FAQ

1. Rapid design-space exploration – Optimised to evaluate thousands of powertrain variants in a single study, so teams can converge on the most promising architectures quickly.

2. Financial and sustainability KPIs alongside engineering performance – Lets you assess technical performance together with cost and sustainability drivers, making trade-offs explicit earlier in the decision process.

3. Fast concept part generation (eMotor, inverter, transmission) – Generate concept components from target specifications to estimate system impact without needing detailed designs upfront, bringing system-level analysis earlier and reducing reliance on specialist tools for early screening.

4. Motor-CAD import – Seamlessly import Motor-CAD motor designs into ePOP to evaluate them in a full system context (drive cycle, architecture trade-offs, overall efficiency).

5. Focused powertrain evaluation and reporting – Not a general-purpose simulation environment; it’s purpose-built for powertrain analysis, with tailored reporting that simplifies comparing concepts and communicating results.

ePOP has a library of parts and drive cycles that can be used so you can get started without providing any data. However if you have specific part data ePOP supports excel import and provides excel templates to be used for import. eMotor data can be imported directly from Ansys Motor-CAD. ePOP outputs a results file as a .mat (matlab) file to be read by the ePOP results viewer.

The tool outputs data in .mat format suitable for viewing in ePOP Results viewer.

Motor-CAD models can be directly imported. Excel format imports allow models from various tools to be imported with some simple/scriptable data manipulation required to fit the ePOP import format.

ePOP simulates the full powertrain and reports efficiency at both the system level and the component level (e.g., inverter, e-motor, transmission), so you can clearly see where losses occur. Once improvement opportunities are identified, you can use ePOP’s component library to swap in alternative designs and re-run the system simulation to quantify the impact. If an off-the-shelf alternative isn’t available, ePOP’s component concepting modules let you rapidly generate new variants and evaluate their efficiency benefit in the same workflow.

C#, Python, JavaScript with Angular, MATLAB/Simulink

Simulink allows a user to build models from scratch in a general purpose block diagram modelling environment for a single or limited number of variants. ePOP provides predefined architectures and validated simulation models combined with a tailored user experience purpose built for screening and optimising powertrain designs in the 1000's. ePOP massively reduces the time and skill required to answer high level power train design questions vs. building models in Simulink.

Ansys Optislang is a CAE optimisation and automation software. It orchestrates other tools which perform the simulation. ePOP is a standalone simulation software which doesn't require any other simulation tools to be installed to run. Ansys Optislang is a general purpose software that can be used for any domain or industry depending on the connected tool and the models being simulated. ePOP is purpose built for screening and evaluating powertrain designs. ePOP also provides answers related to the financial and sustainability dimensions, which is missing from pure engineering simulation alternatives.

Compared with the other options such as Simulink or optiSLang, AVL CRUISE™ M is the closest like-for-like alternative to ePOP for powertrain/vehicle system simulation, with broader multi-domain capability (and typically a deeper, more established ecosystem).

We can differentiate ePOP by being higher-level and faster for concept-stage decisions: stronger emphasis on rapid architecture screening, concept part generation, and integrating financial and sustainability KPIs—accepting less breadth and less high-fidelity multi-physics detail than CRUISE M in return for speed and decision-focused outputs.

As a smaller business, we can also offer more responsive support, tighter feedback loops, and bespoke project work than a large enterprise vendor typically provides.

ePOP is a high level system-level simulation environment. Components are represented using a combination of scalar parameters (to capture limits and key characteristics) and performance maps (to capture behaviour across an operating range). For example, an e-motor might be defined by scalars such as base speed and maximum torque, alongside maps such as efficiency versus speed and torque. This reduced order approach is commonly utilised in the simulation domain and is designed to evaluate many powertrain concepts quickly and support trade-off studies, rather than modelling a single powertrain in high geometric/physics detail.

The ePOP Inverter module enables user-defined construction of the power stage at the semiconductor level, including explicit Die selection and parameterisation. This allows inverter switching and conduction losses, and therefore overall efficiency, to be computed directly for a given motor specification and control operating point, rather than relying on fixed or catalog-based efficiency maps.

The Transmission module (TGEN) lets users build transmission models either by automatically sizing gears from a target application (e.g., required ratios, torque, speed) or by manually defining gear geometry using known parameters (e.g. pressure angles, face widths). Users can recreate known designs as benchmarks or for tool validation as well as quickly creating concepts across a chosen design space - for example varying transmission ratio. This enables rapid transmission concept development for system-level analysis before detailed transmission designs are available. The tool provides first-order estimates of weight, NVH, packaging, and efficiency, helping engineers decide which concepts to take forward into detailed design tools. A wide variety of transmission architectures are supported including multi-stage, multi-speed and planetary designs.

