Thermal Intelligence Platform

Charge faster. Degrade less.

e-TRNL builds thermal intelligence for two-wheeler and EV battery pack engineers. Fingerprint your cells, optimize your charge protocol, and model cycle life before you commit to BMS firmware.

The gap in pack engineering

01

Conservative protocols waste capacity

Standard CC-CV charge schedules are calibrated for worst-case thermal headroom, not for your specific cell geometry, ambient range, or pack topology. The safety margin you inherit is not yours, and it costs you charge time and cycle depth every cycle.

02

Aggressive charge shortens pack life

Pushing C-rate without spatial thermal context generates uneven temperature gradients across the pack. Cells that run hot faster degrade faster, creating a capacity mismatch that compounds over hundreds of cycles until the weakest cell limits the whole pack.

03

Simulation-to-BMS transfer is manual

Bridging electrochemical simulation results to firmware-level charge protocol parameters requires thermal modeling expertise most pack engineering teams build piece by piece. The result is a disconnect between what the model says is safe and what the BMS actually executes.

The e-TRNL platform

Thermal Fingerprinting

Build a spatial thermal model from your cell geometry, impedance spectrum, and temperature readings. The fingerprint maps how heat distributes across your specific pack topology so every downstream decision is grounded in your actual hardware, not a generic cell approximation.

Explore the platform

From cell data to charge protocol

01

Cell Data Input

  • Cell geometry
  • Impedance spectrum
  • Temperature readings
  • Pack topology
02

Thermal Model

03

Charge Protocol

The team

Apoorv Shaligram, CEO
Apoorv Shaligram
CEO

Battery electrochemist with deep background in cell-level thermal characterization and pack integration for high-cycle applications.

Rohan Mathur, CTO
Rohan Mathur
CTO

Embedded systems engineer focused on BMS firmware architecture and real-time thermal signal processing for two-wheeler and EV platforms.

Priya Nair, Head of Research
Priya Nair
Head of Research

Electrochemical modeling specialist with research history in lithium-ion cycle life prediction and multi-physics degradation simulation.

Angel-backed, Bengaluru, 2023

Built for pack engineering teams

For EV and two-wheeler battery engineers working with fast-charge constraints. Join early access and bring thermal intelligence into your protocol development workflow.

Request Early Access

Direct line: [email protected]