Fundamental knowledge of the oil flow in a disengaged wet clutch is essential for optimizing the cooling performance and the drag losses. However, no fundamental information on the oil flow and drag torque generation is available for dip-lubricated wet clutches. Therefore, the oil flow and drag torque generation in the sub-millimeter gap of a dip-lubricated wet clutch was experimentally investigated for three practically relevant oil levels. To enable optical access to the gap, transparent components were used. Further, a high-speed camera was used to capture the oil flow in the gap and grooving. Independent of the set oil level, the gap is oil-filled at low differential speeds, resulting in a single-phase flow. The drag torque increases approximately linearly with increasing differential speed due to the fluid shearing. In certain regions of the waffle grooving, air bubbles form locally. The air bubbles preferably occur in the grooves oriented in the radial direction, while the grooves oriented in the peripheral direction are filled with oil. Above a certain differential speed, the oil is continuously displaced from the gap, starting from the inside, due to the increasing centrifugal force. Consequently, the drag torque increases in a degressive manner until a maximum value is finally reached. The ongoing displacement of oil from the gap eventually results in a decrease in the drag torque. A steady drag torque is generated only when the oil is almost entirely displaced from the gap. Since the oil displacement from the gap already commences at a low differential speed, the cooling performance is limited for dip-lubricated wet clutches. The continuous displacement of oil from the gap can be held up, among other things, by increasing the oil level.

In wet clutches, load-independent drag losses occur in the disengaged state and under differential speed due to fluid shearing. The drag torque of a wet clutch can be determined accurately and reliably by means of costly and time-consuming measurements. As an alternative, the drag losses can already be precisely calculated in the early development phase using computing-intensive CFD models. In contrast, simple analytical calculation models allow a rough but non-time-consuming estimation. Therefore, the aim of this study was to develop a methodology that can be used to build a data-driven model for the prediction of the drag losses of wet clutches with low computational effort and, at the same time, sufficient accuracy under consideration of a high number of influencing parameters. For building the model, we use supervised machine learning algorithms. The methodology covers all relevant steps, from data generation to the validated prediction model as well as its usage. The methodology comprises six main steps. In Step 1, the data is generated on a suitable test rig. In Step 2, characteristic values of each measurement are evaluated to quantify the drag loss behavior. The characteristic values serve as target values to train the model. In Step 3, the structure and quality of the dataset are analyzed and, subsequently, the model input parameters are defined. In Step 4, the relationships between the investigated influencing parameters (model input) and the characteristic values (model output) are determined. Symbolic regression and Gaussian process regression have both been proven to be suitable for this task. Lastly, the model is used in Step 5 to predict the characteristic values. Based on the predictions, the drag torque can be predicted as a function of differential speed in Step 6, using an approximation function. The model allows a user-oriented prediction of the drag torque even for a high number of parameters with low computational effort and sufficient accuracy at the same time.

Fundamental knowledge of wet clutches’ drag loss behavior is essential for designing low-loss clutch systems. In contrast to the widely investigated injection lubrication, more comprehensive knowledge is needed on the drag loss behavior of dip-lubricated wet clutches. In the development phase, data-driven models allow drag loss predictions with low computational effort and, at the same time, sufficient accuracy. Therefore, this study aimed to deepen and expand knowledge of the drag loss behavior of dip-lubricated wet clutches based on experimental investigations. Moreover, the investigations were designed and conducted so that the generated data and findings can be used in further research for building data-driven prediction models. The investigations were conducted on two clutch systems from automotive and industrial applications. The practice-relevant parameters of clearance, oil level, oil viscosity, and plate shape were investigated based on a mixed-level full factorial design. The evaluation shows that a reduction in drag loss can be achieved primarily by increasing the clearance, reducing the oil viscosity, and choosing waved plates. The obtained drag loss behavior can be traced back to the form of oil displacement from the gaps. The displacement process, in turn, is influenced by the operating and geometry parameters. Although the flow in the gaps develops differently for dip and injection lubrication over differential speed, the study shows comparable integral effects of the influencing parameters for both types of lubrication. The generated datasets contain the investigated parameters as features and characteristic drag loss values as targets. The findings can support the selection and configuration of the machine learning algorithm and the validation of the trained models. The described procedure can serve as a template for generating and analyzing datasets for data-driven modeling of wet clutches’ drag losses.

Wet brakes and clutches are crucial drivetrain components widely used in many sectors. However, their drag losses can account for a considerable share of the overall drivetrain losses. Therefore, investigation of the drag loss behavior of wet brakes and clutches has been the subject of research since the 1970s. This paper aims to provide a comprehensive overview of existing knowledge of drag loss behavior and identify future research directions. To this end, a scoping review was conducted according to the PRISMA guideline. It was found that research mainly, but not only, focused on gaining a fundamental understanding of the drag loss generation process, investigating the integral drag loss behavior, analyzing the flow in the sub-millimeter gaps, and modeling and calculating the drag losses. The review summarizes the state of the art and may support researchers and engineers in developing low-loss wet brake and clutch systems. Several research gaps were identified and are provided.

For wet disk clutches, the energy input is strongly influenced by its friction behavior. However, the friction behavior cannot be simulated and therefore is mostly derived from experimental data for specific clutch systems. This paper presents a new approach for the identification and validation of linear friction models using analysis of variance (ANOVA) and stepwise regression. Therefore, we use experimental data of three different friction systems with paper- and carbon-based friction lining. The designed experiments support an efficient parameter-based analysis of the friction behavior. The obtained models can be used as an input for thermal simulations, for example, but can also support a better understanding of the main influencing factors and are applicable to various friction systems. For validation, the obtained models are applied to measured data. A good correspondence between the simulated and measured friction behavior can be shown for speeds in the investigated operating range. The presented procedure can be easily adapted, for different factors and operation modes, as well as other applications.

High-speed electric drive units promise improved power density and, theoretically, driving range of battery electric vehicles. An essential step of the development process is extensive testing of the drive unit on a test rig. In particular, at a high rotational speed level, experimental testing can be challenging. This paper describes a test rig for investigating the overall function of a high-speed drive unit and the transmission’s efficiency and dynamics. The high-speed drive unit developed in the Speed4E research project was the reference drive unit. The test rig is based on the concept of electrical power circulation. Thus, the test rig can be used universally for different drive unit designs and operating modes. A reaction torque measurement unit was developed to enable measurements at high rotational speeds. Simultaneously, this unit allows robust measurements at low costs. The expected measurement uncertainties of torque, rotational speed, transmission efficiency, and power losses were calculated using the Monte Carlo method. The results demonstrate that the developed torque measurement unit combines precise torque measurement with a robust design and low costs, making it competitive with state-of-the-art solutions for torque measurement at high speeds.