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BATTERY INTELLIGENCE PLATFORM

Predict battery failures
before they happen.

The only physics-based battery safety system that's FAA-certifiable. 7-9 days of advance warning. No training data. No black boxes.

r = 0.996
Peak Correlation
7-9 Days
Early Warning
500+
Cells Validated
96
Patent Claims

Batteries fail without warning.

Current battery monitoring can't predict novel failures. They wait for known signatures — by then, it's too late.

Reactive, not predictive

Conventional BMS monitors thresholds. By the time voltage or temperature spikes, thermal runaway is already beginning.

Hidden degradation

Internal cell damage is invisible to voltage and capacity measurements until catastrophic failure occurs.

Black-box ML

Machine learning needs thousands of failure examples. New chemistries and edge cases remain unpredictable.

One metric. Universal physics.

STABILITY MARGIN INDEX (SMI)
SMI

A single, physics-derived metric that quantifies battery stability in real-time.

SMI ≥ 1.0 Stable
0.7-1.0 Marginal
<0.7 Critical

Why it works

  • Chemistry-agnostic — works on any battery chemistry without calibration
  • Uses standard BMS data — no new sensors required
  • Deterministic — same input always produces same output
  • Predicts novel failures — first principles, not pattern matching
VALIDATED RESULTS

SMI detects degradation at cycle 90.
State of Health still shows 90% healthy.

r = 0.983
NASA PCoE DATASET B0007
7.5 days early warning before capacity fade reached critical threshold.
Conventional metrics: no warning
r = 0.995
CALCE CS2_38 DATASET
31 days early warning. SMI flagged instability while SOH still showed the cell as healthy.
31 days the BMS missed entirely
$5.3B+
COST OF NOT KNOWING
GM Bolt ($2B), Hyundai Kona ($900M), plus ongoing recalls across the industry. All traced to manufacturing defects undetectable by conventional QC.
Manufacturing defects. Missed at the factory.

Real-time battery intelligence.

Cell-level monitoring, fleet-wide visibility, and FAA-certifiable safety insights — all from one interface.

Pack Monitoring

Real-time SMI for every cell in your pack. Identify weak links before they fail.

Fleet Operations

Multi-aircraft dispatch authority with go/no-go decisions based on stability margin.

Certification Support

ARP4761 safety assessment, DO-178C compliance, and full audit trails.

Physics, not patterns.

Traditional battery monitoring relies on thresholds and machine learning. SEIONICS takes a fundamentally different approach — applying first-principles physics to predict instability before it occurs.

The Core Principle

Our proprietary methodology uses fundamental physical laws to calculate a real-time Stability Margin Index (SMI). This deterministic approach requires no training data and works across any battery chemistry.

Unlike black-box ML models, SMI is fully explainable and FAA-certifiable.

PROPRIETARY METHODOLOGY

  • Grounded in fundamental physical laws
  • Protected by 96 patent claims across 4 applications
  • No training data required — works from first principles
  • FAA-certifiable deterministic algorithm

Same physics.
Universal results.

SMI is validated across multiple electrochemical domains — proving the underlying physics are fundamental, not curve-fitted.

r = 0.996
Peak Correlation
500+
Cells Tested
3
Domains Validated
0
Training Data Required

One Method. Three Systems.

LI-ION BATTERIES
r = 0.996
500+ cells • 4 chemistries
VALIDATED
PEM FUEL CELLS
r = 0.995
IEEE PHM 2014 • 8-19 day warning
CONFIRMED
MICROBIAL FUEL CELLS
r = 0.995
Mendeley MFC • Living biological
CONFIRMED
Same method works across systems = Universal Physical Principle

Where certainty matters.

Battery safety is mission-critical across industries. SMI provides the certainty needed for high-stakes applications.

Urban Air Mobility

$1T+ by 2035

FAA certification requires physics-based safety monitoring. DO-178C compliant.

Defense & DOD

$15B+ battery spend

Unmanned systems, ground vehicles, portable power, naval electrification.

Electric Vehicles

$900B+ by 2030

Warranty optimization, fast-charging stress, second-life qualification.

Grid Storage

$35B+ by 2030

200+ BESS fires since 2017. Real-time stability monitoring at scale.

Cell Manufacturing

$100B+ globally

15-30% scrap rates. Detect instability during formation, not after aging. See Manufacturing Intelligence →

Maritime

$10B+ market

Electric ferries, hybrid cargo, offshore energy, mining equipment.

Space & Satellites

$400B+ by 2030

Lunar missions, space stations, satellite constellations, launch vehicles.

Submarines & UUV

$30B+ market

Attack subs, ballistic missile subs, autonomous underwater vehicles.

Catch bad cells before they ship.

SMI integrates directly into cell manufacturing lines — using V, I, and T data you're already collecting to reduce scrap, optimize yield, and trace defects to their root cause.

