diesel engine system design || diesel engine system dynamics, transient performance, and electronic...

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909

14Diesel engine system dynamics, transient

performance, and electronic controls

Abstract: This chapter forms the foundation of dynamic engine system design based on transient performance and engine controls. The system dynamics approach adopted shares a common ground with the engine controls community. The chapter begins with discussions on the differences between steady-state and transient performance and the effects of hardware design and software control strategy on transient turbocharged engine performance. It further covers mean-value models, crank-angle-resolution high-fidelity real-time models, air path model-based controls, and fuel path controls. It also illustrates virtual sensor modeling with an example of the exhaust manifold gas temperature sensor. Furthermore, it is pointed out that analytical controller design is an important area where engine system design can make significant contributions.

Key words: system dynamics, transient performance, engine controls, mean-value model, real-time model, model-based controls, sensor, controller design.

14.1 Overview of diesel engine transient performance and controls

14.1.1 The roles of engine hardware design and software controls

Engine controls fulfill the requirements derived from performance, emissions, and durability. Moreover, electronic controls enable flexible hardware designs in modern engines, such as variable valve actuation (VVA) and high-bandwidth-controlled variable combustion systems. Over the years, electronic controls, consisting of software and calibration, have become a core part of engine fundamental architecture. When engine hardware design and electronic controls are integrated, two challenges are faced: (1) What hardware should be controlled (i.e., which sensors and controllers should be able to achieve the settings quickly and reliably)? (2) How should the hardware be controlled (i.e., how to create the optimum algorithm and seamlessly integrate it in the hierarchical structure of the system design with the best trade-offs to minimize transient emission spikes and pumping loss)? Engine air system hardware needs to be designed to have acceptable transient capability to minimize turbocharger lag, transient pumping loss,

910 Diesel engine system design


fuel consumption and emissions, and to ensure fast warm-up. Transient simulation plays a critical role in the development process, from predicting vehicle transients to evaluating electronic control strategies. The commonly encountered vehicle transients include load response, vehicle launch, acceleration, and driving cycles. Moreover, predicting the transients can help to analyze both performance and durability issues, and hence reveal their parametric dependency. For example, compressor wheel fatigue life can be evaluated from the transient load calculations for a turbocharger, and the impact of gear shifting during driving cycles on component durability life can be assessed as well.

14.1.2 The difference between steady-state and transient performance

The transient acceleration or deceleration process of naturally aspirated diesel engines can be approximated by a continuous series of steady-state operating conditions. However, in turbocharged diesel engines, during transients the turbine power is not equal to the compressor power, and the turbocharger speed is affected by turbocharger inertia and turbocharger power imbalance. There is a turbocharger lag during which the compressor boost pressure gradually changes to reach a new steady state. The EGR circuit has a certain volume and there is a transient dynamic response of an EGR purging and filling process in the intake manifold. The transient response is usually in the order of several engine cycles. There is also an air–fuel ratio smoke limit for the maximum fueling, depending on the available air flow. During transients, air–fuel ratio, EGR rate and in-cylinder metal wall temperature (due to thermal inertia) are all different from those in the steady state. The resulting deteriorated combustion efficiency and pumping loss cause differences in emissions and fuel economy between steady-state and transient. Valve overlap also has a large impact on transient acceleration performance. With a large overlap, at the beginning of fast acceleration, the exhaust manifold pressure can be much higher than the intake manifold pressure for various reasons (e.g., EGR valve closed). The high engine delta P results in a large reverse flow of residue gases from the exhaust manifold into the cylinder and the intake manifold. The increased residue gas fraction reduces the air–fuel ratio and retards the vehicle acceleration. In contrast, a small valve overlap helps transient acceleration for the same reason.

14.1.3 Controlling engine transient performance

Steady-state emissions testing establishes appropriate NOx, PM, HC, and CO throughout the engine speed–load domain by setting the engine calibration

911Diesel engine transient performance and electronic controls


parameters including fuel injection timing and pressure, VGT vane opening, EGR valve opening, and intake throttle valve opening. Coordinated controls of the EGR valve and the VGT and coordinated controls of the EGR valve and the intake throttle valve have been studied extensively (Nieuwstadt, 2003). Boost pressure (or air–fuel ratio) and EGR rate are mapped in the speed–load domain during engine steady-state calibration. But the combined control of the EGR valve and the VGT for transient EGR rate and air–fuel ratio is challenging. Steady-state engine calibration may set the VGT vane and the EGR valve opening positions to establish an air–fuel ratio and EGR rate, but those steady-state air–fuel ratios and EGR rates are not achievable during fast transients due to turbocharger lag, even if the transient gains are applied to the steady-state set points of the positions in the lookup table of the calibration maps. Compared to steady-state emissions levels, there are three strategies for designing the transient emission profiles in engine controls: (1) overshoot spike vs. time; (2) undershoot or slow approach vs. time; and (3) a compromise to closely match a predetermined target without excessive overshoot or undershoot. These strategies depend on the transient fueling rate changes. Each strategy has different transient trade-offs between NOx and soot and different characteristics of transient engine delta P and pumping loss. For example, a transient NOx spike is normally caused by shutting off EGR during fast acceleration. Design of each transient emissions profile with proper combination of hardware and electronic controls is important. The difference on emissions margin between steady-state and transient can be predicted at the early stage of engine development. In addition, the requirements of transient emissions and driving cycle fuel economy can be met through proper designs. Meeting a composite emissions target in the US supplemental emissions test (SET) 13-mode steady-state does not mean the engine can meet the same emissions target during an FTP transient cycle. In fact, as the NOx emission regulation becomes more stringent, the relative difference between the FTP transient emissions and the steady-state emissions becomes larger. It would translate to an unreasonably low steady-state emissions development target in order to meet the FTP transient emissions target. Therefore, a dynamic high EGR rate and adjustments of fuel injection timing and injection pressure during transients must be used to reduce the transient emissions. During transient operation, the purpose of engine air system controls is to calculate and regulate the EGR valve and the turbocharger actuator settings to achieve minimum emissions and maintain drivability. The settings may be obtained from the position lookup tables or may be calculated by model-based control algorithms. Various engine control approaches can be used with different effects on engine transients. The conventional approach uses various gain-based transient controllers



to regulate the EGR valve and the VGT vane opening. But even with the most sophisticated map-based gain settings, it is still difficult to achieve a prescribed emissions trade-off during speed and load transients. Model-based control is promising in shaping the transient emissions profiles as a function of time.

