Fuel Efficiency Optimization using Rapid Transient Engine Calibration

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Fuel Efficiency Optimization using Rapid Transient Engine Calibration

2014-01-2359 Published 09/30/2014

Christopher Atkinson Atkinson LLC

CITATION: Atkinson, C., "Fuel Efficiency Optimization using Rapid Transient Engine Calibration," SAE Technical Paper 2014-01-2359, 2014, doi:10.4271/2014-01-2359. Copyright © 2014 SAE International

Abstract Pending GHG emissions reduction legislation for medium- and heavy-duty vehicles will require the development of engines and powertrains with significantly increased mechanical and electronic complexity. Increasing powertrain efficiency will require the simulation, control and calibration of an expanding number of highly interdependent air, fuel, exhaust, combustion and energy transfer subsystems. As a result of these increases in complexity, engine and powertrain control is becoming significantly more sophisticated and costly to develop and difficult to optimize. The high cost of developing engines and powertrain systems that demonstrate greater fuel efficiency and emissions benefits than the engines of today, is undeniable. The increased calibration burden and the complexity of optimization require the development and adoption of entirely new methods for transient engine calibration and optimization to achieve maximum vehicle fuel efficiency and lowest regulated emissions. Model-based rapid transient engine calibration can be used to optimize the performance, fuel efficiency and emissions of high degree-of-freedom engines, off-line, in a timely and efficient fashion.

Introduction Future engines and powertrains will incorporate significant mechanical and electronic complexity. The drive for everincreasing powertrain efficiency will require the simulation, control and calibration of an expanding number of highly interdependent air, fuel, exhaust, combustion and energy transfer subsystems. As a result, engine and powertrain control is becoming significantly more sophisticated to develop and difficult to optimize.

The high cost and increased effort required to develop engines and powertrain systems that demonstrate greater fuel efficiency and emissions benefits than the engines of today is evident. The increased calibration burden and the complexity of optimization require the development and adoption of entirely new methods for controlling and calibrating engines and vehicles to achieve maximum fuel efficiency and lowest regulated emissions. Model-based methods offer significant advantages over traditional testing-intensive methods of control algorithm development and calibration. As a sub-set of model-based methods, data-driven techniques use a limited quantity of actual engine and vehicle operating, performance and emissions data in the development of fully predictive dynamic models. Once developed, these transient data-based models can be used in the off-line environment for calibration optimization, or in the on-line environment for real-time engine control. It should be noted that while the data-driven modeling methods can be generalized, the resultant models are both engine-specific and limited by the engine test operating envelope used to gather the underlying data. These data-based methods are a pragmatic means that allows for the transfer of a significant proportion of the engine control strategy development, refinement and calibration optimization tasks from the high-cost engine and vehicle test cell to the computational simulation environment. This has the potential to reduce development time and costs, and to increase the robustness of the final control and calibration work product.

The Function of Engine Calibration Consider an engine with X interacting subsystems, that utilizes a control system that employs S sensors to provide information that is ultimately used to control A actuators. (Here we assume that multiple similar actuators - for example

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for the control of each cylinder of a multi-cylinder engine - are counted once and not individually). Examples of such engine subsystems might include •

air management, induction or boost management,

•

fuel injection,

•

exhaust gas recirculation management,

•

ignition or combustion management,

•

aftertreatment control and

•

waste energy recovery

•

and may include transmission control and hybridization or integration of the engine and electrical machines.

Examples of sensors might include those responsible for the measurement of mechanical speeds and positions, flows, pressures and temperatures, and chemical composition. Examples of actuators might include those employing force and position to control flows (EGR valves, VGT actuators, intake and exhaust valves, and fuel injector solenoids), and mechanical and electrical engagement. Typically, for adequate control there should be at least as many actuators as subsystems, and at least one sensor per actuator. So this implies that A ≥ X, and S ≥ A, which implies further that S ≥ X or more specifically S ≥ A ≥ X.

