Physical Test Bench Research Articles

Model based integrated control strategy for effective brake energy recovery to extend battery longevity in electric two wheelers

This study aims to achieve optimal regenerative braking performance in the form of a reduced decline in battery SOC for a BLDC electric machine with peak torque of 10 Nm for use in electric two-wheelers. This is conducted via a comparison of control algorithms based on Direct Look-up table, Fuzzy Logic and their combination with PID control. The whole vehicle model and the energy recovery control strategies are designed using MATLAB Simulink by benchmarking the design with the parameters of the Ola Electric S1. A physical motor-dynamometer test bench is utilised to obtain a complete motor operating range to derive a realistic efficiency map that is used in the model. WLTP Class 2 and NYCC standard drive cycles are implemented for vehicle simulation. Two live-recorded driving patterns are also used to validate the model to analyse the adaptability of the control strategies. After obtaining the required motor speed, torque values and the range by matching the theoretical drive cycle profile, the control strategy is further optimised using the PID auto-tuning toolbox in Simulink. Using physical testbench data, the effect of various regenerative braking control strategies on overall vehicle performance is more accurately realised. The Fuzzy PID control strategy exhibits the most optimal gains in terms of energy recovery for electric two-wheelers, allowing for the highest battery SOC levels of 41.88% and average motor regenerative torque of 7.25 Nm for the standard drive cycles. An analogous trend is observed for the on-road driving pattern as Fuzzy PID provides highest battery SOC and average motor regenerative torque of 48.34% and 7.5 Nm respectively. For the driving scenarios aforementioned, this provides a 17% and 44% increase in SOC respectively when compared to a non-regenerative braking-based system.

Neural network based detecting induction motor defects supplied by unbalanced grid

<span lang="EN-US">Predictive diagnosis of motor defects can reduce repair and downtime costs of electrical equipment. This paper presents the results of a research on the effectiveness of the defect classification and recognition system based on convolutional neural network to detect defects in a squirrel-cage induction motor based on the stator supply voltage and output phase currents when supplied from an industrial grid of limited power with possible voltage asymmetry and harmonic distortion taken into account. In this work a simulation model, implemented in MATLAB/Simulink, is proposed to investigate unbalanced conditions. The mathematical model has been verified on a physical test bench and then used to create a database of currents from measured asymmetric grid voltages in the presence of defects such as short circuit between turns of one phase, rotor bar breakage, rotor eccentricity and bearing defects. The classification quality of all defects in the neural network was 72%, with the exclusion of intercoil short circuit defect and the merging of different bearing defects, the quality of the model was achieved 91%. A further increase in the quality of the defect recognition system is due to a building up modification of the neural network architecture.</span>

Super Twisting Sliding Mode Control with Compensated Current Controller Dynamics on Active Magnetic Bearings with Large Air Gap

Due to their unique properties, like no mechanical contact and therefore no wear and no lubrication needed, Active Magnetic Bearings (AMBs) have been a dynamic field of research in the past decades. The high non-linearities of AMBs generate many challenges for the control of the otherwise unstable system, thus they need to be addressed to deliver the performance that modern applications require. Integrating the current controller dynamics into the model of a position controller in a cascading control loop helps to improve the performance of the control loop compared to a plain current controlled schema. Further, this nested control loop guarantees the predefined current dynamics of the current controller, tuned according to an industrial criterion. The systems dynamics are modelled and the proposed controller is validated experimentally on a physical test bench. The experimental results show a performant position control with a nested and explicit current controller on an AMB, even with a large air gap and star-connected coils. The trajectory range of the rotor was reduced by 87% to ±20 μm, compared to a plain current-controlled model. The proposed control strategy lays the foundation for further research, especially concerning sensorless position estimation techniques since these usually have limited bandwidth and benefit from a predefined current dynamic.

Dual-Energy Cone-Beam CT with Three-Material Decomposition for Bone Marrow Edema Imaging.

