A Soft ECU Approach to Develop a Powertrain Control Strategy

THESIS

Presented in Partial Fulfillment of the Requirements for the Degree Master of Science in the Graduate School of The Ohio State University

Andrew Spiegel, B.S.

Graduate Program in Mechanical Engineering

The Ohio State University

2015

Thesis Committee:

Professor Shawn Midlam-Mohler, Advisor

Professor Marcello Canova

Andrew Spiegel

2015

ABSTRACT

Automotive control systems are becoming increasingly complex as consumers and government regulations demand vehicles with better fuel economy, reduced emissions, improved safety, and increased functionality while maintaining performance. The short development time frames of embedded control software in automotive Electronic Control

Units (ECUs) has put additional attention on methods for rapid control development.

Model-based control design is used widely in the automotive industry for the development of embedded control systems for attributes such as reducing development times, lowering cost, and preventing revisions while ensuring quality of complex control systems [1].

The Ohio State University’s Center for Automotive Research (CAR) is working on a research project to develop a novel powertrain control strategy using model-based design techniques. To develop the new control strategy, the Ohio State University (OSU) needed a simplified version of a target vehicle’s state-of-the-art powertrain control strategy.

Because OSU is under time constraints to develop and demonstrate the control strategy, a soft ECU model representing the target vehicle’s powertrain control strategy was developed. The soft ECU model will help speed the development process of the novel powertrain control strategy by serving as a benchmark and starting point for control design.

This thesis describes the approach of developing, verifying, and utilizing a soft ECU model ii in the development process of the novel powertrain control strategy. The soft ECU model was developed in Simulink using model-based control techniques. The soft ECU model, a driver model, and a vehicle plant model were combined to create a complete vehicle model.

The complete vehicle model was verified through Model-In-the-Loop simulations. The accuracy of the components in the complete vehicle model were compared with their respective isolated system accuracies.

The complete vehicle model’s components show good accuracy to their corresponding drive cycle data collected from the target vehicle. The soft ECU model is an accurate benchmark of the target vehicle’s powertrain control strategy. Further modifications can be made to the soft ECU model and driver model to improve the overall accuracy of the complete vehicle model. The soft ECU model will be used as a starting point for the design of the new powertrain control strategy.

iii

Dedicated to my family and friends.

iv ACKNOWLEDGEMENTS

I would first like to thank my advisor, Dr. Shawn Midlam-Mohler, for his guidance and support throughout both undergraduate and graduate school. I would also like to thank

Dr. Marcello Canova for his inclusion on this research project. I would also like to thank the following people who have helped me in my time at the Center for Automotive

Research: Stephanie Stockar, Cristian Rostiti, Vincenzo Colandrea, Salvatore Riccardo,

Luigi Angelino, and Luca D’Avico. Finally, I would like to thank my family and friends for their endless support.

v VITA

March 12, 1991 ...... Born – Bucyrus, Ohio

2009...... Bucyrus High School

2013...... B.S. Mechanical Engineering,

The Ohio State University

August 2013 to Present ...... Graduate Research Associate, Department

of Mechanical Engineering,

The Ohio State University

FIELDS OF STUDY

Major Field: Mechanical Engineering

vi TABLE OF CONTENTS

ABSTRACT ...... ii

ACKNOWLEDGEMENTS ...... v

VITA ...... vi

TABLE OF CONTENTS ...... vii

CHAPTER 1: INTRODUCTION ...... 1

1.1 Motivation...... 1

1.2 Scope...... 2

1.3 Thesis Outline ...... 3

CHAPTER 2: LITERATURE REVIEW ...... 4

2.1 Introduction...... 4

2.2 Automotive ECUs ...... 4

2.3 Model-Based Control Design ...... 6

2.3.1 V-Model ...... 7 2.3.2 Development Phases ...... 8 2.3.2.1 Conceptualization Phase ...... 8 2.3.2.2 System Development Phase ...... 9 2.3.2.3 Verification and Validation...... 10 2.4 Soft ECU Models ...... 13