While we can write a set of (usually linearized) equations that describe the majority of the physical effects that control the charge mass behavior, in the end we resort to a modeled concept or mathematical artifact such as the notion of a “volumetric efficiency”, ηv. It is into this parameter that we offload a large portion of our lack of knowledge of how the induction process actually works on a fully transient basis. It is also this parameter that then requires significant further calibration. Here we define the procedure of calibration as fitting a nominal parameter to a set of actual operational data, where the parameter is described in discrete terms as a function of other more readily measured engine operating variables (such as speed and load in this case, with corrections for engine operating and intake manifold temperatures, for example).

The “Curse of Dimensionality” in Engine Calibration The industry has seen a 98% reduction in both NOx and PM for heavy-duty CI engines since 1998 (since the first promulgation of US EPA exhaust gas emissions regulations) with the introduction of ∼6 independent control and calibration variables (see Table 1). This corresponds to the addition of roughly ∼1 additional parameter every 2 years. The result of adding control and calibration complexity at this rate has been a ∼15-20% reduction in NOx and PM per calibration parameter added in the intervening years. Table 1. Control and calibration variables added since the 1990s to ensure NOx and PM control in heavy-duty CI engines.

With regard to the control of such an engine, we can develop a set of algorithms or strategies that utilizes individual mappings from sensors to actuators. To first order, the number of unique mappings that are required to be developed for effective control is S × A, which is at least of the order of A2. As an example of a set of mappings that pertains to the operation of a single engine subsystem, we consider the induction process of a turbocharged DI CI engine. We understand that the mass of the charge inducted into each cylinder is a complex function of the engine intake geometry, the intake valve timing and valve lift profile, the engine speed, the intake temperature and pressure, the in-cylinder temperature and the exhaust backpressure, along with other secondary effects. (We know further that internal and external rebreathing and externally controlled EGR have significant impacts on the inducted charge mass). Assume further that we have sensors to measure each of these parameters, thus giving rise to the Sinduction mappings required to predict and hence control just the induction process, as a subset of the total number of sensors S required to control the whole engine. Once the relationships implied by these mappings have been developed, there remains a calibration and optimization task to be completed. This arises due to the fact that the fundamental physics and chemistry of the individual mappings are not fully understood or described, or that the equations developed do not completely capture the full physical, thermodynamic and chemical processes at work.

It should be remembered that there is, to first order, a 10× increase in full factorial potential calibration space per additional calibration parameter. If the period from the 1990s to the present day has been the era of emissions reduction for heavy-duty CI engines, then the next 10-15 years will mark the era of fuel efficiency optimization. It is anticipated that peak engine brake thermal efficiency will increase from ∼45% today to ∼55% by 2025 (as anticipated by the USDOE Super Truck targets). According to Table 2 these improvements in fuel efficiency will require an increase in the number of independent calibrateable

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parameters from ∼6 today to ∼25 by ∼2030 (which corresponds to close to 2 additional parameters every year). We will see a roughly 1% reduction in CO2 per calibration parameter added (for a total of 20-25% reduction), and a 10× increase in the full factorial potential calibration space per additional calibration parameter. This is a clear example of the “curse of dimensionality” in which the addition of each new control parameter designed to bring about an increase in fuel efficiency, also brings about an unsustainable increase in the overall calibration space, leading to an intractable calibration burden. Table 2. Anticipated additional complexity expected to be added to allow future engines to meet fuel efficiency and GHG emissions regulations in heavy-duty CI engines.

