improvement. More info here.
  • EV-EVSE communication using ISO 15118.
  • Error reporting using OCPP and OCPI standards.
  • Customized DL solutions for SOC/SOH/RUL estimation in Lithium-ion batteries.
  • Explainable AI for advanced battery prognostics and diagnostics.
 
 
 
 
 
Ph.D. Researcher
Sep 2019 – Apr 2023 Dearborn, Michigan

My top research projects include:

  • Safety of Autonomous Vehicles (AVs) - SOTIF & ISO 21448.
  • Virtual verification and validation of AVs.
  • Exploration of Vehicle-to-Infrastructure (V2I) and Vehicle-to-Vehicle (V2V) technologies for safer trajectory predictions using GAN-based models. More info here.
  • Customized DL solutions for SOC/SOH/RUL estimation in Lithium-ion batteries. More info here.
  • Development of explainable AI and Bayesian methods for warranty analytics. More info here.
 
 
 
 
 
Research Intern
Jun 2021 – Aug 2021 Dearborn, Michigan
  • Developed ROS-based pythonic library for Software-In-Loop (SIL) and Hardware-In-Loop (HIL) testing for L3+ ADAS features.
  • Developed and deployed framework for corner-case realization within self-driving stacks.
  • Replicated processed sensor fusion (Camera, LiDAR, Radar)outputs for real-time communication with driving policies.
  • Developed OpenDrive maps for integration with CARLA simulator.
 
 
 
 
 
Industrial Engineering Program Manager
May 2017 – May 2019 Dearborn, Michigan
  • Responsible for cost, value, and feasibility analysis for Stellantis, Ford, BMW, and Volvo Laser/LiDAR scanning programs.
  • Implemented continuous improvement practices to reduce CAD modeling defects by over 20%.
  • Implemented risk mitigation plans and root cause analysis using a 5 Why’s system.
 
 
 
 
 
Assistant Systems Engineer
Dec 2015 – Jul 2016 Bangalore, India
  • Assisted in the development and maintenance of DVP&R and APQP activities such as DFMEA, PFMEA, CP, RCA, and GD&T.

Recent Publications

Filter here.
(2024). Customer Focussed Key Performance Indicators of Electric Vehicle Charging. Idaho National Laboratory.

PDF Cite

(2023). Implementation Guide for Minimum Required Error Codes in Electric Vehicle Charging Infrastructure. Idaho National Laboratory.

PDF Cite

(2023). Recommendations for Minimum Required Error Codes. Idaho National Laboratory.

PDF Cite

Recent & Upcoming Talks

Informs Annual Meeting 2022
INFORMS Annual Meeting 2022

Contact