WO2011033661A1 - Internal combustion engine control device - Google Patents
Internal combustion engine control device Download PDFInfo
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- WO2011033661A1 WO2011033661A1 PCT/JP2009/066416 JP2009066416W WO2011033661A1 WO 2011033661 A1 WO2011033661 A1 WO 2011033661A1 JP 2009066416 W JP2009066416 W JP 2009066416W WO 2011033661 A1 WO2011033661 A1 WO 2011033661A1
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- engine
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F02—COMBUSTION ENGINES; HOT-GAS OR COMBUSTION-PRODUCT ENGINE PLANTS
- F02N—STARTING OF COMBUSTION ENGINES; STARTING AIDS FOR SUCH ENGINES, NOT OTHERWISE PROVIDED FOR
- F02N3/00—Other muscle-operated starting apparatus
- F02N3/02—Other muscle-operated starting apparatus having pull-cords
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F02—COMBUSTION ENGINES; HOT-GAS OR COMBUSTION-PRODUCT ENGINE PLANTS
- F02D—CONTROLLING COMBUSTION ENGINES
- F02D41/00—Electrical control of supply of combustible mixture or its constituents
- F02D41/02—Circuit arrangements for generating control signals
- F02D41/04—Introducing corrections for particular operating conditions
- F02D41/06—Introducing corrections for particular operating conditions for engine starting or warming up
- F02D41/062—Introducing corrections for particular operating conditions for engine starting or warming up for starting
- F02D41/064—Introducing corrections for particular operating conditions for engine starting or warming up for starting at cold start
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F02—COMBUSTION ENGINES; HOT-GAS OR COMBUSTION-PRODUCT ENGINE PLANTS
- F02D—CONTROLLING COMBUSTION ENGINES
- F02D41/00—Electrical control of supply of combustible mixture or its constituents
- F02D41/02—Circuit arrangements for generating control signals
- F02D41/04—Introducing corrections for particular operating conditions
- F02D41/06—Introducing corrections for particular operating conditions for engine starting or warming up
- F02D41/062—Introducing corrections for particular operating conditions for engine starting or warming up for starting
- F02D41/065—Introducing corrections for particular operating conditions for engine starting or warming up for starting at hot start or restart
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F02—COMBUSTION ENGINES; HOT-GAS OR COMBUSTION-PRODUCT ENGINE PLANTS
- F02D—CONTROLLING COMBUSTION ENGINES
- F02D41/00—Electrical control of supply of combustible mixture or its constituents
- F02D41/02—Circuit arrangements for generating control signals
- F02D41/14—Introducing closed-loop corrections
- F02D41/1401—Introducing closed-loop corrections characterised by the control or regulation method
- F02D41/1405—Neural network control
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F02—COMBUSTION ENGINES; HOT-GAS OR COMBUSTION-PRODUCT ENGINE PLANTS
- F02D—CONTROLLING COMBUSTION ENGINES
- F02D2200/00—Input parameters for engine control
- F02D2200/02—Input parameters for engine control the parameters being related to the engine
- F02D2200/04—Engine intake system parameters
- F02D2200/0414—Air temperature
- F02D2200/0416—Estimation of air temperature
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F02—COMBUSTION ENGINES; HOT-GAS OR COMBUSTION-PRODUCT ENGINE PLANTS
- F02D—CONTROLLING COMBUSTION ENGINES
- F02D2200/00—Input parameters for engine control
- F02D2200/02—Input parameters for engine control the parameters being related to the engine
- F02D2200/10—Parameters related to the engine output, e.g. engine torque or engine speed
- F02D2200/1012—Engine speed gradient
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F02—COMBUSTION ENGINES; HOT-GAS OR COMBUSTION-PRODUCT ENGINE PLANTS
- F02N—STARTING OF COMBUSTION ENGINES; STARTING AIDS FOR SUCH ENGINES, NOT OTHERWISE PROVIDED FOR
- F02N2200/00—Parameters used for control of starting apparatus
- F02N2200/02—Parameters used for control of starting apparatus said parameters being related to the engine
- F02N2200/022—Engine speed
Definitions
- the present invention relates to a control device for an internal combustion engine, and more particularly to a control device that uses a neural network.
- Patent Document 1 discloses a parameter estimation device that estimates an air-fuel ratio using a neural network that inputs parameters indicating an operating state of an internal combustion engine, for example, throttle valve opening, intake pressure, engine speed, intake air temperature, and the like. It is shown.
- a plurality of engine operation areas are set according to the input parameters, and the calculation path in the neural network to be used is changed according to the engine operation area.
