Author Archives: Tamara Dardanoglu

Future Mobility

The Balance Between Complex Testing Tasks and Effective Development in Testing Environments

11. September 2017 | Software & Testing Solutions

The Balance Between Complex Testing Tasks and Effective Development in Testing Environments

The areas of mobility and transportation are undergoing a heavily accelerated process of change. Traditional global mega-trends encompass widely differentiated user-specific and customer-specific requirements, different laws, ever stricter environmental regulations, limited resources and the electrification of drive systems. In addition, issues such as autonomous vehicles and customized, on-demand mobility solutions are growing in importance. Coming up with solutions and products requires completely new approaches on top of the established technologies, where constant advancements are needed. Examples of such approaches include digitization, information technology and networking.

The increasing complexity and differentiation result in stricter requirements that cannot be met in acceptable time frames at an acceptable cost level using established methods and processes. New, customized and specifically designed solutions and products must be developed to overcome these challenges.

Over the past years, advanced and high-performance simulation processes have become established as a main pillar in the portfolio of vehicle and drive system development. Numerous conventional methods of testing and proving have been partially or completely replaced by computer simulation. Increasing system complexity and validation requirements today and in the future, however, require specifically tailored testing solutions to validate functional reliability, quality, etc.
The suitably powerful testing capacity available provided in today’s cutting-edge development environment is no longer limited to mere logistical and technological solutions such as test rigs and measurement systems. For services to be highly efficient, all stakeholders need to be appropriately involved in the entire process of development. Besides designing and equipping the testing environment, this includes areas such as personnel structure and expertise, methods, operational organization, highly efficient logistics, information networking, and more.

Future Testing Environment Strategies

The design of future testing environments must be based on medium-term and long-term strategic product development planning and the testing requirements derived from it. They must do more than meet the simple engineering, operational, and logistical requirements. In particular, they need to be cost-effective, have a balance between operating capacity and personnel resources (and ensure they are utilized to the greatest possible extent), and assign work sensibly between what covers their own needs and what is commissioned by customers.

Testing Environment Organization and Processes

The organization and processes of modern testing environments have changed fundamentally in recent years. In the past, workers from development departments were often very extensively involved in the testing environment operations, sometimes even having a say in how they were conducted. The testing environment staff essentially represented the facility’s capacity, resources, and operators. Responsibility for defining the testing program and performing evaluations rested with the engineering department.

Over the past few years, various tasks have been shifted from engineering to the testing environments. Today, many testing environment crews are, for the most part, independently in charge of generating all test results. It takes adequate human resources and engineering capacity to perform these additional duties. That includes making the testing environment responsible for all processes and their design as well as medium-term and long-term structural orientation and investment budgeting.

Staff: Structure and Authority

The increasing transfer of numerous duties from engineering to the testing environment’s area of responsibility is creating a need for a greater number of engineers in current personnel structures and ranges of expertise. In the past, the largest share of a testing environment’s staff by far consisted of mechanics and some electricians and foremen with very few engineers. Today, the percentage of technicians and engineers has risen sharply. Added to that are IT and other specialists of various disciplines, who take care of the sophisticated test rig automation systems, measurement systems, and various software tools.

Logistics, Plus Flows of Information, Materials, and Data

Organizing modern testing environment operations to be efficient and economical requires the implementation of processes structured to be efficient and flexible from end to end and which can be adapted to meet changing requirements. This includes information management, which encompasses the handling and distribution of all incoming, internally circulating, and outgoing information, the management of experimental and measurement data plus material flows and logistics, quality management, and more. All primary and support processes need to mesh with each other smoothly and require continuous assessment, adjustment, and optimization.

Graphic - FEV Testcenter efficiency

Working and Testing Methods

Today’s advanced testing environments already use IT-based methods and tools to a large extent for organizing processes and performing work. Examples include databases used to aid in the preparation of project-specific test rig set-up and program plans, largely standardized test rig and measurement systems, highly automated running of test programs, integrated computer simulation tools, automated evaluation of test runs, and databases used to file test results.

The FEVFLEX information management software offered by FEV is a powerful solution for managing tasks, procedures, devices, media, test objects, test rigs, measurement data, and test projects, thereby contributing sustainably to a testing center’s efficiency. In addition, FEV MORPHEE significantly lowers the variety of software applications conventionally needed on test rigs. No matter if ECU (HIL), component, engine, powertrain, vehicle, or others: MORPHEE adapts to any kind of test environment.

Further reductions in time and costs of the development process can be achieved with Online and Offline-DoE tools for virtual calibration. FEV xCAL combines best-in-class modeling algorithms with an intuitive, workflow-based interface, thus enabling virtual and efficient calibration of a wide variety of powertrains and other applications.

Structure and Equipment

Today’s high-tech testing environments provide environmental simulation systems as well as traditional equipment such as test benches for engines, transmissions, vehicles, system components, and measuring equipment. In the future, there will be more new testing systems for conducting every test necessary in the field of autonomous vehicles. In recent years, high-performance computer simulation tools have replaced conventional testing methods, with new methods and procedures for testing being created. One example of this is the real-time networking of various subsystem test rigs with the built-in simulation of the system components of an entire powertrain that are not available in physical form.

For instance, FEV and the Institute for Combustion Engines (VKA) have developed the “virtual shaft” as an important tool. The test environment consists of physically separate test benches that are linked by a real-time EtherCAT connection. Thanks to the virtual shaft, the dynamometers in both component test benches are controlled in such a way that system behavior matches that of a real mechanical shaft. This enables us to recreate interactions – such as between an engine and a transmission – as early as the prototype stage before the two components can be physically adapted. That saves valuable time in development. Other benefits mainly include a protected test environment and a high number of options for monitoring individual test objects. This way, damage to prototypes can be effectively prevented. In addition, the virtual shaft allows the testing of hybrid powertrain combinations that are not yet mechanically compatible and would otherwise have to undergo extensive adaptation.

Graphic - FEV Testcenter efficiency

Virtual wave between two testing facilites


Ready for future mobility

FEV´s e-motor test beds

9. September 2017 | Engineering Service

FEV´s e-motor test beds

Electromobility is constantly increasing in importance, and is currently a significant part of daily challenges. Hybrid and electric vehicles continue to represent a minority, but the number of research projects is increasing rapidly. FEV guides its customers on the path to electric mobility and offers solutions for multifaceted and complex challenges.

New Drives, New Challenges

In addition to various hybrid concepts, the focus is on fully electric drives. As a development service provider, FEV manufactures and distributes test bed components and complete test beds with which drive components can be tested.

