Search

Homogeneous charge compression ignition or gasoline controlled auto-ignition combustion is characterized by a strong coupling of consecutive cycles, which is caused by residuals from the predecessor cycle. Closed-loop combustion control is considered a promising technology to actively stabilize the process. Model-based control algorithms require precise prediction models that are calculated in real time. In this article, a new approach for the transient measurement of the auto-ignition process and the data-driven modeling of combustion phasing and load is presented. Gasoline controlled auto-ignition combustion is modeled as an autoregressive process to represent the cycle-to-cycle coupling effects. The process order was estimated by partial autocorrelation analysis of steady-state operation measurements. No significant correlations are found for lags that are greater than one. This observation is consistent with the assumption that cycle coupling is mainly caused by the amount of exhaust gas that is directly transferred to the consecutive combustion. Because steady-state operation results in a hard coupling of actuation and feedback variables, only minor variations of the test data can be achieved. The steady-state tests delivered insufficient data for the generalized modeling of the transient autoregressive effects. A new transient testing and measurement approach is required, which maximizes the variation of the predecessor cycle’s characteristics. Dynamic measurements were performed with the individual actuation of the injection strategy for each combustion cycle. A polynomial model is proposed to predict the combustion phasing and load. The regression analysis shows no overfitting for higher polynomial orders; nevertheless, a first-order polynomial was selected because of the good extrapolation capabilities of extreme outliers. The prediction algorithm was implemented in MATLAB/Simulink and transferred to a real-time-capable engine control unit. The verification of the approach was performed by test bench measurements in dynamic operation. The combustion phasing was precisely predicted using the autoregressive model. The combustion phasing prediction error could be reduced by 53% in comparison to a state-of-the-art mean value-based prediction. This work provides the basis for the development of a closed-loop autoregressive model-based control for gasoline controlled auto-ignition combustion. [ABSTRACT FROM AUTHOR]

Innovative low-temperature combustion modes for internal combustion engines, such as gasoline-controlled auto-ignition, impose very high requirements on the process control. On one hand, fast reference tracking for the engine load and the combustion phasing is needed, while at the same time, numerous disturbances acting on the highly sensitive process have to be rejected in order to guarantee stable operation at a wide operating range. Model-based predictive control concepts have a great potential to fulfill these requirements. In this contribution, a model-based predictive control consisting of a stationary and dynamic optimization stage is introduced. It is able to account for the characteristic cycleto- cycle dynamics which occur in gasoline-controlled auto-ignition and also handle constraints imposed on the manipulated and controlled variables of the process. [ABSTRACT FROM AUTHOR]

Gasoline controlled auto-ignition combustion offers high potential for CO2 emission reduction, but faces challenges regarding combustion stability and high sensitivity to changing boundary conditions. Combustion chamber recirculation allows a wide operation range, but results in a strong coupling of consecutive cycles due to residuals that are transferred to the subsequent combustion cycle. The cycle coupling leads to phases of unstable operation with reduced efficiency and increased emission levels. State-of-the-art control algorithms use data-driven models of gasoline controlled autoignition combustion to achieve cycle-to-cycle control of the process or use offline calibration and optimization. A closed-loop control is proposed and implemented on a rapid control prototyping engine control unit. The control algorithm continuously calculates the current residual fuel in the combustion chamber. The heat release is observed and compared with the theoretical heat release of the injected fuel mass. The rate of unburned fuel mass transferred to the subsequent cycle is calculated offline by a detailed gas exchange model. Based on this information, the control algorithm adapts the injected fuel quantity for each cycle individually using an inverse injector model. In this article, a concept for decoupling consecutive cycles is presented to reduce the deviations of the indicated mean effective pressure and thus the heat release. Unstable sequences are analyzed in the time domain, and unburned residuals are identified as a strong correlating factor for consecutive cycles. Using real-time cylinder pressure analysis based on a field programmable gate array enables the online calculation of unburned residual fuel. Based on this calculation, the injection of each cycle can be adapted individually to decouple consecutive cycles and avoid unstable operation. The results of the control algorithm and the stabilization of the gasoline controlled auto-ignition combustion are validated using a single-cylinder research engine and compared to steady-state operation. [ABSTRACT FROM AUTHOR]

