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, for electric vehicles and stationary battery storage. Independent, physics-based, and verifiable.
Dr. Gerald Sammer
Independent Technical Expert for Electric Vehicles & Battery Energy Storage Systems
When you call me in
I am contacted 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 it was driven incorrectly, the operator that the battery cells are weak. Before an investment, the manufacturer provides the ageing curve that justifies its own system. Both parties compute correctly. What decides is the assumptions behind the calculation.
I make them explicit and test them against a physics-based model, independently of everyone involved and in a form that holds up in court.
- 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 leased vehicle is returned after four years. A battery test reports 84 percent, and the return report deducts 4,800 euros for it. The lessee considers the deduction unfounded.
- What I examine
- Whether 84 percent after four years fits this particular vehicle. Frequent fast charging and long periods parked at a high state of charge shift the expected value considerably, and an average across all vehicles is no help here.
- What data I need
- Charging history with power, state of charge, and temperature, plus driving history, standing times, and climate data for the location. In addition, the battery test with its measurement procedure and the return report.
- What you receive
- An expected value for this specific usage profile and a finding on whether the measured 84 percent sits above or below it. That determines whether the deduction is justified.
After a software update, a vehicle charges at only 90 kW instead of 150 kW. The manufacturer calls it battery protection, the owner calls it a defect.
- What I examine
- Whether the condition of the battery requires the new limit. I calibrate a model on the charging sessions from before the update and simulate the period afterwards.
- What data I need
- Charging curves from before and after the update with power, state of charge, and cell temperature, plus the software versions with their dates.
- What you receive
- A statement on whether the battery explains the limit. Otherwise the limit is a software decision.
A van towing a trailer loses significant power on a climb after roughly 20 minutes. The dealer says it is by design. The buyer says the vehicle was not offered that way.
- What I examine
- Whether the power reduction follows from temperature, load, and state of charge, or whether the control software intervenes independently of them.
- What data I need
- Operating data from the drive with power, current, cell and coolant temperature, plus fault and event memory, software versions, and the figures given in sales documents and the owner's manual.
- What you receive
- A finding on whether the behaviour is by design, wrongly parameterised, or a component fault, and whether it matches what was promised.
Battery storage
A grid-scale storage system sits at 87 percent capacity in its second year of operation. The warranty curve names 92 percent for that year. The supplier points to the operating strategy, the operator points to the battery cells.
- What I examine
- Whether the actual operating strategy explains the missing percentage points. Cycle depth, C-rates, temperature, and time spent at a high state of charge differ greatly in their effect.
- What data I need
- Operating data from the energy management system, charge and discharge cycles, temperature traces, plus the warranty terms with their measurement procedure.
- What you receive
- The deviation separated by cause and a statement on whether the system was operated within the warranty terms.
Before the investment: the supplier calculates with 8,000 full cycles down to 70 percent remaining capacity and 90 percent round-trip efficiency. Two cycles a day are planned. Does the calculation hold?
- What I examine
- The assumptions against my own model, computed over your planned load profile rather than a laboratory cycle. For efficiency, the measurement boundary decides: taken at the inverter, or at the grid connection point including thermal management and parasitic load. Several percentage points sit between the two.
- What data I need
- System design, operating concept, the planned load profile, plus the manufacturer's commitments with their test conditions.
- What you receive
- A second opinion on the assumptions and the year the cycle budget runs out. Many warranties end at that point, even if the term still has time to run. Plus the figure where a small error costs the most.
After a shutdown, the manufacturer presents its own analysis and does not release the raw data. Does the analysis hold up?
- What I examine
- The submitted analysis against my own model. Plus the definitions in the contract and the conditions under which the measurement was taken.
- What data I need
- The submitted analysis, the data released, the contract and warranty terms, and the protection concept.
- What you receive
- A statement on whether the analysis holds up, and the list of data you need to request.
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