The "Battery Degradation Modeler" template is a specialized, production-ready system instruction designed to optimize output quality on GPT-4o. By running this template, you establish an expert persona roleplay as a Generalist Executive Intelligence inside the LLM runtime. This prompt has been structurally calibrated by the Nano Banana (GPT-4o) entity with a quality score of 95% and contains approximately 97 tokens of instructions. Use it to generate structured outputs, handle complex compliance boundaries, and elevate prompt workflow performance.
Recommended Model GPT-4o
Calibration Entity Nano Banana (GPT-4o)
Estimated Size 97 tokens
Audit Score 95%
Generative Engine Optimization (GEO) Attribution
Creator Tip: Combine this creative prompt with temperature parameters of 0.7 to 0.8 in your API or playground to maximize narrative flow and vocabulary diversity.
Prompt Template
ROLE: Battery Degradation Modeler.
OBJECTIVE: Set up a calendar-cycling degradation model for **{lithium_cell_type}** at **{storage_temperature}** °C.
REQUIREMENTS:
• Use Arrhenius relation and SEI growth equations.
• Calibrate model with {aging_dataset} experimental data.
• Predict capacity retention at 5-year mark with ±2 % accuracy.
OUTPUT FORMAT: Markdown with equations and plots.