The increasing requirements to further reduce pollutant emissions, particularly with regard to the upcoming Euro 7 (EU7) legislation, cause further technical and economic challenges for the development of internal combustion engines. All the emission reduction technologies lead to an increasing complexity not only of the hardware, but also of the control functions to be deployed in engine control units (ECUs). Virtualization has become a necessity in the development process in order to be able to handle the increasing complexity. The virtual development and calibration of ECUs using hardware-in-the-loop (HiL) systems with accurate engine models is an effective method to achieve cost and quality targets. In particular, the selection of the best-practice engine model to fulfil accuracy and time targets is essential to success. In this context, this paper presents a physically- and chemically-based stochastic reactor model (SRM) with tabulated chemistry for the prediction of engine raw emissions for real-time (RT) applications. First, an efficient approach for a time-optimal parametrization of the models in steady-state conditions is developed. The co-simulation of both engine model domains is then established via a functional mock-up interface (FMI) and deployed to a simulation platform. Finally, the proposed RT platform demonstrates its prediction and extrapolation capabilities in transient driving scenarios. A comparative evaluation with engine test dynamometer and vehicle measurement data from worldwide harmonized light vehicles test cycle (WLTC) and real driving emissions (RDE) tests depicts the accuracy of the platform in terms of fuel consumption (within 4% deviation in the WLTC cycle) as well as NOx and soot emissions (both within 20%). [ABSTRACT FROM AUTHOR]

The power density of electric machines for future battery electric vehicles must be further increased to improve customer benefits. To this end, this paper compares two state-of-the art electrical traction machines and evaluates the potential for increasing the power density using a third, novel high-speed machine design. The analysis is performed using an electromagnetic finite element analysis, a thermal network with lumped parameters, and a coupled electromagnetic–thermal simulation. The simulations of the three machines evaluate the potential for increasing the power density and overload margins, as well as reducing material consumption. With regard to the active thermal control, the new design aims for reduced thermal capacities and increased loss density to optimize the thermal controllablity and overall performance. The thermal active control is analyzed in thermal transient simulations and electromagnetic simulations with different magnet temperatures. The results show that higher magnet temperatures benefit efficiency and reduce losses for low torque at high speeds. However, a colder magnet is needed for maximum torque at base speed. A maximum loss reduction of 24% is achieved with a 100 °C magnet-temperature difference at maximum speed and low torque. [ABSTRACT FROM AUTHOR]

In this paper, a deep neural network (DNN)-based nonlinear model predictive controller (NMPC) is demonstrated using real-time experimental implementation. First, the emissions and performance of a 4.5-liter 4-cylinder Cummins diesel engine are modeled using a DNN model with seven hidden layers and 24,148 learnable parameters created by stacking six Fully Connected layers with one long-short term memory (LSTM) layer. This model is then implemented as the plant model in an NMPC. For real-time implementation of the LSTM-NMPC, an open-source package acados with the quadratic programming solver HPIPM (High-Performance Interior-Point Method) is employed. This helps LSTM-NMPC run in real time with an average turnaround time of 62.3 milliseconds. For real-time controller prototyping, a dSPACE MicroAutoBox II rapid prototyping system is used. A Field-Programmable Gate Array is employed to calculate the in-cylinder pressure-based combustion metrics online in real time. The developed controller was tested for both step and smooth load reference changes, which showed accurate tracking performance while enforcing all input and output constraints. To assess the robustness of the controller to data outside the training region, the engine speed is varied from 1200 rpm to 1800 rpm. The experimental results illustrate accurate tracking and disturbance rejection for the out-of-training data region. At 5 bar indicated mean effective pressure and a speed of 1200 rpm, the comparison between the Cummins production controller and the proposed LSTM-NMPC showed a 7.9% fuel consumption reduction, while also decreasing both nitrogen oxides ( NO x ) and Particle Matter (PM) by up to 18.9% and 40.8%. [ABSTRACT FROM AUTHOR]

