Continuous slow dynamic slope approach for stationary base internal combustion engine mapping

Engine control optimization, with its always growing complexity, is in permanent focus of engine researchers and developers all over the world. Automotive engines are dominantly used in dynamic conditions, but generally, steady-state operating points are used for building up mathematical models which are later subject to the numerical optimization. For this purpose, a large amount of steady-state regimes needs to be evaluated through experimental work at the engine test stand, which is an extremely time and funds consuming process. Consequently, the methodology for data gathering during engine dynamic excitation could lead to significant savings at the expense of acceptable data accuracy loss. The slow dynamic slope method starting from a stationary operating point was evaluated by several authors in the past. In this paper, slow dynamic slope method with exclusively transient excitation will be presented drawing attention to some of its advantages and drawbacks. The rate of change of engine load as a main control parameter during dynamic test is of great importance for the quality of the final data and for total test duration. In this regard, several tests of different duration were applied for fixed engine speed values to cover engine speed-load usage domain. An approximation of stationary testing results obtained in this way could be used for evaluation of the map gradients and thus as a guideline for additional stationary tests based on design of experiment method

Continuous slow dynamic slope approach for stationary base internal combustion engine mapping

Engine control optimization, with its always growing complexity, is in permanent focus of engine researchers and developers all over the world. Automotive engines are dominantly used in dynamic conditions, but generally, steady-state operating points are used for building up mathematical models which are later subject to the numerical optimization. For this purpose, a large amount of steady-state regimes needs to be evaluated through experimental work at the engine test stand, which is an extremely time and funds consuming process. Consequently, the methodology for data gathering during engine dynamic excitation could lead to significant savings at the expense of acceptable data accuracy loss. The slow dynamic slope method starting from a stationary operating point was evaluated by several authors in the past. In this paper, slow dynamic slope method with exclusively transient excitation will be presented drawing attention to some of its advantages and drawbacks. The rate of change of engine load as a main control parameter during dynamic test is of great importance for the quality of the final data and for total test duration. In this regard, several tests of different duration were applied for fixed engine speed values to cover engine speed-load usage domain. An approximation of stationary testing results obtained in this way could be used for evaluation of the map gradients and thus as a guideline for additional stationary tests based on design of experiment method

Engine calibration: multi-objective constrained optimization of engine maps

International audienceWe present two new approaches to address the optimization problem associated with engine calibration. In this area, the tuning parameters are traditionally determined in a local way, i.e., at each engine operating point, via a single-objective minimization problem. To overcome these restrictions, the first method we propose is able to cope with several objective functions simultaneously in the local formulation. The second method we put forward relies on a global formulation, which allows the whole driving cycle to be taken into account while remaining single-objective. At the practical level, the two methods are implemented by combining various existing techniques such as the LoLiMoT (Local Linear Model Tree) parameterization and the MO-CMA-ES (Multi-Objective Covariance Matrix Adaptation Evolution Strategy) algorithm. A better compromise appears to be achieved on real case applications. Keywords Engine calibration · Response surface · LoLiMoT · Multi-objective optimization · Evolutionary algorithm Nomenclature Abbreviations (by alphabetical order

Multi-objective constrained optimization of engine maps

International audienceNowadays, automotive manufacturers are submitted to strong constraints in engine calibration: lowest fuel consumption, emission-control legislation and driver requests for driving comfort and performances. These constraints lead to an increasingly complexity of the engines and thus an increasingly number of parameters to be tuned, making the empirical engine calibration by a scan of parameter values impossible at engine test-bench. New methodologies in automated engine calibration based on statistics and optimization have emerged in order to limit the number of experimental tests to be run. The optimization problem of engine calibration consists in the determination of engine tuning parameter maps that minimize the cumulated fuel consumption and pollutant emissions, under combustion noise constraints, on a driving cycle. The usual way to get this result is to select specific operating points representing this cycle in the engine working range and to define upper bounds applied on the different engine responses (allocations) for each of them, in order to obtain a weighted sum of these local responses respecting the global targets. The underlying problem is a multi-objective optimization problem: different compromises between fuel consumption, noise and pollutant emissions on each operating point are possible. We propose an adapted optimization method based on the MO-CMA-ES method (Multi-objective Covariance Adaptation Evolution Strategy) which takes into account the non trivial limits of the engine parameter variation domains and some robustness constraints. An other point addressed in this paper is the map optimization which consists in a global optimization of engine responses cumulated on the driving cycle. This method avoids the cumbersome choice of allocations for each considered operating point and includes directly the map regularity constraints in map parameterizations. Finally, application on real dataset obtained at automated test-bench for a diesel engine are presented