2.5 Artificial Neural Networks ...... 14

2.6 Model-In-The-Loop Techniques ...... 16 vii CHAPTER 3: CONCLUSIONS AND FUTURE WORK ...... 18

3.1 Conclusions ...... 18

3.2 Future Work ...... 19

BIBLIOGRAPHY ...... 21

APPENDIX: LIST OF SYMBOLS AND ABBREVIATIONS ...... 23

viii LIST OF FIGURES

Figure 1: Engine Control System [4] ...... 5

Figure 2: Transmission Control System [4] ...... 6

Figure 3: V-Model for Model-Based Software Development [5] ...... 8

Figure 4: Model-Based Control Development Approach [6] ...... 10

Figure 5: Verification and Validation Processes [6] ...... 11

Figure 6: Model of an Artificial Neuron [9] ...... 15

Figure 7: Artificial Neural Network [9] ...... 15

ix CHAPTER 1: INTRODUCTION

1.1 Motivation

Automotive control systems continue to become more complex as consumers and government regulations demand that vehicles achieve better fuel economy, reduced emissions, improved safety, and increased functionality while maintaining performance. In addition, car manufacturers must meet consumer demands within relatively short timeframes. These challenges have driven engineers to look for methods to rapidly develop complex control software for automotive embedded systems.

One of the major industry responses for fast development of embedded software in automotive Electronic Control Units (ECUs) is model-based control design. Model-based control design features a variety of standardized techniques that enable efficient development of control systems in a cost-effective way [2]. Model-based design is used widely throughout the automotive industry to reduce development times, lower cost, and prevent revisions while ensuring quality of complex control systems [1].

1 1.2 Scope

Members from the Ohio State University’s Center for Automotive Research (CAR) are working on a research project to develop a novel powertrain control strategy using model-based design techniques. The control strategy’s main purpose is to optimize vehicle powertrains for fuel economy and drivability.

During the project definition stage of control development, the Ohio State

University (OSU) needed a simplified version of a target vehicle’s powertrain control strategy. The purpose of the simplified control strategy is to simulate the behavior of the complete vehicle powertrain when coupled with a driver model and exercised over desired velocity profiles to establish benchmarks of fuel economy, drivability, and performance metrics. A soft ECU model representing the vehicle’s powertrain control strategy was therefore developed to approximate the complex engine and transmission control logic.

The soft ECU model will enable fast development of the new control strategy by serving as a benchmark and starting point for control design. This thesis describes the approach of developing, verifying, and utilizing a soft ECU model in the development of a novel powertrain control strategy.

2 1.3 Thesis Outline

Note: This thesis has been abridged from its original version to conceal proprietary information. To access the complete version, contact Andrew Spiegel, Shawn Midlam-

Mohler, or Marcello Canova.

This thesis document is set up as follows:

o Chapter 2: Literature Review

Chapter 2 gives background information from recent literature related to the

major topics of this thesis: automotive ECUs, model-based control design,

soft ECU models, artificial neural networks, and Model-In-the-Loop

techniques.

o Chapter 3: Conclusions and Future Work

Chapter 3 summarizes the results of the research and provides directions for

the future work.

3 CHAPTER 2: LITERATURE REVIEW

2.1 Introduction

The purpose of this chapter is to give the reader a background on the major topics discussed in this document through the review of relevant literature. The chapter explores topics that are relevant for using a soft ECU model to develop a complex control strategy.

The topics discussed in this chapter include automotive ECUs, model-based control design, soft ECU models, artificial neural networks, and Model-In-the-Loop techniques.

2.2 Automotive ECUs

An Electronic Control Unit (ECU) is a computer with embedded software responsible for the operation of a mechatronic system. The usage of ECUs for automotive applications became widespread in the 1980s with the advent of electronic fuel injection.

Vehicles today use ECUs for the control of several major systems: powertrain, instrumentation, suspension, steering, braking, and infotainment [3]. Properly developed

ECU software can improve performance, efficiency, drivability, and reduce emissions in automotive powertrains [3]. Figures 1 and 2 below show the implementation of an ECU in an engine and a transmission control system respectively. The main ECU for most vehicle powertrains is referred to as the Powertrain Control Module (PCM). The PCM is a

4 combination of both the Engine Control Module (ECM) and Transmission Control Module

(TCM) and may consist of several computers.

Figure 1: Engine Control System [4]

Figure 2: Transmission Control System [4]

2.3 Model-Based Control Design

Model-based control design is becoming an increasingly common practice for the development of automotive ECU software. The model-based control design process involves developing control software around a system or plant model representing the controlled device. A combination of software and hardware techniques are used successively to rapidly develop complex control algorithms for hardware implementation.

The primary benefits for the widespread use of model-based control design are “cycle time reduction, quality improvement, and defect reduction” [5].

6 2.3.1 V-Model

The V-model is a common visual representation used to detail the systems engineering development process. The left side of the V-model involves the design and definition of the system requirements, and the right side of the V-model represents the validation of the corresponding requirements. The two sides meet at the bottom of the V with the implementation of low-level system designs.