In this work, different analytical methods for calculating the mechanical stresses in the rotors of permanent magnet machines are presented. The focus is on interior permanent magnet machines. First, an overview of eight different methods from the literature is given. Specific differences are pointed out, and a brief summary of the analytical approach for each method is provided. For reference purposes, a finite element model is created and simulated for each rotor geometry studied. A total of seven rotors rom representative automotive powertrains are considered in their specific speed range. The analytical methods are used to determine the maximum mechanical stress concentration factors for the seven rotor geometries, in which we are determined to find maximum mechanical stress as a final step of the analytical process. For each geometry and each respective operating speed range, the deviations from the finite element reference are determined. In addition, the error in the selected geometry variations is evaluated. A recommendation for the method with the lowest error considering all cases studied is given specifically for the stress in the airgap bridge and the central bridge. [ABSTRACT FROM AUTHOR]

• Measurement algorithm for Homogeneous Charge Compression Ignition has been developed. • Wider database for Homogeneous Charge Compression Ignition is generated. • Homogeneous Charge Compression Ignition model quality is improved using this database. • Models are able to predict misfires under certain conditions. Homogeneous Charge Compression Ignition offers a great potential for increasing the efficiency of combustion engines while simultaneously reducing nitrogen oxide raw emissions. However, the broad application has not yet been realized for production engines, mainly owing to low combustion stability at the edges of the operating range and high sensitivity to changing boundary conditions. Owing to strong cycle-to-cycle coupling by negative valve overlap, the cylinder state of the last combustion has an enormous influence on the subsequent combustion. Therefore, the condition of the previous combustion must be taken into account to control the following combustion event. For this purpose, the interactions between feedback variables and cycle individual control interventions need to be measured in a wide operating range. Against this backdrop, a new measurement methodology is presented in this article, which sets up the transient limitations for Homogeneous Charge Compression Ignition combustion, while maintaining the limits of stability, maximum pressure gradient, and other factors automatically. Hence, an algorithm has been developed that sets the manipulated variables on a cyclic basis, dependent on the previous cycle in several dimensions. The new algorithm was then used to gain dynamic measurement data that were used to train artificial neural networks. It is demonstrated that the models are able to predict misfires under certain conditions. Additionally, a feasibility study regarding the usability of the newly gained models was performed based on a data-driven control algorithm, which was carried out and validated on a single-cylinder test engine. [ABSTRACT FROM AUTHOR]

The effect of H 2 O injection on the combustion process of iso -octane was investigated with the aim to better understand the suitability of water addition as a potential engine control parameter for homogeneous-charge compression ignition (HCCI) combustion. Several experiments were combined including premixed low-pressure flames, a jet-stirred reactor (JSR) and a plug-flow reactor (PFR), both at atmospheric pressure, and a single-cylinder research engine (SCRE) operated with either iso -octane or RON 98 gasoline. The thermal effect of H 2 O addition was determined in laminar premixed iso -octane/O 2 /Ar flames (equivalence ratio Φ=1.4, 40 mbar) with H 2 O mole fractions of 0 to 0.22, where water addition reduced the temperature measured by laser-induced fluorescence (LIF) by up to 110 K. Speciation data were obtained from these flames as well as in the JSR (Φ=0.65, 933 mbar) and PFR experiments (Φ=0.65, 970 mbar) with and without H 2 O addition in the low- to intermediate temperature regime from 700–1100 K. The chemical analysis in these flame and reactor experiments was performed using molecular-beam mass spectrometry (MBMS) employing either electron ionization (EI) in the PFR and premixed flame or single-photon ionization (PI) by tunable vacuum-ultraviolet radiation in the JSR. The effects on species mole fractions were small which is supported by predictions from chemical-kinetic simulations. Quantitative speciation data of the exhaust gas of the SCRE were obtained by using Fourier-transform infrared (FTIR) spectroscopy. A very similar species pool was detected in the laboratory-scale experiments and for the engine operation. It is thus assumed that these results could assist in guiding both the improvement of fundamental chemical-kinetic as well as HCCI engine control models. [ABSTRACT FROM AUTHOR]

