-40% Range.
Defect or Physics?

AI-powered simulation makes battery condition, aging, and range measurable.

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Dr. Gerald Sammer Dr. Gerald Sammer Founder & CEO

Interactive Range Simulation

Adjust the controls and see live how temperature, age, driving mode, and HVAC affect range.

487 km
Simulated electric vehicle with visible battery pack
SOH: 98%

This is what I can do for you. Only with your real data.

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Case Study: Real-World Range Loss

A simulation-based analysis – step by step.

The Problem

A fleet operator reports 40% range loss after 3 years

22 Electric Light-Duty Trucks in a delivery fleet in northern Germany. Drivers report drastically declining range since the second winter. The leasing company demands an independent assessment.

-41% range loss measured vs. WLTP in the 3rd winter

Is this normal? Is there a battery defect?

Step 1 – Data Analysis

Analyzing charging history and usage patterns

OBD data and charging logs reveal: the fleet charges exclusively via DC fast charger. Average 1.2 fast charges per day, often at SOC <15%.

DC >100kW
73%
AC
20%
AC <11kW
7%
Charging behavior is extremely stressful for cell chemistry.
Step 2 – Simulation

Modeling temperature × charging behavior × aging

Simulation combines real usage profile with electrochemical aging model and climate data from Hamburg.

100% 95% 90% 85% 0 1 year 2 years 3 y. Simulation Measurement (<3% dev.)
Model matches reality.
Step 3 – Root Cause Decomposition

What causes the 41%? A decomposition.

Simulation enables isolated analysis of each individual factor.

Temperature (-8°C)
-19%
HVAC load
-11%
Degradation
-7%
Driving profile
-4%
No battery defect. 7% degradation after 3 years is within expected range. Most loss is temperature/HVAC-related and reversible.
Result

Fact-based clarification instead of speculation

The fleet operator receives a robust report with reproducible simulation. The leasing company accepts the result. An expensive dispute is avoided.

3 weeks project duration
0 vehicles with actual defect

This is how I work. Do you have a similar case?

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Services

Range analysis – driver with range warning

Range Analysis

Challenge Real-world range is difficult to predict because it is strongly influenced by driving style, ambient temperature, traffic, road profile, and battery condition.
Solution AI-supported simulation combines vehicle, battery, route, and environmental data to deliver precise range predictions and allows reproducible parameter variations.
Battery aging – factors influencing battery health

Battery Health & Aging

Challenge Battery aging is complex and depends on charging behavior, temperature exposure, load profiles, calendar aging, and operating conditions.
Solution AI-supported battery health simulation identifies aging patterns, estimates state of health reproducibly, and identifies key influencing factors of battery degradation.
Damage analysis – simulation-based root cause investigation

Damage Analysis

Challenge Malfunctions in the electric powertrain – power loss, increased wear – are hard to quantify and cannot be objectively explained through inspection alone.
Solution Simulation-based damage analysis reproduces the observed behavior under real operating conditions, isolates root causes, and delivers robust, expert-report-ready results.

Methodology

I use simulation-supported analyses to reproduce real-world technical situations in a structured and traceable way under defined boundary conditions.

  • Based on publicly available technical information, manufacturer data, and – where applicable – measurement, test, or field data
  • Systematic examination of usage profiles, temperature, load conditions, charging history, and environmental conditions
  • Reproducible analyses and robust, transparent conclusions
  • Objective clarification of complex interdependencies and traceable evaluation of scenarios
Simulation-based methodology – vehicle and data analysis

About

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 focused on AI, simulation, and quality management for electric mobility.

My focus: range analysis, battery aging, and predictive AI-supported modeling for fact-based clarification, for example in the fields of damage analysis or valuation.

Until 2025, I led global battery and EV projects at AVL as Principal Business Field Manager. In addition, I was a member of the technical steering committee of ASAM for 15 years, a standardization body for automotive standards in measurement technology, simulation, and autonomous driving.

My academic background in economics, computer science, and electrical engineering has shaped my conviction that solutions must not only be technically convincing, but above all create a clear value for the user.

If you are wondering how my expertise in automotive engineering, combined with simulation and AI, can improve your results – let's talk.

Dr. Gerald Sammer
Founder & CEO, simotive.ai

Dr. Gerald Sammer – Founder and CEO of simotive.ai

Contact

Do you have a technical question about electric mobility? Let's discuss how simulation-based analysis can help you.

gerald.sammer@simotive.ai

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