14.1.4 Analysis of hardware design for engine transients

Turbocharged diesel engines cannot respond to a sudden change in speed or load as fast as naturally aspirated diesel engines because the change in compressor air flow lags behind the change in the fueling rate. The reasons for turbocharger lag include: (1) because the manifold has certain volumes, it takes time (usually several engine cycles) to gradually build up the gas pressure in the exhaust manifold and the intake manifold; (2) during fast acceleration, after the EGR valve is closed it takes time to purge EGR out of the intake manifold; and (3) because the turbocharger rotor assembly has a certain moment of inertia, it takes time for the turbine to gradually accelerate the compressor to a higher speed under the difference between the turbine power and the compressor power. For smoke control, fueling rate and engine power are restricted during the fast acceleration transients according to the available intake boost pressure. Reducing the volume of the intake manifold and the exhaust manifold can reduce turbocharger lag. For example, pulse turbocharging with a smaller manifold volume has a better transient response than constant-pressure turbocharging. Matching the turbine size with a small flow area for low speed–load conditions can help build up the turbine power faster during transients and reduce turbocharger lag. Note that at high speeds and high loads, a wastegated turbine or VGT needs to be used to prevent over-boosting. Another way to reduce turbocharger lag is to use low turbo inertia, such as: (1) reducing the turbine size; (2) using two smaller turbochargers to replace one big unit; (3) using a low-inertia small high-pressure-stage turbocharger in a two-stage turbocharger system; and (4) using a ceramic turbine rotor. Other methods that may reduce turbocharger lag and improve the smoke limit and transient response include: (1) placing the EGR valve close to the intake manifold to minimize the EGR purging time; (2) reducing the heat losses in the cylinder and the exhaust manifold; (3) using a small valve overlap; (4) improving the transient combustion efficiency; (5) retarding fuel injection timing to increase the turbine inlet exhaust temperature; and (6) using boost-assisting devices such as injecting extra air or mechanical supercharging during fast acceleration. One important consideration for turbocharger selection is controlling compressor surge during transients. On the compressor map, the operating trace of a fast speed-increase transient is located at the right side of the steady-state point. The operating trace of the transient of a fast load-increase



or fast speed-decrease is located on the left side of the steady-state point, possibly resulting in compressor surge. The compressor surge during fast transients happens when the compressor flow rate, which responds quickly to engine speed change, decreases much faster than the lagged compressor boost pressure. The boost pressure is affected by turbocharger shaft speed and turbo inertia. There is a need to simulate the fast deceleration to check compressor surge in turbocharger matching. In addition to the turbocharger, other engine hardware design evaluations for transient acceleration performance usually include the effects of engine inertia, charge air cooler or inter-stage cooler volume and cooling medium temperature, manifold volume and piping, EGR circuit volume and EGR purging time, and the location of the aftertreatment components. Engine electronic controls are reviewed by Stobart et al. (2001). Reviews on diesel engine controls are provided by Shigemori (1988), Gant and Alves (1990), Anderson (1991), Winterbone and Jai-In (1991), Hirschlieb et al. (1995), Guzzella and Amstutz (1998), Guzzella and Onder (2004), and Grondin et al. (2004). Overviews on engine model-based controls are provided by Hafner (2001), Lehner et al. (2001), Smith et al. (2007), Turin et al. (2007, 2008), Šika et al. (2008), Stobart et al. (1998), Atkinson et al. (2009), and Guzzella (2010). Engine control process development is decribed by Kämmer et al. (2003), Baumann et al. (2004), and Erkkinen and Breiner (2007).

14.2 Turbocharged diesel engine transient performance

Turbocharged diesel engine transient performance has been extensively researched experimentally and analytically since the 1970s. The transient performance of variable-geometry turbochargers was investigated by Lundstrom and Gall (1986), Pilley et al. (1989), Brace et al. (1999), and Filipi et al. (2001). Dynamic optimization and the differences between the steady-state and transient conditions were researched by Wijetunge et al. (1999). They addressed the subsystem coordination and interaction during the dynamic transients. The transient performance of the EGR circuit in a diesel engine was evaluated by Serrano et al. (2005). Diesel engine transient operation was systematically summarized in a book authored by Rakopoulos and Giakoumis (2009). Computer simulations on turbocharged diesel engine transient performance were conducted by Ledger and Walmsley (1971), Watson and Marzouk (1977), Winterbone et al. (1977), Marzouk and Watson (1978), Watson (1981), Ma and Gu (1990), Qiao et al. (1992), Ma and Agnew (1994) and a group of researchers at the Universidad Politecnica de Valencia in Spain (Payri et al., 1999, 2002; Benajes et al., 2000, 2002), as well as a group of researcher at National Technical University of Athens in Greece (Rakopoulos



et al., 1997b, 2004, 2005, 2007; Rakopoulos and Giakoumis, 2006, 2007, 2009; Theotokatos and Kyrtatos, 2001). Combustion heat release rate models were explored by Watson et al. (1980), Felsch et al. (2009), and Serrano et al. (2009a, 2009b). Unlike previous research that focused on the impact of engine hardware on transient performance, Watson (1984) proposed a modeling methodology that linked the relationship between transient performance and engine controls. The needs of engine performance simulation with electronic controls have started to emerge ever since.