A virtual engine laboratory for teaching powertrain engineering

A virtual engine laboratory application for use in automotive engineering education is proposed to allow the practical teaching of powertrain calibration. The laboratory software is built as a flexible Matlab tool that can easily be transferred for applications in other disciplines and promotes the link between teaching and research

Faster Development of AUTOSAR compliant ECUs through simulation

International audienceVirtualization allows the simulation of automotive ECUs on a Windows PC executing in a closed-loop with a vehicle simulation model. This approach enables moving many development tasks from road or test rigs and HiL (Hardware in the loop) to PCs, where they can often be performed faster and cheaper. Technical challenge: How to port ECU tasks and basic software to Windows PC with reasonable effort, so that key development tasks can be performed on a PC, without the need of accessing real hardware such as vehicle prototypes, test rigs or HiL facilities. This paper presents a new solution for the use case of ECUs developed within the emerging AUTOSAR standard: First, the AUTOSAR authoring tool AUTOSAR Builder (Dassault Systèmes) is used to design the application software and system aspects of a single ECU or an distributed embedded system which is then stored as AUTOSAR XML descriptions. The application code can either be developed in the AUTOSAR Builder environment or auto-generated by tools such as Embedded Coder (MathWorks), TargetLink (dSPACE) or Ascet (ETAS). Once tested in AUTOSAR Builder, selected software components or compositions can be exported including an AUTOSAR OS (Operating System) and RTE (Run- Time Environment) as an FMU (Functional Mockup Unit). FMU [4] is a new exchange format for models that has been developed in the EU-funded MODELISAR project (2008 - 2011) and since then gained considerable acceptance across multiple industries and tools. The FMU can then be imported into the virtual ECU tool Silver (QTronic), where it can be co-simulated with vehicle models originating from a wide range of simulation tools, including Dymola, SimulationX, MapleSim and AMESim. Vehicle models are again provided as FMUs, or via proprietary binary export formats, typically Windows DLLs. Tools for measurement and calibration such as CANape (Vector Informatik) or INCA (ETAS) can then be connected to the virtual ECU running on PC, to directly measure or tune its parameters, like an engineer would do in a real car. Virtual ECUs are also used to move testing activities from test rigs and HiLs to Windows PC

Application of Permutation Genetic Algorithm for Sequential Model Building–Model Validation Design of Experiments

YesThe work presented in this paper is motivated by a complex multivariate engineering problem associated with engine mapping experiments, which require efficient Design of Experiment (DoE) strategies to minimise expensive testing. The paper describes the development and evaluation of a Permutation Genetic Algorithm (PermGA) to support an exploration-based sequential DoE strategy for complex real-life engineering problems. A known PermGA was implemented to generate uniform OLH DoEs, and substantially extended to support generation of Model Building–Model Validation (MB-MV) sequences, by generating optimal infill sets of test points as OLH DoEs, that preserve good space filling and projection properties for the merged MB + MV test plan. The algorithm was further extended to address issues with non-orthogonal design spaces, which is a common problem in engineering applications. The effectiveness of the PermGA algorithm for the MB-MV OLH DoE sequence was evaluated through a theoretical benchmark problem based on the Six-Hump-Camel-Back (SHCB) function, as well as the Gasoline Direct Injection (GDI) engine steady state engine mapping problem that motivated this research. The case studies show that the algorithm is effective at delivering quasi-orthogonal space-filling DoEs with good properties even after several MB-MV iterations, while the improvement in model adequacy and accuracy can be monitored by the engineering analyst. The practical importance of this work, demonstrated through the engine case study, also is that significant reduction in the effort and cost of testing can be achieved.The research work presented in this paper was funded by the UK Technology Strategy Board (TSB) through the Carbon Reduction through Engine Optimization (CREO) project

Optimization of adaptive test design methods for the determination of steady-state data-driven models in terms of combustion engine calibration

This thesis deals with the development of a model-based adaptive test design strategy with a focus on steady-state combustion engine calibration. The first research topic investigates the question how to handle limits in the input domain during an adaptive test design procedure. The second area of scope aims at identifying the test design method providing the best model quality improvement in terms of overall model prediction error. To consider restricted areas in the input domain, a convex hull-based solution involving a convex cone algorithm is developed, the outcome of which serves as a boundary model for a test point search. A solution is derived to enable the application of the boundary model to high-dimensional problems without calculating the exact convex hull and cones. Furthermore, different data-driven engine modeling methods are compared, resulting in the Gaussian process model as the most suitable one for a model-based calibration. To determine an appropriate test design method for a Gaussian process model application, two new strategies are developed and compared to state-of-the-art methods. A simulation-based study shows the most benefit applying a modified mutual information test design, followed by a newly developed relevance-based test design with less computational effort. The boundary model and the relevance-based test design are integrated into a multicriterial test design strategy that is tailored to match the requirements of combustion engine test bench measurements. A simulation-based study with seven and nine input parameters and four outputs each offered an average model quality improvement of 36 % and an average measured input area volume increase of 65 % compared to a non-adaptive space-filling test design. The multicriterial test design was applied to a test bench measurement with seven inputs for verification. Compared to a space-filling test design measurement, the improvement could be confirmed with an average model quality increase of 17 % over eight outputs and a 34 % larger measured input area