When applying the V-model to model-based software development, the traditional systems engineering development is modified to include model simulation. The use of model simulation allows the software developers to define, design, and validate software requirements. In addition, model simulation allows developers to validate requirements before reaching the right, “validation” side of the V-model thus reducing the development cycle time and improving software quality. Figure 3 details an example V-model procedure for model-based software development [5].

Figure 3: V-Model for Model-Based Software Development [5]

2.3.2 Development Phases

The general development process for model-based control design can be divided into three distinct phases: the conceptualization phase, system development phase, and verification and validation phase [6].

2.3.2.1 Conceptualization Phase

Model-based control development begins with the conceptualization and definition of the control problem. Initially, opportunities for a new control design are identified based on the capabilities of the organization and personnel, needs of customers, and knowledge of existing problems. After several opportunities have been screened and evaluated, one is selected and put into engineering context known as the problem definition. The problem 8 definition gives the scope of the control being developed and creates a starting point on which requirements and specifications can be derived. With the problem definition, several control concepts to achieve the requirements and specifications are created, and the best concept is selected for development [6].

2.3.2.2 System Development Phase

The next step in model-based control development is to develop the individual control and plant models. One method for developing plant models is system identification

[6]. System identification is the process of modeling a real physical system with prior knowledge of the behavior of both the system and its external inputs. In some cases, a high- fidelity plant model of the system may already exist and can be used or simplified for control and simulation applications. Control model development begins by determining the appropriate control logic that is both suitable for the plant model and meets the defined requirements. After the control logic has been determined, the control model is developed in individual subsystems starting with its high-level structure. Figure 4 below is a pictorial example of a model-based control development approach.

Figure 4: Model-Based Control Development Approach [6]

Further details on the model-based control conceptualization and development can be found in [6].

2.3.2.3 Verification and Validation

The final development stages of model-based control design are characterized by a series of simulation techniques. The processes known as verification and validation (V&V) occur at each of these development stages and exist to ensure that the product achieves both the requirements and specifications defined in the beginning of the control design process. Verification is used to meet control software specifications, and validation is used to meet control software requirements. Specifications are defined as the way the software completes a desired task, and requirements are defined as the tasks that the software

10 completes. Figure 5 below gives a representation of the verification and validation processes.

Figure 5: Verification and Validation Processes [6]

Model-based control design consists of several well-defined simulation techniques in the verification and validation phase. The major simulation techniques in order of application are Model-In-the-Loop (MIL), Software-In-the-Loop (SIL), Processor-In-the-

Loop (PIL), and Hardware-In-the-Loop (HIL). The simulation techniques are described in more detail below.

Model-In-the-Loop – The first simulation technique involves combining plant, control, and additional models (ex. driver and environment) in a closed-loop system in a program such as Simulink. MIL simulations are fast and easy to implement; therefore, the user can make numerous adjustments to achieve the best control design.

11 Software-In-the-Loop – After the control model is developed in MIL, it is converted into software code by hand or through automatic code generation. The SIL simulation technique uses control software, commonly in C or C++ code, to simulate the plant model in the plant’s original environment. SIL simulations are fast and verify that the control software functions like the control model from MIL.

Processor-In-the-Loop – PIL is an intermediate step between SIL and HIL and involves uploading the control software onto ECU hardware or a microprocessor. The ECU or microprocessor connects with the plant model in its simulation environment through ethernet or another interface. PIL simulations are used to verify that the control software will execute properly based on the ECU hardware specifications.

Hardware-In-the-Loop – HIL is usually the last simulation technique used before implementing a new control strategy in the ECU of a real vehicle. HIL is characterized by simulating the ECU and its control software with a computer representing the real, controlled system or systems through a wiring harness. The purpose of HIL is to verify how the ECU will interact with the real, controlled components in real-time. A benefit of

HIL simulations are to test operating conditions that are normally dangerous with the real components.

Further details on Model-In-the-Loop techniques are discussed in section 2.6.

12 2.4 Soft ECU Models

A soft ECU or ECU behavioral model is a simplified version of the control software implemented in an ECU. Soft ECUs are usually developed in Simulink and only run in virtual environments. A soft ECU is different from a virtual ECU in complexity and functionality. Although no exact definition separates a soft ECU from a virtual ECU, a virtual ECU usually has the same software components and comparable functionalities as the real ECU. Soft ECUs can be used in all phases of control development. The soft ECU described in this thesis is used in the conceptualization phase to define the state-of-the-art control strategy and in the system development phase as a starting point for control system development. Soft ECUs are commonly used in the verification and validation phase through MIL, SIL, and HIL techniques [7].