14.3 Mean-value models in model-based controls

Mean-value real-time transient models are the primary simulation approach used in gasoline and diesel engine controls. In order to reduce computing time, the mean-value model uses engine maps such as the volumetric efficiency map and the exhaust manifold temperature map as a function of other dependent parameters such as engine speed, load, and boost pressure. The model does not have the resolution at a crank-angle level. The engine thermodynamic and flow states are represented by their respective single mean values over the engine cycle. Important progress in this area are detailed as follows. Real-time modeling of diesel engines was started in the 1980s by Shamsi (1980), Hendricks (1989), and Jensen et al. (1991). The real-time mean-value models on gasoline engines are summarized by Hendricks et al. (1996) on intake manifold dynamics, Chevalier et al. (2000) on the validity of the mean-value models, and Buckland et al. (2000) on the application for direct injection gasoline engines. Important research on the turbocharged diesel engine modeling for nonlinear engine controls was performed by Kao and Moskwa (1995). More advanced modeling of mean-value models after the 1990s was investigated by Moraal and Kolmanovsky (1999), Allmendinger et al. (2001), Eriksson (2002), Jung et al. (2002), Chung et al. (2005), Fiorani et al. (2006), Eriksson (2007), Pettiti et al. (2007), Chen (2008), and Olin (2008). A lot of the above-mentioned modeling work was conducted using MATLAB/Simulink or other programming languages. GT-POWER is a leading software tool used for engine system simulation, commercially available from Gamma Technologies. Mean-value modeling in GT-POWER including controller simulations has been presented by Silvestri et al. (2000), Papadimitriou et al. (2005), He (2005), He et al. (2006), and He and Lin (2007). The above discussion is for the engine air system. On the fuel system side, a real-time model of fuel injection dynamics used for hardware-in-the-loop (HIL) test is presented in Woermann et al. (1999).



14.4 Crank-angle-resolution real-time models in model-based controls

The high-fidelity crank-angle-resolution real-time model is more advanced than the mean-value model. Instead of using the prescribed cycle-averaged ‘mean value’ maps, it can predict the in-cylinder cycle process details at a crank-angle level without losing the real-time capability. This type of model is the direction for future development. The real-time crank-angle-resolution model without manifold gas wave dynamics is presented in Schulze et al. (2007). The real-time crank-angle-resolution model including the manifold gas wave dynamics features is developed by Pacitti et al. (2008). Other related work has been reported by Wurzenberger et al. (2009).

14.5 Air path model-based controls

14.5.1 Lookup table approach in air system controls for engine transients

In the traditional lookup table controls, the set point is usually either an actuator position such as the EGR valve duty cycle or a performance parameter such as MAF (mass air flow), MAP (manifold air pressure), lambda (equivalent air–fuel ratio) or air–fuel ratio, and intake manifold oxygen concentration. During transients, the air system component actuator such as the EGR valve or VGT vane can be driven to a pre-determined position, which is obtained from the steady-state calibration at a given engine speed and load and then superpositioned with the transient PID gains and the transient algorithms with a feedback PID control. The moving part within the actuator has transient response characteristics against time. The transient delay produces a non-instant response to the demand from the engine control unit, and the delay is due to a characteristic time constant. In the MAF control, the actuator such as the EGR valve is modulated to achieve a preset air–fuel ratio through a PI or PID controller fed by the feedback difference between the required MAF and the sensor signal. The sensor signal can be either the actual measured signal or a calculated signal from a virtual sensor. The lookup table approach is not flexible enough to handle real-world engine variations. One example of the limitation of the lookup table is the aftertreatment dynamic dosage control, e.g., hydrocarbon dosing in active deNOx catalyst or urea dosing in SCR. The lookup table approach cannot compensate for the interferences from factors such as production variation, component aging, and engine acceleration or deceleration transients. During transient turbocharger lag or valve lag, the dosage rate determined from the steady-state calibration lookup tables may not be suitable and could result in an unacceptable slip amount.



14.5.2 Model-based controls for engine transients

Traditional engine controls used lookup tables. The calibration complexity increases exponentially as new functions and the number of associated control tables or maps increase in the development of modern turbocharged EGR engines. Electronic controls have been evolving toward the mathematical model-based controls, either open loop or closed loop, which have become an important part of system design and diagnosis. The success of online model-based controls relies largely on the accuracy of the thermodynamic cycle performance models. The models can be built for various operating conditions such as new and aged engines, normal and extreme climates. Model-based air system controls use the sensors and the actuators to sense and control the gas fl ows inside and outside the cylinder, such as using engine valve actuation, EGR valve, intake throttle valve, exhaust back-pressure valve, and turbine vane actuator. Model-based cooling system controls use the sensors and actuators for coolant fl ows. In order to provide fl exible cooling as needed to minimize driving power consumption and enhance engine performance, the desirable cooling system in the future is to use more electronic controllers in addition to the current pump, fan, and thermostat. The gas-side data and coolant-side data may be linked by a model of heat rejection and other engine performance parameters. In the coordinated model-based control of EGR valve and turbine actuator, according to the theory in Chapter 4, the valve opening and the actuator setting may be calculated by specifying two performance targets among the following three parameters: fresh air fl ow rate, EGR rate, and intake manifold boost pressure. The engine performance parameters used in model-based controls can be either the measured signals from real sensors, or the simulated data from virtual sensors. The opening position of valves (e.g., EGR valve, intake throttle valve, exhaust back-pressure valve) can be calculated based on target performance parameters. For example, the gas mass fl ow rate through an EGR valve orifi ce can be modeled by using the equation of isentropic compressible fl ow of the ideal gas:

