Multiphysics simulation tools for designing motors for traction applications in hybrid and electric vehicles

Motor manufacturers are facing a difficult challenge in designing traction motors for the latest generation of hybrid and all-electric vehicles. The efficiency with which these motors can perform is critical, as it impacts on the vehicle range and battery life. Many of the issues involved in the motor design have a complex nature which requires multiple fields of physics such as electromagnetics (EM), mechanics and thermal analysis. All these physics are usually interdependent and have to be considered collectively in order to obtain optimal performance for a particular scenario. This paper presents a multiphysics simulation tool that was implemented to address this situation. The Opera FEA software suite [1] was developed to include a multiphysics analysis that can link several EM, thermal and stress analyses. Opera’s Machines Environment (parameterised template software for designing motors and generators) has been extended to allow easy setup of coupled multiphysics analyses such as EM to thermal and EM to stress. In order to further facilitate the coupling of different analyses, a link to the Python programming language was embedded in Opera FEA software. The embedded Python facility offers options to perform certain post-processing operations during the solving stage and hence allow data transfer between different stages of the multiphysics analysis. It also extends Opera’s capabilities to interact with other FEA software

A Platform-Based Software Design Methodology for Embedded Control Systems: An Agile Toolkit

A discrete control system, with stringent hardware constraints, is effectively an embedded real-time system and hence requires a rigorous methodology to develop the software involved. The development methodology proposed in this paper adapts agile principles and patterns to support the building of embedded control systems, focusing on the issues relating to a system's constraints and safety. Strong unit testing, to ensure correctness, including the satisfaction of timing constraints, is the foundation of the proposed methodology. A platform-based design approach is used to balance costs and time-to-market in relation to performance and functionality constraints. It is concluded that the proposed methodology significantly reduces design time and costs, as well as leading to better software modularity and reliability

Comparison of embedded and added motor imagery training in patients after stroke: Study protocol of a randomised controlled pilot trial using a mixed methods approach

Copyright @ 2009 Schuster et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.Background: Two different approaches have been adopted when applying motor imagery (MI) to stroke patients. MI can be conducted either added to conventional physiotherapy or integrated within therapy sessions. The proposed study aims to compare the efficacy of embedded MI to an added MI intervention. Evidence from pilot studies reported in the literature suggests that both approaches can improve performance of a complex motor skill involving whole body movements, however, it remains to be demonstrated, which is the more effective one.Methods/Design: A single blinded, randomised controlled trial (RCT) with a pre-post intervention design will be carried out. The study design includes two experimental groups and a control group (CG). Both experimental groups (EG1, EG2) will receive physical practice of a clinical relevant motor task ('Going down, laying on the floor, and getting up again') over a two week intervention period: EG1 with embedded MI training, EG2 with MI training added after physiotherapy. The CG will receive standard physiotherapy intervention and an additional control intervention not related to MI.The primary study outcome is the time difference to perform the task from pre to post-intervention. Secondary outcomes include level of help needed, stages of motor task completion, degree of motor impairment, balance ability, fear of falling measure, motivation score, and motor imagery ability score. Four data collection points are proposed: twice during baseline phase, once following the intervention period, and once after a two week follow up. A nested qualitative part should add an important insight into patients' experience and attitudes towards MI. Semi-structured interviews of six to ten patients, who participate in the RCT, will be conducted to investigate patients' previous experience with MI and their expectations towards the MI intervention in the study. Patients will be interviewed prior and after the intervention period.Discussion: Results will determine whether embedded MI is superior to added MI. Findings of the semi-structured interviews will help to integrate patient's expectations of MI interventions in the design of research studies to improve practical applicability using MI as an adjunct therapy technique

A component-oriented programming framework for developing embedded mobile robot software using PECOS model

A practical framework for component-based software engineering of embedded real-time systems, particularly for autonomous mobile robot embedded software development using PECOS component model is proposed The main features of this framework are: (1) use graphical representation for components definition and composition; (2) target C language for optimal code generation with small micro-controller; and (3) does not requires run-time support except for real-time kernel. Real-time implementation indicates that, the PECOS component model together with the proposed framework is suitable for resource constrained embedded systems

Efficient Neural Network Implementations on Parallel Embedded Platforms Applied to Real-Time Torque-Vectoring Optimization Using Predictions for Multi-Motor Electric Vehicles

The combination of machine learning and heterogeneous embedded platforms enables new potential for developing sophisticated control concepts which are applicable to the field of vehicle dynamics and ADAS. This interdisciplinary work provides enabler solutions -ultimately implementing fast predictions using neural networks (NNs) on field programmable gate arrays (FPGAs) and graphical processing units (GPUs)- while applying them to a challenging application: Torque Vectoring on a multi-electric-motor vehicle for enhanced vehicle dynamics. The foundation motivating this work is provided by discussing multiple domains of the technological context as well as the constraints related to the automotive field, which contrast with the attractiveness of exploiting the capabilities of new embedded platforms to apply advanced control algorithms for complex control problems. In this particular case we target enhanced vehicle dynamics on a multi-motor electric vehicle benefiting from the greater degrees of freedom and controllability offered by such powertrains. Considering the constraints of the application and the implications of the selected multivariable optimization challenge, we propose a NN to provide batch predictions for real-time optimization. This leads to the major contribution of this work: efficient NN implementations on two intrinsically parallel embedded platforms, a GPU and a FPGA, following an analysis of theoretical and practical implications of their different operating paradigms, in order to efficiently harness their computing potential while gaining insight into their peculiarities. The achieved results exceed the expectations and additionally provide a representative illustration of the strengths and weaknesses of each kind of platform. Consequently, having shown the applicability of the proposed solutions, this work contributes valuable enablers also for further developments following similar fundamental principles.Some of the results presented in this work are related to activities within the 3Ccar project, which has received funding from ECSEL Joint Undertaking under grant agreement No. 662192. This Joint Undertaking received support from the European Union’s Horizon 2020 research and innovation programme and Germany, Austria, Czech Republic, Romania, Belgium, United Kingdom, France, Netherlands, Latvia, Finland, Spain, Italy, Lithuania. This work was also partly supported by the project ENABLES3, which received funding from ECSEL Joint Undertaking under grant agreement No. 692455-2