Do the technical assumptions hold up?
Independent plausibility check and technical second opinion before an investment, a purchase or a release.
When everything depends on a technical answer.
I provide it. Independent, physics-based, and verifiable.
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Dr. Gerald Sammer
Independent Technical Expert for Electric Vehicles & Battery Energy Storage Systems
When you call me in
I am brought in in three situations. The service is the same in all of them: an independent technical assessment when the standard answer is not enough.
What actually happened, technically?
Systematic root-cause analysis from operating data, engineering and physics-based simulation.
Which explanation survives scrutiny?
Traceable technical reasoning for private expert opinions, negotiations and proceedings.
Who I work for
- Law firms
- Technical disputes need an independent specialist assessment.
- Vehicle appraisers and claims adjusters
- The claims process is their field, the technical depth on high-voltage battery, battery management, and system simulation is what I add.
- Insurers and their service providers
- They have to judge cause, extent of damage, and technical necessity.
- Dealers and workshop groups
- Individual cases on warranty, battery replacement, or contested technical behaviour.
- Storage operators and asset owners
- High economic value per asset, plus performance and warranty questions.
- Investors and lenders
- Technical assumptions decide the economics.
What sets me apart
Software delivers the analysis. I examine the assumptions it rests on.
In a dispute, the manufacturer demonstrates that the system was cycled too hard, the operator that the cells are weak. Before an investment, the supplier provides the degradation curve that justifies its own system. Nobody computes wrongly. What decides is the assumptions.
I make them explicit and test them against a physics-based model, independently of everyone involved. And I defend the result when someone challenges it.
- Independent of manufacturers, integrators, and tool vendors
- System understanding of vehicle, battery, BMS, software, and energy systems
- A physics-based model, from 1D system simulation to multi-body simulation, disclosed rather than a black box
- Holds up under scrutiny, formulated and argued as a clear conclusion
Is your case among these?
Concrete questions from practice.
Electric vehicles
A vehicle performs worse after a software update than before. Is it the update or the battery?
- What I examine
- Whether the change is explained by the battery, temperature, and usage. I calibrate a model on the data from before the update and use it to recompute the period afterwards.
- What data I need
- Charging and driving data from both periods including temperature and state of charge, plus the software versions with their dates.
- What you receive
- The share of the change explained by battery, temperature, and usage, and the share that remains unexplained without disclosure of the update.
A vehicle does not hold the figures it was sold with. Is it the vehicle, the usage, or the conditions?
- What I examine
- The deviation broken down into temperature, auxiliary loads, driving profile, charging behaviour, and component condition.
- What data I need
- Charging and driving history, climate data for the area of use, and the manufacturer's figure with its test conditions. For Euro 7 vehicles, the certified energy value and its history.
- What you receive
- The quantified share per cause and a statement on whether the deviation is explained by the usage. Plus an assessment of whether the condition figure the vehicle displays matches the stress it has seen.
A vehicle derates itself during operation. Is that by design or a fault?
- What I examine
- The relationship between temperature, load, state of charge, and the interventions of the control software.
- What data I need
- Operating and event data, fault memory, and software versions with their dates.
- What you receive
- A finding on whether the behaviour is by design, wrongly parameterised, or a component fault.
Manufacturer and customer explain the same facts differently. Which explanation is physically plausible?
- What I examine
- Both explanations against the same physics-based model and the same operating data.
- What data I need
- The accounts of both parties, measurement and diagnostic data, and technical documentation.
- What you receive
- A reasoned statement on which explanation the data supports and which it does not.
Battery storage
A storage system does not reach its promised capacity or efficiency. Is the cause the battery, the operation, the system design or the specification?
- What I examine
- The separation between cell ageing, operating strategy, system design and what was contractually promised.
- What data I need
- Operating data from the energy management system, charge and discharge cycles, temperature traces, and warranty terms.
- What you receive
- The deviation attributed to its causes and an assessment of the warranty position.
Are the supplier's service-life and performance assumptions technically realistic?
- What I examine
- Degradation, throughput and round-trip efficiency assumptions against my own model, computed over the planned operating strategy.
- What data I need
- System design, operating concept, manufacturer commitments, and the planned operating strategy.
- What you receive
- A second opinion on the assumptions the business case rests on.
A system has failed or shut itself down. What triggered it?
- What I examine
- Condition and stress before the event, the time sequence of the measured quantities, and the response of the protective functions.