m Cm C

A p

R TEGm CEGm CR Em CR Em CEGR EEGm CEGm CR Em CEGm C GR

A pEGRA pupstream

gasR TgasR TupsR TupsR T treamm C= m Cm CR Em C= m CR Em C

¥ Ê

ËÁÊÁÊËÁË

ˆ¯̃ˆ˜ˆ¯̄̃

2– 1

· – 2/k

k

kt

t upstream

dp pˆp pˆ̂˜ˆp pˆ˜ˆdownsp pdownstreap ptreamp pm

t

pownsdownsdoownsododownsdod tream

upstream

t t

pÊp pÊp pËÁÊÁÊp pÊp pÁ

p pÊp pËÁË

ˆ¯̃ˆ˜ˆ¯̄̃

( +t t( +t t1)t t1)t t/t t/t tk kt tk kt tk kt tk kt tk kt tk kt t( +k k( +t t( +t tk kt t( +t t1)k k1)t t1)t tk kt t1)t t/k k/t t/t tk kt t/t t

14.1

where AEGR is the theoretical effective fl ow area of the EGR valve orifi ce at a given opening and CEGR is a variable correction coeffi cient. AEGR is obtained from the correlation between valve lift and valve fl ow area. CEGR



is calibrated by measurement data at various engine flow conditions and is used to correct any inaccuracy in the theoretical valve flow area. To meet the valve actuator control demand, the required valve flow area can be calculated first from the desired EGR mass flow rate at any given engine speed and load by rearranging equation 14.1. Then, the valve area can be converted to the valve opening position or lift. In model-based controls, in order to achieve a desirable transient EGR rate (not necessarily the minimum deviation from the steady-state EGR rate), a target transient rate profile may be flexibly defined to a certain shape as a function of the rate of transient speed–load change. For example, the target transient EGR rate can be imposed by a fixed EGR percentage multiplied by the measured total engine gas mass flow rate. It is worth noting that in model-based control the dynamic behavior of the component can also be modeled. To illustrate the effect of different engine control methods on transient performance, a step increase in the fueling rate followed by a step decrease is shown in Fig. 14.1. As a result, engine speed and brake torque change through three steady-state modes, named A, B, and C. Such an event is a typical representation of real-world driving cycles. Due to turbocharger lag, there are inevitable delays in the exhaust manifold pressure and the intake manifold boost pressure during the fast transient of fueling rate change. The instant fueling increase results in a fast decrease in the air–fuel ratio at the beginning of the transient event, possibly reaching the smoke limit. The level of EGR rate commanded by the engine controls has a direct impact on the air–fuel ratio, NOx and soot emissions during the transient. For example, in the method of EGR valve position control, the EGR valve opening undergoes sharp changes from the steady-state calibration opening of mode A to those of modes B and C. The resulting EGR flow reduces the air–fuel ratio and increases the transient soot while keeping the transient NOx low. On the other hand, in the method of lambda (i.e., equivalent air–fuel ratio) control or MAF control, in order to maintain the steady-state setting of air–fuel ratio during transients, the EGR valve is commanded to close when the fueling rate suddenly increases, and to open more when the fueling rate suddenly decreases. As a result, during a step load increase, less or even zero EGR flow is obtained. Consequently, a higher air–fuel ratio and lower transient soot are achieved, but the transient NOx and pumping loss (due to a higher engine delta P) may be higher compared with the position control. The transient soot spike is an inevitable phenomenon during fast accelerations due to turbocharger lag. The task of the EGR control is to regulate the EGR flow to minimize the transient emission spikes or to achieve the best NOx–soot trade-off with the best compromise on drivability. Meanwhile, EGR control needs to be coordinated with turbocharger control. The intake manifold boost pressure can then be properly controlled. Changes in intake and exhaust restrictions, production variations, and changes in ambient conditions can

© W

oodhead Publishing Limited, 2011

Transient responses of position control method

Transient responses of lambda control (solid) and model-based control (dashed lines)

Turbo lag

Mode C

NOx

Soot

Time

Time

Time

Time

Use pre-set position of valve opening

A Steady state mode B

Air–fuel ratioBoost

pressure

Engine delta P

EGR rate

EGR rate

Fuel rate

EGR rate

Reach smoke limit

High soot due to EGR on

High pumping loss (lambda control)

Reduced pumping loss

Transient EGR rate target inmodel-based control

Reach zero to match pre-set A/F ratio in lambda control

Low NOx due to EGR on

Air–fuel ratio

NOx

Soot

Above smoke limit

Transient soot in model-based control

Lower soot dueto higher A/F

ratio

Low transient NOx in model-based

control

Transient A/F ratio target in model-

based controlHigh Nox due to EGR off in lambda control

14.1 Illustration of transient engine controls and air system performance.



be promptly responded to. It is difficult for the position-based control to accomplish these tasks. The MAF or MAP control can only partially fulfill the requirements. A promising model-based control may finally compute the actuator position such as the EGR valve opening or the VGT vane position accurately with a calibrated model based on the preset desirable engine performance parameters. The preset parameters may include the desirable transient EGR flow rate profile or the dynamic transient fuel injection control parameters, or the desirable NOx or soot limit if the real-time NOx or soot model is available. Another advantage of model-based control is that its steady-state engine calibration set points and transient calibration gains are much less hardware-dependent than in the position-based controls, because the set points are more fundamental engine performance parameters, such as air–fuel ratio and EGR rate, rather than the valve opening or the vane opening of a particular EGR valve or VGT. Figure 14.1 illustrates the concept of model-based EGR rate control or air–fuel ratio control in order to reduce transient NOx and pumping loss. It also demonstrates how to optimize the trade-off between NOx and soot during load increase and decrease. The VGT effective area opening or turbine wastegate opening can be modeled similarly with an orifice flow equation (4.57) and by using the turbine power equation. Model-based turbocharger control may reduce turbocharger lag and prevent transient compressor surge. A detailed theory of model-based VGT and EGR nonlinear controls was outlined by Ammann et al. (2003). In summary, hardware design needs to match electronic control strategies so that the inherent transient difficulties (e.g., turbocharger lag or high transient pumping loss) can be best alleviated. Air path control strategies for turbocharged diesel engines usually include the research topics of coordinated EGR–VGT control algorithms, controls by MAP, MAF or exhaust manifold pressure, and controller designs. These topics were extensively researched by Watson and Banisoleiman (1988), Winterbone and Jai-In (1988), Gissinger et al. (1990), Duffy et al. (1999), Shirawaka et al. (2001), Wijetunge et al. (2004), Nieuwstadt et al. (2000), Osborne and Morris (2002), Nieuwstadt (2003), Ammann et al. (2003), Kolmanovsky and Stefanopoulou (2000, 2001), Kolmanovsky et al. (1999), Yokomura et al. (2004), Mueller et al. (2005), Kobayashi et al. (2005), Darlington et al. (2006), Black et al. (2007), Luján et al. (2007), Das and Dhinagar (2008), Plianos and Stobart (2008), and Moulin et al. (2009).

