Multibody model based estimation of multiple loads and strain field on a vehicle suspension system

In order to enhance the dynamic performance of surface-mounted permanent magnet synchronous machines (PMSMs), this letter proposes an improved finite control set (FCS) model predictive direct speed control (MPDSC) strategy that only contains one speed control loop. In comparison with the traditional two-step prediction method, the new controller is achieved by estimating the current at first and then the speed with only one prediction horizon. The practical problem that the optimal switching state cannot be selected by the common implementation procedures of the FCS model predictive control algorithms can be solved by applying the proposed strategy, which conforms more to the real situations. Moreover, in order to solve the other critical problem that the load torque (LT) in the predicting plant is unknown, a novel sliding mode LT observer is developed. The simulation and experimental results prove that the proposed algorithms are effective.

SM Load Torque Observer-Based FCS-MPDSC With Single Prediction Horizon for High Dynamics of Surface-Mounted PMSM

Recently, there has been significant attention given to the electrification of transportation due to concerns about fossil fuel depletion and environmental pollution. Conventional drive systems typically include a clutch, reduction gear, and mechanical differential, which results in power loss, noise, vibration, and additional maintenance. However, in-wheel motor drive technology eliminates the need for these components, providing benefits such as higher system efficiency, improved wheel control, and increased passenger comfort. This article offers a comprehensive review of the technology and development of in-wheel motor drives. It begins with an overview of in-wheel motor drives in electric vehicles, followed by an exploration of the types of electric motors suitable for in-wheel motor drives. The paper then presents an industrial state of the art of in-wheel motors, comparing them with conventional motor drives, and reviews the implemented power electronics, control system, and cooling systems. Finally, the paper concludes by providing an outlook on the challenges and future trends of in-wheel drive systems.

/pdf/in-wheel-motor-drive-systems-for-electric-vehicles-state-of-3soz6wiu.pdf

In-Wheel Motor Drive Systems for Electric Vehicles: State of the Art, Challenges, and Future Trends

Abstract The aim of this work is to provide a thorough research on the implementation of some non-linear Kalman filters (KF) using multibody (MB) models and to compare their performances in terms of accuracy and computational cost. The filters considered in this study are the extended KF (EKF) in its continuous form, the unscented KF (UKF) and the spherical simplex unscented KF (SSUKF). The MB formulation taken into consideration to convert the differential algebraic equations (DAE) of the MB model into the ordinary differential equations (ODE) required by the filters is a state-space reduction method known as projection matrix-R method. Additionally, both implicit and explicit integration schemes are used to evaluate the impact of explicit integrators over implicit integrators in terms of accuracy, stability and computational cost. However, state estimation through KFs is a closed-loop estimation correcting the model drift according to the difference between the predicted measurement and the actual measurement, what limits the interest in using implicit integrators despite being commonly employed in MB simulations. Performance comparisons of all the aforementioned non-linear observers have been carried out in simulation on a 5-bar linkage. The mechanism parameters have been obtained from an experimental 5-bar linkage and the sensor characteristics from off-the-shelf sensors to reproduce a realistic simulation. The results should highlight useful clues for the choice of the most suitable filters and integration schemes for the aforementioned MB formulation.

https://lirias.kuleuven.be/bitstream/123456789/369519/1/Pastorino_Richiedei_Cuadrado_Trevisani_fullpaper.pdf

State estimation using multibody models and nonlinear Kalman filters

Braking energy recovery is one of the main technologies affecting the economic performance of an electric vehicle. To improve economy from as much recovered braking energy as possible on the premis...

A swarm intelligence-based predictive regenerative braking control strategy for hybrid electric vehicle

Diffusion of electric and hybrid vehicles is accelerating the development of innovative braking technologies. Calibration of accurate models of a hydraulic brake plant involves availability of large amount of data whose acquisition is expensive and time consuming. Also, for some applications, such as vehicle simulators and hardware in the loop test rig, a real-time implementation is required. To avoid excessive computational loads, usage of simplified parametric models is almost mandatory. In this work, authors propose a simplified functional approach to identify and simulate the response of a generic hydraulic plant with a limited number of experimental tests. To reproduce complex nonlinear behaviours that are difficult to be reproduced with simplified models, piecewise transfer functions with scheduled poles are proposed. This innovative solution has been successfully applied for the identification of the brake plant of an existing vehicle, a Siemens prototype of instrumented vehicle called SimRod, demonstrating the feasibility of proposed method.

Fast Modelling and Identification of Hydraulic Brake Plants for Automotive Applications

Development of a Novel Test Setup for Validation of Virtual Sensing on Mechatronic Drivetrains

The importance of electric vehicles to the automotive sector has been growing fast over the last years. These vehicles feature novel electric powertrains, designed and sized using model-based engineering approaches. In this context, they would benefit from knowledge on operational torque loads. However, driveline torque loads are very expensive, and often not feasible, to measure in operational conditions. Therefore, this paper presents a method to obtain a model-based prediction of the motor torque applied on the driveline during on-the-road vehicle operation. As such, it aims to close the loop of model-based system engineering. The method is developed for the case of an induction machine as traction motor. In contrast to classical parameter identification tests requiring a motor test bench, the experimental test campaign for the model parameter identification can be performed on a chassis dynamometer, with the E-powertrain mounted in the vehicle.