The Manufacturing Problem

The GM Bolt recall cost $2 billion. The Hyundai Kona recall cost $900 million. Both traced back to manufacturing defects that passed every quality check on the line.

End-of-line testing catches failures too late — after the cell is built, formed, and aged. Capacity and impedance checks look normal. SOH shows 90%+. Meanwhile, the thermodynamic signature of instability is already there, invisible to conventional metrics.

SMI changes the equation: detect instability during formation cycling — not after weeks of aging — using only the voltage, current, and temperature data your cyclers already produce.

15–30%
Industry Scrap Rate
~2%
SMI Target Rejection
0
New Sensors Required

WHY CELLS FAIL SILENTLY

Slurry & Coating Variability Viscosity drift, uneven loading, and coating defects create cells that pass inspection but degrade prematurely.
Formation Protocol Sensitivity Small variations in temperature, C-rate, and rest time during formation create SEI quality differences invisible to capacity testing.
Calendering & Assembly Stress Mechanical damage during calendering and stacking introduces latent defects that surface only after deployment.
SOH Masks Early Degradation State of health can show 90%+ while internal thermodynamic efficiency is already declining. SMI catches what SOH misses.
$5.3B+
in EV battery recall costs — all traced to manufacturing defects that passed conventional QC

These recalls started on the production line.

$2.0B
GM — Chevrolet Bolt EV
143,000 vehicles recalled. Every Bolt sold since 2016. Battery fires while parked and charging. LG paid $1.9B to GM. Production halted for months.
ROOT CAUSE: TORN ANODE TAB + FOLDED SEPARATOR
SMI would flag thermodynamic instability from these defects during formation cycling — before the cell ever left the factory.
$900M
Hyundai — Kona Electric
82,000 vehicles recalled globally. 15+ fires. Battery packs replaced in every affected vehicle. $11,000 per car. Owners told to park 50 feet from buildings.
ROOT CAUSE: LG CELL SHORT CIRCUIT — MANUFACTURING DEFECT
Misaligned cells create detectable entropy signatures during formation. SMI catches the instability. Capacity testing does not.
$2.4B+
Industry-Wide — Ongoing
Ford, BMW, Mercedes, Stellantis — all have issued EV battery recalls. 200+ grid storage fires since 2017. 15–30% manufacturing scrap rates across the industry.
ROOT CAUSE: DEFECTS INVISIBLE TO CONVENTIONAL QC
Every one of these failures passed end-of-line testing. Every one showed "healthy" on standard metrics. SMI measures what they cannot.

What the dashboard does.

DETECTION

Early Rejection Prediction

Flag unstable cells during formation cycling — before they move to aging. Reduce end-of-line scrap by catching defects at the source.

OPTIMIZATION

Recipe Auto-Optimization

Real-time correlation between process parameters and SMI scores. Adjust slurry composition, drying profiles, and formation protocols to maximize yield.

TRACEABILITY

Root Cause Analysis

Trace every underperforming cell back to the specific process deviation — loading variance, viscosity drift, temperature excursion — at the parameter level.

UNIVERSAL

Chemistry-Agnostic

Works across NMC-811, NMC-622, NMC-532, NCA, LFP, and LCO. No retraining or recalibration when switching chemistries.

PHYSICS-BASED

No Black Box

Deterministic, explainable results. No machine learning models to train, no historical failure datasets required. Physics in, physics out.

INTEGRATION

Uses Existing Data

Plugs into your existing cycler infrastructure. Only requires voltage, current, and temperature — data every manufacturer already collects.

A fundamentally different approach.

Conventional QC

Capacity and impedance checks after aging
Statistical sampling — misses cell-level defects
ML models require thousands of failure examples
Retraining needed for every new chemistry
Root cause analysis is manual and slow
Catches failures after cost is sunk

SMI Manufacturing Intelligence

Detects instability during formation cycling
Every cell scored individually — 100% coverage
Physics-based — zero training data required
Chemistry-agnostic across all major chemistries
Automated root cause tracing to process parameters
Catches failures before cells leave formation

The next $2B recall starts with one bad cell that passed QC.

We're partnering with select manufacturers for pilot validation. No new hardware. No lengthy integration. Just better insight from data you already have.

Request Pilot →

Built on first principles.

We're building the definitive battery safety platform — grounded in physics, protected by patents, validated by science.

Our Mission

Make battery failures predictable and preventable. We believe physics-based approaches are the only path to truly safe electrification — from urban air mobility to grid-scale storage.

Intellectual Property

Patent Applications 4 Filed
Total Claims 96
Core Innovation SMI Methodology
2024
Founded
Underway
National Lab Partnerships
8+
Target Markets
96 Claims
IP Protection

Request access.

See how SEIONICS can protect your battery systems. Our team will schedule a personalized demo.

Live platform demonstration
Technical deep-dive on SMI methodology
Custom analysis of your use case