Components such as high capacity energy storage devices (traction batteries), inverters and electric motors are relatively new and have no application in conventional drive concepts. In addition to the quality of individual components, their combined application in vehicles must also be ensured. Manufacturers and suppliers face a wide range of requirements. For example, both hybrid solutions and purely electric drives require the most modern, high-capacity energy storage devices. The currents and voltages employed in these reach up to 1,500 amperes or 1,000 volts, respectively. Consequently, test beds must be oriented according to the corresponding safety conditions, and employees must be trained.

Component Tests

The testing of an electric motor for mobile application in a vehicle should absolutely be compared to that for a combustion engine. However, the individual components such as inverters and batteries are also tested separately. Special battery test rigs enable tests of battery cells, battery packs, and complete traction batteries. In order to test the lifespan of the batteries, for example, these are subjected to different charging and discharging cycles in climatic chambers.
Decisive differences from combustion engines can be found in the use of current as the drive power and in the far higher rotational speeds of electric motors. Two years ago, the maximum rotational speed was limited to about 15,000 rpm. FEV is currently delivering E-Motor test rigs with 20,000 rpm speed capability. Future test beds will be capable of up to 30,000 rpm.


System Tests

The components are also tested together, and as closely to reality as possible, in the context of a system test. In this context, it is necessary to consider both hybrid drives, meaning the integration of electrical components into the powertrain, and purely electric drive concepts. In addition to traditional drive concepts, there is now a so-called “E-axis” coming into play. This involves a highly integrated electric drive unit, arising from the combination of an electric motor, transmission, inverter and control unit.

The inspection of the E-axis is carried out by connecting right and left dynamometers in order to emulate road stress.

Additional challenges arise from the testing requirements, the design, and the electrical system components. In addition to the familiar driver-vehicle simulation, a simulation of climatic conditions is also carried out. This involves trials in temperatures ranging from -40°C to 120°C. Comprehensive conditioning systems for the test subject’s cooling media, high-precision measurements of rotational speed, torque, voltage and current are only a few of the new challenges FEV meets in this area in order to be able to offer expert solutions.

The Service Range of FEV

FEV operates, plans and implements electromobility test beds for internal and external customers. This involves the use of FEV dynamometers and conditioning systems as well as tailor-made solutions for customer-specific requirements. The MORPHEE automation system offers a high degree of flexibility and simple configuration for various test bench types. In addition, FEV not only supplies the interfaces necessary for the integration of power analyzers (measurement of voltage and currents of up to 1,500 A and 1,000 V), but also provides its customers with advice on the configuration of test rigs with regard to safety and machinery directives for the achievement of CE conformity.

Battery - E-Motor test beds

FEV operates, plans and implements electromobility test beds for internal and external customers.


Hybrid system benchmarking

Synergetic testing, simulation and design assessment

28. August 2017 | Engineering Service

Synergetic testing, simulation and design assessment

FEV has developed a system engineering approach focused on benchmarking. In this approach, the synergetic combination of testing, simulation and design assessment provides fundamental information in the concept phase of hybrid vehicle development programs. The proposed method includes a multi-level framework – from overall vehicle architecture down to detailed component level.

As a benchmarking example, a P2 plug-in hybrid vehicle was evaluated with respect to market assessment, drivetrain layout, components, operation strategy and additional starting devices. In the particular case of the starter device, the proposed approach guarantees correct market positioning (in terms e.g. of comfort, NVH, etc.) and virtual confirmation of vehicle targets.

Benchmarking of P2 PHEV SUV

The system level analysis of the P2 PHEV SUV includes on-road and chassis dyno testing, system simulation and final design assessment in terms of drivetrain layout, components and operation strategy. The selected vehicle is a mid-size SUV currently available on the European market with a P2 hybrid layout.

Market Analysis

FEV offers a comprehensive benchmarking database. Depending on the configuration, different parameters can be compared with each other to evaluate technical solutions and evaluate market trends. With respect to P2 PHEV SUVs, it can be observed that while most OEMs offer P2 PHEV solutions featuring a 25–40 km electric range, only few vehicles reach 50 km to target the Chinese subsidy limit. However, an increase in the electric range up to 70–80 km can be expected in the near future due to higher regulatory restrictions and technological battery improvements (energy density and cost per kWh). Concerning installed electric power, most OEMs currently position themselves in the range of 60 kW–100 kW as a function of brand identity (“sporty” vs. “comfortable”), vehicle mass and projected specified electric driving performance. An increase in the installed power to approximately 80 kW–110 kW as a function of the vehicle mass is also foreseen. Similar conclusions can be derived in terms of other vehicle targets (e.g. acceleration, maximum velocity) and reference component specifications (e.g. electric machine torque, battery size, charging system power).

Vehicle Analysis

In FEV Level 1 benchmarking, actual vehicles are analyzed in terms of performance and fuel/energy economy. These properties are examined by means of minimum, non-intrusive testing equipment within standard benchmarking procedures, including the evaluation of 0–100 km/h acceleration, 50–80 km/h elasticity or fuel/electric consumption in driving cycles relevant to both legislation and real-world customers.

In addition to the cross-checking of catalogue values, standard driving cycles are used within repeatable boundary conditions to analyze and compare the effects of different powertrain configurations and the energy management strategies implemented.

Real-world driving also provides information about off-cycle fuel economy, electric energy consumption or electric range on the one hand and the calibration of driving modes depending on battery SOC and routing information on the other.

Powertrain Analysis

In the level 2 assessment, component efficiencies, energy flow and operating strategies are analyzed.

In the first case the transmission clutch changes to slipping mode in the beginning and P2 EM speed is increased slightly. For the subsequent ICE ramp-up, the separation clutch between the ICE and P2 is closed and P2 torque is increased to crank the ICE up to target speed, whereas P2 speed remains constant. After the ICE is fired up, driving torque is transferred from P2 EM to the ICE before the transmission clutch is closed. In the case of an ICE start with a dedicated starter the EM operates at peak torque before, during and after the ICE start event until the ICE can take over the EM torque. The starting device starts the ICE, which is then synchronized and connected to the driveshaft to provide torque to the wheels.

These measurements are used, for example, to analyze and compare system performance in terms of time demand, comfort of ICE start and effect on hybrid operating strategy. The pros and cons for each configuration are documented in the FEV database, which provides important input for the system design.

