Driving Range | Battery Health | Powertrain Efficiency

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Dr. Gerald Sammer, Founder & CEO, simotive.ai

In over 30 years in automotive, I have learned one thing: the best technologies don't fail because of physics, they fail because of quality.

That is why I founded simotive.ai - an independent consultancy at the intersection of AI, simulation, and BEV quality management.

My focus: range analysis, battery aging, predictive models, and AI-driven quality assurance that prevents recalls before they happen.

Until 2025, I led global battery and BEV projects as Principal Business Field Manager at AVL. I served as a member of the ASAM Technical Steering Committee for 15 years.

My Ph.D. in Economics sharpened my conviction that solutions must deliver business value, not just technical brilliance. My M.Sc. in Computer Science gave me the tools – from software architecture to modern AI methods.

If you are asking how AI can help you with your challenges – let's talk!

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My Services

Range Analysis

Challenge: Real-world range is difficult to predict because it is strongly influenced by driving style, temperature, traffic, road profile, payload, and battery condition.
Solution: AI-based range analysis combines vehicle, battery, route, and environmental data to deliver more reliable range predictions, early warnings, and better charging or routing recommendations.

Battery Health & Aging

Challenge: Battery aging is complex and depends on charging behavior, temperature exposure, load profiles, calendar aging, and operating conditions, making degradation difficult to assess accurately.
Solution: AI-supported battery health analysis identifies aging patterns, estimates state of health more precisely, and enables predictive maintenance, optimized charging strategies, and extended battery lifetime.

Powertrain Efficiency

Challenge: Powertrain losses arise from electrical conversion, thermal effects, operating point inefficiencies, and component interactions, which reduce overall vehicle efficiency.
Solution: AI-based efficiency analysis detects loss drivers, optimizes energy flow and thermal management, and improves operating strategies to increase efficiency, range, and system performance.