An important bottleneck in the design, operation and exploitation of mechatronic powertrains is the lack of accurate knowledge of broadband external loading. This is caused by the intrusive nature of regular torque measurements. This paper proposes a novel non-intrusive approach to obtain torsional load information on mechatronic powertrains. Online coupled state/input estimation is performed through an augmented nonlinear Kalman filter. This estimation approach exploits general lumped parameter physics-based models in order to create a widely applicable framework. This work considers both extended (EKF) and unscented Kalman filtering (UKF) approaches. Contrary to previous works, no considerable difference in accuracy is obtained from experiments, with a considerably lower computational load for the EKF. This work reveals the benefits of including rotational acceleration measurements from a theoretical perspective, which is demonstrated through experimental validation. This drastically increases the broadband accuracy. The result of this work is an accurate and non-invasive virtual torque sensor with a sufficiently broad bandwidth for use in condition monitoring, control and future design optimization.
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.
Improving vehicle passenger safety is of major importance in modern automotive industry. Within this framework, vehicle stability controllers play a key role, as they actively contribute to maintain vehicle driveability even in potentially dangerous situations. An example of such a controller is Electronic Stability Control (ESC), that brakes individual wheels to generate a direct yaw moment to stabilize the vehicle (e.g. from excessive understeer or oversteer). This paper presents the realtime implementation of a stability controller based on measured (and/or estimated) yaw rate and sideslip angle and on phase-plane related stability criteria. The control strategy is first developed in MATLAB-Simulink environment with a simplified vehicle model. Then, the controller is assessed via software-in-the-loop using a full vehicle model developed in Simcenter Amesim, before implementing it on a real-time platform. Results are promising, endorsing the implementation of hardware-in-the-loop using an Electronic Control Unit.
The embedded software in some classes of contemporary cyber-physical systems (CPS) is required to run models of its physical environment in real time and with high accuracy and precision. The question to which extent the embedded software can achieve these requirements largely depends on the properties of the embedded platform (i.e. embedded hardware and middleware). In this paper, we focus on the modelling of some of the properties of an embedded platform and on the co-simulation of these properties with the physics-based models. This will result in an assessment of the influence of the embedded properties on the performance of the physics-based models in early stages of the development of embedded software for CPS.
A rapidly shifting market and increasingly stringent environmental regulations require the automotive industry to produce more efficient low-emission Eletric Vehicles (EVs). Regenerative braking has proven to be a major contributor to both objectives, enabling the charging of the batteries during braking and a reduction of the load and wear of the brake pads. The optimal sizing of such systems requires the availability of good simulation models to improve their performance and reliability at all stages of the vehicle design. This enables the designer to study both the integration of the braking system with the full vehicle equipment and the interactions between electrical and mechanical braking strategies. This paper presents a generic simulation framework for the identification of thermal and wear behaviour of a mechanical braking system, based on a lumped parameter approach. The thermal behaviour of the system is coupled back to the friction coefficient between the pad and the disk to assess its effect on braking performance. Additionally, the effect of wear and temperature on the generation of airborne particles is investigated. Subsequently, experimental data collected on a real EV is used to validate and tune the previously described simulation model, following a proposed validation procedure. The instrumentation method and challenges, as well as the experimental procedure used to collect the data on a chassis dynamometer and in real-world driving conditions, are described. Finally, simulation results for different driving scenarios are used to compare virtual and experimental results.
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