CN112606822A - Two-stage double-model prediction control method for energy management of hybrid electric vehicle - Google Patents

Two-stage double-model prediction control method for energy management of hybrid electric vehicle Download PDF

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CN112606822A
CN112606822A CN202011582217.XA CN202011582217A CN112606822A CN 112606822 A CN112606822 A CN 112606822A CN 202011582217 A CN202011582217 A CN 202011582217A CN 112606822 A CN112606822 A CN 112606822A
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周维
张宁峰
张维刚
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Hunan University
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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W10/00—Conjoint control of vehicle sub-units of different type or different function
    • B60W10/24—Conjoint control of vehicle sub-units of different type or different function including control of energy storage means
    • B60W10/26—Conjoint control of vehicle sub-units of different type or different function including control of energy storage means for electrical energy, e.g. batteries or capacitors
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W10/00—Conjoint control of vehicle sub-units of different type or different function
    • B60W10/04—Conjoint control of vehicle sub-units of different type or different function including control of propulsion units
    • B60W10/06—Conjoint control of vehicle sub-units of different type or different function including control of propulsion units including control of combustion engines
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W20/00—Control systems specially adapted for hybrid vehicles
    • B60W20/10—Controlling the power contribution of each of the prime movers to meet required power demand
    • B60W20/15—Control strategies specially adapted for achieving a particular effect
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/10—Geometric CAD
    • G06F30/15—Vehicle, aircraft or watercraft design
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W2510/00—Input parameters relating to a particular sub-units
    • B60W2510/24—Energy storage means
    • B60W2510/242—Energy storage means for electrical energy
    • B60W2510/244—Charge state
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W2530/00—Input parameters relating to vehicle conditions or values, not covered by groups B60W2510/00 or B60W2520/00
    • B60W2530/209—Fuel quantity remaining in tank

Abstract

The invention discloses a two-stage and two-model predictive control method for energy management of a hybrid electric vehicle, which comprises the following steps: dividing a prediction domain into two continuous stages, wherein a first-stage RC model with reduced order is constructed in the first stage, and a pure internal resistance model is constructed in the second stage; respectively acquiring the state feasible domain boundary of each time step in two stages, and dispersing the state feasible domain of each time step; screening a global cost optimal path based on a forward dynamic programming algorithm and obtaining an optimal state point x corresponding to the optimal path at the last time step N*(N) and transition from time step N-1 to time stepN optimal control input Pe *(N-1) and obtaining the optimal control input P from the initial state to the time step 1 by reverse recursion in turne *(0) (ii) a With Pe *(0) Performing power distribution control as the target output power of the engine at the current moment; the above steps are then repeated with a scrolling of time steps. The method utilizes a reduced-order high-precision one-order RC model to process the battery power constraint at the front stage of the prediction domain, and adopts a simple pure internal resistance model at the rear stage of the prediction domain, so that the high-efficiency and safe energy distribution is realized on the basis of not increasing the computational complexity.

Description

Two-stage double-model prediction control method for energy management of hybrid electric vehicle
Technical Field
The invention relates to the technical field of automobile energy management, in particular to a hybrid electric vehicle energy management control method.
Background
The hybrid electric vehicle can not only greatly improve the fuel economy of the vehicle, but also improve the dynamic property of the vehicle. The power system of the hybrid electric vehicle mainly comprises an engine, a motor, a battery pack, a control system and other components, and is divided into plug-in hybrid power and non-plug-in hybrid power according to whether the battery can be charged by an external power supply or not.
