Method for determining fuel blend in a dual fuel mixture

Abstract

A method is provided for determining fuel blend in a dual fuel mixture including a first and a second fuel in an internal combustion engine. The method includes the steps of measuring multiple engine parameters using sensors during transient cycle operation for a predetermined range of engine loads and fuel blends; using system identification of transient time series of the measurements to determine one or more relevant engine parameters; determining a model for estimation of the fuel blend based on said one or more engine parameters; using the model for determining a current fuel blend during transient operation using current measured values of the one or more engine parameters, and using the calculated current fuel blend for controlling the amount of dual fuel mixture injected into each cylinder of the internal combustion engine. A vehicle and a computer program product using the method are also provided.

Claims

1. A method for determining fuel blend in a dual fuel mixture comprising a first and a second fuel in an internal combustion engine, comprising: measuring multiple engine parameters using sensors during transient cycle operation for a predetermined range of engine loads and fuel blends; using system identification of transient time series of the measurements to determine one or more relevant engine parameters; determining a model for estimation of the fuel blend based on the one or more engine parameters; training the model in transient mode using data from engine tests performed with a predetermined fuel blend; using the model for determining a current fuel blend during transient operation using current measured values of the one or more engine parameters, and using the calculated current fuel blend for controlling an engine in response to the current fuel blend.

2. A method according to claim 1, comprising using actual and time delayed, linear and cross-terms, in-data during system identification to determine the relevant parameters.

3. A method according to claim 1, wherein at least one engine parameter comprises exhaust manifold temperature.

4. A method according to claim 1, wherein at least one engine parameter comprises engine speed.

5. A method according to claim 1, wherein at least one engine parameter comprises exhaust manifold pressure.

6. A method according to claim 1, wherein at least one engine parameter comprises exhaust gas recirculation mass flow.

7. A method according to claim 1, wherein at least one engine parameter comprises the integral portion of the regulation for the fuel injection.

8. A method according to claim 1, wherein at least one engine parameter comprises intake manifold pressure.

9. A method according to claim 1, wherein at least one engine parameter comprises intake manifold temperature.

10. A method according to claim 1, wherein at least one engine parameter comprises a torque value demanded by the engine control unit.

11. A method according to claim 1, wherein at least one engine parameter comprises variable geometry turbocharger position.

12. A method according to claim 1, wherein at least one engine parameter comprises cooling water temperature.

13. Vehicle wherein the vehicle comprises an internal combustion engine arranged to be controlled by a method according to claim 1.

14. A computer comprising a computer program for performing all the steps of claim 1 when the program is run on the computer.

15. A non-transitory computer program product comprising program code stored on a non-transitory computer readable medium for performing all steps of claim 1 when the program product is run on a computer.

16. A non-transitory storage medium, comprising a computer readable program code to perform the method claim 1.

Description

BRIEF DESCRIPTION OF DRAWINGS

(1) In the following text, the invention will be described in detail with reference to the attached drawings. These schematic drawings are used for illustration only and do not in any way limit the scope of the invention. In the drawings:

(2) FIG. 1 shows a schematically illustrated vehicle provided with means for controlling an engine using a method according to the invention;

(3) FIG. 2 shows a flow chart of a part of the FQM algorithm;

(4) FIG. 3A shows a diagram plotting Duty Cycle calibration results with 7% RME content;

(5) FIG. 3B shows a diagram plotting Duty Cycle calibration results from FIG. 3A for residual RME;

(6) FIG. 4A shows a diagram plotting Duty Cycle calibration results with 100% RME content;

(7) FIG. 4B shows a diagram plotting Duty Cycle calibration results from FIG. 4A for residual RME;

(8) FIG. 5A shows a diagram plotting WHTC calibration results with 7% RME content;

(9) FIG. 5B shows a diagram plotting WHTC calibration results from FIG. 5A for residual RME;

(10) FIG. 6A shows a diagram plotting WHTC calibration results with 100% RME content;

(11) FIG. 6B shows a diagram plotting WHTC calibration results from FIG. 6A for residual RME;