- the present invention has been made paying attention to this point, and appropriately uses a neural network not only in a steady operation state of an engine but also in a transient operation state, and while controlling the calculation load during operation of the engine, the control is performed. It is an object of the present invention to provide a control device for an internal combustion engine that can improve accuracy.
- the present invention provides a control apparatus for an internal combustion engine, which corresponds to the steady operation of the engine and outputs a predetermined operating parameter (THCMD) of the engine using a neural network (SOMSS), and A control parameter for calculating the engine control parameter (IDTH) using a neural network (SOMTS) and a transient state model that outputs the predetermined operation parameter (THCMD) in response to the transient operation of the engine
- the control parameter calculating means includes a transient state determining means for determining whether or not the engine is in the transient operation state, and the steady state model and the transient state according to a determination result by the transient state determining means. Selection means for selecting one of the state models, and depending on the output of the selected model And calculates the serial control parameter (THCMD).
- a steady state model that outputs a predetermined operation parameter using a neural network corresponding to the steady operation of the engine, and a predetermined operation parameter that outputs a predetermined operation parameter corresponding to the transient operation of the engine are output.
- the engine control parameters are calculated using the transient state model. Specifically, it is determined whether or not the engine is in a transient operation state, one of the steady state model and the transient state model is selected according to the determination result, and the control parameter is determined according to the output of the selected model. Calculated. Therefore, operation parameters suitable for each of the steady operation state and the transient operation state of the engine can be obtained, and the control accuracy by the control parameters calculated using the operation parameters can be improved.
- the steady state model is input with a first set of input parameters (GAIRCMD, PB, PI, NE), and the transient state model has a second set of input parameters different from the first set ( DGAIRCMD, DPB, DPI, DNE) are input, and the control parameter calculation means preferably calculates the model output only for the model selected by the selection means.
- the transient state determination means determines that the engine is in a transient operation state when at least one change amount of input parameters (GAIRCMD, PB, PI, NE) of the steady state model is larger than a predetermined change amount. It is desirable.
- FIG. 1 is a diagram showing a configuration of an internal combustion engine and a control device thereof according to an embodiment of the present invention.
- An internal combustion engine (hereinafter referred to as “engine”) 1 is a diesel engine that directly injects fuel into a cylinder, and a fuel injection valve 9 is provided in each cylinder.
- the fuel injection valve 9 is electrically connected to an electronic control unit (hereinafter referred to as “ECU”) 20. It is controlled by the ECU 20.
- ECU electronice control unit
- the engine 1 includes an intake pipe 2, an exhaust pipe 4, and a turbocharger 8.
- the turbocharger 8 includes a turbine 11 having a turbine wheel 10 that is rotationally driven by the kinetic energy of exhaust, and a compressor 16 having a compressor wheel 15 connected to the turbine wheel 10 via a shaft 14.
- the compressor wheel 15 pressurizes (compresses) air sucked into the engine 1.
- the turbine 11 has a plurality of variable vanes 12 (only two are shown) that are driven to change the flow rate of exhaust gas blown to the turbine wheel 10, and an actuator (not shown) that drives the variable vanes to open and close.
- the flow rate of the exhaust gas blown to the turbine wheel 10 can be changed by changing the opening degree of the variable vane 12 (hereinafter referred to as “vane opening degree”) ⁇ vgt so that the rotational speed of the turbine wheel 10 can be changed. It is configured.
- the actuator that drives the variable vane 12 is connected to the ECU 20, and the vane opening degree ⁇ vgt is controlled by the ECU 20. More specifically, the ECU 20 supplies a control signal with a variable duty ratio to the actuator, thereby controlling the vane opening ⁇ vgt.
- the configuration of a turbocharger having a variable vane is widely known, and is disclosed in, for example, Japanese Patent Laid-Open No. 1-208501.
- An intercooler 18 is provided on the downstream side of the compressor 16 in the intake pipe 2, and a throttle valve 3 is provided on the downstream side of the intercooler 18.
- the throttle valve 3 is configured to be opened and closed by an actuator 19, and the actuator 19 is connected to the ECU 20.
- the ECU 20 controls the opening degree of the throttle valve 3 via the actuator 19.
- the exhaust gas recirculation passage 5 is provided with an exhaust gas recirculation control valve (hereinafter referred to as “EGR valve”) 6 for controlling the exhaust gas recirculation amount (EGR amount).
- the EGR valve 6 is an electromagnetic valve having a solenoid, and the valve opening degree is controlled by the ECU 20.