In-Vehicle Identification of an Induction Machine Model for Operational Torque Prediction

Mechatronic drivetrains have significant advantages over traditional engine-driven mechanical drivelines in terms of – amongst others – energy efficiency, control bandwidth and noise, vibration and harshness (NVH) [1]. Hence, traditional drivelines in industrial applications are more and more replaced by their mechatronic counterpart. The same holds for vehicles, hybrid-electric ships being a particularly successful mechatronic application. With respect to energy conversion, mechatronic drivetrains are not only an indispensable part of turbines, but the electro-mechanical coupling has been shown to be of key importance to operational loading on modern wind turbines [2]. In contrast to the successful combination of electrical and mechanical power components, however, a big potential is still left unused in combining electrical and mechanical sensor data. Using a simple electromechanical model, structural health monitoring is proven to be possible based on electrical current measurements [3]. Furthermore, state-of-the-art model-based design approaches open up the possibility to use high-fidelity models in coupled state, parameter and input estimation [4]. In order to successfully combine the information from mechanical and electric sensors, a coupled electro-mechanical model needs to be constructed. In a general framework this could be obtained through a co-simulation approach. The mechanical part of the system can then be modelled using flexible multibody simulation to take the proper dynamic behaviour into account. However, this approach will lead to unacceptable computational loads for estimation purposes. This paper therefore investigates how a typical mechatronic driveline, consisting of an electric motor and a (nonlinear) flexible cardan shaft can be modelled in a 1D environment and compares these results for a range of dynamic excitations with the results obtained from a fully coupled simulation (figure 1). Figure 1: Virtual sensing on a mechatronic drivetrain. Firstly, two different driveline models are discussed. Both models describe an induction motor which is connected to an inertial load through a double-cardan transmission in Z-configuration (figure 1), and where an external load is applied to the otherwise constant load inertia. The first model is a lumpedparameter description of both the induction motor and the driveline, including the nonlinear torsional dynamics induced by the presence of Hooke’s joints and flexibility in the intermediate shaft. The second model treats the driveline as a flexible multibody system, where both torsional and bending dynamics of the intermediate shaft are represented by means of a modal description of this component. Secondly, this paper also demonstrates some initial estimation results. The more detailed model, with a flexible multibody description of the mechanical part of the driveline, serves as a reference and is used to generate a priori ideal measurements, to which realistic sensor noise is then added. Using the simplified lumped-parameter torsional model as the basis for an Unscented Kalman Filter (UKF, see [5],[6] and [7]) stable estimation results are obtained, which accurately represent the driveline’s torsional behaviour. This is verified by comparing two estimates of dynamic quantities with the corresponding ones in the reference simulation, namely the a priori unknown load torque and the speed of the motor. In order to be able to virtually measure other relevant quantities, to include e.g. bending dynamics, the lumped-parameter model no longer suffices. Therefore, extending the proposed approach to include a flexible multibody model in the UKF is believed to be a valuable next step in this research track. References [1] N. Amann, J. Böcker, F. Prenner. Active damping of drive train oscillations for an electrically driven vehicle. IEEE/ASME Transactions on Mechatronics, Vol. 9, No. 4, pp. 697-700, 2004. [2] B. Blockmans, J. Helsen, F. Vanhollebeke, W. Desmet. Dynamic response of a multi-megawatt wind turbine drivetrain under voltage dips using a coupled flexible multibody approach. In ASME 2013 Power Transmission and Gearing Conference, art. nr. DETC2013-12458, Portland, Oregon, 2013. [3] J.R. Ottewill, M. Orkisz. Condition monitoring of gearboxes using synchronously averaged electric motor signals. Mechanical Systems and Signal Processing, Vol. 38, No. 2, pp. 482-498, 2013. [4] F. Naets, J. Croes, W. Desmet. An online coupled state/input/parameter estimation approach for structural dynamics. Computer Methods in Applied Mechanics and Engineering, Article in Press, doi:10.1016/j.cma.2014.08.010, 2013 [5] D. Simon. Optimal State Estimation. John Wiley & Sons, Inc. Hoboken, New Jersey, 2006. [6] S. J. Julier, J. K. Uhlmann. Unscented filtering and nonlinear estimation. Proceedings of the IEEE, Vol. 92, No. 3, pp. 401-422, 2004. [7] R. van der Merwe, E. A. Wan, S. J. Julier. Sigma-point Kalman filters for nonlinear estimation and sensor-fusion – applications to integrated navigation. In Proceedings AIAA Guidance, Navigation, and Control Conference, pp. 16-19, Providence, Rhode Island, 2004.

Virtual sensing on mechatronic drivetrains using multiphysical models

Virtual Torque Sensing: A Model-based Approach for Indirect Measurement of Dynamic Operational Loads on Mechatronic Powertrains

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