One integrated system.
- Direct solar input from the first usable voltage
- Controlled storage with optimised charging
- Minimal conversion steps between source and consumer
- Intelligent energy management that matches supply and demand in real time
At its core sits a proprietary control algorithm, developed to regulate energy in real time — capture, store, deliver.
The battery is a buffer, not a dependency.
The system runs on a minimal storage strategy: battery capacity bridges periods without solar input, nothing more. Less storage loss, less wear, less complexity.
No overproduction logic. No inventory just in case. Energy is delivered the moment it is requested.
Four innovations, one principle.
First photon capture
Energy capture starts at minimal voltage and current. No activation threshold. Early-stage solar input is stored instead of lost.
Ramp charging
A gradual charging curve reduces thermal stress and energy loss. Estimated total system loss: ~0.25%.
Minimal conversion architecture
Direct energy routing. Reduced AC/DC conversion. Optimisation at system level rather than per component.
Demand-aligned delivery
Energy is delivered based on real usage. No overproduction. Heating and cooking are accounted for in the system’s behaviour.
Under the hood.
| Component | Specification |
|---|---|
| Battery | 24S dual-chemistry — LiFePO4 and Sodium-ion (96V charging) |
| Solar input | up to 105V DC, with soft-start / pre-charge |
| Inverter | 400W pure sine wave |
| DC-DC | 30A adjustable (0–72V) |
| Tool charger | 21V CC/CV |
| BMS | 30A continuous, >1A active balancing |
| IoT | ESP32-S3 — 4G/LTE-M, GPS, Wi-Fi, BT 5.0, MQTT/JSON telemetry |
| Connectivity | cloud integration, remote monitoring, offline FIFO logging |
| GPS | location tracking, anti-theft, deployment monitoring |
Working prototypes. Real conditions.
TRL 7 — tested in the Netherlands and Afghanistan under real operational conditions.
Next phase:
- Independent validation
- Field testing at scale
- Preparation for series production
For researchers, investors and engineers.
The full project context is available as a machine-readable AI Research Package. Paste it into your own LLM and ask your questions directly — no manual reading required.
Zeronic AI Research Package
Innovations, system architecture, battery & control logic, validation status, energy output, founder context — structured for LLM analysis.