- the EGR valve 6 is provided with a lift sensor 7 for detecting the valve opening degree (valve lift amount) LACT, and the detection signal is supplied to the ECU 20.
- the exhaust gas recirculation passage 5 and the EGR valve 6 constitute an exhaust gas recirculation device.
- the intake pipe 2 includes an intake air flow rate sensor 21 that detects an intake air flow rate GA, a boost pressure sensor 22 that detects an intake pressure (supercharge pressure) PB downstream of the compressor 16, and an intake air temperature that detects an intake air temperature TI.
- a sensor 23 and an intake pressure sensor 24 for detecting the intake pressure PI are provided. These sensors 21 to 24 are connected to the ECU 20, and detection signals from the sensors 21 to 24 are supplied to the ECU 20.
- a lean NOx catalyst 31 that is a NOx purification device that purifies NOx contained in the exhaust, and particulate matter (mainly composed of soot) contained in the exhaust are collected.
- a particulate matter filter 32 is provided.
- the lean NOx catalyst 31 captures NOx in a state where the oxygen concentration in the exhaust is relatively high, that is, in a state where the concentration of the reducing components (HC, CO) is relatively low, and captures in a state where the concentration of the reducing component in the exhaust is high. NOx is reduced by the reducing component and released.
- a rotation speed sensor 28 is connected to the ECU 20, and detection signals from these sensors are supplied to the ECU 20.
- the engine speed sensor 28 supplies the ECU 20 with a crank angle pulse generated at every predetermined crank angle (for example, 6 degrees) and a TDC pulse generated in synchronization with the timing at which the piston of each cylinder of the engine 1 is located at the top dead center. To do.
- the ECU 20 shapes input signal waveforms from various sensors, corrects the voltage level to a predetermined level, converts an analog signal value into a digital signal value, a central processing unit (hereinafter referred to as “CPU”).
- CPU central processing unit
- the ECU 20 performs an engine operation state (mainly, fuel injection control by the fuel injection valve 9, exhaust gas recirculation control by the EGR valve 6, supercharging pressure control by the variable vane 12 in accordance with the engine speed NE and the engine load target value Pmecmd).
- the engine load target value Pmecmd is calculated according to the accelerator pedal operation amount AP, and is set to increase as the accelerator pedal operation amount AP increases.
- the ECU 20 calculates the target throttle valve opening THCMD according to the target intake air amount GAIRCMD [g / sec] using a neural network to which the self-organizing map algorithm is applied (hereinafter simply referred to as “self-organizing map”).
- the actuator 19 is driven so that the detected throttle valve opening TH matches the target throttle valve opening THCMD.
- the target throttle valve opening is performed using the steady state model self-organizing map SOMSS corresponding to the steady operating state of the engine 1 and the transient state model self organizing map SOMTS corresponding to the transient operating state of the engine 1.
- the degree THCMD is calculated.
- An input data vector xj composed of N elements is defined by the following equation (1), and a weight vector wi of each neuron constituting the self-organizing map is defined by the following equation (2).
- the number of neurons is M. That is, the parameter i takes a value from 1 to M.
- the initial value of the weight vector wi is given using a random number.
- the Euclidean distance DWX
- between the input data vector xj and the corresponding neuron weight vector wi is calculated, and the neuron with the smallest distance DWX is defined as the winner neuron.
- the Euclidean distance DWX is calculated by the following formula (3).
- the weight vector wi of the neuron included in the winner neuron and its neighboring neuron set Nc is updated by the following equation (4).
- ⁇ (t) is a learning coefficient
- t is the number of learnings.
- the neuron weight vector wi not included in the neuron set Nc maintains the previous value as shown in the following equation (5).
- wi (t + 1) wi (t) (5)
- the neuron set Nc is also a function of the learning count t, and is set so that the neighborhood range is narrowed as the learning count t increases.
- the weight vectors of the winner neuron and the neighboring neurons are corrected so as to approach the input data vector.
- the arrangement of M neurons reflects the distribution state of the input data vectors.
- the input data vector is represented as a two-dimensional vector for the sake of simplicity and the arrangement is represented on a plane, and the input data vector is uniformly distributed on the plane, the neuron arrangement after learning is flat. Distributed uniformly on the top.
- the distribution of input data vectors is biased (dense / dense), the distribution state of neurons is the same biased distribution state.
- the self-organizing map obtained in this way may be made to have a more appropriate arrangement of neurons by further applying a learning vector quantization (LVQ) algorithm.
- LVQ learning vector quantization
- FIG. 2 shows a steady-state model self-organizing map SOMSS for calculating the target throttle valve opening THCMD in the present embodiment as a two-dimensional map.
