METHOD AND SYSTEM FOR CONTROLLING A HYBRID PROPULSION SYSTEM OPTIMIZING FUEL CONSUMPTION AND POLLUTING EMISSIONS

Abstract

The invention relates to a method of controlling a hybrid propulsion system of a vehicle, wherein a control (COM) (minimizing consumption and pollutant emissions at the after-treatment system outlet) is defined. The control method is based on minimizing a cost function (H) of a model (MOD) of the propulsion system. Thus, the method according to the invention allows simultaneous minimizing of fuel consumption and pollution emissions by accounting for after-treatment system efficiency.

Claims

1.-14. (canceled)

15. A method of controlling a hybrid propulsion system comprising at least one electric machine, at least one thermal engine, at least one electrical energy storage system supplying electrical energy to the electric machine, a kinematic chain for coupling the electric machine and the thermal engine, and a pollution emissions after-treatment system at an outlet of the thermal engine with a torque setpoint of the propulsion system being acquired, comprising: a) discretizing at least part of allowable control of the propulsion system to reach the torque setpoint of the propulsion system; b) constructing a model of the propulsion system connecting a cost function to a control of the propulsion system, the cost function being a function of consumption of the propulsion system and of the pollution emissions at an outlet of the pollution emissions after-treatment system; c) determining a control for the propulsion system by minimizing the cost function of the model of the propulsion system for the allowable discretized controls; and d) controlling the hybrid propulsion system by applying the determined control to the hybrid propulsion system.

16. A method as claimed in claim 15, wherein the control is at least one of a torque setpoint of the thermal engine, a torque setpoint of the electric machine and a control setpoint of the kinematic chain.

17. A method as claimed in claim 15, wherein the torque setpoint of the propulsion system is filtered.

18. A method as claimed in claim 16, wherein the torque setpoint of the propulsion system is filtered.

19. A method as claimed in claim 17, wherein a torque setpoint of at least one of the thermal engine and a torque setpoint of the electric machine is determined by use of the filtered torque setpoint of the propulsion system and steps a) to c) are repeated to determine a control setpoint of the kinematic chain by use of an unfiltered torque setpoint and the controls are applied to the hybrid propulsion system.

20. A method as claimed in claim 18, wherein a torque setpoint of at least one of the thermal engine and a torque setpoint of the electric machine is determined by use of the filtered torque setpoint of the propulsion system and steps a) to c) are repeated to determine a control setpoint of the kinematic chain by use of an unfiltered torque setpoint and the controls are applied to the hybrid propulsion system.

21. A method as claimed in claim 15, wherein the discretizing at least part of the allowable control accounts for a state of at least one of charge of the electrical energy storage system and speed of the propulsion system.

22. A method as claimed in claim 16, wherein the discretizing at least part of the allowable control accounts for a state of at least one of charge of the electrical energy storage system and speed of the propulsion system.

23. A method as claimed in claim 17, wherein the discretizing at least part of the allowable control accounts for a state of at least one of charge of the electrical energy storage system and speed of the propulsion system.

24. A method as claimed in claim 18, wherein the discretizing at least part of the allowable control accounts for a state of at least one of charge of the electrical energy storage system and speed of the propulsion system.

25. A method as claimed in claim 19, wherein the discretizing at least part of the allowable control accounts for a state of at least one of charge of the electrical energy storage system and speed of the propulsion system.

26. A method as claimed in claim 20, wherein the discretizing at least part of the allowable controls accounts for a state of at least one of charge of the electrical energy storage system and speed of the propulsion system.

27. A method as claimed in claim 15, wherein the cost function of the hybrid propulsion system model is written with an equation:
H(u.sub.1, u.sub.2, x, t)=f(u.sub.1, u.sub.2, t)+(t){dot over (x)}(u.sub.1, u.sub.2, x, t)
with:
f(u.sub.1, u.sub.2, t)=(1){dot over (m)}.sub.f(u.sub.1, u.sub.2, t)+{dot over (m)}.sub.NO.sub.x .sub.TP(u.sub.1, u.sub.2, t), wherein: u.sub.1 is torque control of the thermal engine T.sub.eng; u.sub.2 is control of the kinematic chain ECC; x is a state of charge of the electrical energy storage system; m.sub.f is fuel consumption of the thermal engine; m.sub.NO.sub.x .sub.TP is Nox emissions at the outlet of the after-treatment system; is a calibration variable; is a Lagrange multiplier; and t is time.

28. A method as claimed in claim 16, wherein the cost function of the hybrid propulsion system model is written with an equation:
H(u.sub.1, u.sub.2, x, t)=f(u.sub.1, u.sub.2, t)+(t){dot over (x)}(u.sub.1, u.sub.2, x, t)
with:
f(u.sub.1, u.sub.2, t)=(1){dot over (m)}.sub.f(u.sub.1, u.sub.2, t)+{dot over (m)}.sub.NO.sub.x .sub.TP(u.sub.1, u.sub.2, t), wherein u.sub.1 is torque control of the thermal engine T.sub.eng; u.sub.2 is control of the kinematic chain ECC; x is a state of charge of the electrical energy storage system; m.sub.f is fuel consumption of the thermal engine; m.sub.NO.sub.x .sub.TP is NOx emissions at the outlet of the after-treatment system; is a calibration variable; is a Lagrange multiplier; and t is time.