During the course of the study, FEV investigated repeated accelerations of the vehicle in electric driving and hybrid driving mode. In the first case, the HV system is allowed to work at peak power without any requested torque reserve to start the ICE due to the presence of the starting device. As a consequence, the first electric acceleration is aggressive, but the thermal derating starts soon thereafter in the 2nd acceleration test.

With hybrid accelerations, there is no quantifiable system degradation for 10 repetitive 0-100 km/h accelerations. The EM is operated up to the continuous torque (no thermal derating) and the full load periods during the hybrid acceleration events are much shorter than for pure electric acceleration.

In general, the results of level 2 benchmarking analyses provide important input for the system design in terms of achievable performance depending on component specifications and control strategy.

Drawing - Hybrid vehicle benchmarking

Vehicle topology: P2 PHEV


The level 1 simulation approach is typically used by FEV to support the benchmarking of electrified vehicles in terms of energy management, component assessment and analysis of the operation strategy.

The first step in any simulation activity is the collection of the input data and the model validation. When benchmarking electrified vehicles, this phase is performed by FEV within the course of a multi-step, systematic approach. First, energy consumption at the wheels is determined during the propulsion and braking phases. Afterwards the driveline losses are validated in terms of transmission efficiency and oil warm-up. The electric components are then verified based on the cycle time histories of the mechanical and electrical signals measured during electric driving. Finally, the internal combustion engine and the auxiliaries are validated.

A semi-empirical temperature model for the ICE is fitted along the measured cycles and the fuel consumption time history is checked against the measurements. If no component measurements for transmission, electric machine, ICE or HV Battery are available, the initial map is based on the extended FEV database and is reshaped afterwards to match the losses measured during constant-speed operation. The simulation model is controlled by a rule-based operation strategy, parameterized to reflect the operation points of the actual vehicle. The resulting model is validated with 5% of cycle accuracy in terms of fuel economy and performance time.

The simulation model is used to assess the powertrain in terms of performance and energy economy, component sizing, technology packages and operation strategy.
FEV’s system simulation toolchain also includes the mathematical optimization of the operation strategy along the selected driving cycle by means of Discrete Dynamic Programming (DDP).

Impact on System Design

During the concept phase of a hybrid development program, several details need to be fixed in terms of hybrid layout. Depending on the integration effort, cost target, synergies with other platforms and requirements in terms of start comfort, performance, efficiency or cold start capability, the most fitting solution must be defined. The selection criteria are supported by a dedicated measurements campaign and simulation activities.

Specifically, since the P2 EM is capable of starting the engine, no additional starting device is absolutely necessary. However, when using only the P2 for starting the engine, either an acceleration drop during the start event can occur for high-load EV operation or a power reserve for the ICE start must be provided. These drawbacks can be avoided by means of an additional starting device.

The cheapest starting device is a 12V pinion starter, which fulfills all requirements in terms of a reliable engine start and especially cold cranking. Belt-driven devices are the most suitable to fulfill the increased requirements regarding NVH and start comfort as well as change-of-mind scenarios. HV belt starter generators can also be beneficial, depending on the availability of modular components, maximum power output and the energy reserve of the high voltage (HV) battery.

Graphic - Hybrid vehicle benchmarking

In the course of the study, FEV investigated repeated accelerations of the vehicle in electric driving and hybrid driving mode.

Graphic - Hybrid vehicle benchmarking

As the P2 EM is capable of starting the engine, no additional starting device is strictly necessary.



Contact-free Measurement of Combustion Air on Engine Test Benches

28. August 2017 | Software & Testing Solutions

Contact-free Measurement of Combustion Air on Engine Test Benches

Significantly increased requirements regarding the environmental compatibility of combustion engines require more specific measures for reductions in consumption and emissions. Every modification to the engine must be examined on a test bench with regard to its impact on emissions and fuel consumption, among other things. In this context, the exact measurement of the combustion air mass flow is very important. For this purpose, FEV has developed the FEV AirRate, which is now available in an improved and completely redesigned version. The AirRate is used for the contactless measurement of gas velocity, pressure, humidity, and temperature of the combustion air on engine test benches. The air mass flow in kg/h is calculated and displayed using these parameters.

High Measurement Accuracy

The ultrasonic gas flow meter with 8 ultrasonic transducers in 4 measurement paths enables very high accuracy in measurement throughout the entire measurement area. The extremely fast response time of the system ensures reproducible air quantity measurements, even for highly dynamic processes in the induction tract. Due to the low pressure loss in the AirRate measurement section, engine behavior is not affected. Due to the large measurement range spread of the AirRate 100 and AirRate 150 measurement systems, the complete range, from single cylinder engines to heavy duty engines, can be covered with only 2 device sizes.

Compact Design in a Single Casing

The AirRate requires very little space; thanks to the compact design, the entire measurement technology fits in a single casing, and there is no need for wiring between the measuring unit and the output unit.

The flow rectifiers integrated in the device effectively reduce turbulence in the combustion air, thereby enabling the system, for example, to be installed directly behind a pipe elbow without any extension of the inflow section whatsoever. Integration in test benches, with or without combustion air conditioning, is thus very easy, as is a quick conversion for operation with or without AirRate.

Clear Operation

During the redesign, special attention was paid to an increase in measurement frequency and simple and clear operation.

Compared to the previous device, the measurement frequency has more than doubled; in addition to pressure and temperature measurements, a humidity measurement has been added for mass determination. The four-path design with a total of eight titanium ultrasonic sensors guarantees very high accuracy in measurement, even under difficult flow conditions. In addition, a plausibility check is carried out between the paths, so that the drift of a path can be detected and reported. Due to the path compensation functionality, the malfunction of an entire path can be compensated for by the device, with no loss of measurement accuracy.

The AirRate is operated via the 7” touchscreen display with easily readable graphic elements, via the web browser, or via the WiFi interface. The latter in particular enables very easy operation and settings adjustments – even in difficult and inaccessible installation conditions – through the use of a smartphone, for example.

All settings are password protected; as a result, any unauthorized or accidental incorrect adjustment of the settings is excluded. The operation surface and the web menus are available in several languages and can be expanded by the user.

Low Maintenance and Calibration Requirements

In addition to the power output (4 to 20 mA), a tension output is now also available (0 to 10 V). The series interface with the AK protocol is fully compatible with the previous version. A simple replacement is therefore possible, since the mechanical connection dimensions were maintained. The AK protocol is also available via TCP/IP in addition to the series interface.

The pressure, temperature, and humidity sensors all communicate via a digital bus protocol. This enables them to be easily replaced by factory-calibrated spare parts in case of a defect; a recalibration of the device is not necessary in this case.

