Risk Management

Safety First

Because your battery is an investment, not an experiment.

The Stakes

Batteries are chemistry.Chemistry doesn't forgive.

Push a lithium cell too hard and it degrades silently. Over-discharge and capacity fades. Thermal runaway is rare but real. A $15,000 investment demands respect.

Aggressive trading algorithms can destroy battery health chasing short-term gains. We built AET differently.

Core Principle

The optimizer proposes.The safety system disposes.

Profit never compromises battery health.

Defense in Depth

Four Independent Layers

Every setpoint must pass all four gates before reaching hardware. Any layer can veto. Defense in depth means no single point of failure.

01

Policy Compiler

Human intent to Machine constraints

Your policy compiles to machine-checkable constraints. Priority tiers: Safety (inviolable), Regulatory (legal), Economic (preferences). The compiler proves consistency before the system starts.

02

Physics Simulation

Predict before acting

A digital twin simulates every proposed setpoint before execution. Models thermal dynamics, efficiency curves, SOH-adjusted capacity. If simulation shows any limit violation, the setpoint is rejected.

03

Formal Verification

Mathematically proven

Interval arithmetic proves safety even with measurement uncertainty. State-space reachability analysis ensures the system cannot enter dangerous configurations. Mathematical proof, not probabilistic hope.

04

Guard Modules

Runtime protection

Independent runtime guards—Thermal, Boundary, SOH—each with veto power. Even if simulation and verification pass, guards can reject setpoints based on real-time conditions.

State Estimation

Self-Learning Digital Twin

The model adapts to your specific battery. Online learning updates parameters as the battery ages. What worked at 100% SOH might damage at 80%.

RLS
Recursive Least Squares

Online parameter identification. Tracks internal resistance, capacity fade, and efficiency changes in real-time.

Kalman
Kalman Filter

State estimation with uncertainty bounds. Knows what it doesn't know. Conservative when uncertain.

Arrhenius
Arrhenius Model

Thermal aging prediction. Every degree of temperature, every percentage of SOC has a degradation cost.

Constraints

Hard Invariants

These limits are never violated. Not for any price. Not for any profit.

InvariantLFPNMC
Max cell temp60C55C
Derating start50C45C
SOC bounds2-98%2-98%
Low SOH C-rate0.3C0.3C
Min rest time30s30s
The Outcome

Maximize profit.Maximize lifespan.

These aren't competing goals—they're aligned. A battery that dies early costs more in lost revenue than any single trade could gain. The optimizer knows this.

Every trade is evaluated against its lifetime cost. Short-term greed is filtered out. What remains is sustainable profit that compounds over years, not days.