29. A method as claimed in claim 17, wherein the cost function of the hybrid propulsion system model is written with an equation:
H(u.sub.1, u.sub.2, x, t)=f(u.sub.1, u.sub.2, t)+(t){dot over (x)}(u.sub.1, u.sub.2, x, t)
with:
f(u.sub.1, u.sub.2, t)=(1){dot over (m)}.sub.f(u.sub.1, u.sub.2, t)+{dot over (m)}.sub.NO.sub.x .sub.TP(u.sub.1, u.sub.2, t), wherein u.sub.1 is torque control of the thermal engine T.sub.eng; u.sub.2 is control of the kinematic chain ECC; x is a state of charge of the electrical energy storage system; m.sub.f is fuel consumption of the thermal engine; m.sub.NO.sub.x .sub.TP is NOx emissions at the outlet of the after-treatment system; is a calibration variable; is a Lagrange multiplier; and t is time.

30. A method as claimed in claim 19, wherein the cost function of the hybrid propulsion system model is written with an equation:
H(u.sub.1, u.sub.2, x, t)=f(u.sub.1, u.sub.2, t)+(t){dot over (x)}(u.sub.1, u.sub.2, x, t)
with:
f(u.sub.1, u.sub.2, t)=(1){dot over (m)}.sub.f(u.sub.1, u.sub.2, t)+{dot over (m)}.sub.NO.sub.x .sub.TP(u.sub.1, u.sub.2, t), wherein u.sub.1 is torque control of the thermal engine T.sub.eng; u.sub.2 is control of the kinematic chain ECC; x is a state of charge of the electrical energy storage system; m.sub.f is fuel consumption of the thermal engine; m.sub.NO.sub.x .sub.TP is NOx emissions at the outlet of the after-treatment system; is a calibration variable; is a Lagrange multiplier; and t is time.

31. A method as claimed in claim 21, wherein the cost function of the hybrid propulsion system model is written with an equation:
H(u.sub.1, u.sub.2, x, t)=f(u.sub.1, u.sub.2, t)+(t){dot over (x)}(u.sub.1, u.sub.2, x, t)
with:
f(u.sub.1, u.sub.2, t)=(1){dot over (m)}.sub.f(u.sub.1, u.sub.2, t)+{dot over (m)}.sub.NO.sub.x .sub.TP(u.sub.1, u.sub.2, t), wherein u.sub.1 is torque control of the thermal engine T.sub.eng; u.sub.2 is control of the kinematic chain ECC; x is a state of charge of the electrical energy storage system; m.sub.f is fuel consumption of the thermal engine; m.sub.NO.sub.x .sub.TP is NOx emissions at the outlet of the after-treatment system; is a calibration variable; is a Lagrange multiplier; and t is time.

32. A method as claimed in claim 28, wherein the fuel consumption of the thermal engine is obtained by using a map.

33. A method as claimed in claim 28, wherein the pollution emissions m.sub.NO.sub.x .sub.TP are obtained with an equation:
{dot over (m)}.sub.NO.sub.x .sub.TP(u.sub.1, u.sub.2, x, t)={dot over (m)}.sub.NO.sub.x .sub.EO(u.sub.1, u.sub.2, x, t)(1.sub.AT(T.sub.AT)) with: m.sub.NO.sub.x .sub.EO being pollution emissions an outlet of the thermal engine, .sub.AT being efficiency of the after-treatment system, and T.sub.AT being temperature of the after-treatment system.

34. A method as claimed in claims 30, wherein the pollution emissions m.sub.NO.sub.x .sub.TP are obtained with an equation:
{dot over (m)}.sub.NO.sub.x .sub.TP(u.sub.1, u.sub.2, x, t)={dot over (m)}.sub.NO.sub.x .sub.EO(u.sub.1, u.sub.2, x, t)(1.sub.AT(T.sub.AT)) with m.sub.NO.sub.x .sub.EO being pollution emissions an outlet of the thermal engine, .sub.AT being efficiency of the after-treatment system, and T.sub.AT being temperature of the after-treatment system.

35. A method as claimed in claim 34, wherein the pollution emissions m.sub.NO.sub.x .sub.EO at the outlet of the thermal engine are determined by using a model or a map.

36. A method as claimed in claim 34, wherein temperature of the after-treatment system is estimated by use of a formula: $T_{\mathrm{AT}}\left(t\right)=T_{\mathrm{AT}}\left(t-.Math..Math.t\right)+.Math..Math.t\frac{h_1.Math.\left(t\right)+h_2\left(t\right)}{I}$ with: h.sub.1(t)=k.sub.1(T.sub.0T.sub.AT(tt))
h.sub.2(t)=k.sub.2[T.sub.AT QS(u.sub.1(tt), u.sub.2(tt))T.sub.AT(tt)] T.sub.AT QS(u.sub.1(tt), u.sub.2(tt)) measured steady-state temperature at the after-treatment, with t being time interval; k1 being equivalent thermal resistance of exchanges with an outside; k2 being equivalent thermal resistance of exchanges with exhaust gases; and I being thermal inertia of the after-treatment system.

