I am writing to propose a high-value feature for the FoxCloud 2.0 App, specifically designed for users with large-scale storage (like my 42kWh setup) and Time-of-Use (TOU) or EV plans.
Current Pain Point:
Currently, the "Force Charge" function only allows for a static SoC target (e.g., 80%). In winter or variable weather conditions, users must manually guess how much "buffer" to leave for the next day's solar yield.
If we overcharge at night, we waste solar energy by exporting it to the grid at a very low Feed-in Tariff (1c/kWh).
If we undercharge, we end up buying expensive peak power in the evening.
Proposed Solution: "Predictive Smart Charging"
I suggest implementing an intelligent charging algorithm with the following three components:
1. Predictive Yield Estimation:
The system should integrate local weather forecast APIs with historical generation data from the specific plant. By analyzing predicted cloud cover and daylight hours, the App can estimate the next day’s total solar yield (kWh).
2. Dynamic Night-time SoC Adjustment:
Based on the predicted yield, the system should automatically calculate the optimal overnight charging target.
Logic: Night Charge Target = 100% - (Predicted Solar Yield / Total Battery Capacity).
Goal: Ensure the battery reaches exactly 100% just before sunset, minimizing exports and maximizing the use of cheap off-peak (EV Plan) power.
3. User Control & Permission Management:
To ensure user confidence, this feature should offer two modes:
Auto-Pilot: The system executes the calculated SoC target autonomously.
Advisory Mode: The App sends a push notification (e.g., at 10:00 PM) stating: "Tomorrow will be sunny. Suggested night charge limit: 65% to save room for 15kWh of solar. Do you approve?"
Value Proposition:
This feature would position FoxESS as a leader in "Smart Energy Management" rather than just a hardware provider. It optimizes ROI for users, reduces grid stress, and maximizes the utilization of green energy.
I believe this update would be a game-changer for the FoxESS community, especially in markets like Australia with highly volatile electricity prices.