The calibration interval of the AirRate is 2 years and is therefore significantly better than comparable hot film measuring devices with a 6-month calibration interval. Upon request, a DAkkS calibration of the AirRate is possible.


Connected Vehicle Validation 2.0

FEV TST: Early validation of connected vehicle systems and cyber security

28. August 2017 | Software & Testing Solutions

FEV TST: Early validation of connected vehicle systems and cyber security

Vehicle apps, smartphone connections, GPS, Bluetooth, WiFi, 4G/LTE, and soon 5G are just a few of connectivity features a modern car has: Connected Vehicle is no longer a vision but has become reality and along with it serious complexity and challenges (i.e. Cyber Security) for the integration of all these features and functions into the Smart Vehicle eco-system. As a leading development services provider, FEV has supported these developments from their formative stages and has developed unique expertise from development, implementation, integration, through validation. To support these various program development cycles or stages FEV has developed the “Telematics System Tester” (FEV TST) which has become an important tool for integrating and validating increasingly complex connected vehicle components and systems, even when used in the very early development stages. This test system enables the simulation of relevant connected vehicle components, applications as well as signals and data in a controlled environment with the ability to also replay recorded scenarios. After successfully completing several series-production development, integration, and validation projects with connected vehicles, the project results indicate that the FEV TST is able to reduce time and effort by up to 30%, which is especially relevant in the context of shortening innovation cycles. Further, tremendous benefits are achieved using this test system platform for continuous and regression testing including for Cyber Security.


“In today’s connected vehicle and certainly tomorrow’s Smart Vehicles, connectivity will be a must-have upon which not only telematics and infotainment systems rely on but also the coming autonomous driving features. Connectivity will enable the Smart Vehicle and its reliability will allow OEMs to offer a wide range of additional applications for the driver and society as a whole”, explained Stephan Tarnutzer, Vice President Electronics and Global Center of Excellence Smart Vehicle at FEV. “The vehicle of the future will be part of the Internet of Things (IoT) contributing Terra-bytes of data and receiving or consuming large amounts of data when driving from various sources both inside and certainly outside the vehicle.” For this reason, Connected Vehicle systems need to be validated on an “end-to-end” basis with a Connected System Thinking approach and methodology, where the vehicle is only one part of the system. In addition to the “standard or traditional” vehicle functions, all of the other services and communication structures must also be considered during the validation phase as well as all components outside of the car (cloud, back-end, apps, etc.). Not the least of the challenges associated with the Connected Vehicle is Cyber Security for which new threats are to be addressed and validated on a daily basis. “A system validation that meets all these demands can only be tackled successfully through the use of automated test systems or the task is overwhelming and there are not enough people to do this work manually, reliably, and consistently”, resumed Tarnutzer.



Complex Connected Vehicle Systems

The FEV TST platform makes it possible to simulate relevant signals and data in a controlled environment or to replay recorded scenarios. These signals include the vehicle communication buses, cellular network, GPS, Bluetooth, and WiFi and the simulation of Smartphone apps as well as connection to the Internet for backend services, which are necessary in order to develop the required use cases for the connected vehicle system and used for end-to-end testing. In addition, the FEV TST can simulate different scenarios for mobile network and GPS signals. For example, the influence of weak or bounced satellite signals or tower-to-tower cellular signal hand-over scenarios can already be assessed in the laboratory. “With FEV’s TST the connected system under test can be validated easily and in a short amount of time against hundreds of different scenarios and evaluated in a controlled environment”, outlined Tarnutzer. “An additional back-office application maps a simulated chain of information – for example, the data flow of a door opening command from the smartphone, over the backend, to the vehicle’s telematics unit and the vehicle’s CAN bus.”
The latest addition to the FEV TST platform is for the automation of various Cyber Security related tests involving various industry standard cyber attack tools through the numerous threat vectors present in a car (i.e. Bluetooth, WiFi, CAN, etc.) and simulated on the TST. The FEV system allows for the automation of such Cyber Security related testing and validation which is very helpful during development as well as regression testing. The TST has shown to reduce the manual testing effort related to such activities by over 50% allowing resources to be deployed for other types of testing.

Early Development

Modern connectivity systems for a car alone usually consist of over 5 different components, usually from different suppliers. Frequently, not all of these components are available at the same time during the development phase for integration and validation testing. The FEV TST can be included from the beginning in the process to support the development effort as well as test and validation. The FEV TST can be configured so that it closely simulates the real system to quickly help verify requirements for each of these components. For the Cyber Security related tasks within a development program, the TST can support such activities as well from the start and help identify cyber security implementation gaps in components early on.


Conquering the Ever Growing Electronics Technologies in the Vehicle

FEV’s Smart Vehicle Center of Excellence

8. August 2017 | Engineering Service

FEV’s Smart Vehicle Center of Excellence

The features of next generation Smart Vehicle programs pose a major challenge to the automotive industry as OEMs race to develop systems which enable reliable, efficient integration of vehicle and cloud-based ecosystems. The FEV team is capable and experienced to meet customer’s needs and expectations while delivering a cost-effective and reliable solution through the utilization of subject matter and domain expertise and specifically the industry first usage of true Smart Vehicle HIL systems.

To focus and enhance FEV’s capabilities in the areas of Infotainment, Telematics, ADAS, and Autonomous Driving, FEV recently formed the Global Center of Excellence Smart Vehicle. Included in that are also such key topics as Functional Safety and Cyber Security.

Barriers to a Successful Program

Each generation of automotive electronics, none more than any of the Smart Vehicle related areas, presents product teams with the challenge of finding new ways to address an old industry paradox: Develop new features and functions in a shorter amount of time and at a lower cost.
Within the world of smart vehicles, these demands require development to be performed by teams, distributed worldwide, working in an agile development environment with requirements and standards that in many cases are little more than a guideline.

Inevitably, integrating connectivity or ADAS solutions from multiple suppliers will result in the identification of incompatibilities between components and systems. The issue is further complicated by the compromises which are often made in the requirements development phase of a project. With deadlines looming, OEMs and Tiers are often placed in the unenviable position of haggling over who is the “least wrong”.

Proven Leaders in Smart Vehicle Integration Services

The above scenario illustrates why the system integrator role is critical to the success of smart vehicle related programs. FEV’s experience in all phases of product development has positioned us to be the leader in this space.