Soft ECU models have several advantages for control development. Because the soft ECU model is simple, it can be developed and calibrated in a short amount of time. In addition, the simplicity of the soft ECU allows fast simulations to facilitate algorithm development and diagnostics testing. Soft ECUs can be used in instances where an ECU is unavailable such as a prototype ECU for HIL simulations. The soft ECU described in this paper is used to both create a benchmark of a control strategy’s capabilities and use its components when developing a new control system [8], [7].

Although the advantages of soft ECU models for control development are notable, the models have several limitations. Because soft ECU models are simplified, the models lack the fidelity and functionality of a virtual or real ECU. Depending on the level of 13 complexity, the soft ECU model may only be representative of the ECU software under certain conditions. For example, the soft ECU model described in this document was developed to work in steady-state operating conditions in a fully-warmed engine.

2.5 Artificial Neural Networks

An Artificial Neural Network (ANN) is a type of heuristic model used to represent the input-output behavior of a system. Artificial neural networks were originally developed when trying to find mathematical models to mimic the human brain. The base element of an ANN is the artificial neuron as shown in Figure 6 below. An artificial neuron derives its outputs by multiplying its inputs by weights, summing the values, and entering them into a sigmoid function. An ANN is comprised of several artificial neurons arranged in layers as shown in Figure 7. The input layer receives the input values and sends them to the first hidden layer of neurons. Depending on the complexity of the ANN, several hidden layers may exist, and each hidden layer receives inputs from the previous layer’s outputs. The last layer of artificial neurons is called the output layer and receives its inputs from the last hidden layer [9].

Figure 6: Model of an Artificial Neuron [9]

Figure 7: Artificial Neural Network [9]

15 When an ANN is being developed, its capabilities are defined by its structure and weights. The structure of the ANN “is chosen so that the system provides a sufficient nonlinearity” [9]. Because the ANN is a type of “learning” algorithm, the ANN needs a sufficient amount of training data for the learning process. The training data must consist of a variety of both the inputs and desired outputs for a good approximate of the modeled system. The ANN “learns” by having its weights modified usually with a backpropagation algorithm and an optimization with the training data. The backpropagation algorithm is a method of minimizing the error between the ANN’s output and the desired output starting from the output layer to the input layer [9].

Artificial neural networks have several advantages and disadvantages for control design. ANNs can be used to represent a plant or control without any physical modeling involved. Additional advantages include adaptability from its ability to learn, robustness to errors in inputs or component failures, and its representation of nonlinear systems.

Disadvantages of ANNs include errors in convergence to training data and the need to train before usage. Additional disadvantages include the requirement of a large amount of training data and high software and hardware requirements [9].

2.6 Model-In-The-Loop Techniques

As described previously, Model-In-the-Loop denotes the technique of simulating plant, control, and additional models in a closed-loop system. The purpose of the MIL is to run tests on a virtual system to study and characterize how a real, physical system will react. MIL simulations most frequently run in software programs like Simulink or 16 LabVIEW that allow users to build and evaluate system models. In model-based control development, MIL techniques allow verification and validation to occur early in the development process [10].

Model-In-the-Loop techniques have many positive attributes for model-based control development. Because MIL simulations allow early verification and validation of control software, it reduces both development time and costs. In addition, MIL gives engineers an early understanding of the control system being developed and thus enables good communication among developers. MIL simulations are fast and relatively simple to implement; therefore, the developer can make numerous adjustments to achieve the best control design. Finally, because MIL testing occurs in a virtual environment, engineers can simulate cases that are normally not feasible or safe with the real, physical system [10].

Model-In-the-Loop techniques are used in a variety of applications in the literature.

Reference [11] uses MIL to verify and validate a reliable model-based control for a variable valve engine. Reference [12] uses MIL techniques to test the implications of new engine features, control and plant calibrations, and powertrain architectures. In this thesis, MIL techniques are used to demonstrate a proof-of-concept soft engine control model and create a complete vehicle model to benchmark a control strategy.

17 CHAPTER 3: CONCLUSIONS AND FUTURE WORK

3.1 Conclusions

This thesis describes an approach of creating and verifying a soft ECU model to aid in the development of a novel powertrain control strategy. The soft ECU model developed in the course of this project is able to capture the target vehicle’s control strategy for its powertrain actuators. The soft ECU model concept was developed using a bottom-up approach and verified.

Once the soft ECU model concept was developed, the remaining individual components of a complete vehicle model were developed and integrated. Additional members of the OSU research team created an energy-based vehicle plant model to characterize the powertrain and vehicle dynamics of the target vehicle. The isolated soft

ECU model was integrated with a driver model and the vehicle plant model to create a complete vehicle model.