• New architecture for modeling homogenous charge compression ignition. • Heat release in intermediate compression as correlation for combustion phasing. • Closed-loop in-cycle control using direct water injection. • Combustion stabilization in terms of combustion phasing and load. Homogeneous charge compression ignition offers a high potential for the reduction of CO 2 and NO x raw emissions; however, its use entails problems that are associated with low combustion stability, especially at the limits of the operating range. The recirculation of exhaust gases inside the combustion chamber by using a negative valve overlap leads to a strong coupling of consecutive cycles. The cyclic coupling induces phases of unstable operation after the occurrence of stochastic outlier cycles with misfire or incomplete combustion. These unstable phases are marked by reduced efficiency and increased emissions. Two in-cycle closed-loop control algorithms, which focus on the heat release in the intermediate compression, are presented in this article. To control the combustion process, direct water injection is used to ensure a direct influence on the temperature level in the combustion chamber; subsequently this influences combustion phasing. The decoupling of consecutive cycles serves to reduce deviations in the indicated mean effective pressure and crank angle position of 50% mass fraction burned. To develop a suitable controller, a first-order autoregressive model of homogeneous charge compression ignition combustion is split into intermediate compression and main combustion phases. Moreover, unstable sequences are analyzed in the time domain to identify appropriate in-cycle control concepts. The control concepts are developed based on the heat release in the intermediate compression as a strong correlation factor for consecutive cycles. To realize fast control interventions, a real-time cylinder pressure analysis as well as the control algorithms are implemented on a field-programmable gate array. The control algorithms are validated on a single-cylinder research engine and compared with conventional operation without in-cycle control. Results show a significant increase in the stability of combustion phasing and load by means of in-cycle control. [ABSTRACT FROM AUTHOR]

Range extender modules are one option to compensate for short drive ranges of electric vehicles. The close interaction of combustion engine and generator poses new challenges in development. A key requirement for range extender systems is to be light and virtually imperceptible in operation. High-speed electrical machines aim at increasing power density. However, their introduction in a range extender requires a gearbox. The combustion engine torque fluctuations can lead to rattle in the gearbox. The rattle can be overcome by a dual mass flywheel. An interdisciplinary model is developed and used to analyse three different range extender systems: one with a low speed generator without gearbox, one with a high-speed generator, and one with a high-speed generator and a dual mass flywheel. The efficiency was found to be higher for the system with a low speed generator, whereas the power density and the costs are beneficial for the high-speed concept. A dual mass flywheel eliminates the changes of torque direction in the gearbox. It reduces the speed fluctuations of the gearbox and generator by over 90 % compared to the low speed setup. But it increases rolling moment and subsequently chassis excitation compared to a setup with only a gearbox. [ABSTRACT FROM AUTHOR]

In this paper, a Field Programmable Gate Array (FPGA) was used to implement a real-time cylinder pressure analysis. The goal of the project was to improve the accuracy of calculated heat release and center of combustion calculations to enhance the precision of engine control functions. Compared to today’s real-time pressure analysis systems, several additional physical effects were taken into account for this objective. The wall heat transfer was calculated based on the approach published by Hohenberg. A chemical equilibrium with six substances was assumed for the mixture composition and a real-time calculation method was developed. Furthermore, a two-zone model was adapted and implemented for this realtime analysis. The validation of the results and the rating of the improvement in precision were based on GT-SUITE simulation results as an offline reference tool. Compared to state-of-the-art analysis systems, it was possible to reduce the average error of the center of combustion position from 1.6° to 0.5° crank angle (CA) by taking the investigated effects into account. Moreover, it was possible to significantly reduce the time required for the calculation from one complete combustion cycle to 0.2°CA at an engine speed of 3,000 rpm by using a continuous calculation method on the FPGA. This led to an additional improvement of the ability to control the engine, especially under highly dynamic operation conditions. [ABSTRACT FROM AUTHOR]