- This two-dimensional map is defined by a target intake air amount GAIRCMD and a supercharging pressure PB, which are two input parameters that are the most dominant factors.
- the input data vector xTH is defined by the following formula (10). That is, the input parameters are the target intake air amount GAIRCMD, the supercharging pressure PB, the intake pressure PI, and the engine speed NE.
- xTH (GAIRCMD, PB, PI, NE) (10)
- each region RNR i Is defined.
- the map shown in FIG. 2 is obtained by performing learning corresponding to a standard engine (a new engine and an engine having an average operating characteristic).
- the input data applied to learning is plotted with black circles.
- the target throttle valve opening THCMD is calculated by applying the weight coefficient vector Ci and the input data vector xTH associated with the above to the following equation (12).
- This expression (12) corresponds to a mathematical expression that defines the steady state model in the present embodiment.
- THCMD C1i x GAIRCMD + C2i x PB + C3i * PI + C4i * NE + C0i (12)
- the change amount of the input parameter of the above-described steady state model self-organizing map SOMSS is applied as an input parameter. That is, a target intake air amount change amount DGAIRCMD, a supercharging pressure change amount DPB, an intake pressure change amount DPI, and a rotational speed change amount DNE are calculated by the following equations (21) to (24), and the transient state model self-organization is calculated.
- a target intake air amount change amount DGAIRCMD a supercharging pressure change amount DPB, an intake pressure change amount DPI, and a rotational speed change amount DNE are calculated by the following equations (21) to (24), and the transient state model self-organization is calculated.
- Applied as an input parameter for the map SOMTS. “K” in these mathematical expressions is a discretization time discretized at the calculation cycle TC of the target throttle valve opening THCMD.
- DGAIRCMD GAIRCMD (k) ⁇ GAIRCMD (k ⁇ 1) (21)
- DPB PB (k) ⁇ PB (k ⁇ 1) (22)
- DPI PI (k) ⁇ PI (k ⁇ 1) (23)
- DNE NE (k) ⁇ NE (k ⁇ 1) (24)
- FIG. 3 shows, as a two-dimensional map, a transient state model self-organizing map SOMTS for calculating the target throttle valve opening THCMD in the present embodiment.
- This two-dimensional map is defined by the target intake air amount change amount DGAIRCMD and the supercharging pressure change amount DPB.
- CDi (CD0i, CD1i, CD2i, CD3i, CD4i) (26)
- THCMD CD1i ⁇ DGAIRCMD + CD2i ⁇ DPB + CD3i ⁇ DPI + CD4i ⁇ DNE + CD0i
- FIG. 4 is a diagram showing the relationship between the intake air amount GAIR [g / sec] and the throttle valve opening TH, and the curves L1 to L5 indicate that the engine speed NE is 1000, 1500, 2000, 2500, and This corresponds to the state of 3000 rpm.
- the target intake air amount GAIRCMD set according to the accelerator pedal operation amount AP and the engine speed NE multiplies the maximum intake air amount GAIRMAX by a predetermined threshold coefficient KTH (for example, 0.95).
- the target throttle valve opening THCMD is set to the maximum opening THMAX (for example, “90 degrees”). Thereby, the calculation load of the CPU of the ECU 20 can be reduced without impairing the controllability of the intake air amount.
- the target throttle valve opening THCMD is calculated using the above-described self-organizing map. As a result, the optimum throttle valve opening can be set for controlling the actual intake air amount GAIR to the target intake air amount GAIRCMD.
- FIG. 5 is a flowchart of a process for calculating the target throttle valve opening THCMD, and this process is executed by the CPU of the ECU 20 every predetermined time TC.
- a GAIRCMD map (not shown) is searched according to the accelerator pedal operation amount AP and the engine speed NE to calculate a target intake air amount GAIRCMD.
- the GAIRCMD map is set so that the target intake air amount GAIRCMD increases as the accelerator pedal operation amount AP increases, and the target intake air amount GAIRCMD increases as the engine speed NE increases.
- step S12 a GAIRMAX map (not shown) is searched according to the engine speed NE and the boost pressure PB, and the maximum intake air amount GAIRMAX is calculated.
- the GAIRMAX map is set so that the maximum intake air amount GAIRMAX increases as the engine speed NE increases, and the maximum intake air amount GAIRMAX increases as the boost pressure PB increases.
- step S13 the determination threshold GAIRTH is calculated by multiplying the maximum intake air amount GAIRMAX by a predetermined threshold coefficient KTH.