37. A method as claimed in claim 35, wherein temperature of the after-treatment system is estimated by use of a formula: $T_{\mathrm{AT}}\left(t\right)=T_{\mathrm{AT}}\left(t-.Math..Math.t\right)+.Math..Math.t\frac{h_1.Math.\left(t\right)+h_2\left(t\right)}{I}$ with:
h.sub.1(t)=k.sub.1(T.sub.0T.sub.AT(tt))
h.sub.2(t)=k.sub.2[T.sub.AT QS(u.sub.1(tt), u.sub.2(tt)T.sub.AT(tt)] T.sub.AT QS(u.sub.1(tt), u.sub.2(tt)) measured steady-state temperature at the after-treatment, with t being time interval; k1 being equivalent thermal resistance of exchanges with an outside; k2 being equivalent thermal resistance of exchanges with exhaust gases; and I being thermal inertia of the after-treatment system.

38. A method a claimed in claim 15, wherein minimization is carried out by use of Pontryagin's minimum principle.

39. A computer program recorded on a tangible computer storage medium or a controller which executes, program code instructions for implementing the method as claimed in claim 15, when the computer program is executed.

40. A hybrid propulsion system of a vehicle, comprising at least one electric machine, at least one thermal engine, at least one electrical energy storage system supplying electrical energy to the electric machine and at least one system for after-treatment of the pollution emissions of the thermal engine, wherein the propulsion system is controlled by the method as claimed in claim 15.

41. A motor vehicle, comprising a hybrid propulsion system as claimed in claim 40.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Other features and advantages of the method according to the invention will be clear from reading the description hereafter of embodiments given by way of non limitative example, with reference to the accompanying figures wherein:

[0057] FIG. 1 schematically illustrates the steps of the method according to the invention;

[0058] FIG. 2 illustrates a first embodiment of the method according to the invention;

[0059] FIG. 3 illustrates a second embodiment of the method according to the invention;

[0060] FIG. 4 illustrates an example of a hybrid propulsion system;

[0061] FIG. 5 shows a curve comparing the measured after-treatment temperature and the estimated after-treatment temperature as a function of time;

[0062] FIG. 6 illustrates, for one example, comparative curves of the vehicle speed for two control methods according to the prior art, and for an embodiment according to the invention;

[0063] FIG. 7 illustrates, for the same example, comparative curves of the state of charge of the battery for two control methods according to the prior art, and for an embodiment according to the invention,;

[0064] FIG. 8 illustrates, for the same example, comparative curves of the cumulative NOx emissions at the engine outlet for two control methods according to the prior art, and for an embodiment according to the invention;

[0065] FIG. 9 illustrates, for the same example, comparative curves of the cumulative NOx emissions at the after-treatment system outlet for two control methods according to the prior art, and for an embodiment according to the invention;

[0066] FIG. 10 illustrates, for the same example, comparative curves of the after-treatment system temperature for two control methods according to the prior art, and for an embodiment according to the invention; and

[0067] FIG. 11 illustrates, for the same example, comparative curves of the after-treatment system efficiency for two control methods according to the prior art, and for an embodiment according to the invention.

DETAILED DESCRIPTION OF THE INVENTION

[0068] The method according to the invention allows reduction of the fuel consumption and the NOx emissions at the after-treatment system outlet for a hybrid propulsion system.

[0069] According to the invention, the method allows controlling a hybrid propulsion system of a vehicle, notably a motor vehicle, comprising at least one electric machine and at least one thermal (diesel or gasoline) engine. The electric machine is powered by an electrical energy storage system. The term electrical energy storage system includes any electrical energy storage such as a battery, an accumulator, a pack, modules, supercapacitors, etc. In the rest of the description, the term battery is used to designate any electrical energy storage. The hybrid propulsion system further comprises a kinematic chain for coupling the thermal engine and the electric machine. It can be a series or parallel or mixed series/parallel kinematic chain. The kinematic chain can comprise a reduction mechanism such as a gearbox, reducers, etc., coupling such as clutches, etc. The hybrid propulsion system further comprises a system for after-treatment of the pollutant emissions (notably NOx) of the thermal engine. The most usual NOx reduction systems are exhaust gas recirculation and selective catalytic reduction. Furthermore, a particle filter can be used for hydrocarbons HC, carbon monoxide CO and fine particles.

