Robust two-degrees of freedom linear quadratic gaussian position control for the front axle actuator of a steer-by-wire system

The Steer-by-Wire (SbW) system is a key technology for highly automated driving. For automated lateral vehicle guidance, the precise position control of the SbW Front Axle Actuator is an essential prerequisite. This contribution presents the modeling, control design, nominal performance, and stability analysis as well as the robustness analysis of the position control for the SbW Front Axle Actuator. Based on a nonlinear model of the plant a simplified linear system model is derived. This model yields the basis for the design of a Two-Degrees of Freedom Linear Quadratic Gaussian Control (2DOF LQG control), which allows an independent design of the command and the disturbance response. Besides an evaluation of the nominal control behavior, μ-analysis is applied to assess the robustness of performance and stability. Finally, real vehicle tests for different driving maneuvers are presented to verify simulation results.

     

Parking lock integration for electric axle drives by multi-objective design optimization

Due to safety considerations, electric axle drives (e-drives) are often equipped with a parking lock system, which prevents vehicle movement while parking in redundancy with the parking brake. In order to integrate the parking lock into the e‑drive, various mounting positions inside the e‑drive are eligible, which have a direct influence on the e‑drive packaging. Furthermore, engaging the parking lock may happen at small vehicle velocities and while driving downhill, leading to high loads on the e‑drive components. These loads depend on the mounting position of the parking lock and have to be considered in the design phase to prevent failure of the system. That way, the designs of shafts, gear wheels and bearings of the gearbox are affected by the parking lock integration. A suboptimally integrated parking lock system can thus lead to undesirably high costs and reduced energy efficiency of the entire e‑drive—all alongside the packaging aspect. Consequently, finding the best suitable parking lock integration for a certain e‑drive is a complex task for the design engineers. To reduce the level of problem complexity, an established computer-based system design method for e‑drives by means of a multi-objective optimization is extended to be capable of considering the parking lock integration. The proposed method is applied to a case study and the impact of the parking lock on the optimality of an exemplary e‑drive system is shown.

     

Navigation method utilizing floor-integrated inductive power supply modules for omnidirectional AGVs

This paper answers the research question: Can the contactless induced energy supply from a novel inductive floor be used to navigate omnidirectional automated guided vehicles (AGVs)?

In contrast to existing systems a novel inductive floor enables AGVs traveling through production without charging breaks. This floor consists of tiles with inductive modules, which supply the AGV with energy. In addition to supplying power to the AGV, the inductive modules are also intended to guide the vehicle through production. To enable such a guidance sensors placed in the AGV measure the induced voltages of the floor. To answer the research question these voltages are calculated with the help of an electromagnetic simulation of the AGV’s travel on the inductive tiles. To estimate the position as well as rotation of the AGV depending on the simulated voltages as inputs a novel algorithm is presented. During the travel the AGV is able to move in arbitrary directions independently of its orientation. To control the omnidrectional AGV consistently without singularities, a transformation in Omni-Curve-Parameters (OCP) is proposed. As simulation case study a four wheeled steering- and velocity controlled AGV is introduced. For the evaluation a novel motion model depending on the input OCP is presented. This model is compared to the estimation of the position to verify the accuracy and the reproducibility of the algorithm.

     

Reducing mass while improving the operational behavior: form optimization of planetary gearbox housings

The optimization of load sharing between planets is one of the most important goals in planetary gearbox design. Unevenly distributed load will cause locally higher flank pressures and therefore, less durability of gears and bearings. Furthermore, unevenly distributed or fluctuating loads can cause excitations in the gear mesh and structural vibrations. The load sharing in planetary gear stages depends on the individual stiffness conditions in each mesh position. The stiffness is not only influenced by the gear geometry but also by the surrounding structural elements like shafts, housings and torque arms. In wind industry these components are often designed very stiff in order to reduce their effect on the operational behavior.

