Range Prediction

Use AI to predict EV battery range accurately and detect early degradation for safety and uptime

Business impact

• Operational uptime — Predictive range models reduce unexpected downtime by anticipating battery issues early
• Safety incidents — Early detection of battery degradation lowers risk of failures and accidents
• Battery lifespan — Optimized battery usage through AI extends overall battery life
• Maintenance costs — Proactive maintenance reduces costly emergency repairs and replacements
• Customer Experience — Accurate range predictions enhance user trust and satisfaction with EVs
• Operational Efficiency — Better battery management improves fleet utilization and energy use
• Product Reliability — AI diagnostics increase confidence in battery performance and vehicle dependability

Data requirements

• Battery sensor data (Numeric) — Monitors voltage, current, temperature to assess battery health
• Vehicle telematics (Structured) — Provides usage patterns and environmental conditions affecting battery
• Historical degradation records (Numeric) — Used to train predictive models on battery aging trends
• Cloud connectivity logs (Structured) — Enables real-time data aggregation and remote diagnostics

AI methods and techniques

• Predictive AI — Forecasts battery degradation and remaining range based on historical and real-time data
• Agentic AI — Automates decision-making for maintenance scheduling and alerts
• Symbolic AI — Incorporates domain knowledge and rules for safety-critical battery management

AI models and model families

GPT-4, Llama 2, Custom ML models for battery prognostics

Industries

Real-world evidence

2 documented case studies on record.

Companies using this: LG Energy Solution, Tata Elxsi.