Adaptive control of a boost-buck converter for thermoelectric generators

Thermoelectric generators (TEGs) are used to recover waste heat of the exhaust gas and convert it into electric energy in automotive applications. The temperature of the waste heat influences the voltage and internal resistor of a TEG. For the electric linking of TEGs to the on-board power supply, a DC-DC converter may be used. The control of the DC-DC converter must be robust against dynamic changes and additionally has to track the maximum power point (MPP) of the TEG. This paper presents a digital cascade controller for a boost-buck converter to charge a vehicle battery and to supply the load. To track the MPP, a hill climbing (HC) algorithm is implemented, which is also used for photovoltaics. The conversion time of the HC is minimized with an adaptive step size. Width variations of electric parameters of TEG influence the dynamic and stability of the controllers. With a closed loop identification, the parameter variation is estimated, and the control parameters can be redesigned. An experimental result show the efficiency of the adaptive control.BMBF, 03X3553E, Thermoelektrische Generatoren 202

Time-synchronous sampling in wireless sensor networks

Peer Reviewe

Maximum power point controller for thermoelectric generators to support a vehicle power supply

The growing mobility increases the world-wide fuel consumption. Yet the amount of fossil fuel is limited and the environmental burden is increasing dramatically as well. Many governments have enacted laws to regulate and reduce the fuel consumption as well as the CO2 emissions of combustion engines. An idea to save fuel and to reduce the environmental burden is to use thermoelectric generators (TEGs) to recover the waste heat of the exhaust gas and convert into electric energy in automotive applications. For the linking of TEGs to the vehicle is power supply, a DC-DC converter can be used. To support a wide range of TEGs with different electric parameters, the control of DC-DC converter must be robust. Further, the control should track the maximum power point (MPP) of the TEG for an efficient power recovery. This paper presents a digital cascade controller for a boost-buck converter that charges a vehicle battery and supplies the load. To model and analyze the discontinuous converter, the state-space-averaging (SSA) is used. The tracking of the MPP is realized with a gradient algorithm and an input current control. An adaptive step size algorithm reduces the conversion time of the maximum power point tracking algorithm (MPPT). Experiments verified the controller design and the efficiency of the MPPT.BMBF, 03X3553E, Thermoelektrische Generatoren 202

Autoencoder Based Iterative Modeling and Multivariate Time-Series Subsequence Clustering Algorithm

This paper introduces an algorithm for the detection of change-points and the identification of the corresponding subsequences in transient multivariate time-series data (MTSD). The analysis of such data has become more and more important due to the increase of availability in many industrial fields. Labeling, sorting or filtering highly transient measurement data for training condition based maintenance (CbM) models is cumbersome and error-prone. For some applications it can be sufficient to filter measurements by simple thresholds or finding change-points based on changes in mean value and variation. But a robust diagnosis of a component within a component group for example, which has a complex non-linear correlation between multiple sensor values, a simple approach would not be feasible. No meaningful and coherent measurement data which could be used for training a CbM model would emerge. Therefore, we introduce an algorithm which uses a recurrent neural network (RNN) based Autoencoder (AE) which is iteratively trained on incoming data. The scoring function uses the reconstruction error and latent space information. A model of the identified subsequence is saved and used for recognition of repeating subsequences as well as fast offline clustering. For evaluation, we propose a new similarity measure based on the curvature for a more intuitive time-series subsequence clustering metric. A comparison with seven other state-of-the-art algorithms and eight datasets shows the capability and the increased performance of our algorithm to cluster MTSD online and offline in conjunction with mechatronic systems.Comment: 26 pages, 11 figures, for associated python code repositories see https://github.com/Jokonu/mt3scm and https://github.com/Jokonu/abimca; Minor spelling and grammar corrections, fixed wrong bibtex entry for SOStream, some improvements and corrections in formulas of section

RIANN—A robust neural network outperforms attitude estimation filters

Inertial-sensor-based attitude estimation is a crucial technology in various applications, from human motion tracking to autonomous aerial and ground vehicles. Application scenarios differ in characteristics of the performed motion, presence of disturbances, and environmental conditions. Since state-of-the-art attitude estimators do not generalize well over these characteristics, their parameters must be tuned for the individual motion characteristics and circumstances. We propose RIANN, a ready-to-use, neural network-based, parameter-free, real-time-capable inertial attitude estimator, which generalizes well across different motion dynamics, environments, and sampling rates, without the need for application-specific adaptations. We gather six publicly available datasets of which we exploit two datasets for the method development and the training, and we use four datasets for evaluation of the trained estimator in three different test scenarios with varying practical relevance. Results show that RIANN outperforms state-of-the-art attitude estimation filters in the sense that it generalizes much better across a variety of motions and conditions in different applications, with different sensor hardware and different sampling frequencies. This is true even if the filters are tuned on each individual test dataset, whereas RIANN was trained on completely separate data and has never seen any of these test datasets. RIANN can be applied directly without adaptations or training and is therefore expected to enable plug-and-play solutions in numerous applications, especially when accuracy is crucial but no ground-truth data is available for tuning or when motion and disturbance characteristics are uncertain. We made RIANN publicly available.DFG, 414044773, Open Access Publizieren 2021 - 2022 / Technische Universität Berli