Within this paper, a method is presented, which allows combining the structural optimization process with a tooth contact analysis for planetary gearboxes. By means of this combined approach, it is possible to optimize the housing structure of the ring gear in terms of mass reduction while keeping the operational behavior in focus. With a weighted design objective function, it is possible to decide whether the main objective should be load distribution, excitation behavior, low mass or a balanced design.

     

Identification of relevant acoustic transfer paths for WT drivetrains with an operational transfer path analysis

The aim of a transfer path analysis (TPA) is to view the transmission of vibrations in a mechanical system from the point of excitation over interface points to a reference point. For that matter, the Frequency Response Functions (FRF) of a system or the Transmissibility Matrix is determined and examined in conjunction with the interface forces at the transfer path. This paper will cover the application of an operational TPA for a wind turbine model. In doing so the path contribution of relevant transfer paths are made visible and can be optimized individually.

     

Efficient lubrication of a high-speed electromechanical powertrain with holistic thermal management

In order to increase the power density of BEVs (Battery Electric Vehicles), high-speed concepts are being progressively developed. With increased speed, the power of the electrical machine can be maintained with reduced torque and therefore size, resulting in cost and package advantages. In the joint research project Speed4E with seven industrial and five university partners, such high-speed electromechanical powertrain is being developed and investigated. The electrical machines will run at a maximum rotational speed of 50,000 rpm in the test rig and 30,000 rpm in the test vehicle. The developed lubrication system for the Speed4E transmission aims for high efficiency and optimized heat balance, via a demand-oriented oil flow. In this context, this study investigates how an efficient lubrication system can be designed with respect to the holistic thermal management of the vehicle. Therefore, a hybrid lubrication consisting of dip and injection lubrication is realized. For the analysis and evaluation, efficiency calculations and CFD (Computational Fluid Dynamics) simulations of the oil distribution are presented.

     

Emulation of grid-forming inverters using real-time PC and 4-quadrant voltage amplifier

A test bed for the evaluation of novel control methods of inverters for renewable power generation is presented. The behavior of grid-following and grid-forming control in a test scenario is studied and compared.

Using a real-time capable control platform with a cycle time of 50 µs, control methods developed with Matlab/Simulink can be implemented. For simplicity, a three-phase 4‑quadrant voltage amplifier is used instead of an inverter. Thus, the use of modulation and switched power semiconductors can be avoided. In order to show a realistic behavior of a grid-side filter, passive components can be automatically connected as L‑, LC- or LCL-filter. The test bed has a nominal active power of 43.6 kW and a nominal voltage of 400 V.

As state-of-the-art grid-following control method, a current control in the d/q-system is implemented in the test bed. A virtual synchronous machine, the Synchronverter, is used as grid-forming control method. In combination with a frequency-variable grid emulation, the behavior of both control methods is studied in the event of a load connection in an island grid environment.

     

Development of a wind turbine gearbox virtual load sensor using multibody simulation and artificial neural networks

A current development trend in wind energy is characterized by the installation of wind turbines (WT) with increasing rated power output. Higher towers and larger rotor diameters increase rated power leading to an intensification of the load situation on the drive train and the main gearbox. However, current main gearbox condition monitoring systems (CMS) do not record the 6‑degree of freedom (6-DOF) input loads to the transmission as it is too expensive. Therefore, this investigation aims to present an approach to develop and validate a low-cost virtual sensor for measuring the input loads of a WT main gearbox. A prototype of the virtual sensor system was developed in a virtual environment using a multi-body simulation (MBS) model of a WT drivetrain and artificial neural network (ANN) models. Simulated wind fields according to IEC 61400‑1 covering a variety of wind speeds were generated and applied to a MBS model of a Vestas V52 wind turbine. The turbine contains a high-speed drivetrain with 4‑points bearing suspension, a common drivetrain configuration. The simulation was used to generate time-series data of the target and input parameters for the virtual sensor algorithm, an ANN model. After the ANN was trained using the time-series data collected from the MBS, the developed virtual sensor algorithm was tested by comparing the estimated 6‑DOF transmission input loads from the ANN to the simulated 6‑DOF transmission input loads from the MBS. The results show high potential for virtual sensing 6‑DOF wind turbine transmission input loads using the presented method.