The complete vehicle model was verified over several drive cycles through Model-

In-the-Loop simulations to meet accuracy requirements. The driver model’s proportional- integral (PI) controller gains were tuned to give the best representation of the real driver pedal positions before beginning simulations. The vehicle plant model shows good

18 accuracy with data collected from the real vehicle. The soft ECU model also had good accuracy with corresponding data from the real vehicle. Complete vehicle model simulations showed a decrease in accuracy from isolated model simulations due to the combination of errors from individual components.

3.2 Future Work

Although the components of the complete vehicle model showed good accuracy with the real vehicle, several improvements can be made. The soft ECU model can use further modifications to gain accuracy in both the current and desired engine air values which impact all set-points. In particular, the throttle model accuracy can use improvement at high throttle conditions. Implementing a simplified lock-up strategy will yield partial and complete lock-up conditions and improved lock accuracy. The vehicle model can also be implemented with a wheel slip model to improve the engine torque and vehicle speed accuracies. Finally, additional modifications should be made to improve the driver accelerator pedal at high speed conditions.

The soft ECU model will be used further in the development of the novel powertrain control strategy. Benchmark values on fuel economy, drivability, and performance metrics will be collected over several drive cycles from the complete vehicle model. The new powertrain control strategy will be integrated with the vehicle plant model to demonstrate improvements from the benchmarks set by the soft ECU model. The soft

ECU model will be used as a starting point in the development of the new powertrain

19 control strategy. Finally, the soft ECU model’s algorithms responsible for the actuator set- points will be used in the first two generations of the powertrain control strategy.

20 BIBLIOGRAPHY

[1] Michaels, L., Pagerit, S., Rousseau, A., Sharer, P., Halbach, S., Vijayagopal, R.,

Kropinski, M., Matthews, G., Kao, M., Matthews, O., Steele, M., Will, A. "Model-

Based Systems Engineering and Control System Development via Virtual

Hardware-in-the-Loop Simulation." SAE Technical Paper, 2010-01-2325, 2010.

[2] "Model-Based Design for Production Real-Time Embedded Systems."

http://www.mathworks.com/services/consulting/proven-solutions/model-based-

design.html/

[3] "Electronic Control Unit (ECU) - Webinar, Basics of Automotive ECU."

www.etas.com/data/group_subsidiaries_india/20140121_ETAS_Webinar_ECU_

Basics.pdf/

[4] Yurkovich, Steve. ECE 5554, Lecture 4. Spring 2015.

[5] Dong, Y., Li, M., Josey, R. "Model Based Software Development for Automotive

Electronic Control Units." SAE Technical Paper, 2004-21-0038, 2004.

[6] Gurusubramanian, Sabarish. “A Comprehensive Process for Automotive Model-

Based Control.” Master’s thesis, The Ohio State University, 2013.

21 [7] Himmler, Andreas. “From Virtual Testing to HIL Testing – Towards Seamless

Testing.” SAE Technical Paper, 2014-01-2165, 2014.

[8] Cain, Trevor., Mallory, Ryan., Fabien, Brian., and Reinhall, Per. “University of

Washington Modeling and Simulation White Paper.” SAE Technical Paper, 2014.

[9] B. Baumann, G. Rizzoni and G. Washington, “Intelligent Control of Hybrid

Vehicles Using Neural Networks and Fuzzy Logic,” SAE Technical Paper, 981061

1998.

[10] Bringmann, E., Kramer, A., “Model-based Testing of Automotive Systems,”

Software Testing, Verification, and Validation, 2008 1st International Conference

on, vol., no., pp. 485, 493, 9-11 April 2008.

[11] K. Suzuki, S. Asano, “Model-Based Technique for Air-Intake-System Control

Using Thermo-Fluid Dynamic Simulation of SI Engines and Multiple-Objective

Optimization,” SAE Technical Paper, 2011-28-0119, 2011.

[12] Lomonaco, J., DiValentin, E., and Black, M. “Powertrain Modeling Advances,”

SAE Technical Paper, 2007-01-1463, 2007.

22 APPENDIX: LIST OF SYMBOLS AND ABBREVIATIONS

ANN Artificial Neural Network

CAR Center for Automotive Research

ECM Engine Control Module

ECU Electronic Control Unit

HIL Hardware-In-the-Loop

MIL Model-In-the-Loop

OSU The Ohio State University

PCM Powertrain Control Module

PI Proportional-Integral

PIL Processor-In-the-Loop

SIL Software-In-the-Loop

TCM Transmission Control Module

23 V&V Verification and Validation