We investigate the feasibility of bone marrow edema (BME) detection using a kV-switching Dual-Energy (DE) Cone-Beam CT (CBCT) protocol. This task is challenging due to unmatched x-ray paths in the low-energy (LE) and high-energy (HE) spectral channels, CBCT non-idealities such as x-ray scatter, and narrow spectral separation between fat (bone marrow) and water (BME). We propose a comprehensive DE decomposition framework consisting of projection interpolation onto matching LE and HE view angles, fast Monte Carlo scatter correction with low number of tracked photons and Gaussian denoising, and two-stage three-material decompositions involving two-material (fat-Aluminium) Projection-Domain Decomposition (PDD) followed by image-domain three-material (fat-water-bone) base-change. Performance in BME detection was evaluated in simulations and experiments emulating a kV-switching CBCT wrist imaging protocol on a robotic x-ray system with 60 kV LE beam, 120 kV HE beam, and 0.5° angular shift between the LE and HE views. Cubic B-spline interpolation was found to be adequate to resample HE and LE projections of a wrist onto common view angles required by PDD. The DE decomposition maintained acceptable BME detection specificity (<0.2 mL erroneously detected BME volume compared to 0.85 mL true BME volume) over +/-10% range of scatter magnitude errors, as long as the scatter shape was estimated without major distortions. Physical test bench experiments demonstrated successful discrimination of ~20% change in fat concentrations in trabecular bone-mimicking solutions of varying water and fat content.

Improving gearshift controllers for electric vehicles with reinforcement learning

During a multi-speed transmission development process, the final calibration of the gearshift controller parameters is usually performed on a physical test bench. Engineers typically treat the mapping from the controller parameters to the gearshift quality as a black-box, and use methods rooted in experimental design – a purely statistical approach – to infer the parameter combination that will maximize a chosen gearshift performance indicator. This approach unfortunately requires thousands of gearshift trials, ultimately discouraging the exploration of different control strategies. In this work, we calibrate the feedforward and feedback parameters of a gearshift controller using a model-based reinforcement learning algorithm adapted from pilco. Experimental results show that the method optimizes the controller parameters with few gearshift trials. This approach can accelerate the exploration of gearshift control strategies, which is especially important for the emerging technology of multi-speed transmissions for electric vehicles.

Investigation of deviations in SI-engine behaviour due to manufacturing tolerances in cylinder heads

Cast engine components are experiencing ever tighter tolerance requirements and at the same time a more complex cast design. The geometries, some of which are inaccessible, are tested for quality assurance on the basis of relevant component characteristics, among other things. The position check measures the actual position of a feature in a spatial dimension. Information about the alignment and geometry of the combustion chamber cannot be derived from the measurement methods applied. The use of three-dimensional measuring methods, e.g., imaging by computer tomography, can additionally record the spatial component position and the component geometry. Further measurement data can be derived from this, which serves to increase process reliability and component quality, and to increase component quality within an entire component batch. On the one hand, the cylinder head limits the working space by the roof of the combustion chamber, on the other hand, the cylinder head has a significant influence on the charge movement, especially at the beginning of the intake flow, due to the geometry of the intake ducts. On account the high demands of modern gasoline engines with tumble combustion process paired with Miller operation at partial load, variable timing, etc., mixture formation is important for efficient operation. Mixture formation in air- and wall-guided combustion processes depends on the components air duct and injection. From the point of view of cylinder head production, the mixture formation component air guiding is an elementary development approach for implementing efficient and sustainable component production while ensuring component properties. From this, the question can be derived as to what influence, for example, different dimensional tolerances in the combustion chamber size have on engine operation. To address this question, 3D simulations and physical test bench measurements were performed. With a variation of the above-mentioned intake duct and combustion chamber geometries and due to manufacturing tolerances, simulation results and measurement data were evaluated, analysed and presented in this paper. The influence of manufacturing-relevant tolerance deviations in the early process step of cylinder head production on combustion engine operation can be recognised in different ways.