- step S14 it is determined whether or not the target intake air amount GAIRCMD is smaller than the determination threshold value GAIRTH. If the answer to step S14 is affirmative (YES), the SOM calculation process shown in FIG.
- the target throttle valve opening THCMD is calculated using the map SOMSS or SOMTS (step S15).
- step S14 when the target intake air amount GAIRCMD is equal to or larger than the determination threshold GAIRTH, the target throttle valve opening THCMD is set to the maximum opening THMAX.
- step S21 in FIG. 6 the target intake air amount change amount DGAIRCMD, the supercharging pressure change amount DPB, the intake pressure change amount DPI, and the rotation speed change amount DNE are calculated by the above-described equations (21) to (24).
- step S22 it is determined whether or not the target intake air amount change amount DGAIRCMD is larger than the predetermined air amount change amount DGATH. It is determined whether or not the amount of change DPBTH is greater (step S23). If the answer is negative (NO), it is determined whether or not the intake pressure change amount DPI is greater than a predetermined intake pressure change amount DPITH (step S23). S24) If the answer to step S25 is negative (NO), it is determined whether or not the rotational speed change amount DNE is greater than a predetermined rotational speed change amount DNETH (step S25).
- Step S27 If the answer to any of steps S22 to S25 is affirmative (YES), it is determined that the engine 1 is in a transient operation state, and the target throttle valve opening THCMD is calculated using the transient state model self-organizing map SOMTS. (Step S27).
- step S25 when the answer to step S25 is negative (NO), it is determined that the engine 1 is in a steady operation state, and the target throttle valve opening THCMD is calculated using the steady state model self-organizing map SOMSS (step S25). S26).
- the CPU of the ECU 20 calculates a drive parameter IDTH for driving the actuator 19 so that the detected throttle valve opening TH matches the target throttle valve opening THCMD calculated by the processing of FIGS. Valve opening control (intake air amount control) is performed.
- the steady state model that outputs the target throttle valve opening THCMD using the steady state model self-organizing map SOMSS corresponds to the steady operation of the engine 1, and the transient operation of the engine 1.
- the drive parameter IDTH of the actuator 19 is calculated using a transient state model that outputs the target throttle valve opening THCMD using the transient state model self-organizing map SOMTS. That is, it is determined whether or not the engine 1 is in the transient operation state by steps S22 to S25 in FIG. 6, and one of the steady state model and the transient state model is selected according to the determination result, and the output of the selected model is selected.
- the drive parameter IDTH is calculated according to the target throttle valve opening THCMD. Therefore, the target throttle valve opening THCMD suitable for each of the steady operation state and the transient operation state of the engine 1 can be obtained, and the control accuracy by the drive parameter IDTH calculated using the target throttle valve opening THCMD can be improved.
- a target intake air amount GAIRCMD, a supercharging pressure PB, an intake pressure PI, and an engine speed NE are input to the steady state model self-organizing map SOMSS, while those are input to the transient state model self-organizing map SOMTS.
- the amount of parameter change is input, only the calculation for the steady state model self-organization map SOMSS is executed in the steady operation state of the engine, and only the calculation for the transient state model self-organization map SOMTS is executed in the transient operation state of the engine. Is done. Therefore, it is not necessary to always execute the calculations of the two self-organizing maps in parallel, and the calculation load can be suppressed.
- the engine 1 Since it is determined that the engine is in the transient operation state, it is possible to appropriately determine the transient operation state to which the engine 1 should apply the transient state model self-organizing map SOMTS.
- the ECU 20 constitutes a control parameter calculation unit, a transient state determination unit, and a selection unit. That is, the target throttle valve opening THCMD corresponds to a predetermined operation parameter, the drive parameter IDTH of the actuator 19 corresponds to an engine control parameter, the process in FIG. 5 corresponds to a part of the control parameter calculation means, and the process in FIG. Corresponds to transient state determination means and selection means.
- the present invention is not limited to the above-described embodiment, and various modifications are possible.
- the target throttle valve opening THCMD is a predetermined operation parameter.
- the NOx amount exhausted from the engine 1 the exhaust gas recirculation rate (or the exhaust gas recirculation amount or the target exhaust gas recirculation amount)
- the intake air amount may be set as a predetermined operation parameter
- the fuel injection amount (control parameter) may be calculated according to the calculated predetermined operation parameter.