Electric vehicle sales have accelerated in recent years due to a wider customer acceptance. However, the still limited driving range continues to be a barrier to purchase for many customers. To improve the driving range, it is particularly important to further reduce all losses in the powertrain. This applies in particular to the temperature-dependent motor and inverter losses. In this context, this paper presents a high-fidelity motor model based on MotorCAD and ANSYS Maxwell, which is thermally controlled using an economic Model Predictive Control (MPC) approach to reduce the temperature dependent losses. A detailed explanation of the high-fidelity motor model is given, followed by a system-level validation including the thermal system model. The setup is used to simulate three different cycles, namely a highway drive, a rural road drive, and a long urban drive. A comparison between the MPC, which actively controls the rotor, winding and the inverter junction temperature, and a rule-based strategy is used to analyse the motor-level losses in detail. While the total MPC savings system level are up to 2.86% at 35 °C ambient temperature, 0.82% of this is saved due to increased temperatures of the motor caused by reduced cooling while driving first highway and then into the city. For a pure highway cycle the motor savings increase up to 1.12%. One major outcome, which is enabled by the detailed modelling approach, is that the majority of those motor savings are originating from reduced ac-copper losses at elevated temperatures. The reason was found to be a lower magnet flux density and a higher winding resistance which both lead to less eddy current losses in the winding. Moreover, the inverter losses were reduced by cooling the inverter prior to acceleration from standstill. By using the NTC behaviour of the IGBTs in low current region, temperature-dependent savings of up to 0.56% were achieved. [Display omitted] • Utilizing temperature dependent efficiencies of motor and inverter by economic MPC. • Detailed modelling of motor losses to explain thermal effects on loss shares. • Up to 2.86% system efficiency increase for a mixed highway & city drive. • Motor efficiency increase – up to 1.12% – mainly by reduced ac-copper losses. • In city drives 0.56% lower power consumption by temperature dependence of inverter. [ABSTRACT FROM AUTHOR]

The conventional automotive development process for embedded systems today is still time- and data-inefficient, and requires highly experienced software developers and calibration engineers. Consequently, it is cost-intensive and at the same time prone to sub-optimal solutions. Reinforcement Learning offers a promising approach to address these challenges. The evolved agents have proven their ability to master complex control tasks in a close-to-optimal manner without any human intervention, but the training procedures are hardly compatible with current development processes. As a result, Reinforcement Learning has rarely been used in powertrain development until now. This work describes an integration of Reinforcement Learning in the embedded system development process to automatically train and deploy agents in transient driving cycles. Using the example of exhaust gas re-circulation control for a Diesel engine, an agent is successfully trained in a fully virtualized environment, achieving emission reductions of up to 10 % in comparison to a state-of-the-art controller. Further investigations are carried out to quantify the impact of the driving cycle and ambient conditions on the agent's performance. To demonstrate the transferability between different levels of virtualization, the experienced agent is then tested in closed-loop with a real hardware controller to operate the physical actuator. By confirming the reproducibility of the learned strategy on real hardware, this article serves as proof-of-concept for a sustainable, Reinforcement Learning based path to automatically develop embedded controllers for complex control problems. • A framework for RL-based development of embedded control functions is presented. • The method is applied to the multi-objective task of EGR control for a Diesel engine. • The automatically generated control function reduced pollutant emission by up to 10%. • Real-time performance of the function is verified on a Hardware-in-the-Loop platform. [ABSTRACT FROM AUTHOR]