transient calibration is far higher, adding the extra temporal dimension, as well as a range of variation in each parameter spanning a wide range of time scales. These time scales vary from a few crank angle degrees in the case of injection and combustion-related phenomena, to a few engine cycles for induction and EGR processes, to many tens of seconds (or minutes) for fast (or slow) thermal or heat transfer processes. In summary, for conventional engine control and calibration techniques, a total of A2 strategies or algorithms are required to be developed, the total number of individual calibration parameters that needs to be developed is of the order of 1000 × A, and in the process of calibration optimization a minimum of 10A+2 discrete engine operating setpoints is required to be investigated. It is clear that as A increases due to increasing engine complexity, the capability to develop suitably calibrated and optimized strategies will in the future become a limiting process (see Figure 1). To put this issue into perspective, for a 7-10 actuator engine today (see Table 1), ∼70-100 strategies need to be developed and roughly 10 000 individual calibration parameters are required to be optimized through an investigation of potentially hundreds of millions of potential engine operating setpoints, steady state and dynamic. Through the use of such techniques as design of experiments (DoE), the potential calibration optimization space can be reduced by as much as two orders of magnitude [1], but these techniques are well-suited only to nominally steady operation, and a significant transient calibration burden remains.

The Scope of the Engine Calibration Problem Returning to the calibration of the notional engine introduced earlier, to first order, the magnitude of the calibration requirement for this engine (the number of discrete calibration parameters that are required to be developed for A actuators) is of the order of 1000 × A. This number accounts for the fact that the calibration “fit” typically varies with engine speed and load (or fueling), engine operating temperature, ambient temperature, ambient pressure and many other variables specific to each calibrateable parameter - normally specified in a tabular form, or “map”. Assuming that we exercise such an engine through a range of 10 discrete setpoints in each of the calibrateable parameters, implies that the number of discrete engine operating setpoints that this engine will visit is of the order of 10A+2. Speed and load (torque or fueling) provide the additional 2 degrees of freedom. Note that this total assumes nominally steady-state (or quasi-steady) engine operation, and the requirement for

Figure 1. The full factorial calibration space for current and future engines showing the exponential increase in the calibration burden as the number of independent control parameters increases.

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Model-Based Methods for Engine Calibration It is generally acknowledged in the automotive industry that model-based methods represent a significant step forward in the development of improved engine control and calibration systems [2, 3]. Model-based methods allow for the transfer of many engine control and calibration tasks from the high cost test cell or test vehicle environment to the much lower cost desktop computational environment. However it is unreasonable to assume that we can readily and cost effectively develop fully transparent, first principles physicsand chemistry-based models for highly complex engines with 7 or more major subsystems in the case of modern SI or CI engines (air management, induction or boost management, fuel injection, exhaust recirculation management, ignition or combustion management, aftertreatment control and waste energy recovery) and ultimately as many as 30 independent actuators. As an alternative to the exhaustive development of hundreds of control strategies and algorithms, we have developed a pragmatic data-driven heuristic approach that requires of the order of 4-5 hours of engine operating data per major control subsystem (or of the order of 30-40 total test hours for a next generation engine). The engine data that is collected is used to develop fully predictive transient engine performance, fuel consumption and emissions models that are then either used in the off-line environment for the calibration of an existing engine control system, or in the on-line real-time environment for engine control. Data-driven models can be generated using any one of a number of statistical or mathematical techniques such as polynomial functions, Volterra series or Kalman filters [4]. We prefer the use of artificial neural networks (ANNs) with the use of engine operating history (to capture transient engine operation). ANNs are favored due to their distinct advantages in efficiently learning the non-linear associations between multiple inputs and outputs, and in their generalization and predictive capabilities [5]. Moreover the choice of model inputs is biased towards the use of fundamental engine control and operating variables, and not derived parameters or mathematical constructs. This is done to avoid having modeled parameters as the inputs to further models, thus preventing a set of cascading or compounding modeling errors.

Data-Driven Methods for Engine Calibration Having established that the calibration burden for a specific engine is a strong function of the control system complexity for that engine, an improved engine control process would be one that requires the development of fewer strategies or algorithms, be self-calibrating, requires fewer engine setpoints to be visited, and be automatically optimized for transient operation [2, 3, 6]. Alternatively in the case of optimizing existing (legacy) engine control systems, we can use data-driven modeling techniques to develop fully-predictive engine models for off-line simulation of engine operation for the purposes of optimizing engine calibration data-sets. Here we employ transient

data-driven forward predictive engine models (for real-time engine performance, fuel consumption and emissions production) for calibration optimization purposes. The datadriven engine model form and logic are shown below.