- the input parameters of the steady state model self-organizing map for calculating the NOx amount include engine speed NE, fuel supply amount (fuel injection amount), air-fuel ratio, temperature of exhaust gas flowing into the turbine 11, and supercharging pressure PB. ,
- the intake pressure PI, and the intake air amount GAIR are applied, and the change amount of the input parameter of the steady state model self-organizing map is applied as the input parameter of the transient state model self-organizing map.
- the input parameters of the steady-state model self-organizing map for calculating the exhaust gas recirculation rate include boost pressure PB, intake pressure PI, EGR valve opening, intake air amount GAIR, fuel / air ratio, engine speed NE, turbine 11 vane opening degree ⁇ vgt and recirculation exhaust gas temperature are applied, and the input parameter variation of the steady state model self-organizing map is applied as the input parameter of the transient state model self-organizing map.
- the throttle valve opening TH As input parameters of the steady state model self-organizing map for calculating the intake air amount, the throttle valve opening TH, the supercharging pressure PB, the intake pressure PI, and the engine speed NE are applied, and the transient state model self-organizing is applied.
- the transient state model self-organizing As an input parameter of the map, a change amount of the input parameter of the steady state model self-organizing map is applied.
- the self-organizing map is used as the neural network.
- the present invention is not limited to this, and a neural network known as a so-called perceptron may be used.
- the present invention can also be applied to the control of a marine vessel propulsion engine such as an outboard motor having a vertical crankshaft.
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Abstract
Description
この装置では、入力パラメータに応じて複数の機関運転領域が設定されており、使用するニューラルネットワークにおける演算経路が機関運転領域に応じて変更される。
In this apparatus, a plurality of engine operation areas are set according to the input parameters, and the calculation path in the neural network to be used is changed according to the engine operation area.
図1は本発明の一実施形態にかかる内燃機関、及びその制御装置の構成を示す図である。内燃機関(以下「エンジン」という)1は、シリンダ内に燃料を直接噴射するディーゼルエンジンであり、各気筒に燃料噴射弁9が設けられている。燃料噴射弁9は、電子制御ユニット(以下「ECU」という)20に電気的に接続されており、燃料噴射弁9の開弁時期及び開弁時間は、すなわち燃料噴射時期及び燃料噴射量は、ECU20により制御される。 Embodiments of the present invention will be described below with reference to the drawings.
FIG. 1 is a diagram showing a configuration of an internal combustion engine and a control device thereof according to an embodiment of the present invention. An internal combustion engine (hereinafter referred to as “engine”) 1 is a diesel engine that directly injects fuel into a cylinder, and a
N個の要素からなる入力データベクトルxjを下記式(1)で定義し、自己組織化マップを構成する各ニューロンの重みベクトルwiを下記式(2)で定義する。ニューロンの数はM個とする。すなわち、パラメータiは、1からMまでの値をとる。重みベクトルwiの初期値は乱数を用いて与えられる。
xj=(xj1,xj2,…,xjN) (1)
wi=(wi1,wi2,…,wiN) (2) The self-organizing map will be described in detail below.
An input data vector xj composed of N elements is defined by the following equation (1), and a weight vector wi of each neuron constituting the self-organizing map is defined by the following equation (2). The number of neurons is M. That is, the parameter i takes a value from 1 to M. The initial value of the weight vector wi is given using a random number.
xj = (xj1, xj2,..., xjN) (1)
wi = (wi1, wi2, ..., wiN) (2)
wi(t+1)=wi(t)+α(t)(xj-wi(t)) (4) Next, the weight vector wi of the neuron included in the winner neuron and its neighboring neuron set Nc is updated by the following equation (4). In equation (4), α (t) is a learning coefficient, and t is the number of learnings. The learning coefficient α (t) is set such that the initial value is set to “0.8”, for example, and decreases as the number of learning times t increases.
wi (t + 1) = wi (t) + α (t) (xj−wi (t)) (4)
wi(t+1)=wi(t) (5) The neuron weight vector wi not included in the neuron set Nc maintains the previous value as shown in the following equation (5).
wi (t + 1) = wi (t) (5)
xTH=(GAIRCMD,PB,PI,NE) (10) FIG. 2 shows a steady-state model self-organizing map SOMSS for calculating the target throttle valve opening THCMD in the present embodiment as a two-dimensional map. This two-dimensional map is defined by a target intake air amount GAIRCMD and a supercharging pressure PB, which are two input parameters that are the most dominant factors. The input data vector xTH is defined by the following formula (10). That is, the input parameters are the target intake air amount GAIRCMD, the supercharging pressure PB, the intake pressure PI, and the engine speed NE.
xTH = (GAIRCMD, PB, PI, NE) (10)
Ci=(C0i,C1i,C2i,C3i,C4i) (11) During learning of the self-organizing map, the weighting coefficient vector Ci (i = 1 to 1) represented by the following equation (11) is used by using the input data vector xTH and the throttle valve opening TH corresponding to the input data vector xTH. M) is calculated and stored. The weight coefficient vector Ci is calculated and stored corresponding to each neuron NRi.