[0070] For the method according to the invention, the following steps are carried out: [0071] acquisition of a torque setpoint TPT.sub.sp of the propulsion system, [0072] discretization of at least part of the controls allowable by the propulsion system, allowing reaching of the torque setpoint of the propulsion system, [0073] construction of a model of the propulsion system connecting a cost function to a control of the propulsion system with the cost function being a function of the consumption of the propulsion system and of the pollution emissions at the outlet of the after-treatment system, [0074] determination of a control of the propulsion system by minimizing the cost function for the discretized allowable controls, and [0075] application of the control to the propulsion system.

[0076] According to the invention, the determined control can be at least one of a torque setpoint of the thermal engine T.sub.eng_sp and a torque setpoint of the electric machine T.sub.mot_sp and a control setpoint of the kinematic chain ECC.sub.sp, such as for example a control setpoint for the gearbox ratio of the kinematic chain.

[0077] The torque setpoint of the propulsion system TPT.sub.sp corresponds to the wheel torque requested by the driver.

[0078] The control method according to the invention is performed online in real time. Thus, it determines the control without knowing in advance the path of the vehicle.

[0079] Notations

[0080] The following notations are used in the description hereafter:

TABLE-US-00001 N.sub.e Thermal engine speed [rpm] SOC State of charge of the battery [%] TPT.sub.sp Raw (unfiltered) driver's wheel torque setpoint [Nm] TPT.sub.flt.sub..sub.sp Filtered driver's wheel torque setpoint [Nm] v.sub.adm Allowable control vector [] T.sub.eng Thermal engine torque [Nm] T.sub.eng.sub..sub.v Allowable thermal engine torque vector [Nm] T.sub.eng.sub..sub.sp Thermal engine torque setpoint [Nm] T.sub.mot Electric machine torque [Nm] T.sub.mot.sub..sub.sp Electric machine torque setpoint [Nm] V.sub.veh Vehicle speed [km/h] ECC State of the kinematic chain at time t [] ECC.sub.sp Kinematic chain state setpoint [] ECC.sub.v Allowable kinematic chain state vector [] H.sub.v Hamiltonian vector (cost function) [equivalent W] .sub.AT After-treatment system efficiency [] T.sub.AT After-treatment system temperature [ C.] T.sub.AT QS After-treatment system steady-state temperature [ C.] C.sub.nom Nominal battery (or equivalent) capacity [C] OCV Battery (or equivalent) open-circuit voltage [V] DCR Battery (or equivalent) internal resistance [] Lagrange multiplier [] u.sub.1 Thermal engine torque control [Nm] u.sub.2 Kinematic chain state control [] x State of charge of the battery [%] Calibration variable [] m.sub.f Thermal engine fuel consumption [kg/h] m.sub.NO.sub.x .sub.TP NOx emissions at after-treatment system outlet [g/h] m.sub.NO.sub.x .sub.EO NOx emissions at thermal engine outlet [g/h] I Equivalent thermal inertia of the after-treatment system [W/K] k.sub.1 Thermal resistance of exchanges with the outside [W/K] k.sub.2 Thermal resistance of exchanges with the exhaust gas [W/K] t Time interval [s] R1 Reduction ratio of the reducer coupled with the electric machine [] RBV Ratio of the gearbox coupled with the thermal engine [] Pelec Power of the inverter supplying the electric machine [W] .sub.sp Calibration variable [] K.sub.p Calibration variable []

[0081] The time derivative is indicated by a point above the variable.

[0082] FIG. 1 illustrates the various steps of the method according to the invention: [0083] acquisition of a torque setpoint TPT.sub.sp of the propulsion system, [0084] discretization DIS of at least part of the controls v.sub.adm allowable by the propulsion system, allowing to reach the torque setpoint of the propulsion system TPT.sub.sp. [0085] construction of a model MOD of the propulsion system connecting a cost function H to a control of the propulsion system, cost function H being a function of the consumption of the propulsion system and of the pollutant emissions at the after-treatment system outlet, and [0086] determination of a control COM of the propulsion system by minimizing the cost function for the discretized allowable controls v.sub.adm.

[0087] 1) Discretization

[0088] In this stage, all the allowable controls enabling torque setpoint TPT.sub.sp of the hybrid propulsion system to be obtained are discretized. Discretization grids all of the allowable control solutions. The grid pitch can be selected according to a compromise between the precision of the solution (fine grid) and the acceleration of the computation time (coarse grid).

[0089] One possible discretization method produces a regular grid. This means that the grid pitch which is the distance between two elements, is constant. In this case, each element of the allowable control vector is obtained by use of the following equations:

v.sub.adm(i)=T.sub.eng.sub.mini+*(i1)

where , the grid pitch, is simply obtained by setting the number of elements of the grid N (for example, one can select N=10, which is an order of magnitude allowing to obtain a good compromise between precision and computational speed). For example:

[00002]$=\frac{T_{{\mathrm{eng}}_{\max }}-T_{{\mathrm{eng}}_{\min }}}{N-1}$

with T.sub.eng.sub.max being the maximum allowable torque of the thermal engine allowing reaching the torque setpoint, and T.sub.eng.sub.min being the minimum allowable torque of the thermal engine allowing reaching the torque setpoint.