14.6 Fuel path control and diesel engine governors

Fuel path control is another important area in diesel engine controls. The modeling work includes three main areas: (1) engine speed and governor controls; (2) real-time modeling of fuel system hydraulic dynamics for HIL; and (3) fuel delivery unevenness detection and misfire detection, and their



model-based controls. The fuel path dynamics of a diesel engine is very different from that in a port-injection gasoline engine because the diesel engine does not have such issues as wall-wetting and mixing/evaporation during the fuel transporting process. Mechanical governors and modern electric governors have always been used in diesel engines to control engine speed (SAE J830, 1999). Diesel fuel control and governor designs are part of the fuel system development. Diesel engine governors and fuel path control were investigated by Gant (1984), Okazaki et al. (1990), Bazari (1990), Rakopoulos et al. (1997a), Mruthunjaya and Dhariwal (2000), Stefanopoulou and Smith (2000), Larisch and Sobieszczanski (2001), Makartchouk (2002), Chatlatanagulchai et al. (2009), and Deng et al. (2010). Fuel delivery unevenness detection and misfire detection are important for fuel injection quantity control, especially real-time correction of fuel injection failures. The unevenness refers to the difference in fuel quantity between the cylinders or cycles. Macián et al. (2005, 2006a, and 2006b) explored this area of fault diagnostics and proposed improved control algorithms and controller designs. The conventional diagnosis techniques use the instantaneous crankshaft speed during an engine cycle as input information. Although these techniques are able to detect misfire at low engine speeds, they are not very effective at high speeds, especially at low loads (Macián et al., 2006a). Other engine system performance parameters were explored as better alternatives to replace or supplement the crankshaft speed signal for the purpose of fault diagnosis and control of the injection and combustion processes in order to better detect fuel delivery unevenness and misfire at high engine speeds. These parameters explored included exhaust manifold pressure, instantaneous turbocharger speed, and the mean temperature in the exhaust port. Macián et al. (2006b) reported that using instantaneous turbocharger speed as input signal was effective for fuel quantity control in order to achieve satisfactory correction on fuel injection unevenness. A system-level fault diagnosis model can be developed in engine system design by combining the thermodynamic performance and engine dynamics submodels to facilitate fuel system control development.

14.7 Torque-based controls

Torque-based controls are widely used in both gasoline and diesel engines to facilitate the overall powertrain control demands. Engine system design models can play a key role in developing the more accurate torque-based control models because the diesel engine system design engineers analyze engine torques in their daily work and have a thorough understanding of the engine torque behavior during steady-state and transient conditions. System engineers can complement control engineers very well in this area.



For example, the friction torque and pumping loss torque calculations/estimates used in the torque-based controls can be refi ned and enhanced by the system design. Moreover, more advanced parametric dependency of the engine indicated torque and brake torque can be developed by the engine system design engineers for the torque-based control models in order to accurately calculate the engine torques under various operating or aging conditions. Torque-based controls are discussed by Ginoux and Champoussin (1997), Müller and Schneider (2000), Greff and Günther (2001), Heintz et al. (2001), Lee et al. (2001), Park and Sunwoo (2003), Wang and Chu (2005), Katsumata et al. (2007), and Livshiz et al. (2004, 2008) for gasoline engines; and Maloney (2004), Li et al. (2002), Grünbacher et al. (2003), Chauvin et al. (2004), Brahma et al. (2008), Tian et al. (2008), and Oh et al. (2009) for diesel engines.

14.8 Powertrain dynamics and transient controls

14.8.1 Transient performance simulation

Transient engine testing is much more diffi cult and expensive than steady-state testing. Using engine cycle performance simulation to study transients is a very effective approach, especially for real-time transient modeling. The engine crankshaft lumped model has the governing equation 5.20. In transient engine torque simulation, the ‘fi lling-and-emptying’ method is usually used to model the zero-dimensional manifold gas dynamics in order to reduce the computing time. There are several key issues related to model accuracy in transient cycle simulation. They are listed below.

1. During the fast acceleration transient, a sudden increase in fueling causes a rapid decrease in the air–fuel ratio that may result in incomplete combustion. Combustion effi ciency as a function of air–fuel ratio was usually assumed as input in the thermodynamic cycle transient simulation. Engine testing or sophisticated combustion simulation that tries to quantify such effi ciency change remains challenging.