- What data I need
- High-resolution operating data from before the event, event and fault memory, and the design and protection concept.
- What you receive
- A reasoned statement on the triggering process and on whether the protective functions acted as designed.
The manufacturer holds the operating data and presents its own analysis. Does it hold up?
- What I examine
- The submitted analysis against my own model, together with the definitions in the contract and the conditions under which the measurement was taken.
- What data I need
- The submitted analysis, the raw data released, and the contract and warranty terms.
- What you receive
- A statement on whether the analysis holds up, and a list of the data you still need to request for a defensible answer.
One case, step by step
How a measured loss becomes a reasoned finding.
The Case
41 percent less range than the data sheet states
22 electric light-duty trucks in a delivery fleet in northern Germany. Since the second winter, drivers have reported sharply declining range. The operator suspects a battery defect, the leasing company holds the usage responsible.
-41% range measured against WLTP in the third winter
Normal ageing or a defect?
Step 1 – Data Analysis
Analyzing charging history and usage patterns
OBD data and charging logs reveal: the fleet charges predominantly via DC fast charger. Average 1.2 fast charges per day, often at SOC <15%.
Charging behavior is extremely stressful for cell chemistry.
Step 2 – Simulation
Modeling temperature × charging behavior × aging
The aging model is parameterised from cell chemistry and vehicle type, not from this fleet. All it receives from the fleet are the boundary conditions, namely the usage profile, the charging behaviour and climate data from Hamburg, and from these it predicts the capacity these vehicles ought to have under this duty. Only then do I reconstruct the actual capacity from the charging logs, energy charged against the SoC window, independent of the battery management system's own estimate.
The model never saw the measured capacity, and matches it to within 3% across three years.
Step 3 – Root Cause Decomposition
What causes the 41%? A decomposition.
Simulation enables isolated analysis of each individual factor.
No battery defect. 7 percent degradation after three years is within the expected range. Most of the loss comes from temperature and cabin heating, and is 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?
Request an initial consultationMethodology
A state-of-health value describes a single point in time. From it I assess what a vehicle or a storage system delivers in real-world use across its service life.
- Physics-based simulation, calibrated against measurement, test, and field data
- Usage profile, temperature, load states, and charging history as inputs
- Deviations broken down into their causes, each share quantified separately
- Reproducible, traceable, and defensible in a dispute
Environment or component?
Range cases are decided on whether a deviation can be explained by the environment or goes back to the condition of the battery. Move temperature and battery age independently of each other. That same separation is what I deliver in every assessment.
508 km
SOH: 98%
About
I founded simotive.ai to resolve technically contested cases independently. My work begins where measurement and diagnostics no longer give a clear answer.
- Experience
- 27 years at AVL List GmbH, more than 20 of them in leadership, most recently responsible for the Electrified Powertrain business unit. Alongside that, 15 years until 2026 on the technical steering committee of ASAM, the standardization body of the automotive industry.
- Education
- Dipl.-Ing. in Telematics, TU Graz, with a thesis on artificial intelligence and machine learning. Dr. techn. in Mechanical Engineering and Business Economics with the dissertation "Success Factors for Automotive Testing". Advanced training in battery systems and electric vehicles, TH Ingolstadt 2022.
- Fields
- Battery, battery management, vehicle software, electrified powertrain, and stationary storage systems.
- Speaking
- Regularly at international technical conferences.
Dr. Gerald Sammer
Founder and Managing Director, simotive.ai
Why now
- 29 November 2026. Euro 7 makes battery durability part of type approval. New car types must still deliver 80 percent of their certified energy after five years or 100,000 km, and 72 percent after eight years or 160,000 km.
- 9 December 2026. The new EU Product Liability Directive treats software as a product, introduces disclosure obligations, and eases the burden of proof for claimants. Cases become arguable that previously failed on the evidence.
- 18 February 2027. Electric-vehicle and industrial batteries above 2 kWh need a digital battery passport carrying service-life and condition figures. What it states has to hold.
- Grid-scale storage. By the end of March 2026 Germany had 489 grid-scale systems of 1 MWh or more on the register. In the first quarter of 2026 their additions exceeded home storage for the first time. In Austria the roughly 3.2 GWh installed by mid-2026 still consisted mostly of units below 50 kWh. The warranty cases are still ahead.
Contact
Are you facing a decision with real exposure, an unexplained failure, or a technical dispute? In an initial conversation we establish whether an independent technical analysis helps.
gerald.sammer@simotive.aiRequest an initial consultation