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Model portability, model fidelity and Real-Time capability are becoming critical requirements in the new era of virtual engineering. These characteristics provide the foundation to ensure continuity, reliability, and scalability both for physical and control model representations, along all the product development phases. The adoption of the digital twin model design is key to enhancing the pre-concept phase, to anticipating possible issues, to being competitive in the time-to-market especially for high performance BEV vehicles. Novel simulation methodologies have been developed in a joint effort between Maserati and Gamma Technologies with the aim of reducing computation time and of preserving model fidelity.

An increasing demand for reducing cost and time effort of the design process via improved CAE (Computer-Aided Engineer) tools and methods has characterized the automotive industry over the past two decades. One of the main challenges involves the effective simulation of a vehicle’s propulsion system dealing with different physical domains: several examples have been proposed in the literature mainly based on co-simulation approach which involves a specific tool for each propulsion system part modeling. Nevertheless, these solutions are not fully suitable and effective to perform statistical analysis including all physical parameters. In this respect, this paper presents the definition and implementation of a new simulation methodology applied to a propulsion subsystem.

Historically, electrical-equivalent modeling of battery systems has been the preferred approach of engineers when modeling hybrid and electric vehicles at the system level. This approach has provided modeling engineers good boundary conditions for batteries, with accurate terminal voltage and state of charge (SOC) calculations; however, it fails to provide insight into the electrochemical processes taking place in their Lithium-ion cells, necessary to optimize control algorithms and predict aging mechanisms within the battery. In addition, the use of predictive battery models that simulate electrochemical mechanisms empowers engineers with the ability to predict the performance of a Lithium-ion cell without requiring cells to be manufactured. If hardware is already available and tested, the use of physics-based battery models allows the simulation of the cell to be done well beyond the conditions at which the battery has been tested.

The V-Cycle is a well accepted and commonly implemented process model for systems engineering. The concept phase is represented by the upper-left portion of the V, in which very high level system simulations are the predominant modeling activity. Traveling down the V toward the vertex, sub-system level and component level simulations are employed as one enters the development phase. Finally, the test and validation phase is completed, and is represented by the right side of the V. Simulation tools have historically been used throughout some phases of the V-cycle, and with the ever increasing computing power, and the increasingly accurate and predictive simulation tools available to the engineer, today it is common that simulation is used in every phase of the cycle, from concept straight through the test and validation phases.

One promising solution for increasing vehicle fuel economy, while still maintaining long-range driving capability, is the plug-in hybrid electric vehicle (PHEV). A PHEV is a hybrid electric vehicle (HEV) whose rechargeable energy source can be recharged from an external power source, making it a combination of an electric vehicle and a traditional hybrid vehicle. A PHEV is capable of operating as an electric vehicle until the battery is almost depleted, at which point the on-board internal combustion engine turns on, and generates power to meet the vehicle demands. When the vehicle is not in use, the battery can be recharged from an external energy source, once again allowing electric driving. A series of models is presented which simulate various powertrain architectures of PHEVs. To objectively evaluate the effect of powertrain architecture on fuel economy, the models were run according to the latest test procedures and all fuel economy values were utility factor weighted.

As engines advance toward greater efficiency and lower emissions, there is increasing need for accurate real-time residual models for engine control. Both the formulation of real-time-capable models and the development of methods for measuring or estimating residuals during engine calibration have been difficult and longstanding problems. This paper describes development of a low-cost, easy-to-use tool for on-line residual estimation in all cylinders of an IC engine. The basic method, hardware required, and software structure are described. The residual estimation tool was applied to estimate residuals over the operating map in all cylinders of a six-cylinder direct-injection SI engine equipped with dual-independent phasers. The data was used to calibrate a real-time residual model integrated into the engine management system. Validation data confirming accuracy of the model are presented.

Engines and vehicle systems are becoming increasing complex partly due to the incorporation of emission abatement components as well as control strategies that are technologically evolving and innovative to keep up with emissions requirements. This makes the testing and verification with actual prototypes prohibitively expensive and time-consuming. Consequently, there is an increasing reliance on Software-In-the-Loop (SIL) and Hardware-In-the-Loop (HIL) simulations for design evaluation of system concepts. This paper introduces a methodology in which detailed chemical kinetic models of catalytic converters are transformed into fast running models for control design, calibration or real time ECU validation. The proposed methodology is based on the use of a hybrid, structured, semi-automatic scheme for reducing high-fidelity models into fast running models.

This paper presents a novel approach in real-time engine modeling. Unlike standard practices, which involve system level modeling, the presented methodology is a hybrid physical/system domain solution. Specifically, for each subsystem that the engine is divided into, a physical, map-based, or combination physical/map-based solution is chosen depending on the available computational power and the desired model detail. The resulting semi-physical engine models are suitable for real-time applications, such as Hardware-In-Loop (HiL) simulations, and, at the same time, re-usable to a large extent when model updates are required. In addition, since the proposed methodology allows for variable level of detail -from models as simple as pure map-based look-ups for torque, airflow, and exhaust temperature, all the way to models capable of predicting crank angle resolved cylinder pressure- it provides natural adjustability to the ongoing growth of computer power.

A structured, semi-automatic method for reducing a high-fidelity engine model to a fast running one has been developed. The principle of this method rests on the fact that, under certain assumptions, the computationally expensive components of the simulation can be substituted with simpler ones. Thus, the computation speed increases substantially while the physical representation of the engine is retained to a large extent. The resulting model is not only suitable for fast running simulations, but also usable and updatable in later stages of the development process. The thrust of the method is that the calibration of the fast running components is achieved by use of automatically selected neural networks. Two illustrative examples demonstrate the methodology. The results show that the methodology achieves substantial increase in computation speed and satisfactory accuracy.