2. Another diffi culty is related to the turbocharger. The instantaneous turbocharger speed NTC (revolution per second) is given by

4 d

d =

– 2 ,p INdNd W W W

NTCTC T CW WT CW W – W W – T C – W W – TC

TCtfW fW

14.2

where ITC is the moment of inertia of the turbocharger, WTWTW is the turbine power, WCWCW is the compressor power, and

is the moment of inertia of the turbocharger,

is the moment of inertia of the turbocharger, W TCfW fW , is the turbocharger

bearing friction power. The turbine instantaneous temperature, pressure and effi ciency affect NTC and turbocharger lag. Transient turbocharging performance modeling is usually approximated by a quasi-steady-state approach, which computes the instantaneous varying turbocharger



parameters within an engine cycle by looking up the steady-state turbocharger maps. Understanding the difference between the steady-state turbine efficiency and the transient efficiency remains challenging. There are large discrepancies between the conclusions from different authors (Capobiano et al., 1989; Westin and Angstrom, 2002) who obtained their data using different measurement methods (e.g., pulsating gas stands or on engine in-situ) or CFD simulations. As to the turbine flow rate, it was reported that the turbine under unsteady conditions has a 3–6% higher swallowing flow capacity (i.e., the corrected mass flow rate) than that measured at steady state (Capobiano et al., 1989). The heat losses, volume, and thermal inertia of the exhaust manifold also have significant effects on the accuracy of turbocharger lag and engine transient performance simulation.

3. Transient engine simulation and accurate prediction of driving cycle fuel economy demand accurate engine control models that reflect the engine calibration set points in the speed–load domain, actual transient control strategies, and the dynamic response of the sensors and the actuators.

4. Accurate simulations of cold start, warm-up and hot start transients are important for analyzing transient emission cycles.

5. Transient simulation also requires accurate models of mechanical losses from main friction components (i.e., piston–ring–liner assembly, bearings and valvetrain) and parasitic losses from the accessories (Taraza et al., 2007).

14.8.2 Real-time high-fidelity versus mean-value transient simulations

Real-time high-fidelity simulation with a detailed instantaneous in-cylinder process and manifold gas wave dynamics is currently available. Mean-value models have been used to conduct real-time simulations for engine control design and vehicle driving cycle analysis prior to the real-time high-fidelity modeling. The mean-value models use a simplified map-based approach without calculating the in-cylinder cycle process at a crank-angle-level resolution. Typically, volumetric efficiency, indicated efficiency, and exhaust energy fraction are built in maps as functions of certain parameters such as engine speed, fueling rate, intake manifold pressure, and air–fuel ratio. The mean-value model has some disadvantages: (1) tremendous efforts need to be spent to run the DoE data upfront to build the maps that are actually hardware-specific and primitive; and (2) the model cannot predict the turbocharger lag transient conveniently and accurately. Mean-value models may use a large time step in the order of one engine cycle, while a detailed in-cylinder process model requires 1–5∞ crank angle resolution. The theory on the mean-value model is provided in Schulten and Stapersma (2003). There is a trend to develop



and use the real-time high-fidelity simulation to replace the mean-value models for future transient analysis in order to increase the predictability of the model. Moreover, in engine control simulations, software-in-the-loop (SIL) and hardware-in-the-loop (HIL) are used to validate the algorithms for both steady state and transient operations.

14.9 Sensor dynamics and model-based virtual sensors

14.9.1 Classification of engine sensors

The model-based controls of valves and turbochargers mentioned above are essentially about actuators. In their equations, it is noted that the engine temperature, pressure, and flow parameters can be from either actual measurement or virtual sensor modeling. Model-based virtual sensors may replace some actual physical sensors in the engine and aftertreatment systems in order to reduce cost or to enable flexible controls. For instance, switching combustion mechanisms between different speed–load regions can be achieved by predicting the in-cylinder parameters based on the combustion process. The development of virtual sensors relies largely on thermodynamic performance modeling. Engine sensors can be classified into two categories, the physical sensors (or real sensors) and the virtual sensors. The physical engine sensors usually include: the crank position sensor and the cam position sensor for sensing engine speed, TDC position, fuel injection timing and duration; the accelerator pedal position sensor (SAE J1843, 2009); the pressure sensors for manifold absolute air pressure (MAP), barometric ambient, DPF inlet pressure, oil, and fuel; the temperature sensors for intake manifold air, exhaust manifold gas, DPF inlet and outlet gases, oil, fuel, and coolant; the mass air flow (MAF) sensor; the exhaust oxygen or lambda sensor; the NOx and NH3 sensors used in aftertreatment; and the oil and coolant level sensors. The virtual sensors can be classified by algorithm into two types: physics model based, and empirical data based (e.g., response surface models or neural network models). Moreover, the virtual sensors can be divided into steady-state virtual sensors and dynamic transient virtual sensors. They require different computing algorithms. An example of the virtual sensors is provided in Section 14.9.3.

14.9.2 Development needs for engine sensors

There are abundant needs for future virtual sensors in both steady-state and transient for the next generation of ‘intelligent’ diesel engines. The needs in future development include the following:



∑ steady-state virtual MAF and MAP sensors∑ other steady-state virtual sensors of gas flow, pressure, and temperature

which can be calculated using the 19 equations (4.39–4.57) governing the engine air system described in Chapter 4

∑ a virtual engine torque sensor in torque-based controls of powertrain central torque-demand coordination

∑ transient virtual sensors for in-cylinder real-time quantities, for example air temperature, pressure and charge equivalence ratio during the compression stroke in HCCI burning control

∑ engine-out NOx, PM/soot, and HC virtual sensors based on the in-cylinder cycle process calculation with heuristic macro-parameter-dependent emissions models

∑ virtual sensors for aftertreatment flow parameters and outlet emissions built based on heuristic models

∑ combining all of the above to enable a whole engine model with affordable computing time for integrated virtual sensing. A fully validated physical model can then be used in real-time predictive algorithms of engine controls.

