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%).

Advances in artificial intelligence (AI) have led to its application in many areas of everyday life. In the context of control engineering, reinforcement learning (RL) represents a particularly promising approach as it is centred around the idea of allowing an agent to freely interact with its environment to find an optimal strategy. One of the challenges professionals face when training and deploying RL agents is that the latter often have to run on dedicated embedded devices. This could be to integrate them into an existing toolchain or to satisfy certain performance criteria like real-time constraints. Conventional RL libraries, however, cannot be easily utilised in conjunction with that kind of hardware. In this paper, we present a framework named LExCI, the Learning and Experiencing Cycle Interface, which bridges this gap and provides end-users with a free and open-source tool for training agents on embedded systems using the open-source library RLlib. Its operability is demonstrated with two state-of-the-art RL-algorithms and a rapid control prototyping system.

In hybrid vehicle development, the operating strategy has a decisive role in meeting the development goals, such as compliance with emission standards and high energy efficiency. A considerable number of interactions and cross-influences on other topics, such as emissions, on-board diagnostics, or drivability, must be considered during the calibration process. In this context, the given time constraints pose further challenges. To overcome these, approaches for virtualization of the calibration process are an effective measure. For this purpose, in the current study, a real engine control unit is embedded into a virtual simulation environment on so-called hardware-in-the-loop (HiL) testbenches, which allow virtual calibration and validation of the complete target vehicle. In this context, the paper presents a novel method for virtual calibration of operating strategies for hybrid-electric propulsion systems. This includes an innovative multi-criterial approach that considers the requirements of several development tasks, such as emission and OBD calibration. Measurement data for this optimization is generated on a HiL testbench setup tailored for the described methodology, including both the electrical setup and the simulation environment. To validate the selection of modeling approaches and the parametrization, the simulation environment is operated in open loop. The results of the open loop validation show promising behavior regarding the proposed use case. Finally, the presented methodology is evaluated regarding time and cost savings compared to a conventional approach.

The complexity and shortening of development cycles in the automotive industry, particularly with the rise in hybrid electric vehicle sales, increases the need for efficient calibration and testing methods. Virtualization using hardware-in-the-loop testbenches has the potential to counteract these trends, specifically for the calibration of hybrid operating strategies. This paper presents a dynamic closed-loop validation of a hardware-in-the-loop testbench designed for the virtual calibration of hybrid operating strategies for a plug-in hybrid electric vehicle. Requirements regarding the hardware-in-the-loop testbench accuracy are defined based on the investigated use case. From this, a dedicated hardware-in-the-loop testbench setup is derived, including an electrical setup as well as a plant simulation model. The model is then operated in a closed loop with a series production hybrid control unit. The closed-loop validation results demonstrate that the chassis simulation reproduces driving resistance closely aligning with the reference data. The driver model follows target speed profiles within acceptable limits, achieving an R² = 0.9993, comparable to the R² reached by trained human drivers. The transmission model replicates the gear ratios, maintaining rotational speed deviations below 30 min⁻¹. Furthermore, the shift strategy is implemented in a virtual control unit, resulting in a gear selection comparable to reference measurements. The energy flow simulation in the complete powertrain achieves high accuracy. Deviations in the high-voltage battery state of charge remain below 50 Wh in a WLTC charge-sustaining drive cycle and are thus within the acceptable error margin. The net energy change criterion is satisfied with the hardware-in-the-loop testbench, achieving a net energy change of 0.202%, closely matching the reference measurement of 0.159%. Maximum deviations in cumulative high-voltage battery energy are proven to be below 10% in both the charging and discharging directions. Fuel consumption and CO₂ emissions are modeled with deviations below 3%, validating the simulation’s representation of vehicle efficiency. Real-time capability is achieved under all investigated operating conditions and test scenarios. The testbench achieves a real-time factor of at least 1.104, ensuring execution within the hard real-time criterion. In conclusion, the closed-loop validation confirms that the developed hardware-in-the-loop testbench satisfies all predefined requirements, accurately simulating the behavior of the reference vehicle.