The hybrid electric vehicle is provided with two sets of energy systems (fuel oil and electric energy), and the energy management of the hybrid electric vehicle improves the working efficiency of the power system and effectively reduces the fuel oil consumption of the vehicle by adjusting the output power distribution of an engine and a battery. In recent years, model predictive control has been widely used in the field of energy management of hybrid vehicles. For example, patent CN 110696815 a, "a method for energy management prediction for internet-connected hybrid electric vehicles," plans a reference SoC trajectory by using an optimal SoC feature obtained by offline dynamic planning in combination with a neural network, tracks the reference SoC trajectory by using a model prediction method based on dynamic planning, and solves a power distribution optimization problem in a prediction domain. The patent CN 109017809A energy distribution method based on cross-country working condition prediction establishes a vehicle electric transmission power model, a power battery simple internal resistance model, an engine generator model and a system state equation, calculates the future required power of the vehicle based on the information of working condition prediction vehicle speed, gradient, rolling resistance and the like, and adopts a model prediction control strategy of an embedded dynamic programming algorithm to give the optimal energy distribution at the next moment. Although the energy management method based on the model predictive control can improve the fuel economy of the whole vehicle, the battery model adopts a simple pure internal resistance model, and the calculation accuracy of the battery power constraint boundary is low. Particularly, under the condition of extremely sparse control grid division required by real-time calculation, the calculation accuracy of the lower battery power constraint boundary influences the optimality of the result, so that the oil-saving potential of the hybrid electric vehicle cannot be exerted to the maximum extent. If a first-order RC model is directly adopted in the prediction domain, the calculation complexity is greatly increased, and real-time calculation cannot be carried out in the vehicle-mounted embedded controller. Therefore, it is necessary to reasonably simplify the battery model on the premise of ensuring the calculation accuracy of the battery power constraint boundary, so that the algorithm can run in real time and the fuel consumption of the vehicle is reduced to the maximum extent.
Disclosure of Invention
The invention provides a two-stage and two-model predictive control method for energy management of a hybrid electric vehicle, which aims to solve the problem that the fuel-saving potential of the hybrid electric vehicle cannot be furthest exerted because the power constraint of a battery cannot be processed with high precision in the existing energy management control scheme.
The two-stage double-model predictive control method for the energy management of the hybrid electric vehicle comprises the following steps:
acquiring estimated SoC state and polarization voltage state v of current battery pack1And obtaining the internal resistance R of the battery pack0Internal polarization resistance R1Polarization time constant τ1And a prediction domain length N;
dividing a prediction domain into two continuous stages, constructing a first-stage RC model aiming at the first-stage prediction domain, and constructing a pure internal resistance model aiming at the second-stage prediction domain; based on first-order RC model and polarization time constant tau1Obtaining the first stage prediction domain length N1Further obtain the second stage prediction domain length N2；
Respectively acquiring a feasible domain boundary of each time step in the first-stage prediction domain and the second-stage prediction domain;
respectively dispersing the state space based on the feasible domain boundary of each time step in the first-stage prediction domain and the second-stage prediction domain, and expressing the state point in the feasible domain of each time step obtained after dispersion as xi(k)；
Acquiring all state points x from an initial state to a time step Ni(N) optimal feasible path, screening the path with optimal global cost and obtaining the optimal state point x corresponding to the path at the time step N*(N) and transition from time step N-1 to timeOptimal control input P for step Ne *(N-1) and reversely recurrently obtaining the optimal control input P for transferring from the initial state to the time step 1e *(0)；
With Pe *(0) Performing power distribution control as a target output power of the engine; the above steps are repeated with a rolling motion of time steps.
