The goal of this project is to develop and validate a Hybrid Physics–AI Digital Twin for battery energy storage systems (BESS) operating under tropical conditions, enabling safer operation, longer battery life, and scalable adoption across Vietnam’s EV-charging and renewable-energy networks.

The project will:

• Develop and validate 3D electro-thermal and ageing models for tropical BESS by Month 12 (target: ≤15% thermal error; ≥0.85 ageing correlation).
• Build PINN/DeepONet-based reduced-order models for temperature, SOC, and SOH and generate tropical-climate scenarios for training and validation.
• Train physics-guided AI models for early thermal-performance deviation detection and implement uncertainty quantification for safety-critical alerts by Month 12.
• Deploy a cloud–edge hybrid digital twin on Volterra EV Smart Charging by Month 18, integrate a real-time dashboard at ≥2 pilot sites, and deliver a commercialization roadmap.

Expected outcomes include safer and more reliable BESS operation, reduced O&M costs, higher renewable-energy utilization, and improved sustainability and end-of-life planning, benefiting VinFast, VinEnergo, Volterra, and Vietnam’s wider smart-energy ecosystem.