Conventional gear units are installed in almost all vehicles manufactured and developed these days in particular Battery Electric Vehicles (BEV). Despite the already very high sophisticated development of gearboxes, the focus on optimizing mass and efficiency is still important in development. Especially for BEV the efficiency becomes very important. A remarkable feature of a high efficient transmission of a BEV is the low mass and resulting reduced thermal masses. An additional significant challenge is posed by generally higher engine speed of BEVs, which leads to an increase of heat influx in the gearboxes. Overall, the thermal stability of the transmission units decreases, thus the importance of the temperature monitoring is increased. Oftentimes a model is needed for this, especially for topics such as thermal design with regard to temperature distribution or cooling effects on the surface. Under certain conditions having such a model on a test bench facilitates the avoidance of setting a temperature measuring point. In the scope of this work, a potential solution is shown to infer the inner temperature of the gearbox by using measurable temperature on the outer surface. The surface temperatures of the gearbox can be measured on a test bench by sticking sensors onto it. Furthermore using thermal imaging cameras or other methods based on infrared enable contact free measurements. In order to be able to deduce the inner temperature prevailing in the gearbox from these easily determined surface temperatures, a well-known and frequently used thermal model is created, which is based on the simple foundation of thermal networks. For this concrete application, a thermal network with a small number of thermal nodes and therefore low complexity is created. One node represents the point to be observed inside the gearbox, the others are either a heat sink or heat source or a point where the temperature is known or measured. Even these relatively simple models reveal a large number of parameters. These need to be determined, because each thermal source or sink as well as each transition needs to be parameterized. Determining these parameters demands detailed knowledge of the particular transmission unit. To avoid this, a method from the field of machine learning is applied. For this purpose, the gearbox to be modelled or an identical gearbox is analyzed on a test bench. Both, external and internal temperatures are recorded. In addition, data such as speed and power, which are usually available anyway, are required. Based on a sufficiently large data set, the optimization criterion for the approach is created. A method, which is based on evolutionary algorithms is verified using the example of a single-stage differential transmission. For this purpose, measurements on a driveline test bench are collected and evaluated. A model and its parameters are determined and subsequently compared with temperature measurements, derived from the inside of the transmission unit.

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"Oil sump temperature in conventional gearboxes is one of the most important technical framework conditions in terms of research, development and especially in testing of gearboxes. Nevertheless, it is not always possible to set a suitable sensor or to access sensor data with sufficient quality. The need for a simulated oil sump temperature is, among others, for such purposes. In a previous work, an approach for this is presented. A thermal model of a gearbox is generally created on the basis of lumped-element models. Therefore, the thermal masses, such as oil, gears and the gearbox housing, are modeled as lumped-capacitance and the thermal transitions between them as thermal resistances. In addition, there are thermal sources due to heat generation and thermal sinks due to cooling. In principle, a high level of detailed knowledge would be required to parameterize this lumped-element model. To circumvent this, a data-based approach for parameter identification is chosen. For this purpose, an optimization problem is formulated which aims at minimizing the error between simulated and measured values. The methods of genetic algorithms are used to solve the optimization problem. This computationally expensive step is done in advance, apart from the actual use of the model. Thus, a model with little detailed knowledge and without extensive model building can be generated. The preliminary conceptual paper focusses on gearboxes of battery electric vehicle (BEV) with low thermal masses. In contrast, transmissions in conventional and in hybrid electric vehicles (HEV) are steadily increasing in size, number of gears, and thermal mass. The generality of the approach described above is shown by modeling a very large gearbox in the present work. For the specific case, even a gearbox with a coolant/oil heat exchanger is considered. Beside this extension of the model, the former approach has additionally been improved regarding the accuracy of the results. By varying the measured and simulated model inputs, the parameters can be determined more accurately. Furthermore, the final simulation model is upgraded with these additional model inputs as well, which leads to a significant improvement of the simulation results. The model complexity even decreases. These results base on a large measurement data set, which is necessary for the identification of the parameters. In addition, measurement data is needed for the validation of the method. An extensive series of measurements have been taken on an electric powertrain test bench and is presented within this work. Both, generic measurement runs, which are ideal for system identification, and runs similar to a real driving data were recorded."