[0090] Thus in this stage, an allowable control vector v.sub.adm is thus determined.

[0091] According to an embodiment of the invention, allowable control vector v.sub.adm can comprise the vector of the allowable thermal engine torques T.sub.eng_v.

[0092] Moreover, allowable the control vector v.sub.adm can comprise the vector of the allowable kinematic chain states ECC.sub.v.

[0093] 2) Model Construction

[0094] A model of the hybrid propulsion system is constructed in this stage. The hybrid propulsion model is representative of the kinematic chain of the propulsion system. It can further take account of the state of charge of the battery. The hybrid propulsion model connects a cost function to a control of the propulsion system. The cost function is a function of the consumption of the propulsion system and of the pollutant emissions at the after-treatment system outlet. It allows calculation of the cost associated with each possible control, in terms of consumption and pollutant emissions (notably NOx). This cost is a calibratable compromise between the pollutant emissions at the exhaust and the fuel consumption.

[0095] The control method according to the invention applies to all hybrid architectures: series, parallel or mixed series/parallel. Depending on the architecture used, the equations modeling the hybrid traction chain are different, but the overall principle remains the same. Furthermore, the main added value of the invention is independent of the hybrid architecture being considered since it is the consideration of the pollution emissions at the exhaust in addition to the consumption. Therefore, modeling of the hybrid traction chain is presented in a non-limitative manner in the case of a parallel hybrid propulsion system. The considered architecture is illustrated (in a non-limitative manner) in FIG. 4. The hybrid propulsion system comprises a thermal engine ICE, a Stop & Start generator SSG (electric machine that automatically shuts down and restarts a thermal engine), a clutch CL, a gearbox GB and an electric machine EM. Wheels W are coupled to the hybrid propulsion system by coupling mechanisms that are schematically shown.

[0096] For a parallel hybrid propulsion system, the wheel torque balance is written as follows:

TPT.sub.sp(t)=R.sub.1T.sub.mot(t)+RBV(ECC(t))T.sub.eng(t)

[0097] There are thus two degrees of freedom to achieve the driver's requirement. By convention, the control selected here is the thermal engine torque u.sub.1=T.sub.eng(t) and the state of the kinematic chain u.sub.2=ECC(t). It is observed that the thermal engine speed N.sub.e(t)=f.sub.BV(u.sub.2), where f.sub.BV characterizes the gear reduction ratios, is controlled through the state of the kinematic chain.

[0098] These degrees of freedom are used to minimize a calibratable (using parameter ) compromise between fuel consumption m.sub.f and pollutant emissions m.sub.NO.sub.x .sub.TP at the after-treatment system outlet:

J=.sub.t0.sup.tf f(u.sub.1,u.sub.2,t)dt

f(u.sub.1, u.sub.2, t)=(1){dot over (m)}.sub.f(u.sub.1, u.sub.2, t)+{dot over (m)}.sub.NO.sub.x .sub.TP(u.sub.1, u.sub.2, t)

[0099] Furthermore, the dynamics of the state of charge of the battery x(t)=SOC(t) is taken into account. Moreover, this state of charge is not totally free since the capacity of the battery is limited. Concerning the optimization problem, this amounts to adding a state constraint to the problem, so that the charge of the battery at the end t.sub.f of the operation is identical to the state of charge of the battery at the start t.sub.0 of the operation thereof:

SOC(t.sub.f)=SOC(t.sub.0)

[0100] The cost function of the model can be given by a function of the type:

H(u.sub.1, u.sub.2, x, t)=f(u.sub.1, u.sub.2, t)+(t){dot over (x)}(u.sub.1, u.sub.2, x, t)

[0101] Calculation of f(u.sub.1, u.sub.2, t)

[0102] To calculate f at any time, all of the terms of the equation of f are determined.

[0103] is a calibration variable allowing to adjust the compromise between consumption and pollutant emissions. The calibration of parameter cc depends on the emissions level of the engine being considered. In general, a setting ranging between 0.2 and 0.5 can be selected for , which allows ensuring a significant pollution emissions decrease while maintaining a favorable fuel consumption.

[0104] According to an embodiment of the invention, {dot over (m)}.sub.f=MAP(N.sub.e, T.sub.eng) can be obtained by use of a fuel flow map (MAP), generally generated as a result of tests.

[0105] The NOx emissions at the exhaust (after-treatment system outlet) can be modeled with an equation of the form:

{dot over (m)}.sub.NO.sub.x .sub.TP(u.sub.1, u.sub.2, x, t)={dot over (m)}.sub.NO.sub.x .sub.EO(u.sub.1, u.sub.2, x, t)(1.sub.AT(T.sub.AT))

[0106] The NOx emissions at the engine outlet {dot over (m)}.sub.NO.sub.x .sub.EO can be either calculated from a model or simply using a map obtained from bench tests.

[0107] The characteristic of the after-treatment system efficiency .sub.AT as a function of temperature T.sub.AT can result from characterization tests. It is also possible to take account of the influence of other variables such as the gas flow at the exhaust.