     

Calculation of structure-borne sound in a direct drive wind turbine

In this publication, the methods will be presented that are deployed to formulate a multi-physical system model of a direct drive wind turbine in order to calculate structure borne sound. The model includes excitation effect as well as sound radiating behaviour. The mechanical structure as a medium partner between excitation and radiation will be formulated through a multi-body simulation model in the time domain. In the multi-body simulation model, all relevant drivetrain components are considered with their structural eigenmodes in the frequency range of interest. The electromagnetic forces of the multi-pole ring generator are calculated and introduced into the mechanical structure at each stator tooth, rotor pole and various axial positions individually. Similarly, the modelling of the bearings is investigated for a range of available methods. Sound emission is evaluated at the large outer surface structures like tower, blades and nacelle cover. To minimize computational effort, the surface accelerations are not calculated for each surface node, instead a modal approach is used. Through a combination of mode shapes with mode participation factors of the respective structures, the surface accelerations can be regained during a post-processing step. Those results are used as input for airborne sound calculations. Nevertheless, the high number of modal and spatial degrees of freedom results in high computing costs.

     

Holistic simulation of wind turbines with fully aero-elastic and electrical model

Requirements for the design of wind turbines advance facing the challenges of a high content of renewable energy sources in the public grid. A high percentage of renewable energy weaken the grid and grid faults become more likely, which add additional loads on the wind turbine. Load calculations with aero-elastic models are standard for the design of wind turbines. Components of the electric system are usually roughly modeled in aero-elastic models and therefore the effect of detailed electrical models on the load calculations is unclear. A holistic wind turbine model is obtained, by combining an aero-elastic model and detailed electrical model into one co-simulation. The holistic model, representing a DFIG turbine is compared to a standard aero-elastic model for load calculations. It is shown that a detailed modelling of the electrical components e.g., generator, converter, and grid, have an influence on the results of load calculations. An analysis of low-voltage-ride-trough events during turbulent wind shows massive increase of loads on the drive train and effects the tower loads. Furthermore, the presented holistic model could be used to investigate different control approaches on the wind turbine dynamics and loads. This approach is applicable to the modelling of a holistic wind park to investigate interaction on the electrical level and simultaneously evaluate the loads on the wind turbine.

     

A methodology for data-driven modeling and prediction of the drag losses of wet clutches

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.

     

Reconstruction of operating loads in wind turbines with inertial measurement units

Wind turbines are a major source of renewable energy. Load monitoring is considered to improve reliability of the systems and to reduce the cost of operation. We propose a load monitoring system which consists of inertial measurement units. These track the movement of rotor blade, hub and tower top. In addition, wind turbine states, e.g. yaw angle, pitch angle and rotation speed, are recorded. By solving a navigation algorithm with a Kalman Filter approach, the raw sensor data is combined with an error model to reduce the tracking error. In total, five inertial measurement units are installed on the research wind energy converter AD 8–180 on the test site in Bremerhaven. Results show that tracking the blade movement in full operation is possible and that loads can be estimated with a model-based approach. In comparison to simulations, the blade deflections can be approximated by an aeroelastic model. The presented approach can be used as basis for comprehensive load monitoring and observer system with additional increase of system robustness by measurement redundancy.

     

Digital twin modeling for predictive maintenance of gearboxes in floating offshore wind turbine drivetrains

This paper presents a multi-degree of freedom torsional model of drivetrain system as the digital twin model for monitoring the remaining useful lifetime of the drivetrain components. An algorithm is proposed for the model identification, which receives the torsional response and estimated values of rotor and generator torques, and calculates the drivetrain dynamic properties, e.g. eigenvalues, and torsional model parameters. The applications of this model in prediction of gearbox remaining useful lifetime is discussed. The proposed method is computationally fast, and can be implemented by integrating with the current turbine control and monitoring system without a need for a new system and sensors installation. A test case, using 5 MW reference drivetrain, has been demonstrated.