Engine Operation & Input Control/Calibration Parameters k - current time period k-1 - previous time period, etc. s(k) - engine operating trajectory of speed and fueling u(k) - actual engine control inputs at current time period u(k-1) - actual engine control inputs at previous time period (history) y(k+1) - actual, unmeasured, engine outputs (emissions, fuel consumption, performance) at future time Y(k+1) - predicted engine outputs (emissions, fuel consumption, performance) at future time Ui(k+1) - predicted control inputs, subject to variable emissions, fuel consumption and performance targets (denoted i) Bi - modeled forward weights and biases (fixed by the datadriven modeling process) Di - modeled inverse weights and biases (fixed by the datadriven modeling process) Ci - output performance, fuel consumption and exhaust emissions targets (variable and determined by the engine calibrator) Predicted Engine Outputs (calculated using Forward Predictive Engine Models) Y(k+1) = Bn1·u(k) + Bn2·u(k-1) + Bn3·u(k-2) Predicted Controller/Calibration Parameters (calculated using Inverse Dynamic Control Model for a single step look ahead horizon) Ui(k+1) = Ci · [D1 · Y(k+1) + D2 · Y(k) + D3 · Y(k-1)]

Controller Parameter Calibration and Optimization Ûi(k+1) = optimum{Ui(k+1)} Subject to the emissions, engine performance and fuel consumption constraints U(k) ∊ {umin; umax}

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|U(k+1) - U(k)| < ΔUmax slew (imposing a maximum permissible actuator slew rate) |Yi(k+1) - Yi(k)| < ΔYi max variation (including a maximum permissible torque variation) Yi(k+1) < Yi max (instantaneous emissions production and engine mechanical constraints) The final calibration task therefore remains then to transfer the time-based optimized control inputs into the desired tabular or map-based format in the case of legacy engine control systems, Data-driven model-based engine calibration optimization then does not require the exhaustive calibration of multiple control algorithms and strategies, but rather relies upon the development of optimized real-time (or integrated) targets for engine performance, fuel consumption and emissions production [7, 8]. As such, it represents a significant shift in the direction of and the level of effort required to calibrate engine control systems. Each of the forward transient predictive engine models used has a form similar to the following real-time NOx prediction model:

parameters as engine speed, fueling or torque, start of injection timing, injection or rail pressure, and VGT and EGR valve settings - as well as the immediate operating history of those same parameters. It is by employing the immediate history of the engine operating and control parameters that the dynamic behavior of the engine and its measurable outputs can be effectively captured. A typical set of data-driven engine models used in the rapid transient engine calibration process is given in Table 3, where the emissions models are used to predict the emissions outputs that will arise from a given set of engine control inputs (Figure 5, Appendix), and the engine performance models and engine operating state models are utilized to ensure that any commanded set of control inputs does not cause the engine's mechanical limits to be exceeded. This ensures the validity of any specific set of instantaneous control inputs, regardless of whether it is derived from a calibration table or time-based calibration “string”. Figure 5 shows the spread of input data used in engine modeling, comparing the overall data-set used versus the experimental data for the engine operation on a baseline cycle (here the heavy-duty engine FTP) using the base engine calibration. Careful selection of the model inputs is required to allow the fully transient engine operating space to be covered, as well as to ensure that any predictions of the resultant models are as a result of interpolation and not extrapolation. Table 3. Typical forward engine model parameters (with typical units) and the purpose for their prediction.

Figure 2. Typical transient model prediction capability. Top instantaneous NOx emissions (ppm). Bottom - instantaneous CO emissions (ppm). In each case the red trace is the modeled result and blue trace is the actual data.

