Ajay Suresh

Argonne National Laboratory

Led battery systems development and software validation for a DOE program where 12 university teams designed, built, and tested EV battery packs. Now building AI-native engineering and operations tooling alongside battery and vehicle systems R&D.

In the Battery Workforce Challenge, teams were building and testing their first high-voltage battery packs. There was no shared infrastructure to prove that a student-developed BMS was safe to operate on real cells, and twelve teams inventing their own validation frameworks would have meant twelve definitions of safe.

So validation became infrastructure. A staged framework took each team from bench checks through model- and hardware-in-the-loop testing to full-pack tests and simulated fault injection, with defined evidence requirements at each gate. The test platforms were deliberately low-cost and repeatable (MATLAB/Simulink, CAN tooling, Python), and the BMS curriculum put the reasoning behind the gates where every future cohort could reach it.

Teams built full battery packs with production-intent; the strongest ones completed validation across model, hardware, and pack level and went on to vehicle integration and calibration. The frameworks we developed are now described in two IEEE ITEC+EATS 2026 papers, to be published.

Photos: AVTC.

UC Davis EcoCAR

Built a team, program and organizational infrastructure from scratch, recruited and managed junior engineers, and executed the design, testing, and integration of a custom powertrain and autonomous stack into a production Cadillac LYRIQ.

The university had been out of the national Advanced Vehicle Technology Competition series for fifteen years, so the program had to exist before it could deliver anything: recruiting, garage and workspace setup, acquiring automotive-standard tooling, developing a safety and information-security regime, and integrating the digital workflows that coordinated complex vehicle development amongst dozens of students.

The engineering work ran on an adopted V-model. We embraced a detail-oriented approach to systems modeling, component, subsystems, and systems-level xIL testing, and a test-first approach even under a compressed timeline.

The team won multiple awards through the competition years, and was one of the first few teams to get the integrated vehicle functional and on the road, thanks to our rigor and perseverance. The outcome I value most though, is that several of our alumni gained unparalleled hands-on experiences and are now working across some of the best automotive and energy companies in the industry.

Tesla

Product engineering on the Powerwall fleet: customer cases, failure analysis on returned units, install and commissioning quality, and telemetry-driven root cause analysis.

A battery pack spends a majority of its life in the field, and problems arrive one support case at a time. Diagnosing each case in isolation does not scale, and it wastes what the fleet already knows: every unit reports the same telemetry, so one confirmed fault is a pattern you can go look for everywhere.

The working loop: starting from a single case and its data, forming a hypothesis, expressing it as a query the whole fleet can answer, correlating the hits against firmware versions, environmental factors, and usage, confirming against physical failure analysis on returned units, and leaving behind an automated containment system so the next occurrence is caught before a customer notices. Around that loop sat the physical half of the job: inspecting returned units, install and commissioning quality, and on-site debugging.

I also returned to Tesla while completing my master's degree to work the same problem from the design reliability perspective: test fixtures, automation, and the data and modeling pipelines that reliability decisions depended on. Both times, what the field taught me was key to informing future iterations of products.

Solarlytics

First role out of school, at a stealth solar energy startup. Built a self-powered panel-instrumentation system end to end, from silicon to cloud.

At a stealth startup where every engineer wore multiple hats, the instrumentation product had one owner, and with guidance from more experienced engineers, that owner was me: working on circuitry and PCB, register-level firmware for our microcontrollers, secure connectivity / OTA updates, and the APIs, data infrastructure, and a full stack application that turned raw samples into waveforms someone could actually look at.

It is also where I learned that an instrument is only half the product. The value showed up when the signal left the device: high-frequency measurements published reliably enough to reconstruct remotely, and test automation that turned hours of board bring-up into minutes.