[0108] After-treatment temperature T.sub.AT can be estimated at any time from the following equations:

[00003]$T_{\mathrm{AT}}\left(t\right)=T_{\mathrm{AT}}\left(t-.Math..Math.t\right)+.Math..Math.t\frac{h_1.Math.\left(t\right)+h_2\left(t\right)}{I}$

where term h.sub.1 corresponds to the exchanges with the outside:

h.sub.1(t)=k.sub.1(T.sub.0T.sub.AT(tt))

and where term h.sub.2 corresponds to the combustion-related enthalpy release at the exhaust:

h.sub.2(t)=k.sub.2[T.sub.AT QS(tt), u.sub.2(tt))T.sub.AT(tt)]

[0109] Term T.sub.AT QS(u.sub.1(tt), u.sub.2(tt)) can be obtained by use of a map resulting from tests and it corresponds to the temperature measured at the after-treatment under steady state conditions. The values of parameters k1 and k2 can be determined from tests on vehicles. This temperature model, although simplified for computation time constraints related to the integration to the energy management strategy, gives a correct representativity, as illustrated in FIG. 5. This figure compares the measured after-treatment system temperature MES with the estimated temperature EST by use of the above equations, as a function of time and for a portion of the WLTC (Worldwide harmonized Light duty driving Test Cycle) driving cycle. This figure shows that it was possible to validate the model for various vehicle configurations (all thermal, Stop & Start and Full Hybrid: total hybridization, with both the engine and the motor providing motive power) and for the energy supervision strategy according to the invention (optimization criterion for consumption and pollutants at the after-treatment system outlet).

[0110] Calculation of {dot over (x)}(u.sub.1, u.sub.2, x, t)

[0111] Calculation of the dynamics of the system state, which is the state of charge of the battery, can be described in the following equations:

[00004]$\stackrel{.}{x}\left(u_1,u_2,x\right)=-\frac{I_{\mathrm{bat}}\left(u_1,u_2,x\right)}{C_{\mathrm{nom}}}100$

[0112] Using a model of the battery as an electric cell, the battery current can be expressed with an equation of the type:

[00005]$I_{\mathrm{bat}}=\frac{\mathrm{OCV}\left(x\right)}{2\mathrm{DCR}\left(x\right)}-\sqrt{\frac{{\mathrm{OCV}\left(x\right)}^2}{4{\mathrm{DCR}\left(x\right)}^2}-\frac{P_{\mathrm{elec}}\left(u_1,u_2\right)}{\mathrm{DCR}\left(x\right)}}$

with OCV and DCR respectively being the open-circuit voltage and the internal resistance of the battery as a function of the state of charge thereof, and these characteristics can be generated as a result of tests.

[0113] The calculation of power P.sub.elec of the inverter supplying the electric machine can be given by an equation of the form:

P.sub.elec(u.sub.1, u.sub.2, x, t)=f.sub.ME(u.sub.1, u.sub.2)

where f.sub.ME is a map integrating the efficiency of the electric machine and of the inverter, obtained from tests depending on the operating point thereof, and thus implicitly on controls u.sub.1 and u.sub.2.

[0114] Calculation of (t)

[0115] Calculation of the Lagrange multiplier can be carried out with the following equation:

(t)=.sub.SP+K.sub.p(x.sub.SPx(t))

where A.sub.SP and K.sub.p are calibrated to optimally contain the state of charge of the battery within the useful range thereof, x.sub.SP being the mean value of the state of charge of the battery.

[0116] f(u.sub.1, u.sub.2, t,), {dot over (x)}(u.sub.1, u.sub.2, x, t) and (t) can therefore be estimated using these various equations. Thus, the cost function H of the hybrid propulsion model is entirely determined.

[0117] 3) Cost Function Minimization

[0118] Cost function H of the hybrid propulsion system model is minimized in this stage. Minimization is carried out on the allowable controls discretized in step 1). Discretization thus allows reduction of the computation time required for minimization.

[0119] This step minimizes the cost vector (Hamiltonians given by the equation of H) associated with each allowable control in order to determine which is the optimal control.

[0120] According to an embodiment of the invention, minimization is performed using Pontryagin's minimum principle.

[0121] According to an embodiment of the invention, the minimization step allows determination of optimal torque setpoints for at least one of the thermal engine, the electric machine and an optimal control setpoint for the kinematic chain.

[0122] 4) Control Application

[0123] The invention allows determination of torque setpoints for at least one of the hybrid propulsion driving system and a control setpoint for the kinematic chain. Application of these setpoints to at least one of the thermal engine, to the electric machine and the kinematic chain allows obtaining a decrease in pollution emissions and the fuel consumption can also be limited.

[0124] The dynamic optimization period is suited to the physical phenomena involved in the engine, in this instance the production of pollution emissions.

[0125] The method according to the invention can be used for motor vehicles. However, it can be used in the field of road transport, two-wheelers, in the rail sector, the naval sector, the aeronautics sector, for hovercraft and amphibious vehicles.