Using the dead leaves pattern for more than spatial frequency response measurements

Simulation is an established tool to develop and validate camera systems. The goal of autonomous driving is pushing simulation into a more important and fundamental role for safety, validation and coverage of billions of miles. Realistic camera models are moving more and more into focus, as simulations need to be more then photo-realistic, they need to be physical-realistic, representing the actual camera system onboard the self-driving vehicle in all relevant physical aspects – and this is not only true for cameras, but also for radar and lidar. But when the camera simulations are becoming more and more realistic, how is this realism tested? Actual, physical camera samples are tested in laboratories following norms like ISO12233, EMVA1288 or the developing P2020, with test charts like dead leaves, slanted edge or OECF-charts. In this article we propose to validate the realism of camera simulations by simulating the physical test bench setup, and then comparing the synthetical simulation result with physical results from the real-world test bench using the established normative metrics and KPIs. While this procedure is used sporadically in industrial settings we are not aware of a rigorous presentation of these ideas in the context of realistic camera models for autonomous driving. After the description of the process we give concrete examples for several different measurement setups using MTF and SFR, and show how these can be used to characterize the quality of different camera models.

Model-based material decomposition with a penalized nonlinear least-squares CT reconstruction algorithm

Spectral information in CT may be used for material decomposition to produce accurate reconstructions of material density and to separate materials with similar overall attenuation. Traditional methods separate the reconstruction and decomposition steps, often resulting in undesirable trade-offs (e.g. sampling constraints, a simplified spectral model). In this work, we present a model-based material decomposition algorithm which performs the reconstruction and decomposition simultaneously using a multienergy forward model. In a kV-switching simulation study, the presented method is capable of reconstructing iodine at 0.5 mg ml−1 with a contrast-to-noise ratio greater than two, as compared to 3.0 mg ml−1 for image domain decomposition. The presented method also enables novel acquisition methods, which was demonstrated in this work with a combined kV-switching/split-filter acquisition explored in simulation and physical test bench studies. This novel design used four spectral channels to decompose three materials: water, iodine, and gadolinium. In simulation, the presented method accurately reconstructed concentration value estimates with RMSE values of 4.86 mg ml−1 for water, 0.108 mg ml−1 for iodine and 0.170 mg ml−1 for gadolinium. In test-bench data, the RMSE values were 134 mg ml−1, 5.26 mg ml−1 and 1.85 mg ml−1, respectively. These studies demonstrate the ability of model-based material decomposition to produce accurate concentration estimates in challenging spatial/spectral sampling acquisitions.

Research and Bench Test of Nonlinear Model Predictive Control-Based Power Allocation Strategy for Hybrid Energy Storage System

The hybrid energy storage system (HESS), which consists of ultra-capacitors and battery packs, is able to prevent the battery from the large current impact, increase instantaneous power capacity, so the battery is working under the reasonable status and the peak electric power requirement from the vehicle can be fulfilled. In this paper, a type of HESS containing dc–dc converter module, battery pack, and the ultra-capacitor is analyzed. Then, a novel online optimization-based nonlinear model predictive control (NMPC) power allocation strategy for the HESS is proposed. By introducing the vehicle velocity, control-oriented engine model, and the electro-mechanical transmission plant model, the electric power demand of the vehicle in the future can be predicted. The model of HESS is built in MATLAB/Simulink based on the predicted electric power requirement under the circumstance of a certain heavy-duty driving cycle. Moreover, the proposed strategy is verified through the physical test bench. The simulation and bench test results show the advantages of the HESS that is able to enhance the overall instantaneous power and prevent the battery from overload. Meanwhile, compared with the results of single battery storage system and the HESS with logic threshold control method, the proposed NMPC-based power allocation strategy can lower the peak power of the battery in the HESS, reduce the average charge and discharge power of the battery pack, and decrease the change in frequency and amplitude of the electric power. It will prevent the battery pack from over discharging/charging and improve the overall efficiency of the HESS.

Use of 3D modelling and several ultrasound sensors to assess overflow rate

A new instrumentation mode has been put in place in order to determine the overflow rate of five complex CSOs of the system in Sélestat (French), which are subject to self monitoring. 3D and 1D models have made it possible to predict the shapes of the water lines and suggest a location for the ultrasound sensors. In order to validate the instrumentation principle, three overhead sensors were placed on a weir. The overflow laws suggested are of the type Q(overflow) = a1h1b1 + a2h2b2 + a3h3b3. Early results of the overflow rate that have been obtained by applying the law are close to 10 % of the flow measured in the physical test bench. On the actual site, the ultrasound sensors were assembled on a rail covering the entire weir in order to be able to change their position in future after models calibration.