(12) FIG. 7A shows a diagram plotting WHSC calibration results with 7% RME content;

(13) FIG. 7B shows a diagram plotting WHSC calibration results from FIG. 7A for residual RME;

(14) FIG. 8A shows a diagram plotting WHSC calibration results with 100% RME content;

(15) FIG. 8B shows a diagram plotting WHSC calibration results from FIG. 8A for residual RME;

(16) FIG. 9A shows a diagram plotting Duty Cycle validation results with 50% RME content;

(17) FIG. 9B shows a diagram plotting Duty Cycle validation results from FIG. 9A for residual RME

(18) FIG. 10A shows a diagram plotting WHTC validation results with 50% RME content;

(19) FIG. 10B shows a diagram plotting WHTC validation results from FIG. 10A for residual RME;

(20) FIG. 11A shows a diagram plotting WHSC validation results with 50% RME content;

(21) FIG. 11B shows a diagram plotting WHSC validation results from FIG. 11A for residual RME; and

(22) FIG. 12 shows the invention applied on a computer arrangement.

EMBODIMENTS OF THE INVENTION

(23) In future fuels a promising candidate for most diesel engines will include bio-diesel, or RME. The example studied here is RME, mixed in proportions 7%, 50% and 100% in diesel oil (VSD10). A 13 liter Euro V engine has been used as test object. In order to get a cost efficient detection of which blend of fuel the vehicle is filled with, e.g. the % content of bio-diesel, a combination of soft sensors is used.

(24) By analysing the different influences on the engine at 7% and 100% RME a number of engine related parameters that could be measured by available sensors were listed (Table 1).

(25) TABLE-US-00001 TABLE 1 Measured variables No. Variable ID Unit 1 Torque md Nm 2 Exhaust manifold ACM.se_EngExhTemp C. temperature 3 Engine speed APS_EngineSpeed_TS rpm 4 Exhaust manifold pressure ExhaustManifoldPressure kPa 5 EGR mass flow mfv_EgrMassFlowValidated kg/s 6 Fuel Injection parameter 1 rpc_Ipart mg/ str 7 Fuel Injection parameter 2 rpc_MpropFlowDemand mg/ str 8 Fuel Injection parameter 3 rpe_RailPressure bar 9 Boost pressure se_BoostPres kPa 10 Boost temperature se_BoostTemp C. 11 NOx (pre cat) se_NOxInLevel ppm 12 NOx (post cat) se_NOxOutLevel ppm 13 Demanded torque tc_TorqueValue Nm 14 VGT position/demand vsra_VgtPosSRA % 15 Cooling water temperature T_W_I C. (in) 16 Cooling water temperature T_W_O C. (out)

(26) From those parameters, exhaust manifold temperature, engine speed, exhaust manifold pressure, EGR mass flow, a fuel Injection parameter, intake manifold pressure, intake manifold temperature, demanded torque, VGT position/demand and cooling water temperature were selected. All are detectable using current engine sensors used for a standard Euro V truck engine. Optionally, although ten parameters have been selected in this example, fewer or additional sensor signals can be selected.

(27) This model is not directly applicable on other engine models. For instance, a Euro VI (EU standard for 2014) engine might have different sensors and some engine versions will include EGR and turbo-compound. This makes it necessary to recalibrate the model for each engine.

(28) FIG. 1 shows a schematically illustrated vehicle provided with means for controlling an engine using a method according to the invention. The figure shows a schematic engine layout used for testing and validation of the inventive method. FIG. 1 shows a vehicle 1 provided with a compression ignition engine 2 controllable by an electronic control unit (ECU) 3. The engine 2 comprises an air intake manifold which is supplied with charge air from a turbocharger driven by exhaust gas from an exhaust manifold. A portion of the mass flow of exhaust gas is supplied directly to the intake manifold through an exhaust gas recirculation (EGR) conduit. Fuel is supplied to each individual combustion chamber through fuel injectors controlled by the ECU. Said devices are well known component parts of a modern internal combustion engine and will not be shown in the figure or be described in further detail.