FEV’s system integration processes and tools enable and empower individuals from multi-vendor teams to work together. The key to this service is utilizing smart vehicle component and system experienced technical project management and subject matter expert professionals who can synergistically work with resources ranging from software developers to validation engineers. These unique FEV resources technically understand the issues and have the ability to anticipate and communicate any impacts to all stakeholders.

Process examples:

  • Support engineering to remove all technical/non-technical roadblocks
  • Use FEV’s Connected System Thinking methodology to provide component as well as system level expertise and experience
  • Task prioritization to maximize efficiency
  • Identification of program issues which require escalation to mitigate time drain
  • Application of an integrated and effective approach towards defect management and triage
  • Ensure validation resources diagnose the root cause not the symptom

Powerful Tools

To support the design and engineering capabilities with testing and integration services, FEV uses powerful test tools developed over many years. “With test platform tools such as the HMIts (Human Machine Interface Test System) and TST (Telematics System Tester) in our arsenal, we have a distinct advantage in the discovery of a defects root cause”, explained Stephan Tarnutzer, Vice President of Electronics at FEV North America and Global Center of Excellence Smart Vehicle at FEV. “These test tools are developed by our internal resources, which allows us to reconfigure each tool rapidly to characterize and identify root cause when new defects are discovered.”

Finally, as a “one-stop shop” for a system integration and validation project, FEV’s project management and validation teams can lean on FEV’s in-house embedded hardware and software development teams to consult on the deep dive issues which pose the greatest risk to program timing.


FEV’s multi-phased approach to eff iciently integrate a telematics solution as part of an overall Smart Vehicle ecosystem:

Phase 1: Specification Development

Collaborative workshops with customers to develop robust Telematics Requirements, i.e. TCU, TSP, feature implementation, Cyber Security, Functional Safety, etc.

Phase 2: Project Planning

Combined effort with customers to create program timing and milestones.

Phase 3: Project Initiation

Vendor Kickoff /Expectations, Test Methodology Development, etc.

Phase 4: System Integration Services

End-to-End bench testing and modeling, drive testing, root causing defects, etc.

Test system - FEV Smart Vehicle

FEV has developed an advanced automated HMI test system enabling 24/7 validation of telematics enabled multi-media

TST - FEV Smart Vehicle

FEV’s preeminent Smart Vehicle test center featuring the robust Telematics System Testers (TST) which simulates the “connected vehicle” in a lab environment.


FEV Makes Software Quality Measurable

Metrics-based Strategies for Quality Assurance of Automotive Embedded Software

6. August 2017 | Software & Testing Solutions

Metrics-based Strategies for Quality Assurance of Automotive Embedded Software

Digitalization and high innovative demand drive future key technologies in the automotive industry. Increasing software complexity is met by rigorous quality and safety standards, but strict resource constraints on projects are limiting time spent on quality assurance (QA). Therefore, FEV has developed a tailored, optimized QA strategy that is scientifically sound to improve efficiency and quality in software projects. Based on data derived from 13 customer projects, the impact of strategic decisions on the quality of the resulting software was examined, thus leading to a better understanding of how high quality can be achieved and quantified appropriately.


Focus of the Study

Two major subjects are the primary focus of FEV’s research. First, the aim is to analyze, formalize and then capture the quality of a software product using the appropriate metrics. The second goal is to identify how specific properties of the quality assurance strategy influence the outcome of a project.

A pragmatic approach to defining software product quality is its segmentation into intrinsic and extrinsic facets. Extrinsic quality is most often judged on the basis of the customer’s satisfaction, whereas intrinsic quality can best be quantified using established product quality models, such as that found in the ISO 25010.

Key of the Study

For the purpose of the initial study, FEV limited the scope of investigation to certain properties proposed by the ISO 25010. Relevant quality characteristics were then quantified using a set of metrics. Most notably, the requirements conformity and residual defect ratio were considered.

The data from 13 automotive software projects was gathered and analyzed in order to validate the correlation between metric and de facto product quality and to identify any relationships to specific QA strategies. Statistical models, such as correlation coefficients and ANOVA (Analysis of Variance), were considered for analysis purposes.


The research showed that customer satisfaction is strongly correlated with both requirements conformance (positive correlation) and residual defect ratio (negative correlation), which indicates a strong linear relationship between these metrics.
We make the reasonable assumption that intrinsic and extrinsic product quality strongly correlate with each other. Then, both metrics – requirement conformance and residual defect ratio – appear to be suitable indicators for intrinsic product quality, since customer satisfaction is regarded as the major marker for extrinsic quality in literature.


Lastly, the impact of different quality assurance strategies on a project’s outcome was analyzed by first surveying which test methods were applied (respectively), and then examining the relationship between these properties and the identified quality indicators. First results here are promising, and FEV is confident that it will enable the profound derivation of an effective and efficient QA strategy in upcoming projects.

Graphic - automotive software

Relevant software products characteristics and metrics evaluated.


Focus on Reusability

Enhancing Agile Automotive Software Product Line Development by Similarity Analysis

3. August 2017 | Software & Testing Solutions

Enhancing Agile Automotive Software Product Line Development by Similarity Analysis

In the automotive domain, the complexity of software functions and the demanded quality standards, e.g. CMMI, ISO 26262, are still growing, while in the meantime the expected release cycles get shorter and shorter. In addition, the vehicle starts to turn into a smart device which is able to interact with its environment and to react autonomously. As a consequence, further aspects, such as security or privacy, receive a higher prioritization.

This up-to-date scenario is a great example for the frequently changing and hardly predictable demands in today’s automotive industry.

  • Who can ensure that features which are identified as necessary today will be required in a similar form tomorrow?
  • What is the adequate time frame for long-term strategies in the context of shorter and shorter release periods?

FEV’s PERSIST (powertrain control architecture enabling reusable software development for intelligent system tailoring) was established to give first answers to these questions. This approach introduces agile methods to be able to react flexibly on requirement changes and to reduce the duration of one development cycle and project quality risks due to continuous integration. Nevertheless, the establishment and maintenance of a Software Product Line (SPL) in the context of the daily work of a supplier has to face several ongoing projects in parallel. As a consequence, an approach is required which allows the project teams to focus on the implementation of the required product, while it is possible to continuously establish and maintain a SPL in parallel with minimized effort. Therefore, it is necessary to further extend the collaboration between Agile Software Development (ASD) and Software Product Line Engineering (SPLE), resulting in Agile Software Product Line Engineering (APLE).