As a result of this model formulation, the engine performance, emissions, fuel consumption and operating states can be determined, via data-driven modeling, as a function of such

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Figure 6 (Appendix) shows the interactions between the inputs as imposed during the engine testing and the measured engine outputs, emphasizing that the engine modeling (to be developed using this data) is required to result in interpolation, and never extrapolation.

Engine Calibration Optimization A further application of data-driven model-based techniques lies in the optimization of the calibrations of existing or legacy ‘map-based’ engine control systems. In this process a datadriven transient model of the engine (or powertrain) is developed and the calibration optimization is performed using this model in the off-line simulation environment rather than in the engine test cell. Figures 7 and 8 (Appendix) shows the split in tasks between the high-cost, high-effort engine test facility and the low-cost desktop computational environment. Table 4 shows the task list for the rapid transient calibration optimization process. A typical calibration optimization protocol would be: •

Minimization of the engine fuel consumption over a target engine (or vehicle) operating cycle,

•

While maintaining suitably low regulated exhaust emissions (instantaneous or integrated),

•

While not exceeding upper (or upper and lower) bounds for such engine mechanical outputs such as exhaust temperature, turbocharger speed, compressor outlet temperature and pressure, and peak cylinder pressure.

Through the adoption of these methods, calibration optimization is transformed from a test-intensive, physical process to a computational exercise through the use of fully predictive, transient engine and aftertreatment models in place of the physical powertrain for the optimization process. Calibration shifts from being a process where the engineer modifies entries in a table-based set of engine “maps” and tries to determine the net effect of the calibration change, to one where the engineer sets targets for the engine to achieve and relies on the largely automated optimization process to determine the calibration parameters that best achieve those targets, using such techniques as pareto-optimization. This results in a more robust final calibration product, achieved with 4-5 hours of engine testing per engine sub-system (or 30-40 hours per engine), rather than months of testing [9]

Figure 3. Flowchart of the off-line model-based calibration process.

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References 1. Röpke, K.; Gaitzsch, R.; Haukap, C.; Knaak, M.; Knobel, C.; Neßler, A.; Schaum, A.; Schoop, U.; Tahl, S.; DoE - Design of Experiments, Methods and Applications in Engine Development. Verlag Moderne Industrie 2005. 2. Brahma, I. and Chi J.N.: Development of a model-based transient calibration process for diesel engine electronic control module tables - Part 1: data requirements, processing and analysis, Int. Jour. Of Engine Research. 13:77, 2012. 3. Brahma, I. and Chi J.N.: Development of a model-based transient calibration process for diesel engine electronic control module tables - Part 2: modelling and optimization, Int. Jour. Of Engine Research. 13:147, 2012. Figure 4. Example of the results of a table-based calibration optimization process for a heavy-duty CI engine, showing the evolution of an optimized EGR table.

Conclusions Model-based methods have the potential to reduce the time required to develop suitable control systems and optimized calibrations by a significant margin. Data-driven model-based techniques are a practical and expedient tool to reduce powertrain development time and costs. For calibration optimization, the engineering task is transformed into setting real-time targets for emissions and performance (rather than specifying integrated cycle-based emissions limits), while respecting the physical constraints of engine operation (with respect to such parameters as peak combustion pressures, peak turbocharger speeds, exhaust temperatures, compressor outlet temperatures etc.). In the case of data-driven calibration, the majority of the experimental test cell work is performed upfront, in a focused and directed fashion, rather than in a protracted and extended manner. Validation and verification of the optimized calibration data-sets are then performed. Data-driven methods shift the emphasis away from the high cost physical test environment to the lower-cost computational environment, while reducing the total development effort required to manageable levels. In the case of data-driven model-based control, optimized transient engine control becomes ‘map-less’ or algorithm free. The use of accurate transient forward predictive and inverse control models together with real-time optimization results in an extremely powerful tool for reducing complexity in engine control, while increasing robustness. The data-driven methods described here have demonstrated effectiveness in improving engine fuel efficiency while reducing emissions in both the control and in the calibration of legacy systems. Large reductions in development time and effort are possible with data-driven engine modeling, which is a valuable tool to deal with the significant increase in complexity of future engines and powertrains.