Further, the first-stage prediction domain length N is obtained based on the first-stage RC model and the polarization time constant tau1Further obtain the second stage prediction domain length N2The method specifically comprises the following steps:
the maximum n is solved that satisfies the following formula,
Figure BDA0002865396380000021
in the formula, the polarization time constant τ1＝R1C1，C1Is a polarization capacitor; deltarelPresetting a relative tolerance; e is a natural constant;
the maximum N obtained by solving is the length N of the prediction domain of the first stage1Second stage prediction field length N2＝N-N1。
Further, the obtaining of the feasible region boundary of each time step in the first-stage prediction domain and the second-stage prediction domain respectively specifically includes:
solving for the feasible region boundary [ soc ] of each time step in the first-stage prediction domain by the following formulamin(k),v1,max(k)]And [ soc)max(k),v1,min(k)]，k＝1,2,…,N1，
Figure BDA0002865396380000022
Figure BDA0002865396380000023
Figure BDA0002865396380000024
Figure BDA0002865396380000025
Wherein i (k) is calculated by the following formula,
Figure BDA0002865396380000031
solving the feasible region boundary soc of each time step in the second-stage prediction domain by the following formulamin(k) And socmax(k)，k＝N1+1,N1+2,…,N，
Figure BDA0002865396380000032
Figure BDA0002865396380000033
Figure BDA0002865396380000034
In the above formula, k represents a time step, SoC (k) represents a SoC state of the battery; qnomRepresents a rated capacity of the battery; i (k) represents a current; voc(soc (k)) represents the open-circuit voltage of the battery; v. of1(k) Represents the cell polarization voltage; pdmd(k) Representing the required power of the bus; pe(k) Representing engine power; r0(soc (k)) represents the ohmic internal resistance of the cell.
Further, the discretizing the state space based on the feasible domain boundary of each time step in the first-stage prediction domain and the second-stage prediction domain respectively specifically includes:
discretizing the state space based on the feasible region boundaries for each time step within the first stage prediction domain comprises:
based on the determined feasible region boundary of each time step in the first-stage prediction domain, obtaining an approximate linear equation of the feasible region of each time step in the first-stage prediction domain:
Figure BDA0002865396380000035
firstly, dispersing a state variable SoC into
Figure BDA0002865396380000036
(k＝1,2,…,N1) Another state variable polarization voltage v1Based on
Figure BDA0002865396380000037
And the above-mentioned approximate linear equation of feasible domain of each time step is directly dispersed into
Figure BDA0002865396380000038
Discretizing the state space based on the feasible region boundaries for each time step within the second-stage prediction domain comprises:
discretizing state variable SoC into
Figure BDA0002865396380000039
(k＝N1+1,N1+2,…,N)。
Further, the obtaining all state points x from the initial state to the time step Ni(N) optimal feasible path, screening the path with optimal global cost and obtaining the optimal state point x corresponding to the path at the time step N*(N) and optimal control input P transitioning from time step N-1 to time step Ne *(N-1) and reversely recurrently obtaining the optimal control input P for transferring from the initial state to the time step 1e *(0) The method specifically comprises the following steps:
the feasible domains obtained after the dispersion of each time step in the first-stage prediction domain and the second-stage prediction domain are uniformly expressed as the shapesState vector
Figure BDA0002865396380000041
(k-1, 2, …, N), feasible domain
Figure BDA0002865396380000042
The ith inner state point is denoted as xi(k)；
Uniformly dispersing the controlled variable in the constraint range of the controlled variable into
Figure BDA0002865396380000043
Is calculated at
Figure BDA0002865396380000044
Under the action of the action, the ith state point x can be transferred from the time step k to the step k +1iState set of (k +1)
Figure BDA0002865396380000045
In the formula, g () represents an inverse function of a state variable SoC state transition equation, which in the first stage and the second stage respectively is:
Figure BDA0002865396380000046
Figure BDA0002865396380000047
calculating from initial state to state point xiCost of feasible path of (k +1)
Figure BDA0002865396380000048
Figure BDA0002865396380000049
In the formula (I), the compound is shown in the specification,
Figure BDA00028653963800000410
representing last time step state vector
Figure BDA00028653963800000411
To the state point xiThe cost of the transfer of (k +1),
Figure BDA00028653963800000412
state vector representing initial state to last time step