[0126] The method according to the invention is particularly suitable for Full Hybrid propulsion systems, but it can also be suited for Stop & Start or Mild Hybrid type hybridization. Full Hybrid type hybridization corresponds to a completely hybrid system where the electric motor(s) are powerful enough to provide propulsion alone for a limited time. Stop & Start type hybridization corresponds to a control system that switches off the thermal engine when the vehicle is at standstill in neutral gear and restarts it when reactivated by the driver, by use of a low-power electric machine. The Mild Hybrid type propulsion system is equipped with a low-power electric machine and a regenerative braking system that provides additional power at low engine speed or during a high acceleration phase. For a Mild Hybrid propulsion system, the electric machine is not capable of providing traction of a vehicle alone.

Variant Embodiments

[0127] According to an implementation of the invention (that can be combined with all the variant embodiments described), discretization can also be a function of the state of charge SOC of the vehicle and of vehicle speed V.sub.veh.

[0128] According to a first embodiment of the invention, filtering of the torque setpoint of the hybrid propulsion system is performed with, the stages of the process being carried out for the filtered setpoint. Filtering can be a preventive anti-surge filter that filters the driver's torque requirement to limit surges.

[0129] According to a variant of this first embodiment of the invention, the control method determines the torque setpoints of the thermal engine T.sub.eng_sp and of the electric machine T.sub.mot_sp from the filtered propulsion system torque setpoint. FIG. 2 illustrates the steps of the control process for this variant embodiment. According to this variant, discretization is performed from a filtered value TPT.sub.flt_sp of the torque setpoint of the hybrid propulsion system. Without limitation, discretization is also a function of the state of charge SOC of the vehicle and of vehicle speed V.sub.veh. The discretization step allows determination of the allowable thermal engine torques T.sub.eng_v. The modeling and minimization steps remain unchanged in relation to the embodiment described in connection with FIG. 1. The minimization step allows determination of the torque setpoints of the thermal engine T.sub.eng_sp and of the electric machine T.sub.mot_sp.

[0130] According to a second embodiment of the invention, the determined control corresponds to the state of the kinematic chain or for example the control of the gearbox ratio of the kinematic chain. For this embodiment, the calculation principle (discretization, modeling and minimization) is the same as for the torque optimization, except that this allowable control vector v.sub.adm is not limited to all the possible torques T.sub.eng_v, and it also contains all the allowable kinematic chain states ECC.sub.v. Indeed, in order to determine which is the optimal kinematic chain state, the optimal torque distribution over each of the kinematic chain states to be compared is preferably determined beforehand. In fact, it is the comparison of the costs of the optimal torque distributions for each allowable kinematic chain state that allows the optimum to be determined.

[0131] FIG. 3 illustrates the steps of the method according to this second embodiment. Discretization is performed from an unfiltered value TPT.sub.sp of the torque setpoint of the hybrid propulsion system. Without limitation, discretization is also a function of the state of charge SOC of the vehicle and of vehicle speed V.sub.veh. The modeling and minimization steps remain unchanged in relation to the embodiment described in connection with FIG. 1 (or FIG. 2), except that allowable control vector v.sub.adm also comprises all the allowable kinematic chain states ECC.sub.v. The minimization step allows determination of a control setpoint for the kinematic chain ECC.sub.sp, which can be a kinematic chain gear ratio setpoint.

[0132] According to a third embodiment of the invention, at least one of a torque setpoint for the thermal engine T.sub.eng_sp and a torque setpoint for the electric machine T.sub.mot_sp is determined by use of the filtered torque setpoint of the propulsion system TPT.sub.flt_sp. Steps 1) to 3) are repeated to determine a control setpoint for the kinematic chain ECC.sub.sp by use of unfiltered torque setpoint TPT.sub.sp. The controls are applied to the hybrid propulsion system. For this embodiment, steps 1) to 3) are thus repeated twice which is once with a filtered torque setpoint TPT.sub.flt_sp and once with a raw (unfiltered) torque setpoint TPT.sub.sp. These two determinations can be performed in parallel. The advantage of separating the torque optimization from the kinematic chain state optimization lies in the impact of the comfort strategies that modify the driver's wheel torque requirement. These filtering strategies, such as the preventive anti-surge filter, filter the torque requirement of the driver TPT.sub.sp as a function of the current state of the kinematic chain. Optimization of the torque distribution is achieved from this filtered torque setpoint TPT.sub.flt_sp. However, the optimal kinematic chain state is preferably selected using raw torque setpoint TPT.sub.sp. Having to use different input signals justifies the need to perform two optimizations in parallel.

[0133] According to a variant embodiment, repeating steps 1) to 3) can be done according to the variant embodiments illustrated in FIGS. 2 and 3.

[0134] The invention further relates to a computer program product downloadable from a communication network and/or recorded on a computer readable medium and/or controller or server executable. This program comprises program code instructions for implementing the method as described above when the program is executed on a computer or a controller.