4. Isermann, R.; Munchhof, M.: Identification of Dynamic Systems. Springer-Verlag 2011. 5. Norgaard, M.; Ravn, O.; Poulsen N.K.; Hansen, L.K.: Neural Networks for Modelling and Control of Dynamic Systems. Springer-Verlag 2000. 6. Atkinson, C., Allain, M., Kalish, Y., and Zhang, H., “ModelBased Control of Diesel Engines for Fuel Efficiency Optimization,” SAE Technical Paper 2009-01-0727, 2009, doi:10.4271/2009-01-0727. 7. Atkinson, C., Allain, M., and Zhang, H., “Using ModelBased Rapid Transient Calibration to Reduce Fuel Consumption and Emissions in Diesel Engines,” SAE Technical Paper 2008-01-1365, 2008, doi:10.4271/200801-1365. 8. Atkinson, C., Long, T., and Hanzevack, E., “Virtual Sensing: A Neural Network-based Intelligent Performance and Emissions Prediction System for On-Board Diagnostics and Engine Control,” SAE Technical Paper 980516, 1998, doi:10.4271/980516. 9. Atkinson, C. and Mott, G., “Dynamic Model-Based Calibration Optimization: An Introduction and Application to Diesel Engines,” SAE Technical Paper 2005-01-0026, 2005, doi:10.4271/2005-01-0026.

Contact Information Dr. Chris Atkinson Atkinson LLC [email protected]

Abbreviations A - Actuators ANN - Artificial neural network Bi - Model weights and biases CI - Compression ignition CO - Carbon monoxide CO2 - Carbon dioxide DI - Direct injection DoE - Design of experiments DPF - Diesel particulate filter

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EGR - Exhaust gas recirculation (valve setting)

SCR - Selective catalytic reduction

EPA - US Environmental Protection Agency

SI - Spark-ignition

FTP - Federal Test Procedure

SOI - Start of injection (crank angle degrees)

GHG - Greenhouse gas (emissions)

TQ - Torque (Nm)

IMP - Intake manifold (boost) pressure

USDOE - US Department of Energy

IMT - Intake manifold temperature

VCR - Variable compression ratio

LTC - Low temperature combustion

VGT - Variable geometry turbocharger (setting)

N - Engine speed (rpm)

X - Engine subsystems

NOx - Oxides of nitrogen (ppm) PM - Particulate matter ppm - Parts per million RailP - Rail pressure S - sensors

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APPENDIX

Figure 5. Analysis of data from a heavy-duty CI engine (16-liter, in-line 6 cylinder with EGR and VGT), showing the inter-relationships between the input parameters (speed, fueling, injection timing, rail pressure, EGR setting, and VGT setting) used to develop the engine models for off-line calibration optimization.

Figure 6. Analysis of the output data from the same engine as in Figure 5, showing the inter-relationships between engine output torque, NOx, CO, and CO2.

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Figure 7. The engine control system development and calibration optimization process for a heavy-duty on-highway CI engine, showing the development time and testing-intensive sub-tasks.

Downloaded from SAE International by Christopher Atkinson, Saturday, March 12, 2016 Table 4. The rapid transient calibration process optimization sub-tasks as illustrated in Figure 8.

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Figure 8. Rapid transient calibration process showing the interaction of the software-intensive tasks. A task breakdown is given in Table 4.

The Engineering Meetings Board has approved this paper for publication. It has successfully completed SAE’s peer review process under the supervision of the session organizer. The process requires a minimum of three (3) reviews by industry experts. All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording, or otherwise, without the prior written permission of SAE International. Positions and opinions advanced in this paper are those of the author(s) and not necessarily those of SAE International. The author is solely responsible for the content of the paper. ISSN 0148-7191 http://papers.sae.org/2014-01-2359