(29) The ECU 3 is connected to a large number of sensors supplying it with sensor signals necessary for controlling the engine 2.

(30) FIG. 1 schematically indicates a limited number of sensors, including sensors for exhaust manifold temperature 11, engine speed 12, exhaust manifold pressure 13, EGR mass flow 14, a fuel Injection parameter 15, intake manifold pressure 16, intake manifold temperature 17, demanded torque 18, variable geometry turbocharger (VGT) position 19 and cooling water temperature 20. In this case, the fuel Injection parameter is the integral portion of the regulation for the fuel injection. The demanded torque is the torque demanded from the engine by the driver and can be detected using the accelerator position. Variable geometry turbochargers (VGT) are arranged to allow the effective aspect ratio (A/R) of the turbo to be altered as conditions change. The position of a controllable part of the VGT can be detected by a suitable sensor. The cooling water temperature can be measured at both the coolant inlet and the coolant outlet of the engine; in this case the outlet temperature is used.

(31) The ECU 3 comprises a non-volatile memory in which is stored a model for estimating the RME percentage of the fuel mixture currently being injected. By using measured values from the above sensors and the stored model, the current RME percentage can be estimated. The ECU 3 can then control the fuel injectors via a conduit 21 to adjust of the fuel injection rate per engine cycle and the produced power from the engine. In addition, or alternatively, the current fuel blend can be used for controlling parameters such as the exhaust gas recirculation mass flow or the variable geometry turbocharger position.

(32) The engine was operated in a transient cycle using B7 and B100, to measure and collect data and subsequently operated in transient state using B50 in the certification cycles. The model was trained in a transient cycle and tested in a transient cycle. All tests were performed in room temperature.

(33) The engine was tested in different transient points and with the different blends of RME at certain throttle positions. Data for exhaust manifold temperature, engine speed, exhaust manifold pressure, EGR mass flow, a fuel Injection parameter, intake manifold pressure, intake manifold temperature, demanded torque, VGT position/demand and cooling water temperature were measured for each transient point for each RME blend.

(34) The model output at time instant k is computed using a linear function of the predictor variables as where x(k) is a 1-by-nx matrix of predictor variables, y(k) is the scalar response variable, is a (nx+1)-by-1 matrix of regression coefficients, and r is the residual.

(35) To increase the fitting of the model, the model is augmented by including past values (lags) of the predictor variables. If x(k) is the vector of values of the predictor variables at time instant k, then x(k1) is the vector of values of the predictor variables at time instant k1 or lagged one sample.

(36) In this way, the model has more regressors, i.e. not only has the nx input variables but also lags of x(k). For example, when we consider the inputs lagged one sample, the model can be written as:
y(k)=[1x(k)].sub.0+x(k1).sub.1+r(k)=[1x(k)x(k1)]+r(k).

(37) In a general way, the set of regressors with lagged input data values is formulated as:
[x(k),x(kn.sub.x1),x(k2n.sub.x1), . . . ,x(kn.sub.x2)],

(38) where n.sub.x1 and n.sub.x2 are parameters that define the time lags for the input variables. For example, if n.sub.x1=4, n.sub.x2=8, the set of regressors is
[x(k),x(k4),x(k8)].

(39) Finally, cross-product terms are also included in the model. These terms represent interaction effects between the predictor variables. The set of regressors can be expressed as follows
[x(k),x(kn.sub.x1),x(k2n.sub.x1), . . . ,x(kn.sub.x2),
z(k),z(kn.sub.x1),z(k2n.sub.x1), . . . ,z(kn.sub.x2)]

(40) where z is a vector of the cross-product terms
[x.sub.1x.sub.2,x.sub.1x.sub.3, . . . ,x.sub.1x.sub.nx,x.sub.2x.sub.3, . . . ,x.sub.2x.sub.nx, . . . ,x.sub.nx-1x.sub.nx].