Ci = (C0i, C1i, C2i, C3i, C4i) (11)
THCMD=C1i×GAIRCMD+C2i×PB
+C3i×PI+C4i×NE+C0i (12) In actual control calculation, a region RNRi including an operating point on the map at that time determined by the target intake air amount GAIRCMD and the supercharging pressure PB, which are elements of the input data vector xTH, is selected, and the neuron NRi representing the region RNRi is selected. The target throttle valve opening THCMD is calculated by applying the weight coefficient vector Ci and the input data vector xTH associated with the above to the following equation (12). This expression (12) corresponds to a mathematical expression that defines the steady state model in the present embodiment.
THCMD = C1i x GAIRCMD + C2i x PB
+ C3i * PI + C4i * NE + C0i (12)
DGAIRCMD=GAIRCMD(k)-GAIRCMD(k-1) (21)
DPB=PB(k)-PB(k-1) (22)
DPI=PI(k)-PI(k-1) (23)
DNE=NE(k)-NE(k-1) (24) On the other hand, in the transient state model self-organizing map SOMTS, the change amount of the input parameter of the above-described steady state model self-organizing map SOMSS is applied as an input parameter. That is, a target intake air amount change amount DGAIRCMD, a supercharging pressure change amount DPB, an intake pressure change amount DPI, and a rotational speed change amount DNE are calculated by the following equations (21) to (24), and the transient state model self-organization is calculated. Applied as an input parameter for the map SOMTS. “K” in these mathematical expressions is a discretization time discretized at the calculation cycle TC of the target throttle valve opening THCMD.
DGAIRCMD = GAIRCMD (k) −GAIRCMD (k−1) (21)
DPB = PB (k) −PB (k−1) (22)
DPI = PI (k) −PI (k−1) (23)
DNE = NE (k) −NE (k−1) (24)
xTHD=(DGAIRCMD,DPB,DPI,DNE) (25) FIG. 3 shows, as a two-dimensional map, a transient state model self-organizing map SOMTS for calculating the target throttle valve opening THCMD in the present embodiment. This two-dimensional map is defined by the target intake air amount change amount DGAIRCMD and the supercharging pressure change amount DPB. The input data vector xTHD is defined by the following equation (25).
xTHD = (DGAIRCMD, DPB, DPI, DNE) (25)
CDi=(CD0i,CD1i,CD2i,CD3i,CD4i)
(26) A weighting coefficient vector CDi (i = 1 to M) represented by the following equation (26) is calculated by learning and stored in the same manner as the above-described steady state model self-organizing map SOMSS.
CDi = (CD0i, CD1i, CD2i, CD3i, CD4i)
(26)
THCMD=CD1i×DGAIRCMD+CD2i×DPB
+CD3i×DPI+CD4i×DNE+CD0i
(27) When the transient state model self-organizing map SOMTS is used, the target throttle valve opening THCMD is calculated by the following equation (27). This expression (27) corresponds to a mathematical expression that defines the transient state model in the present embodiment.
THCMD = CD1i × DGAIRCMD + CD2i × DPB
+ CD3i × DPI + CD4i × DNE + CD0i
(27)
ステップS11では、アクセルペダル操作量AP及びエンジン回転数NEに応じてGAIRCMDマップ(図示せず)を検索し、目標吸入空気量GAIRCMDを算出する。GAIRCMDマップは、アクセルペダル操作量APが増加するほど目標吸入空気量GAIRCMDが増加し、かつエンジン回転数NEが増加するほど目標吸入空気量GAIRCMDが増加するように設定されている。 FIG. 5 is a flowchart of a process for calculating the target throttle valve opening THCMD, and this process is executed by the CPU of the
In step S11, a GAIRCMD map (not shown) is searched according to the accelerator pedal operation amount AP and the engine speed NE to calculate a target intake air amount GAIRCMD. The GAIRCMD map is set so that the target intake air amount GAIRCMD increases as the accelerator pedal operation amount AP increases, and the target intake air amount GAIRCMD increases as the engine speed NE increases.