Detailed Model of a Hydromechanical Double Clutch Actuator with a Suitable Control Algorithm

Abstract This paper presents the detailed model of a double clutch actuator with a suitable control algorithm. Firstly, there is an introduction into the theory of a double clutch transmission and the aim of this project. The simulation model with a Dymola R and a MATLAB R /Simulink R part is discussed. The library of a vehicle model with a highly detailed hydromechanical clutch is introduced, which includes models with different levels of detail. The modeling of the hydraulic and the mechanic parts of the clutch actuator is discussed, concentrating on the problem of determining the parameters of the actuator modules e.g., the hydraulic valves. Some parts could not be used from existing Dymola R libraries, in those cases, new models are created based on Modelica code. A translational lever is pictured with its source code. Furthermore the non-linear behavior of the clutch actuator and control design is described. To verify this model and the suitable closed loop controller, the algorithm is tested with an up-shift cycle in a vehicle model with a double clutch transmission. The simulation results are presented with a global view of the driver inputs, the speed, the torque of the vehicle model and the gear status. Additionally the local view of the clutch actuator is shown with the cylinder pressure, the clutch position and the clutch capacity (torque). Finally there is a summary and an outlook on the further development of this library

Friction and wear monitoring methods for journal bearings of geared turbofans based on acoustic emission signals and machine learning

In this work, effective methods for monitoring friction and wear of journal bearings integrated in future UltraFan® jet engines containing a gearbox are presented. These methods are based on machine learning algorithms applied to Acoustic Emission (AE) signals. The three friction states: dry (boundary), mixed, and fluid friction of journal bearings are classified by pre-processing the AE signals with windowing and high-pass filtering, extracting separation effective features from time, frequency, and time-frequency domain using continuous wavelet transform (CWT) and a Support Vector Machine (SVM) as the classifier. Furthermore, it is shown that journal bearing friction classification is not only possible under variable rotational speed and load, but also under different oil viscosities generated by varying oil inlet temperatures. A method used to identify the location of occurring mixed friction events over the journal bearing circumference is shown in this paper. The time-based AE signal is fused with the phase shift information of an incremental encoder to achieve an AE signal based on the angle domain. The possibility of monitoring the run-in wear of journal bearings is investigated by using the extracted separation effective AE features. Validation was done by tactile roughness measurements of the surface. There is an obvious AE feature change visible with increasing run-in wear. Furthermore, these investigations show also the opportunity to determine the friction intensity. Long-term wear investigations were done by carrying out long-term wear tests under constant rotational speeds, loads, and oil inlet temperatures. Roughness and roundness measurements were done in order to calculate the wear volume for validation. The integrated AE Root Mean Square (RMS) shows a good correlation with the journal bearing wear volume

Dynamics Modeling of Bearing with Defect in Modelica and Application in Direct Transfer Learning from Simulation to Test Bench for Bearing Fault Diagnosis

In data-driven bearing fault diagnosis, sufficient fault data are fundamental for algorithm training and validation. However, only very few fault measurements can be provided in most industrial applications, bringing the dynamics model to produce bearing response under defects. This paper built a Modelica model for the whole bearing test rig, including the test bearing, driving motor and hydraulic loading system. First, a five degree-of-freedom (5-DoF) model was proposed for the test bearing to identify the normal bearing dynamics. Next, a fault model was applied to characterize the defect position, defect size, defect shape and multiple defects. The virtual bearing test bench was first developed with OpenModelica and then called in Python with OMPython. For validation of the positive effect of the dynamics model in the direct transfer learning for bearing fault diagnosis, the simulation data from the Modelica model and experimental data from the Case Western Reserve University were fed separately or jointly to train a Convolution Neural Network (CNN). Then the well-trained CNN was transferred directly to achieve the fault diagnosis under the test set consisting of experiment data. Additionally, 157 features were extracted from both time-domain and frequency-domain and fed into CNN as input, and then four different validation cases were designed. The results confirmed the positive effect of simulation data in the CNN transfer learning, especially when the simulation data were added as auxiliary to experimental data, and improved CNN classification accuracy. Furthermore, it indicated that the simulation data from the bearing dynamics model could play a part in the actual experimental measurement when the collected data were insufficient