(41) To smooth out fluctuations in the model over time, a cumulative average of all of the response variable estimates up until the current data value,

(42) $\stackrel{\_}{y}\left(k\right)=\frac{y\left(1\right)+\mathrm{.Math.}+y\left(k\right)}{k}=\frac{y\left(k\right)+\left(k-1\right)\stackrel{\_}{y}\left(k-1\right)}{k}$

(43) where y(0)=0

(44) To increase the fitting of the model, and hence the accuracy of it, data values are excluded when a set of conditions is not satisfied. This set states

(45) (I) a range for each model input variable at the current time,
x.sub.i,minx.sub.i(k)x.sub.i,max

(46) i=1 . . . n.sub.x

(47) (II) a minimum engine power demand (power is calculated from demanded torque and speed, and C is a conversion factor),
tc_TorqueValue(k)*APS_EngineSpeed_TS(k)*Cpower_min

(48) When the predictor variables satisfy conditions (I) and (II), the FQM output y(k) is considered valid.

(49) The cumulative average, mentioned at the end of the previous section, calculates the average of only the valid estimates up until the current data value (see FIG. 2).

(50) A PLS model with 10 input variables (listed in Table) and two time lags of 0.4 s (n.sub.x1=4) and 0.8 s (n.sub.x2=8) for each input variable, is developed in order to detect the RME content. Table also shows the minimum and maximum values for each variable. The minimum and maximum values are engine-cycle dependant, i.e. they depend on the engine and cycle used for model calibration, and they are obtained automatically from a Matlab script written for this purpose. As explained in the previous section, all the current measurements of the 10 input variables should be inside the min-max range to get a valid model evaluation, as well as the demanded power should be greater than equal to 75 kW:
x.sub.i,minx.sub.i(k)x.sub.i,max

(51) i=1 . . . 10
tc_TorqueValue(k)*APS_EngineSpeed_TS(k)*C75 kW

(52) TABLE-US-00002 TABLE 2 Model input variables Variable ID Unit Min Max Exhaust manifold temperature ACM.se_EngExhTemp C. 208.66 372.43 Engine speed APS_EngineSpeed_TS rpm 840.4 1864.6 Exhaust manifold pressure ExhaustManifoldPressure kPa 9.23 256.39 EGR mass flow mfv_EgrMassFlowValidated kg/s 0 0.0535 Fuel Injection parameter rpc_Integral part inj. fuel mg/str 4.04 21.22 Boost pressure se_BoostPres kPa 110.55 266.77 Boost temperature se_BoostTemp C. 53.36 64.05 Demanded torque tc_TorqueValue Nm 417.5 1225.5 VGT position/demand vsra_VgtPosSRA % 1.3 46 Cooling water temperature T_W_I C. 86.3 93.3

(53) The model was calibrated using data coming from engine tests performed with 7% and 100% RME content. The engine test cycles used for calibration are Duty Cycle (City3 cycle), WHTC, and WHSC.

(54) Table shows the part of each cycle, using 7% or 100% RME content, that is valid, i.e. the constraints of the input data are satisfied. About 15% of each cycle is used for calibration, and these time instants are distributed along the tests time.

(55) TABLE-US-00003 TABLE 3 Part of the cycle valid for calibration Duty Cycle WHTC WHSC 7% RME content 15.8 15.4 14.8 100% RME content 13.4 13.1 21.8

(56) FIGS. 3A-3B and FIGS. 4A-4B show the results from Duty Cycle with 7% and 100% RME content, respectively. After about 0.2 h of the beginning of the tests, the model converges to values within 8% FSO (full scale output) of the nominal ones, as can be seen in FIGS. 3A and 4A. FIGS. 3B and 4B show that the estimation RME residuals are within 5% units at the end of the cycle. In FIGS. 3A, 4A, 5A and up to 11A, full lines represent estimated values, dashed lines represent nominal values, and dash-dotted lines represent a desired 15% accuracy range on either side of the nominal value.