Software Product Line Development Focused on Application Engineering

FEV searched for a SPL development method which provides essential feedback for the SPL during daily project work but reduces the additional effort for the projects to a minimum. The method does not require long-term decisions, keeps the SPL up-to-date and identifies new potential for reusable components is needed.

The main important development artifacts used during this process are the reference architecture, project architecture, component specification, test cases and the component implementation. The proposed method follows a component-based project-first approach: This means that the main item of the software architecture is a component and any specification for a component is first raised during project development. A component will only be considered to be redesigned in a more general way, if it is proven that a corresponding demand is given and a general component is a realistic opportunity within several projects. In addition, AE shall gain most benefit from the already established product line and components which are currently developed in different projects, without being slowed down by the burden of being dependent of generalized components.


Step by Step Process

1 In the first step, a new component is specified. This is done by performing a first draft of the interface with a related functional description.

2 In addition, its position in the software architecture is estimated. This is done by considering the reference architecture of the SPL. In the context of PERSIST, components are hierarchically arranged in a set of compositions. If a suitable component can be identified, which seems to be similar or identical to the specified component, the name and location of the component will be adapted to the reference architecture. This is the first step, where the SPL supports the project development. Complex architectural decisions can often be supported by experiences from the past which are stored in the reference architecture.

3 In case the component cannot be mapped, a specific position in the project’s software architecture needs to be defined.

4 In a second step the decision made in step 3 is reevaluated by the SPL team to avoid any false negatives.

5 If the component cannot be matched, the complete implementation will be performed from scratch. The new component is added to the reference architecture after its location has been agreed on.

6 If the component specified in the context of the project can be mapped to a component an extrinsical equality regarding the reference architecture can be established. This connection cannot only be used to compare the component under development with general components of the SPL but also to compare it with other individual, but extrinsical, equal components from different projects.

In addition further analysis can be performed on structural (interfaces) and semantical (test cases, function models) level.

7 If a candidate with a similarity lower than 100% is identified, this candidate can be used by the project team to finalize their own specification and the implementation can be based on the provided development artifacts.
The project team does not spend any additional effort on implementing a reusable component which is able to fulfil the requirements of both or more similar components. The identified similarities are only used to speed up the project development.

8 In a parallel step the SPL team evaluates if the identified similarities provide enough potential for a general reusable component. Based on the amount of similar components and the degree of similarity on structural and semantical level a decision has to be made whether to implement the component based on a similar component [7] or to proceed to step [9]: If the potential is high enough, the SPL team will implement a general component which can then be reused in further projects [9].

9 The proposed steps ensure that the project teams always specify components which are as close as possible to the already established reference architecture and available components. In the best case general components can directly be reused. Additionally all derivations of the reference architecture can automatically be spotted and the degree of variation can be evaluated. Therefore the potential degeneration of an established product line can be observed continuously and a synchronization between product and product line can be performed iteratively.


Activities 1 to 5 are already well established, while for activity 6 and 8 the necessary automatization is currently being finalized. General components, which are used in several projects, have already been established, but are always based on intensive manual reviews or a proactive approach. The possibility to investigate a given reference architecture during the specification of a new component is not noticed as an additional effort. Instead the reference architecture provides helpful information to perform architectural decisions.

In the current status the reference architecture consists of 219 components, whereas 125 (57%) components have no extrinsically equal match. 94 (43%) components of the reference architecture are part of more than one project and 61 (28%) of these components are also part of at least three projects.

Method for agile soft ware product line development: The colored activities represent activities which are performed by the SPL team, while the white ones are performed during projects.


The proposed approach provides the possibility of realizing the project-driven implementation work directly in the project without losing the benefits of a corresponding SPL. Each project team can benefit from the established reference architecture to speed up architectural decisions, while the similarity analysis can provide additional foundations for the actual implementation.



Cost Development of Electric Vehicles Considering Future Market Conditions

Market study and cost analysis of electric, hybrid and fuel cell vehicles

24. July 2017 | Consulting

Market study and cost analysis of electric, hybrid and fuel cell vehicles

With a market share of only about 1% of new vehicles sold, battery driven electric vehicles and plug-in hybrid vehicles (“xEVs”) stand, from a European market perspective, far below expectations. In Germany, the xEV share is 0.6%; corresponding to about 25,000 vehicles sold in 2016. Germany is below the EU average. It is clear that the purchase and tax subsidies from the German government have, so far, not had a significant impact: In the first 3 months, only 4,500 sales were realized. Despite the subdued market demand, the number of public charging stations for electric vehicles tripled between 2015 and 2016. Against this background, FEV Consulting conducted a market and cost study to answer the question of how electric vehicle costs will develop in the future under conditions of increased sales volumes, growing demand for raw materials, and developing production capacities. The main objective is to assess the extent to which xEV vehicles can be cost competitive with conventional vehicles and which powertrain type will dominate the market.

FEV’s study answers the following core questions:

  • What are the latest trends in electrification and hybridization?
  • What are key market and technology trends regarding xEVs towards 2025/30?
  • How high are costs for alternative powertrains today, and what will they be in 2025/30?
  • What are the primary cost drivers and how will they develop?
  • Will combustion engines still be the cost leaders in 2025/30?
  • Which additional costs are expected in order to meet statutory and supervisory requirements?
  • How cost competitive will fuel cell technology be in 2025/30?

Driven by “diesel gate”, statutory regulations, regulatory pressure and technological advances, alternative drives (or xEV vehicles) have developed into a key trend in the automotive sector. Many European OEMs are convinced that the tipping point for electric vehicles will soon be reached: OEMs and suppliers are currently investing heavily in the development of their EV fleet and EV component portfolios. Volkswagen just recently released the launch of its xEV platform (MEB) with a goal of achieving a 600 km electric driving range in its compact car concept, “ID.” Daimler showcased an electric SUV Coupé called “Generation EQ,” at the Paris Motor Show that is based on a dedicated EV architecture. Other manufacturers are planning similar concepts, including purely electric as well as hybrid, and fuel-cell electric vehicles with electric ranges exceeding 350 km. Aside from the regulatory and legislative motivation, the financial implications for OEMs over the next 10 years are still not clear. The question of whether xEVs will be able to attain a significant market share largely depends on future price competitiveness compared with their conventionally powered counterparts.