Machine learning (ML) and a nonlinear model predictive controller (NMPC) are used in this paper to minimize the emissions and fuel consumption of a compression ignition engine. In this work machine learning is applied in two methods. In the first application, ML is used to identify a model for implementation in model predictive control optimization problems. In the second application, ML is used as a replacement of the NMPC where the ML controller learns the optimal control action by imitating or mimicking the behavior of the model predictive controller. In this study, a deep recurrent neural network including long–short term memory (LSTM) layers are used to model the emissions and performance of an industrial 4.5 liter 4-cylinder Cummins diesel engine. This model is then used for model predictive controller implementation. Then, a deep learning scheme is deployed to clone the behavior of the developed controller. In the LSTM integration, a novel scheme is used by augmenting hidden and cell states of the network in an NMPC optimization problem. The developed LSTM-NMPC and the imitative NMPC are compared with the Cummins calibrated Engine Control Unit (ECU) model in an experimentally validated engine simulation platform. Results show a significant reduction in Nitrogen Oxides (NO x) emissions and a slight decrease in the injected fuel quantity while maintaining the same load. In addition, the imitative NMPC has a similar performance as the NMPC but with a two orders of magnitude reduction of the computation time. [Display omitted] [ABSTRACT FROM AUTHOR]

To improve the efficiency and lifetime of proton exchange membrane fuel cells, a deeper understanding of the physical and chemical factors affecting the water transport within the fuel cell is required. A factor that is often neglected in corresponding investigations is the polarization of the liquid water molecules in the unevenly distributed electric field. Therefore, in this paper, the effect of polarization on the transport of liquid water in the g as d iffusion l ayers (GDL) is studied. Thereto, numerical simulations are conducted using the structural and physical properties of TORAY TGP-H-060 carbon paper GDL. The simulation findings indicate that the polarization effect has no substantial impact on the shape of liquid water droplets in the GDL. Nonetheless, polarization significantly lowers the internal pressure of the water droplets needed to penetrate the GDL. [Display omitted] • First study on the effect of inhomogeneous electric field in GDL on water transport. • Both, a 3-D and a 2-D digitally stochastic model of GDL is developed. • Liquid water penetration through GDL is simulated using volume of fluid method. • Polarization force applied on liquid water is embedded into Navier-Stokes equation. [ABSTRACT FROM AUTHOR]

• We demonstrate the use of reinforcement learning for eco-driving strategies. • Only minimal data on the traffic situation are provided to the agent. • No explicit prediction of the traffic situation is required. • The energy saving potential was determined to be up to 11% compared with a green light optimal speed advice system. Urban settings are challenging environments to implement eco-driving strategies for automated vehicles. It is often assumed that sufficient information on the preceding vehicle pulk is available to accurately predict the traffic situation. Because vehicle-to-vehicle communication was introduced only recently, this assumption will not be valid until a sufficiently high penetration of the vehicle fleet has been reached. Thus, in the present study, we employed Reinforcement Learning (RL) to develop eco-driving strategies for cases where little data on the traffic situation are available. An A-segment electric vehicle was simulated using detailed efficiency models to accurately determine its energy-saving potential. A probabilistic traffic environment featuring signalized urban roads and multiple preceding vehicles was integrated into the simulation model. Only information on the traffic light timing and minimal sensor data were provided to the control algorithm. A twin-delayed deep deterministic policy gradient (TD3) agent was implemented and trained to control the vehicle efficiently and safely in this environment. Energy savings of up to 19% compared with a simulated human driver and up to 11% compared with a fine-tuned Green Light Optimal Speed Advice (GLOSA) algorithm were determined in a probabilistic traffic scenario reflecting real-world conditions. Overall, the RL agents showed a better travel time and energy consumption trade-off than the GLOSA reference. [ABSTRACT FROM AUTHOR]