Figure BDA00028653963800000413
The optimal cost of (2); wherein
Figure BDA00028653963800000414
xi(k +1) and
Figure BDA00028653963800000415
calculated by the following formula:
Figure BDA00028653963800000416
Figure BDA00028653963800000417
in the formula etae(Pe(k) ) is an optimal efficiency curve for the hybrid system; qlhvIs the low heat value of the fuel oil; zeta is a SoC track deviation penalty coefficient; SoC _ ref (k) is the SoC reference trace, preferably, may be 0.5;
screening from initial state to state point xiOptimal cost of feasible path of (k +1)
Figure BDA00028653963800000418
And obtains the corresponding optimal control input for the transition from the state of time step k to time step k +1
Figure BDA00028653963800000419
Figure BDA00028653963800000420
Repeating the above process to obtain all state points x from the initial state to the time step Ni(N) optimal feasible path, screening the path with optimal global cost and obtaining the optimal state point x corresponding to the path at the time step N*(N) and optimal control input P transitioning from time step N-1 to time step Ne *(N-1) and reversely recurrently obtaining the optimal control input P for transferring from the initial state to the time step 1e *(0)。
Further, the following state and control constraints due to physical constraints and safety are satisfied throughout the prediction domain:
soclb≤soc(k)≤sochb
Pe,min≤Pe(k)≤Pe,max
ΔPe,min≤Pe(k+1)-Pe(k)≤ΔPe,max
Pb,min≤Pb(k)≤Pb,max
wherein, soclbAnd sochbIs SoC minimum and maximum; pb,minAnd Pb,maxThe minimum and maximum instantaneous charge and discharge power of the battery pack is mainly restricted by the terminal voltage of the charge and discharge; pe,minAnd Pe,maxIs the minimum and maximum output power of the engine; delta Pe,minAnd Δ Pe,maxIs the maximum allowable ramp down and ramp up rate of the engine.
Further, for the first-order RC model corresponding to the first-stage prediction domain, P is the state variable because the polarization voltage is the state variableb,minAnd Pb,maxThe corresponding peak power constraint can be directly converted into a voltage constraint, which is calculated as:
Vlow≤Voc(soc(k))-i(k)R0(soc(k))-v1(k)≤Vhigh
for the pure internal resistance model corresponding to the second stage prediction domain, Pb,minAnd Pb,maxThe calculation is as follows:
Figure BDA0002865396380000051
Figure BDA0002865396380000052
wherein, VlowAnd VhighThe discharge cut-off voltage and the charge cut-off voltage of the battery pack are respectively; v. of1(n) is the polarization voltage state of the last time step of the first phase.
Advantageous effects
The invention provides a hybrid electric vehicle energy management control method based on model predictive control, which divides the whole prediction domain into two continuous stages in time domain: within the first-stage prediction domain, applying a reduced-order first-stage RC model to obtain a more accurate power constraint, and adaptively determining a duration of the first-stage prediction domain from the estimated RC model time constant; in the second stage prediction domain, a simple pure internal resistance model is applied to improve the calculation speed so as to improve the capability of meeting the battery power constraint on the premise of ensuring the calculation efficiency. Compared with the traditional method based on the pure internal resistance model, the scheme can more reasonably process the battery power constraint on the premise of not increasing the calculation burden, thereby obtaining better fuel economy than the traditional method under the condition of sparse control grid division, and further improving the fuel economy of the hybrid electric vehicle when being applied to a real vehicle.
Drawings
FIG. 1 is a flow chart of a two-stage dual-model predictive control method for energy management of a hybrid electric vehicle according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of two battery equivalent circuit models, a pure internal resistance model and a first-order RC model, according to an embodiment of the present invention;
FIG. 3 is a diagram illustrating that the feasible region discrete points at each time step in the first-stage prediction domain can be approximately equivalent to a straight line according to an embodiment of the present invention;
fig. 4 is a schematic diagram of an implementation of a hybrid electric vehicle energy management control method according to an embodiment of the present invention.
FIG. 5 is a graph showing the relative percentage of fuel consumption for a two-stage dual-model method and a pure internal resistance model method obtained from simulation testing
Figure BDA0002865396380000061
Detailed Description
The present invention will be described in detail below with reference to the accompanying drawings and specific embodiments.