Graphic - Cost development fo electric vehicles

Exemplary cost split for selected fuel cell
component in 2025 [in €]

Comparison - Cost development fo electric vehicles

Selected vehicle concepts for cost comparison of future xEVs

Methodology and Assumptions

Several alternative powertrain vehicle concepts and a conventional compact vehicle were compared in a cost analysis study. The selected models included typical plug-in hybrids (PHEV), pure battery-electric vehicles (BEV) and fuel-cell electric vehicles (FCEV) in the compact car segment. In order to capture market and technology uncertainties, 3 scenarios were developed that reflect technology development costs and fluctuations in raw material prices. For all 3 scenarios, a set of boundary conditions were determined to allow a fair cost comparison between the different concepts.

Selected boundary conditions for the 2016 cost baseline:

  • Vehicle segment: Compact car
  • Baseline vehicle for cost comparison is a conventional ICE with start-stop and 12V
  • Low production volume for Fuel Cell Vehicles
  • Battery specifications based on current market concepts

Selected boundary conditions for the 2025 cost forecast:

  • Vehicle segment: Compact car
  • Conventional baseline vehicle is MHEV (48V) with an additional 12 kW of electric power
  • Production volume for FCEV has been increased to 50 thousand units
  • Higher specific energy [Wh/kg]

Selected Study Results

In 2016, the manufacturing costs of plug-in hybrids and battery electric vehicles (PHEVs & BEVs) were about one-third higher than a conventional ICE-powered vehicle with a Start/Stop automatic transmission. Fuel cell electric vehicles (VCEV) manufacturing costs are nearly 5 times as high as those for a conventional vehicle. The reasons for this are lower sales volumes and high development cost in 2016.
By 2025, it is expected that the electric range of xEV vehicles will nearly double, with marginal cost savings of approximately 5% (Allrounder EV). Compared to mild hybrid comparison vehicles with 48V technology, the costs are about 20% higher. The cost of fuel cell electric vehicles, with an electrical range of approximately 800 km, is expected to fall to one-fifth of today’s price, leaving a remaining cost gap of 60% compared to the 2025 baseline vehicle (48V mild hybrid). Battery costs are expected to decrease by 50% for traditional OEMs due to economies of scale associated with increased production volumes and improvements in cell technologies. The electric capacity of a typical BEV is expected see a significant increase from 36 to 70 kWh (500-600 km).
In addition to the comparison of the total cost and the delta analysis of the selected xEV vehicle configurations, detailed powertrain cost splits are provided in the study for key components like the electric motor, controller, battery, transmission, etc. Each key component has been further broken down into the main cost drivers, including material costs as well as overhead costs which were determined using the FEV “should cost” methodology. Uncertainties in future production volumes are considered in the “conservative,” “most likely” and “progressive” scenarios.

Impact on the Automotive Industry

Fully electric drivetrains are far less complex than their conventional counterparts with internal combustion engines, since many components of a conventional drivetrain are no longer necessary. The sales potential of injectors, fuel pumps, filter systems and turbochargers is adversely affected by increasing EV sales. Conversely, the strategic importance of new components, such as the electric motor, battery and power electronics increases. For the future, manufacturers need to decide what share of the added value they want to provide from within (vs outsourcing). This decision is strongly influenced by endogenous factors such as cost competitiveness, exogenous factors such as raw material prices, vehicle range and future development of charging infrastructures.
Suppliers – especially those with a product portfolio focusing on conventional powertrains – will have to undergo a fundamental transformation over the next 15 years, which can be subdivided into 3 steps:


Comparison - Cost development of electric vehicles

Modification / Change of powertrain configurations in a 15-year-timeframe

1  Today: Strategic Analysis and Preparation of Realignment

Although the industry is in a state of upheaval, there is still partial restraint. On the one hand, the change to the development of alternative propulsion systems is already visible in the organizations of major manufacturers and large or specialized suppliers. On the other hand, traditional suppliers that are active in the internal combustion engine market are still in the preparatory phase.

2020: Implementation of the Realignment and Transition

As soon as market shares of xEVs have increased, product and service portfolios must be realigned and value chains have to be reorganized. The orchestration of an orderly ramp-down of the traditional business requires a solid strategic plan and dedicated implementation. It is very likely that the early inefficient suppliers will fall victim to the industry transition and exit the market. As a further consequence, the future R&D focus of the OEM’s will shift even more clearly toward electrification and other value-added product offerings, such as automation and (digital) mobility services.

3  2025+: Completion of Transition Phase

Depending on the respective scenario, market shares for conventional powertrains (ICE-only) will shrink significantly. In one radical scenario, ICE vehicle sales are likely to drop to 75% of the 2016 level. On one hand, as a result of shrinking market volumes, further (and even stronger) consolidation of the remaining suppliers in the field of conventional powertrains is expected. On the other hand, market participants will be well-positioned with an early strategic focus on the realignment and transition toward the new boundary conditions for the future xEV market and technology competition.

Graphic -

Iterative transformation for suppliers over the next 15 years


FEV xMOD put into practice

Real-time simulation meets hybrid powertrain

28. June 2017 | Software & Testing Solutions

Real-time simulation meets hybrid powertrain

To ensure a reasonable development time of future complex hybrid powertrains, while maximizing their efficiency, and improving the early prediction of the vehicle behavior, advanced tools and methodologies are mandatory. Relying on its long and proven experience in testing activities, its strong expertise in developing and providing competitive testing products like MORPHEE, and on its recognized know-how in using simulation to create advanced and innovative solutions, FEV decided to merge real and virtual worlds to create and offer a Hybrid Tool Chain to overcome these challenges. This 3-step Hybrid Tool Chain is a practical solution, from the Model-in-the-Loop front-loading phase to the X-in-the-Loop validation phases, “X” standing for combustion engine, battery or electric motor for instance.

FEV´s Hybrid Tool Chain

To ensure the optimization of the development of hybrid powertrains and energy management system (EMS), and benefit from simulation-based methodologies, FEV proposes to use a dedicated HiL step, which allows a seamless transition between the MiL and EiL phases, to create the Hybrid Tool Chain. This Hybrid Tool Chain relies on FEV’s advanced co-simulation platform xMOD, a platform which combines an integration environment for various heterogeneous models, together with a virtual test laboratory, and offers a range of different functionalities, such as the integration of heterogeneous models, the protection of the model contents, when they are imported, the virtual instrumentation, or the test automation. Moreover, xMOD provides simulation functions in various simulation schemes: real-time, extended time or as soon as possible, which are really useful features for MiL, HiL and EiL environments.

The Hybrid Tool Chain in Use

In this first step of the Hybrid Tool Chain, the objective is to set-up a full hybrid vehicle model of the targeted application, and integrate it in the xMOD environment. To that purpose, FEV uses classical simulation tools and software available on the market. The vehicle modelled is a parallel hybrid with an automated manual transmission.