• Dynamic measurements to form a database for artificial neural network training. • Comprehensive in-cylinder information integrating ion current and pressure sensing. • Evidence that the ion current contains information beyond the in-cylinder pressure. • Improvement of model prediction accuracy in terms of combustion phasing and load. • Demonstration of potential using both signals in parallel for control applications. Homogeneous charge compression ignition is a promising low-temperature combustion mode because of its high efficiency and low emissions; however, its strong cycle-to-cycle coupling effect, which caused by the recirculation of exhaust gases, may entail problems with low combustion stability. In this study, a new concept that extracts more comprehensive combustion information in homogeneous charge compression ignition is proposed through the integration of ion current and in-cylinder pressure sensing. To analyze the correlations of combustion parameters and their relationships with the ion current parameters, steady-state measurements were conducted. Dynamic measurements were implemented to form a comprehensive database for artificial neural network training. To investigate the hypothesis that the ion current gives additional information beyond the pressure trace, black-box models based on experimental data are trained. The results show that the baseline model trained purely with the manipulated variables has the worst performance, while the model including both in-cylinder pressure and ion current derived parameters has the best predictability, with the overall root-mean-square error reduced by 2.5% in predicting combustion phasing, compared with in-cylinder pressure based model. It demonstrates that a significant improvement in model quality can be achieved by the combination of ion current and in-cylinder pressure sensing, which indicates that the ion current signal contains information that goes beyond a sole analysis of the pressure trace. By complementing the in-cylinder pressure, the use of the ion current as a "chemical sensor" for low-temperature combustion thus appears very promising for the stable control of homogeneous charge compression ignition combustion. [ABSTRACT FROM AUTHOR]

• Novel simulation framework for collaborative embedded systems in vehicles. • Multiple controlled vehicles and systems under test in one closed-loop simulation. • Generic model architecture for a broad spectrum of collaborative driving use cases. • Simultaneous sensor, ad-hoc network and high fidelity kinematics simulation. • Demonstrated applicability for model-in-the-loop testing of vehicle platooning. The continuous enhancement of X-in-the-Loop (XiL) simulation methods is one key to efficiently test advanced vehicle control functions and ensure high software quality under a rising cost and time pressure. Systems, which control vehicles based on sensor perception can already be evaluated in XiL simulations. However, future vehicles will not exclusively represent stand-alone systems anymore, but involve Cooperative Intelligent Transport Systems (C-ITS), distributed among different traffic participants. While several simulation environments for C-ITS feasibility studies are available, common virtual test frameworks cannot incorporate multiple interacting and communicating road users with the required fidelity to secure C-ITS software, e.g. for platooning applications. Within the scope of this publication, a method for virtual testing and calibration of in-vehicle C-ITS using a high fidelity vehicle platoon model is presented. Multiple closed-loop controlled vehicles and the mutual interferences between them are simulated in a common 3D environment alongside other traffic. Each high fidelity vehicle model individually simulates the longitudinal and lateral response, the generation of sensor data plus the exchange of vehicle-to-vehicle data. As a proof of concept, a model-in-the-loop simulation for a Cooperative Adaptive Cruise Control (CACC) was conducted. The simulation environment included five high fidelity vehicle models forming a platoon, coupled with CACC controller models under test. The results reveal that the developed simulation environment captures the interaction within collaborative vehicle groups while modeling individual vehicles as controlled systems in the same tool. At the same time the method satisfies all requirements for XiL testing. Functional tests of multiple interacting vehicle controllers have been successfully carried out. In the performed feasibility study simulation-based tests uncovered an insufficient control stability of the CACC. Upcoming series developments and homologations of in-vehicle C-ITS software and hardware can be supported by the new virtual test environment to overcome connectivity, reproducibility and cost efficiency limitations of physical test environments. Next steps include proving the method in use for series development and extending it to cover vehicle-to-infrastructure use cases. [ABSTRACT FROM AUTHOR]