Engine performance and emissions modeling is the foundation for building these advanced virtual sensors. This area opens many challenges and opportunities for system integration analysis and design. Automotive sensors are introduced in Westbrook and Turner (1994). An overview of diesel engine sensors (physical sensors) is provided by Challen and Stobart (1998). Physics-based virtual sensors are discussed by Grimes et al. (2005). Overviews on neural network based virtual sensing and sensors are presented by Atkinson et al. (1998) and Nareid et al. (2005). EGR and residue gas sensing has been researched by Müller et al. (2001) and Leroy et al. (2009). Diesel engine air mass flow sensing has been investigated by Höckerdal et al. (2008). Diesel exhaust temperature sensing is evaluated by Culbertson et al. (2008) and Hori and Todo (2009). A virtual sensor of barometric pressure for altitude detection is presented in Olin and Maloney (1999). The diesel engine exhaust pressure sensor for DPF operation is addressed by Ueno et al. (2008). NOx sensors have been investigated by Kato et al. (1999) and Orban et al. (2005). NOx virtual sensors are presented by Re et al. (2005) and Subramaniam et al. (2008). Particulate matter sensors are discussed in Allan et al. (2003), Warey and Hall (2005), Hauser (2006), Diller et al. (2008, 2009), and Cai and Ma (2009). Cylinder pressure sensors are discussed in Ulrich et al. (2001) and Hasegawa et al. (2006). Virtual cylinder pressure sensors are explored in Palma et al. (2004) and Wang et al. (2005).



14.9.3 Virtual sensor modeling of exhaust manifold gas temperature

The virtual sensor for exhaust manifold gas temperature is important for engine durability and EGR rate control. Its modeling is based on the thermodynamic fi rst law energy balance equation 12.2. Assuming

W q m

Q B q

W qE tW qW qhW qW qE tW qhW qE tW qLHV fuel

basQ BbasQ Be–Q Be–Q BcoolantQ BcoolantQ B L

W q =W qW qE tW q =W qE tW q · (W q · (W q )

Q B= Q B · (

W qhW qW qE tW qhW qE tW q

HVHHVHLHLHVLHL fuel

miscellaneousmiscellaneousmiscell fuel

m

Q GmiscellQ GmiscellaneousQ GaneousmiscellaneousmiscellQ Gmiscellaneousmiscell q mLHVq mLHV

)

Q G= Q G · ( ),2Q G,2Q G2

Ï

ÌÌÌÔÏÔÏÔÌÌÌÔÌÌÌÔÔÔ

ÓÔÌÌÌÔÌÌÌ

ÔÓÔÓÔÔÔ

14.3

where hth, B and G2 are constants or known functions of engine speed, load, air–fuel ratio, and fuel injection timing, the steady-state exhaust manifold gas temperature can be calculated using equation 12.2 as:

T

mm

c T q

turbinTturbinT e–inlet

IM

fuelp ic Tp ic Tn Ic Tn Ic T Mn IMn I LHV

= +p i,p i

ÊËÁÊÁÊËÁË

ˆ¯̃ˆ˜ˆ¯̄̃

(((1 – – )

+ 1

2hth

IM

fuelp e,p e, xp exp e

B G – B G –

mm

c

ÊËÁÊÁÊËÁË

ˆ¯̃ˆ˜ˆ¯̄̃

14.4

where mIM is the intake manifold mixture fl ow rate of both fresh air and EGR, and cp,ex and cp,in are the equivalent average specifi c heats of the exhaust fl ow and the intake manifold fl ow, respectively. It is observed that the exhaust manifold gas temperature is a function of the ‘mass-to-fuel’ ratio ( / ) ( / ( /m m( /m m( /IM( /IM( /( /m m( /IM( /m m( / fuel . In equation 14.4, one method to estimate the engine brake thermal effi ciency hth is to calculate the engine brake power using the indicated torque and the engine friction obtained from the torque-based controls. The prediction of the engine indicated torque and the brake torque has been used increasingly as a part of the coordinated shift control in automotive powertrains. In equation 14.4, an accurate estimation of B is a challenge. The value of B depends on engine speed, load, charge mass-to-fuel ratio, fuel injection timing, intake manifold gas temperature, coolant temperature, etc. One approach is to use the equations of the in-cylinder cycle process along with the Woschni heat transfer coeffi cient ag outlined in Chapter 4 to fi rst calculate the transient instantaneous exhaust manifold gas temperature, and then average it over an engine cycle to obtain the steady-state exhaust manifold gas temperature. The other approach is to develop heuristic models for the steady-state values of B in order to build its sensitivity to other engine performance and operating parameters. Steady-state engine performance test data reveal that there is a correlation between the temperature difference across the cylinder and the fuel-to- charge mass ratio (Fig. 14.2), and the slope of the correlation line changes



at different engine speeds. Such a correlation can be explained as follows. If it is assumed that mIM is much greater than m fuel , and cp,in ª cp,ex, equation 14.4 is simplifi ed to:

T Tq B G

turbine–inleT Tturbine–inleT Tt IT Tt IT T Mt IMt Iq BLHVq Bq Bthq B

pT Tt IT T – T Tt IT T

(q B(q B1 – q B1 – q Bq B – q B – )2ª q Bhq Bc ,,

=

ex

fuel

IM

fuel

IM

mm

Cmm

ÊËÁÊÁÊËÁË

ˆ¯̃ˆ˜ˆ¯̄̃

ÊËÁÊÁÊËÁË

ˆ¯̃ˆ˜ˆ¯̄̃

14.5

where the coeffi cient C is a function of engine speed. A simplifi ed empirical model of the virtual sensor of exhaust manifold gas temperature can be developed as follows:

T T C C N C C NturbinT TturbinT Te–T Te–T TinleT TinleT Tt IT Tt IT T M EN CM EN Ct IM Et I ET Tt IT T – T Tt IT T = M E= M E( C C( C CM E( M EC CM EC C( C CM EC CC C+ C C ) N C) N CN C+ N C( N C( N C + )1 2C C1 2C CM E1 2M EC CM EC C1 2C CM EC CC C( C C1 2C C( C CM E( M E1 2M E( M EC CM EC C( C CM EC C1 2C CM EC C( C CM EC CC C+ C C1 2C C+ C CC CM EC C+ C CM EC C1 2C CM EC C+ C CM EC C 3 4C N3 4C N( 3 4( + 3 4+

mm

fuel

IM

ÊËÁÊÁÊËÁË

ˆ¯̃ˆ˜ˆ¯̄̃

14.6

where C1, C2, C3 and C4 are the model tuning constants, and NE is the engine speed. It should be noted that although the model equation 14.6 can predict the steady-state exhaust temperatures reasonably well, it may not be able to accurately predict the exhaust temperatures during fast transients due to the lack of thermal-lag related factors in the model.