Dynamic modelling of diesel engine emissions using the parametric Volterra series

The design of powertrain controllers relies on the availability of data-driven models of the emissions formation from internal-combustion engines. Typically these are in the form of tables or statistical regression models based on data obtained from stabilised experiments. However, as the complexity of engine systems increases, the number of experiments required to obtain the effects of each actuator becomes large. In addition, the models are only valid under stable operating conditions and do not give any information as to dynamic behaviour. In this paper, the use of the Volterra series (dynamic polynomial models) calculated from dynamic measurements is presented as an alternative to the steady-state models. Dynamic measurements of gaseous exhaust emissions were taken for a 2.0 l automotive diesel engine installed on a transient engine dynamometer. Sinusoidally based excitations were used to vary the engine speed, the load, the main injection timing, the exhaust gas recirculation valve position and the fuel injection pressure. Volterra models calculated for nitrogen oxide and carbon dioxide emissions presented high levels of fit with R2 values of 0.85 and 0.91 respectively and normalised r.m.s. error values of 6.8% and 6.6% respectively for a cold-start New European Driving Cycle. Models for carbon monoxide and total hydrocarbon emissions presented poorer levels of fit (normalised r.m.s. errors of 26% and 17% respectively), with difficulties in obtaining the high non-linearities of the measured data, notably for very high emission levels. </jats:p

Optimization for engine calibration

International audienceNowadays, automotive manufacturers are submitted to strong constraints in engine calibration such as: low fuel consumption, emission-control legislation and driver requests for driving comfort and performances. These constraints lead to an increasing complexity of the engines and thus an increasing number of parameters to be tuned, making the empirical engine calibration by a scan of parameter values impossible at engine test-bench. New methodologies in automated engine calibration based on statistics and optimization have emerged in order to limit the number of experimental tests to be run. The optimization problem of engine calibration consists in the determination of engine tuning parameters that minimize the cumulated fuel consumption and pollutant emissions on a driving cycle generally associated with legislation norms. This cycle is decomposed in a set of stationary operating points of the engine characterized by its speed and its torque (the transient behaviors of the engine are not taken into account in the stabilized calibration). Then, the optimal tuning parameters of the engine should be defined for each operating points, the functions defining these parameters on the whole engine operating domain are called the engine maps. These two-dimensional optimal engine maps are then integrated in the engine control unit in the vehicle. We illustrate the difficulties associated with this application and propose adapted optimization methodologies: LoLiMoT models for engine map parameterization in order to handle intrinsic constraints on the map regularity, multi-objective optimization method based on CMA-ES approach. Finally , application on real dataset obtained at IFP automated test-bench for a diesel engine are presented. 2. Keywords: Engine calibration, LoLiMoT, Multi-objective optimization, Evolutionary algorithm 3. Introduction Engine calibration consists in fulfilling the engine tuning maps that are used in engine controls of the vehicle, i.e. in defining the optimal tuning of parameters used by engine control strategies. Due to the highly increased number of these parameters (especially for diesel engines but spark ignition engines are following the same trend) and the reduction of the development schedule available for the calibration process, manual tuning of engine parameters is now replaced by mathematically assisted calibration process. Such a process is based on the design of experiments with associated modeling methods, in order to reduce the number of tests used to build engine response models depending on engine control parameters, and optimization techniques to determine the optimal settings within the model definition domain. In order to perform the tests in a more productive way, these mathematical techniques are generally associated with test automation, requiring well controlled measurement methods and reliable test equipments. This paper describes the optimization methods developed for this application and illustrates their effectiveness on a real case of a common rail diesel Engine. The first section introduces the classical steps of the calibration process and discusses the associated difficulties. In the second section, we propose the Multi-Objective Covariance-Adaptation Evolutionary Strategy method for solving the optimization problem associated with a given engine operating point defined by the engine speed and the engine load. In the third part, an integrated approach is proposed in order to directly optimize the engine maps on the whole driving cycle (associated with legislation norms) instead of the individual optimization of each engine operating point. 4. Engine calibration 4.1. Sketch of the engine calibration process The emission calibration workflow is classically divided into four steps: 1. a preliminary phase consisting in choosing a sample of operating points (referred to as OP in the