Software / AI

Battery Industry Leveraging AI Possible Use Cases Across the Battery Lifecycle Battery Production

Product development

Engineering workflow orchestration Augmented simulations & testing Data-driven design iterations Document generations CAD, req. spec.)

Material discovery

Recipe engineering

Accelerated testing

Automated patent analysis

Knowledge harvesting

Anomaly detection (quality, safety) Predictions (quality, maintenance) Inspections (quality {vision AI, processes, machines) Automation (processes, operations) Planning (shifts, inventory, maintenance) Big data analytics & processing

Battery Operation

Intelligent BMS & TMS

Charging optimization (charge point locations, battery

Recovery

Efficient EOL testing

Learned battery disassembly

Waste / feedstock recognition

preconditioning, charging protocols) EV range and lifetime modelling Field-data analytics EV Fleet / BESS asset management Safety monitoring, risk prevention Energy management & trading

The three main “wishesˮ of battery AI - disruptive, desirable, difficult to achieve: The golden recipe with materials discovered or identified by AI. Find optimal battery material combinations faster and / or better than with traditional trial-and-error methods.

The AI Mastermind that autonomously runs and optimizes a battery production plant. The battery factory of the future is able to learn, adapt and improve manufacturing performance – without human intervention.

Source: Accenture at AABC, PDF Solutions (vision AI, GM AI for charging stations), Voltaiq Panel Discussion at Battery Show NA

The universal AI-based battery lifetime prediction model applicable to all chemistries. This could allow precise forecasting for any type of battery, even for new products that have not been physically tested yet.

Extracted from the PDF; charts and images may read imperfectly.