Directory profiles · Battery Business Directory
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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 “wishes” 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.
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