2 吸気管
19 アクチュエータ
20 電子制御ユニット(制御パラメータ算出手段、過渡状態判定手段、選択手段)
22 過給圧センサ
24 吸気圧センサ
27 アクセルセンサ
28 エンジン回転数センサ DESCRIPTION OF
22
Claims (3)
- 内燃機関の制御装置において、
前記機関の定常運転に対応し、ニューラルネットワークを用いて前記機関の所定運転パラメータを出力する定常状態モデルと、前記機関の過渡運転に対応し、ニューラルネットワークを用いて、前記所定運転パラメータを出力する過渡状態モデルとを用いて、前記機関の制御パラメータを算出する制御パラメータ算出手段を備え、
前記制御パラメータ算出手段は、
前記機関が前記過渡運転状態にあるか否かを判定する過渡状態判定手段と、
前記過渡状態判定手段による判定結果に応じて前記定常状態モデル及び過渡状態モデルの一方を選択する選択手段とを備え、
選択されたモデルの出力に応じて前記制御パラメータを算出することを特徴とする内燃機関の制御装置。 In a control device for an internal combustion engine,
Corresponding to the steady operation of the engine, a steady state model that outputs a predetermined operating parameter of the engine using a neural network, and corresponding to the transient operation of the engine, outputting the predetermined operating parameter using a neural network Using a transient state model, comprising a control parameter calculation means for calculating the control parameter of the engine,
The control parameter calculation means includes
Transient state determination means for determining whether or not the engine is in the transient operation state;
Selecting means for selecting one of the steady state model and the transient state model according to a determination result by the transient state determination unit;
A control device for an internal combustion engine, wherein the control parameter is calculated according to an output of a selected model. - 前記定常状態モデルには入力パラメータの第1の組が入力され、前記過渡状態モデルには、前記第1の組と異なる第2の組の入力パラメータが入力され、前記制御パラメータ算出手段は、前記選択手段により選択されたモデルについてのみ前記モデル出力の演算を行う請求項1の制御装置。 A first set of input parameters is input to the steady state model, a second set of input parameters different from the first set is input to the transient state model, and the control parameter calculation means includes The control apparatus according to claim 1, wherein the model output is calculated only for the model selected by the selection unit.
- 前記過渡状態判定手段は、前記定常状態モデルの入力パラメータの少なくとも1つの変化量が所定変化量より大きいとき、前記機関が過渡運転状態にあると判定する請求項1の制御装置。 The control device according to claim 1, wherein the transient state determination means determines that the engine is in a transient operation state when at least one change amount of an input parameter of the steady state model is larger than a predetermined change amount.
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PCT/JP2009/066416 WO2011033661A1 (en) | 2009-09-18 | 2009-09-18 | Internal combustion engine control device |
JP2011531734A JP5377655B2 (en) | 2009-09-18 | 2009-09-18 | Control device for internal combustion engine |
DE112009005254.1T DE112009005254B4 (en) | 2009-09-18 | 2009-09-18 | Control system for an internal combustion engine |
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Cited By (2)
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EP2853721A1 (en) * | 2012-05-23 | 2015-04-01 | Toyota Jidosha Kabushiki Kaisha | Controller for internal combustion engine with supercharger |
JP2020197165A (en) * | 2019-06-03 | 2020-12-10 | トヨタ自動車株式会社 | Abnormality detection system of exhaust gas recirculation system |
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- 2009-09-18 DE DE112009005254.1T patent/DE112009005254B4/en not_active Expired - Fee Related
- 2009-09-18 WO PCT/JP2009/066416 patent/WO2011033661A1/en active Application Filing
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JPH06249007A (en) * | 1993-02-26 | 1994-09-06 | Toyota Motor Corp | Driving force control device for vehicle |
JPH1011105A (en) * | 1996-06-20 | 1998-01-16 | Yamaha Motor Co Ltd | State control system |
JPH10331701A (en) * | 1998-04-06 | 1998-12-15 | Hitachi Ltd | Controller |
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EP2853721A1 (en) * | 2012-05-23 | 2015-04-01 | Toyota Jidosha Kabushiki Kaisha | Controller for internal combustion engine with supercharger |
EP2853721A4 (en) * | 2012-05-23 | 2016-01-20 | Toyota Motor Co Ltd | Controller for internal combustion engine with supercharger |
JP2020197165A (en) * | 2019-06-03 | 2020-12-10 | トヨタ自動車株式会社 | Abnormality detection system of exhaust gas recirculation system |
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DE112009005254B4 (en) | 2015-11-05 |
JPWO2011033661A1 (en) | 2013-02-07 |
JP5377655B2 (en) | 2013-12-25 |
DE112009005254T5 (en) | 2013-01-10 |
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