(57) FIGS. 5A-5B and FIGS. 6A-6B show the calibration results from a WHTC cycle with 7% and 100% RME content, respectively. After about 0.2 h of the beginning of the tests, the model converges to values within 8% FSO (full scale output) of the nominal ones, as can be seen in FIGS. 5A and 6A. At the end of the cycle, about 0.5 h from the beginning of the cycle, the model converges to values within 15% units, as indicated in FIGS. 5B and 6B.

(58) Finally, FIGS. 7A-7B and FIGS. 8A-8B show the calibration results from a WHSC cycle with 7% and 100% RME content, respectively. After about 0.2 h of the beginning of the tests, the model converges to values within 8% FSO (full scale output) of the nominal ones, as can be seen in FIGS. 7A and 8A. After the initial transient, the estimation RME residuals are always within 5% units of the nominal ones, as indicated in FIGS. 7B and 8B.

(59) The model was validated using data coming from engine tests performed with 50% RME content. The engine test cycles considered for validation are Duty Cycle (City3 cycle), WHTC, and WHSC.

(60) FIGS. 9A-9B, FIGS. 10A-10B and FIGS. 11A-11B show the validation results for Duty Cycle, WHTC, and WHSC, respectively. As can be seen in FIGS. 9A, 10A and 11A the estimated RME content is at the end of all the three cycles within 15% FSO (full scale output) of the nominal RME contents. After the initial transient, the estimation RME residuals are always within 5% units of the nominal ones, as indicated in FIGS. 9B, 10B and 11B.

(61) The model indicated above can provide a desired accuracy for estimation of the RME content of the fuel to optimize both the fuel consumption information of the engine and the torque produced by the engine. The electronic control unit can use the selected model during transient operation. By providing the electronic control unit with measured transient data, the model can be used for estimation of the RME content, allowing for adjustment of the fuel injection rate per engine cycle and the produced power from the engine. In addition, or alternatively, the current fuel blend can be used for controlling parameters such as the exhaust gas recirculation mass flow or the variable geometry turbocharger position.

(62) As indicated above a transient model determined for one engine model is not directly applicable on other engine models. This makes it necessary to recalibrate the transient model for each engine model, using the method described above. When recalibrating the transient model for different engine models it is also possible to select different or other combinations of engine parameters. The selection of engine parameters is dependent on their relevance to the RME content of the fuel.

(63) The present invention also relates to a computer program, computer program product and a storage medium for a computer all to be used with a computer for executing the method as described in any one of the above examples.

(64) FIG. 12 shows an apparatus 100 according to one embodiment of the invention, comprising a non-volatile memory 120, a processor 110 and a read and write memory 160. The memory 120 has a first memory part 130, in which a computer program for controlling the apparatus 100 is stored. The computer program in the memory part 130 for controlling the apparatus 100 can be an operating system.

(65) The apparatus 100 can be enclosed in, for example, a control unit, such as the control unit 3. The data-processing unit 110 can comprise, for example, a microcomputer.

(66) The memory 120 also has a second memory part 140, in which a program for controlling the target gear selection function according to the invention is stored. In an alternative embodiment, the program for controlling the transmission is stored in a separate non-volatile storage medium 150 for data, such as, for example, a CD or an exchangeable semiconductor memory. The program can be stored in an executable form or in a compressed state.

(67) When it is stated below that the data-processing unit 110 runs a specific function, it should be clear that the data-processing unit 110 is running a specific part of the program stored in the memory 140 or a specific part of the program stored in the non-volatile storage medium 150.

(68) The data-processing unit 110 is tailored for communication with the storage memory 150 through a data bus 114. The data-processing unit 110 is also tailored for communication with the memory 120 through a data bus 112. In addition, the data-processing unit 110 is tailored for communication with the memory 160 through a data bus 111. The data-processing unit 110 is also tailored for communication with a data port 190 by the use of a data bus 115.

(69) The method according to the present invention can be executed by the data-processing unit 110, by the data-processing unit 110 running the program stored in the memory 140 or the program stored in the non-volatile storage medium 150.

(70) The invention should not be deemed to be limited to the embodiments described above, but rather a number of further variants and modifications are conceivable within the scope of the following patent claims.