The simulation is made in a forward approach with a 1D model for the vehicle and drivetrain components. Cycle set points are sent to a driver model in Simulink. According to these set points the driver generates accelerator and brake targets for the vehicle supervisory system and transmission target for the gearbox and clutch. The vehicle supervisory system interprets the accelerator and braking targets, manages the energy in the battery, the torque split between the combustion engine and the electric motor, and the drivetrain mode between Electric Vehicle (EV) and Hybrid Electric Vehicle (HEV) modes.

Energy Management System

The Energy Management System (EMS) is located in the vehicle supervisory model. First it estimates the power needed to propel the vehicle and the power needed to follow the battery state of charge requirements. With this power demand, it chooses if the vehicle must run in EV mode or HEV mode according to customizable power levels. In EV mode all the torque is provided by the electric motor. As a consequence, the vehicle supervisory system passes all the torque targets from the driver, to the electric motor. In HEV mode, the torque demand is split between the electric motor and the combustion engine, to optimize the system’s efficiency. In HEV mode the torque demand is not only the torque asked by the driver to move the vehicle, but also a torque estimated to meet battery state of charge target.

Integration in xMOD platform

The integration process in xMOD is composed of 3 main steps. First, the hybrid vehicle model is tested in a fixed step co-simulation environment, to validate the functional behavior of the platform, and ensure, in the end, the real time ability. Then, the hybrid vehicle model is split into blocks that represent the different parts of the Virtual Test Bed and Engine-in-the-Loop configurations:
Block 1: “Test automation block” that sends the cycle set points and manages the information transfer between the blocks. It emulates the automation system environment that is integrated into MORPHEE at steps 2 and 3.

  • Block 1: “Test automation block” that sends the cycle set points and manages the information transfer between the blocks. It emulates the automation system environment that is integrated into MORPHEE at steps 2 and 3.
  • Block 2: “Vehicle, Driver, Energy Management System and Drivetrain models” contains all the models, except the combustion engine model.
  • Block 3: “Combustion Engine model with its test bed environment” contains the combustion engine model, and the correct inputs and outputs that are required at steps 2 and 3.

Finally, these three “blocks” are compiled with the xMOD target, and then integrated into xMOD). At this step a Human Machine Interface (HMI), or dashboard, is also created to be able to visualize the interesting variables and to have access to the relevant parameters of the system. Once this platform is set-up, the hybrid vehicle model capability to follow the driving cycle as well as the behavior of the energy management system are validated.

Using xMOD means:

  • Using a unified representation of all heterogeneous models that is simple and complete enough for them to be integrated and co-simulated, and for the model contents to be protected.
  • Abstracting the modelling language through a virtual instrumentation, such that the models can be easily understood by people other than those who created them, or by people who do not have knowledge of the languages in which they were written.
  • Focusing on using the models (they are always built in the usual modelling environments), and providing ergonomically designed features for interacting with the simulations, running the tests and using the results.

Set-Up of the Virtual Test Bed

Once this first xMOD simulation platform of the hybrid vehicle is created, the second step, called Virtual Test Bed (VTB), becomes relevant. It consists in coupling the xMOD simulation platform, to a test-bed computer, and start the preparation and the validation of the communication protocol. One of the objectives of the VTB is also to allow test bench engineers and technicians to prepare their test procedures. The VTB must thus be able to represent, in a virtual environment, the main behaviors of the engine test bench.

Benefiting from all the work performed in step 1, the following steps are fast:

  • Integration of the full hybrid vehicle model and the EMS on the 2nd computer.
  • Extraction of the combustion engine model with the test bed environment models, and integrating them in the 3rd computer.

Another advantage of the Virtual Test Bed is the possibility to develop and validate specific test procedures, before being at the real engine test bench. For instance, a specific component to master stop and start of the engine was developed, tested and validated at this HiL step. Finally, this “3 computer configuration” of the VTB allows to validate the whole communication protocol and the engine test bench procedures, to train the engine test bench team, and ensures the consistency of the simulation results, in a real time environment.

Graphic - FEV xMOD

Parallel hybrid powertrain

Set-Up of the Final EiL Environment

Due to the previous work done in steps 1 and 2, this last step is pretty fast and does not require extra human resources compared to a classical calibration test phase: At first, the standard test bench procedure to put the previously prepared engine is done. Then the engine installation validation protocol is followed up, in order to ensure the safety of the people and the protection of the engine. Then the engine can be started and the test bench automation system proper functioning is checked: control loops, and plausibility measurements for instance. Depending on the “fresh” state or not of the engine, a run-in phase can be performed. It was not necessary to do so with the engine of this study.

Finally the simulation computer can be disconnected from the VTB and directly connected to the bench automation system (via an S-Link communication protocol). The test bench procedure can be downloaded from the MORPHEE computer of the VTB, directly from the network, and uploaded on the test bench computer.

As soon as all the devices are connected, the test bench operator has access to a library containing different drive cycles. During the test cycle, MORPHEE sends the parameters related to the driving conditions to xMOD, especially the vehicle speed, and gets back from the vehicle and its driver models, the desired operating point of the engine (engine speed / engine torque). Then MORPHEE directly controls the engine speed by driving the dyno and the engine load via a simulation of pedal signals.

During the automatized test cycle, no intervention from the test bench operator is required. The engine is started and stopped automatically and all data (bench sensors, ECU data, xMOD simulated variables and parameters) are collected in the same data file.

The operator can thus focus on calibrating the EMS, thanks to the dashboards of xMOD, or can, for instance, modify the parameters of the hybrid vehicle (mass, gearbox ratios or battery capacity for instance).
One additional advantage of using this Hybrid Tool Chain, and xMOD, is also the possibility to keep, inside the xMOD computer, an engine model at the EiL step. In that case, xMOD is able to send the simulated engine variables to
MORPHEE and these values can be compared to the measurements. It makes thus a strong and reliable monitoring of the engine and the acquisition system possible in order to detect or anticipate any malfunction by comparing physical data with the engine model outputs, and thus save a lot of time and money by stopping the test procedure, as soon as an incoherence is detected.


The Hybrid Tool Chain is an already fully efficient tool chain for hybrid powertrain development. It offers a lot of additional functionalities which are required to develop and validate Advanced Driver Assistance System (ADAS) for instance. Moreover, the versatile and open xMOD co-simulation platform can allow the integration of additional models like environment or traffic models.

Graphic - FEV xMOD

MiL configuration containing 3 main blocks