The ePOP eMotor module supports two workflows. First, it enables Motor-CAD import, allowing motor designs created in Motor-CAD to be brought into ePOP for system-level simulation. Second, it includes Motor Generation (MGEN): an automated motor design tool that generates interior permanent magnet (IPM) synchronous motors from target specifications. MGEN uses analytical equations to define initial motor geometry, then verifies performance using finite element analysis (FEA). MGEN provides an efficient way to create motors that meet requirements—either for immediate use in ePOP or as a starting point for detailed design and optimisation.

The ePOP Thermal module extends the base ePOP Pro framework by replacing fixed-temperature assumptions with a physics-based thermal solution for the power inverter. Inverter losses, which are strongly dependent on semiconductor junction temperature, are computed dynamically across the full operating range as a function of motor control parameters and coolant boundary conditions. The module solves the coupled electro-thermal problem using an appropriate thermal network model, ensuring temperature convergence and loss consistency at every operating point.

The ePOP NVH module, integrated within the Transmission module, computes key analytical predictors including contact ratio, contact and bending safety factors, and static transmission error. These metrics enable early-phase trade-off studies of NVH performance against competing design objectives such as mass and efficiency. While not intended to replace detailed time- and frequency-domain NVH simulations in the detailed design phase, the module provides quantitative guidance to minimise excitation at the source and reduce downstream system-level NVH risk.

The ePOP REEV module enables system-level modeling of Range Extender Electric Vehicle architectures by integrating a parametrized range extender unit into an existing BEV drivetrain representation. It accounts not only for energy flows but also for the control strategy inherent to series-hybrid operation, including battery SOC management. This allows quantitative evaluation of the impact of the range extender on vehicle range, energy consumption, and key system-level performance indicators across representative drive cycles, supporting informed architecture selection and early-phase trade-off studies.

The ePOP Voltage Dependency module allows system-level simulations to be conducted at a user-specified battery voltage. Motor and inverter losses are dynamically computed at the selected voltage, ensuring accurate representation of performance without the need to re-import or regenerate motor and inverter models. This enables rapid evaluation of voltage-dependent efficiency and operating behavior across different electrical architectures.

The term "AI" captures a wide range of solutions and technical approaches. In the simulation domain, optimisation algorithms (a type of AI) have been in use for decades, and ePOP is no different when addressing the challenges of discrete optimisation. Another type of AI is Neural Networks, which can specifically be deployed to some benefit if there are large amount of static real world measurements available, to replace static physics based simulation. As ePOP (and all other simulation software) lack large amount of measurement data, and usually perform optimisation including time domain analysis, Neural Networks provide little/no benefit and are therefore not necessary in ePOP. The challenge of correlation to real world measurements is usually addressed by ISO standards, which do exactly that: fit analytical/empirical models onto real world measurement data. ePOP relies on ISO standards for engineering accuracy. And finally, the use of LLM's (large language models) to support future engineering workflows is an exciting area of AI development. We are all aware of the limitations of LLM systems when it comes to providing physics based answers to engineering questions. ePOP aims to support the agentic ecosystem by providing an MCP that LLM systems can utilise to increase the reliability of such workflows. Direct integration of LLM based workflow assistants and MCP servers is on the product roadmap.

To be more specific, we are talking about LLM systems here, and we need to define what it is we are trying to achieve. If one aims for an answer without the need for validation and verification, LLMs are well suited to provide such answers rapidly. In reality, engineering decisions usually need repeatable, traceable and auditable data/results, which are not possible with LLMs. In summary, LLMs can be used to propose known architectures and starting designs as a 'copilot' but are not a substitute for validated physics based simulation.

ePOP Concept correlates to real-world applications through the use of power-versus-time duty cycles defined at the powertrain output, from which sub-component sizing is derived using first-principles power and energy analysis. The tool has been exercised using load profiles obtained from existing production applications. When the resulting component sizes are compared against the corresponding real-world hardware, the predicted sizing is typically within 10%. To balance limited data availability with computational speed, ePOP Concept employs zero-dimensional, physics-based models that capture the dominant system behaviors relevant to early-phase architecture decisions.

For analysis requiring a higher degree of freedom and time-domain fidelity, ZeBeyond’s other toolset ePOP Pro extends this approach using one-dimensional dynamic simulation incorporating efficiency maps and engine BSFC maps. ePOP Pro has been correlated against real-world measurement data to within <1% and has additionally been cross-validated against established industry-standard simulation environments such as GT-Suite.

ePOP Concept accepts one or more power-versus-time cycles that represent the full range of operating conditions for a given application. Where measured or defined duty cycles are not available, the tool supports derivation of representative load profiles through reverse-engineering from high-level requirements.

Two complementary tiers of output are avilable. First, a technology-level analysis based on built-in scalable component models, which approximates an idealised solution space under the assumption of continuously scalable components. This tier identifies the theoretically optimal system architecture and sizing based on fundamental power and energy constraints.

Second, recognizing that real-world implementations are constrained by discrete component availability, ePOP Concept maps the ideal solution onto a library of commercially available off-the-shelf components and identifies feasible combinations that best satisfy the system requirements. This enables direct translation from conceptual architecture optimization to realizable system configurations.