     

Simulative investigation of wind turbine gearbox loads during power converter fault

Three phase short circuit power converter faults in wind turbines (WT) result in highly dynamic generator torque reversals, which lead to load reversals within the drivetrain. Dynamic load reversals in combination with changing rotational speeds are, for example, critical for smearing within roller bearings. Therefore, an investigation of the correlation between three phase short circuit converter faults and drivetrain component failures is necessary.

Due to the risk of damage and the resulting costs, it is not economically feasible to extensively investigate three phase short circuit converter faults on test benches. Valid WT drivetrain models can be used instead. A WT drivetrain model, which has been developed and validated in a national project at the CWD, is used and a three phase short circuit converter fault is implemented. In this paper, the resulting torque load on the drivetrain for a three phase short circuit converter fault in rated power production is presented. This converter fault leads to a highly dynamic reversing electromagnetic torque which exceeds the rated torque by a factor of three. As a result the load on the rotor side high speed shaft (HSS) bearing oscillates and increases by around 15 per cent compared to rated power production. Simultaneously the rotational velocity of the HSS oscillates with an amplitude of 10 rpm. Therefore an increase in the risk of smearing is expected.

     

Material influence on mechanical properties of gears with alternative microstructures

Case hardening represents the most important heat treatment process to increase the load carrying capacity of gear components. Beside predominantly martensitic surface layers with low proportion of retained austenite, there were investigated material structures with differing properties in preceding research work.

In a previous publication, the results of three variants made of the material 20MnCr5, which is typically used for gears, were presented. The reference heat treatment was a conventional carburizing with subsequent case hardening. The second variant was also gas carburized, but with a high proportion of retained austenite. The last presented variant had a bainitic structure in the surface layer. The second and the third variant showed a similar tooth root bending strength compared to the reference. The numbers of the pitting resistance were significantly higher than for the reference variant.

This paper presents the results of further investigations on the influence of different microstructures on the gear load carrying capacity. For this purpose, gears made of 18CrNiMo7‑6 were tested with regard to their load carrying capacity. 18CrNiMo7‑6 is a case hardening steel like 20MnCr5, too, which is often applied in big gear components. The tooth root bending strength was approximately constant, whereas the pitting resistance decreased compared to the corresponding variants of the material 20MnCr5. In comparison to the carburized 20MnCr5 reference variant, only the 18CrNiMo7‑6 variant with a large proportion of retained austenite showed a higher pitting resistance.

The tooth root bending strength investigations took place in the cycle regime of limited life as well in the high cycle fatigue regime. The pitting resistance was only determined in the cycle regime of limited life.

     

Full-scale field test of a model predictive control system for a 3 MW wind turbine

Model predictive control (MPC) is a strong candidate for modern wind turbine control. While the design of model predictive wind turbine controllers in simulations has been extensively investigated in academic studies, the application of these controllers to real wind turbines reveals open research challenges. In this work, we focus on the validation of a linear time-variant MPC system for a 3 MW wind turbine in a full-scale field test. First, the study proves the MPC’s capability to control the real wind turbine in the partial load region. Compared to the turbine’s baseline PID controller, the MPC system offers similar results for the electrical power output and for the occurring mechanical loads. Second, the study validates a previously proposed, simulation-based rapid control prototyping process for a systematic MPC development. The systematic development process allows to completely design and parameterize the MPC system in a simulative environment independent of the real wind turbine. Through the rapid control prototyping process, the MPC commissioning in the wind turbine’s programmable logic controller can be realized within a few hours without any modifications required in the field. Thus, this study establishes the proof of concept for a linear time-variant MPC system for a 3 MW wind turbine in a full-scale field test and bridges the gap between the control design and field testing of MPC systems for wind turbines in the multi-megawatt range.