[0135] Furthermore, the invention relates to a hybrid propulsion system for a vehicle. The hybrid propulsion system comprises at least one electric machine powered by an electrical energy storage system, a thermal engine, a kinematic chain and a system for after-treatment of the pollutant emissions (notably NOx) of the thermal engine. The hybrid propulsion system comprises a control for carrying out the following steps: [0136] acquiring of a torque setpoint TPT.sub.sp of the propulsion system; [0137] discretizing at least part of the controls allowable by the propulsion system which allows reaching the torque setpoint of the propulsion system; [0138] constructing a model of the propulsion system connecting a cost function to a propulsion system control with the cost function being a function of the consumption of the propulsion system and of the pollution emissions at the outlet of the after-treatment system; [0139] determining a control of the propulsion system by minimizing the cost function for the discretized allowable controls, and [0140] applying the control to the propulsion system.

[0141] The control can be compatible with all the variants of the control method described above.

[0142] Furthermore, the invention relates to a vehicle comprising such a hybrid propulsion system. The vehicle according to the invention can be a motor vehicle. However, it can be any vehicle type in the field of road transport, two-wheelers, in the rail sector, the naval sector, the aeronautics sector, hovercraft and amphibious vehicles.

COMPARATIVE EXAMPLE

[0143] This example shows the advantages of the control method according to the invention. The control method according to the invention is compared with control methods of the prior art. The examples presented here are experimental validations of test bench results.

[0144] Application case:

[0145] Utility vehicle, mass: 2700 kg

[0146] Diesel engine: 120 kW

[0147] Electric motor: 50 kW.

[0148] The control method has been tested and compared on the engine test bench over the WLTC cycle that will be the official certification standard from the Euro 7 emissions standard.

[0149] The test apparatus that is used is a high-dynamic engine test bench with gas analysis cabinet for pollutant emissions measurement.

[0150] The engine used has a wide EGR area, standard in Euro 6c. This is an important point because, so far, many energy supervision works aimed at reducing consumption and pollution emissions have been validated only with Euro 5 engines. Now, a Euro 5 diesel engine comprises two relatively different operating areas: in the rather small area corresponding to the operating points of the NEDC driving cycle, exhaust gas recirculation (EGR) is used. Therefore, this area has low nitrogen oxides levels and a degraded fuel consumption. In the second area, outside the cycle, the engine adjustments are optimized with only a consumption criterion and EGR is not used. It is therefore no surprise that energy supervision manages to greatly vary the compromise between NOx emissions and fuel consumption.

[0151] For this example, the control method according to the invention is compared with a control method of the prior art where only the fuel consumption of the vehicle is optimized, and with a control method of the prior art where the consumption and the emissions at the engine outlet (before after-treatment) are optimized.

[0152] The experimental results are given in Table 1 and in FIGS. 6 to 11. The control method according to the invention is denoted by INV. The method according to the prior art wherein only consumption is optimized is denoted by AA. The method according to the prior art wherein consumption and emissions at the engine outlet are optimized is denoted by AA2. FIG. 6 shows the curves of vehicle speed V.sub.veh as a function of time for the three methods. FIG. 7 relates to the curves of the state of charge SOC of the battery for the three methods. FIG. 8 relates to the cumulative pollutant emissions at the thermal engine outlet m.sub.NO.sub.x .sub.EO for the three methods. FIG. 9 illustrates the cumulative pollutant emissions at the after-treatment system outlet m.sub.NO.sub.x .sub.TP for the three methods. FIG. 10 shows the curves of the after-treatment system temperature T.sub.AT for the three compared control methods. FIG. 11 illustrates the curves of the after-treatment system efficiency .sub.AT for the three control methods.

[0153] In Table 1 and in FIGS. 6 to 11, it is noted to be that each control method indeed allows obtaining the same speed profile (FIG. 6) as the control methods of the prior art AA1 and AA2. Moreover, it can be noted that each method allows the associated parameter to be minimized since the minimum value of the optimized associated parameter is indeed obtained in each one of the three cases. In relation to state of the art AA1, which corresponds to the strategy optimizing the fuel consumption, it appears that it is possible to significantly (55%) reduce the nitrogen oxide NOx emissions at the exhaust, by use of the control method of the invention INV, at the price of a slight consumption increase (2%). The control method according to the invention INV therefore first achieves all-electric motoring or illustrated by the drop in the state of charge of the battery in FIG. 7. Then, the thermal engine is started and used at points that favor actuation of the after-treatment, which is very rapid, as illustrated in FIG. 11, and also is effective as illustrated in FIG. 10. Therefore, the cumulative NOx emissions at the exhaust remain low as illustrated in FIG. 9, while the cumulative NOx emissions at the engine outlet are close to those of the method according to the prior art AA1 (FIG. 8).

TABLE-US-00002 TABLE 1 Comparative example Fuel Nitrogen oxides Nitrogen oxides consumption engine outlet exhaust [L/100 km] [mg/km] [mg/km] AA1 8.25 466 101 AA2 8.57 249 72 INV 8.42 446 45