Novel Approach for Model-Based Development - Part II: Developing Virtual Environment and Its Application

Pandey, Nabal Kishore; Vedula, Sashank Mani; Thimmalapura, Satish; Tellikepalli, KumarPrasad

doi:10.4271/2016-01-0322

Cited by 6 publications

(3 citation statements)

References 21 publications

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“…By this measure to revise the RDE calibration and validation procedures, a lesson learned for the current approaches can be taken into account to overcome the high testing efforts and provide benefits for the future calibration process. The lack of reproducibility 21–23 as a result of unpredictable environmental influences during road tests and the significant increase in possible test cases due to the expansion of the test boundary conditions 8,9,24–27 compared to previous legislation and the increasing complexity of powertrains and engine control units (ECUs) 28 requires the integration of X-in-the-Loop approaches 23,30–34 and supporting tests for monitoring RDE validation on the test bench. 12 Manufacturers mainly investigate validation test matrices consisting of fleet-generic cycles and on-road tests with portable emission measurement systems (PEMSs) to assess whether the type approval tests can be passed.…”

Section: Current and Future Challenges To Comply With The Rde Requirementioning

confidence: 99%

Statistically supported real driving emission calibration: Using cycle generation to provide vehicle-specific and statistically representative test scenarios for Euro 7

Claßen

Pischinger

Krysmon

et al. 2020

International Journal of Engine Research

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The progression of emission legislation has intensified the efforts of the automotive industry to develop improved exhaust gas after-treatment systems. The requirement to fulfill Euro 6d-TEMP in real-world driving scenarios, the already significant calibration effort for Euro 6d and the Euro 7 emission standards in discussion have significantly increased the work load for calibration engineers and the requirements for testing resources. Many original equipment manufacturers are implementing taskforces in order not to have to discard the planned start of production for their products, and some are even already forced to reduce their product portfolio. This is due to the diverse testing matrix required to cover all possible real driving emissions test scenarios. One big challenge is the extension and possible variation of boundary conditions regarding ambient temperatures, traffic conditions, road gradients and other varying driving resistances. Moreover, the test duration can cause considerable differences in the measured emissions, even if the same route is driven repeatedly. Addressing these challenges makes the application of a dedicated, event-targeted emission calibration mandatory. Since only a few sequences of the time-consuming road tests are relevant for improving the emission calibration, the methodology presented in this article focuses on the exact reproduction of these emission events on an emission chassis dynamometer with the aim of implementing calibratable solutions for these events. This is done using a real driving emission-cycle-generator which creates real driving emission compliant severe test scenarios and which focuses on the statistical relevance related to the typical product specific operation. The underlying generation process accesses a large database with real driving emission measurement results focusing on vehicle- or vehicle-group-specific challenges, using statistical approaches. It will be demonstrated how this procedure reduces test time and how it helps to tackle the substantial real driving emission work-load, while providing a dependable base to achieve real driving emission legislation compliance.

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Section: Current and Future Challenges To Comply With The Rde Requirementioning

confidence: 99%

Statistically supported real driving emission calibration: Using cycle generation to provide vehicle-specific and statistically representative test scenarios for Euro 7

Claßen

Pischinger

Krysmon

et al. 2020

International Journal of Engine Research

View full text Add to dashboard Cite

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“…For example, qualitative 'Face Validity' is achieved by an expert user who can 'validate' the model based on their knowledge of how the real system operates under given conditions. [15][16][17][18][19][20][21] More advanced quantitative statistical techniques can range from evaluation of percentage error and root mean square errors (RMSs) between simulation and experimental results for given characteristics, [22][23][24][25][26] through to more comprehensive evaluation of signal correlation such as use of the 'R-Squared' method as shown by Wang et al 27 and some methods which use the frequency domain in their analyses to capture high frequency transient phenomena. 28,29 The single metric approaches have also been extended to multi-step validation procedures, for example, in the approach of Klemmer et al 30 where a two stage method for validation of a vehicle model based on a signal correlation, a correlation rating and a validation coverage rating was presented.…”

Section: Introductionmentioning

confidence: 99%

Multi-fidelity validation algorithm for next generation hybrid-electric vehicle system design

Stevens

Early

Cunningham

et al. 2019

Proceedings of the Institution of Mechanical Engineers, Part D:

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With the evolution of increasingly complex hybrid-electric vehicle powertrains, the process of creating validated system models has become progressively more difficult to achieve. Increasing levels of confidence in how instantaneous vehicle energy states are captured is needed to take full advantage of the fuel consumption and emissions reduction potential of the vehicle to move towards more sustainable transportation systems. While many strategies for model validation exist, the majority rely on ascertaining comparisons with global system characteristics, for instance, total fuel consumption over a fixed driving event. However, these methods do not necessarily account for the rapidly fluctuating energy states which need to be understood to optimise the vehicle’s energy management strategy. The current work proposes a new validation approach which captures these instantaneous characteristics taking advantage of the high signal sampling rates available from modern data acquisition equipment rather than relying on drive cycle average or cumulative global behaviours. The method proposed provides a holistic view of the behaviours demonstrated by the vehicle model and identifies regions of poor system validation targeting areas for further model refinement. The algorithm is demonstrated on a new post-transmission, parallel mild-hybrid-electric bus. The model was developed in the MATLAB Simulink modelling environment. The validation algorithm is tested against vehicle dynamometer and test track data. With an increasing volume of mild and full hybrid vehicle configurations emerging, validation strategies such as the one proposed here are increasingly important for the design of energy management strategies to deliver the full potential benefits of the vehicle. The algorithm is proposed in a step by step method which can be automated to limit required user input.

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“…ISO26262 comprises 10 parts and prescribes 43 requirements and recommendations. Both, hardware and software modules are developed in accordance with the development process of the V-Model that employs a model-based design (MBD) approach, thereby ensuring accurate design of a complicated system [4].…”

Section: Introductionmentioning

confidence: 99%

MBD-based Modeling and Dynamic Characteristic Analysis of Electric Vehicle Key Components and Hybrid Control Unit

Yoon¹,

Lin²,

Kim³

2018

IJCA

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In general, electric vehicles have significantly different structures compared to vehicles running on internal combustion engines owing to their use of electric motors as the source of power and batteries as energy-storage devices. Electric vehicles also demonstrate different driving performance and dynamic characteristics, as they are capable of recovering some of the energy wasted in the form frictional heat at the brake through use of regenerative braking during deceleration. The proposed study discusses a mathematical model constructed by identifying characteristics of certain key components and the hybrid control unit (HCU) of an electric vehicle. Corresponding correlations among these components were analyzed. In addition, a dynamic model of the electric vehicle was designed using the model-based design approach in accordance with the standard V-Model development process ISO 26262-an international standard for automotive safety. All models discussed herein were developed using the MATLAB/SIMULINK package, and dynamic characteristics of the vehicle were analyzed through simulations.

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Novel Approach for Model-Based Development - Part II: Developing Virtual Environment and Its Application

Cited by 6 publications

References 21 publications

Statistically supported real driving emission calibration: Using cycle generation to provide vehicle-specific and statistically representative test scenarios for Euro 7

Statistically supported real driving emission calibration: Using cycle generation to provide vehicle-specific and statistically representative test scenarios for Euro 7

Multi-fidelity validation algorithm for next generation hybrid-electric vehicle system design

MBD-based Modeling and Dynamic Characteristic Analysis of Electric Vehicle Key Components and Hybrid Control Unit

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