     

Impact of age and cognitive demand on lane choice and changing under actual highway conditions

Previous research suggests that drivers change lanes less frequently during periods of heightened cognitive load. However, lane changing behavior of different age groups under varying levels of cognitive demand is not well understood. The majority of studies which have evaluated lane changing behavior under cognitive workload have been conducted in driving simulators. Consequently, it is unclear if the patterns observed in these simulation studies carry over to actual driving. This paper evaluates data from an on-road study to determine the effects of age and cognitive demand on lane choice and lane changing behavior. Three age groups (20–29, 40–49, and 60–69) were monitored in an instrumented vehicle. The 40's age group had 147% higher odds of exhibiting a lane change than the 60's group. In addition, drivers in their 60's were less likely to drive on the leftmost lane compared to drivers in their 20's and 40's. These results could be interpreted as evidence that older adults adopt a more conservative driving style as reflected in being less likely to choose the leftmost lane than the younger groups and less likely to change lanes than drivers in their 40's. Regardless of demand level, cognitive workload reduced the frequency of lane changes for all age groups. This suggests that in general drivers of all ages attempt to regulate their behavior in a risk reducing direction when under added cognitive demand. The extent to which such self-regulation fully compensates for the impact of added cognitive demand remains an open question.     

Topology analysis of electric vehicles,with a focus on the traction battery

Over recent years, the number of battery electric vehicles (BEVs) has drastically increased due to new European Union (EU) regulations. These regulations force vehicle manufacturers to adjust their product range in order to fulfill the imposed carbon dioxide limits. Such an adjustment enforces the usage of battery electric vehicles. However, research into the optimal BEV architectures and topologies is still in progress. Therefore, the aim of this paper is an analysis of all the current electric vehicle topologies. From this analysis, the authors identify different basic battery shapes. Subsequently, these shapes are used to describe the impact of the battery on the passenger compartment. As an initial result of this analysis, the authors create a new denomination method, via which it is possible to cluster the battery topologies. In a second step, the collected data is clustered using the novel denomination method. Finally, this paper presents the benchmark topologies for the analyzed segments.

     

Lane heading difference: An innovative model for drowsy driving detection using retrospective analysis around curves

Driving while sleepy is a serious contributor to automobile accidents. Previous research has shown that drowsy drivers produce systematic errors (variability) in vehicle behavior which are detectable using vehicle monitoring technology. The current study developed a new methodological approach using a vehicle heading difference metric to detect drowsy driving more effectively than other more commonly used methods. Twenty participants completed a driving scenario as well as several measures of fatigue in five testing sessions across a night of sleep deprivation. Each simulated highway driving session lasted 20 min, and was analyzed for lateral lane position variability and vehicle heading difference variability with two statistical methods. Fatigue measures monitored reaction time, attention, and oculomotor movement. The results showed that examining lane heading difference using the absolute value of the raw data detected driving variability better across the night than other statistical models. The results from the fatigue measures indicated an increase in reaction time and response lapses, as well as a decrease in oculomotor reactivity across the night. These results suggest that in fatigued drivers the statistical model using the absolute value of lane heading could be an improved metric for drowsy driving detection that could accurately detect detriments in driving ability at lower levels of fatigue.     

Method to determine the local load cycles of a blade bearing using flexible multi-body simulation

Conventional methods for designing rolling bearings against fatigue assume that a bearing ring is fully rotating and that the load is ideally distributed over the rolling elements. Blade bearings in wind turbines, are operated under oscillating motions and dynamic loads. The load distribution is strongly dependent on the stiffness of the bearing rings and the surrounding structural components. This has been shown in numerous studies using FEM simulations for static load cases. In this paper a method is presented that reduces the calculation effort of the deformation of the bearing rings, so that a flexible integration into an aeroelastic mbs model of a wind turbine is possible. Thereby an average accuracy of 6.5% between FEM and mbs could be achieved. The model allows the determination of time series of the global load distribution of each raceway. By data processing of the simulation results, the number of load cycles and the maximum contact pressure for individual segments of the raceways could be determined and their fatigue probability could be estimated using the linear damage hypothesis according to Palmgren-Miner.