Ttu

rbin

e–in

let–

TIM

(°F

)

1100

1000

900

800

700

600

500

400

300

200

100

00 0.01 0.02 0.03 0.04 0.05 0.06

Fuel-to-charge mass ratio

700 rpm

800 rpm

1000 rpm

1200 rpm

1400 rpm

14.2 Illustration of virtual sensor – correlation between exhaust manifold gas temperature and fuel-to-charge ratio.



14.10 On-board diagnostics (OBD) and fault diagnostics

On-board diagnostics (SAE J1699-2, 1998; J1699-3, 2009; J1930, 2008) is an engine control topic, and has been researched by Grimaldi and Mariani (1999), Geraldo (2006), Vitale et al. (2007), Millo et al. (2009), and Fischer et al. (2009). Engine fault diagnostics is closely related to engine system design and electronic controls because many parameters used in the diagnosis are system-level performance parameters such as the instantaneous gas pressure, temperature, flow, and the component rotational speeds. A system-level dynamics model is usually required for model-based diagnostics. The area of engine fault diagnostics is reviewed by Haddad (1984) and further investigated by Schwarte and Isermann (2002), Macián et al. (2004, 2005, 2006a, and 2006b), and Yan et al. (2007). Diesel engine protection is reviewed by Fouch and Gross (1991).

14.11 Engine controller design

The theoretical foundation of modern control engineering is summarized in the classical textbooks authored by Ogata (2002, 2004), Palm III (2005),Dorf and Bishop (2007), and DiStefano et al. (1990). Internal combustion engine control and modeling is introduced in Guzzella and Onder (2004) and Guzzella and Sciarretta (2005). Engine controller design itself generally does not belong to the scope of diesel engine system design. It falls within the job function of a control engineer. However, dynamic engine system design cannot be implemented without considering controller design. Diesel engine controls (e.g., air path control, fuel system control, speed control, etc.) are nonlinear controls. Linear design and reduced-order design are the common techniques used in controller designs. PI, PD and PID controllers are still the most widely used controllers in engines. Nonlinear numerical simulation of engine system dynamics may provide a virtual confirmation of the controller behavior and the control strategy, but the simulation itself is not a controller design tool. There is a need to bridge the gap between engine system simulation and controller design in order to promote the simulation-based controller design for nonlinear controllers, especially for the air path and powertrain controls. Engine controls and controller designs are presented in Chin and Coats (1986), Weisman (1987), Tsai and Goyal (1986), Tuken et al. (1990), Scotson and Heath (1996), Balfour et al. (2000), Christen et al. (2001), and Malkhede et al. (2005). An adaptive torque controller design is reported by Fullmer et al. (1992). PID controller design theories are presented by Strom and Hagglund (1995, 2005). A PID controller for diesel engine speed control has



been investigated by Mruthunjaya and Dhariwal (2000). Idle speed control was studied by Memering and Meckl (1994, 2002). Diesel engine air system (EGR–VGT) controller designs have been researched by Stefanopoulou et al. (2000) and Utkin et al. (2000). A PID controller tuning for diesel engine EGR–VGT control is investigated in Wahlström et al. (2008). A PID controller design for a gasoline engine is introduced by Lauber et al. (2002). The multi-input multi-output (MIMO) controller has been researched by Stefanopoulou and Smith (2000) to reduce transient smoke for a marine diesel engine. The comparison between a PID controller and a neural network controller is provided by Tsuchiya et al. (2003). A fuzzy logic controller for diesel engines is reported by Plianos et al. (2007). Robust control for marine diesel engines is summarized by Xiros (2002).

14.12 Software-in-the-loop (SIL) and hardware-in-the-loop (HIL)

Software-in-the-loop is introduced by Philipp et al. (2005) and Mitts et al. (2009). Hardware-in-the-loop has been actively applied to engine/powertrain control development and investigated in research (Isermann et al., 1998; Rolfsmeier et al., 2003; Nabi et al., 2004; Shayler et al., 2005; Köhl and Jegminat, 2005; Steiber et al., 2005; Wanpal et al., 2006; Schuette and Ploeger, 2007; Wang et al. 2009; and Dhaliwal et al., 2009).

14.13 Cylinder-pressure-based controls

Good references on cylinder-pressure-based controls for diesel engines are provided by Johnson et al. (1999), Nakayama et al. (2003, 2008), Klein et al. (2007), and Husted et al. (2007). Due to limited space, the details are not discussed here.

14.14 Homogeneous charge compression ignition (HCCI) controls

HCCI control is an important area of combustion control. Detailed discussions on HCCI are given in Section 9.8 in Chapter 9. For completeness, selected references are provided here (Olsson et al., 2001; Zhao and Asmus, 2003; Strandh et al., 2005; Bengtsson et al., 2006; Narayanaswamy and Rutland, 2006; Chauvin et al., 2006; Wang, 2007; Tunestål and Johansson, 2007; Kumar et al., 2007).

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Erkkinen T and Breiner S (2007), ‘Automatic code generation – technology adoption lessons learned from commercial vehicle case studies’, SAE paper 2007-01-4249.

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