For the hybrid electric vehicle energy management method based on model predictive control, because the calculation is simple, a pure internal resistance model is usually adopted, and the circuit schematic diagram is shown in fig. 2(a), the model only has one state variable of SoC, the calculation efficiency is high, but the processing of battery power constraint is rough. The circuit diagram of the first-order RC model is shown in fig. 2(b), and the RC link takes into account the polarization voltage of the battery, so that the first-order RC model has more advantages in processing the power capability of the battery, but also increases the dimension of the system state space and increases the calculation cost.
In order to better process the power constraint of the battery on the premise of ensuring the computational efficiency, the invention provides a two-stage dual-model energy management control scheme embedded in model predictive control. The main difference with the existing scheme is that the entire prediction domain is divided into two consecutive stages in the time domain: within a first phase of the prediction domain, applying a first order RC model to more accurately compute the power constraint boundaries of the battery; in the second stage of the prediction domain, a simple pure internal resistance model is applied to ensure the calculation efficiency; meanwhile, the equivalent order reduction processing within the error allowable range is carried out on the first-order RC model so as to further reduce the calculation cost.
The energy management control scheme provided by the invention aims to improve the fuel economy of the whole vehicle, so that the cost function of the optimal energy management problem is set as the accumulated fuel consumption in a prediction domain, and meanwhile, in order to realize electric quantity maintenance, an SoC track deviation punishment needs to be added. The total cost function is then expressed as:
Figure BDA0002865396380000062
wherein eta ise(Pe(k) ) is an optimal efficiency curve for the hybrid system; qlhvIs the low heat value of the fuel oil; zeta is a SoC track deviation penalty coefficient; SoC _ ref (k) is a SoC reference trace; n is the time length of the prediction domain, k is the time step, in this embodiment, the length of each time step is 1 second, and the time length of the prediction domain is N seconds.
For the pure internal resistance model, SoC is the only state variable, and the state transition equation is as follows:
Figure BDA0002865396380000071
for the first-order RC model, the state variables are SoC and the polarization voltage v1：
soc(k+1)＝soc(k)-i(k)/Qnom (3.1)
Figure BDA0002865396380000072
Wherein R is1And τ1The polarization internal resistance and the polarization time constant, tau, of the first-order RC model circuit in FIG. 2(b) are shown1＝R1C1，C1Is a polarization capacitor; the instantaneous current i (k) may be based on a control input Pe(k) Calculated by the following formula:
Figure BDA0002865396380000073
at the same time, the following state and control constraints due to physical constraints and safety considerations must be satisfied throughout the prediction domain:
soclb≤soc(k)≤sochb (5.1)
Pe,min≤Pe(k)≤Pe,max (5.2)
ΔPe,min≤Pe(k+1)-Pe(k)≤ΔPe,max (5.3)
Pb,min≤Pb(k)≤Pb,max (5.4)
wherein, soclbAnd sochbIs the minimum and maximum values of SoC; pb,minAnd Pb,maxThe minimum and maximum instantaneous charge and discharge power of the battery pack is mainly restricted by the terminal voltage of the charge and discharge; pe,minAnd Pe,maxIs the minimum and maximum output power of the engine; delta Pe,minAnd Δ Pe,maxIs the maximum allowable ramp down and ramp up rate of the engine.
For the first-order RC model corresponding to the first-stage prediction domain, P is the state variable because the polarization voltage is the state variableb,minAnd Pb,maxThe represented peak power constraint can be directly translated into a voltage constraint, calculated as:
Vlow≤Voc(soc(k))-i(k)R0(soc(k))-v1(k)≤Vhigh (6.1)
for the pure internal resistance model corresponding to the second stage prediction domain, Pb,minAnd Pb,maxThe calculation is as follows:
Figure BDA0002865396380000074
Figure BDA0002865396380000075
wherein, VlowAnd VhighThe discharge cut-off voltage and the charge cut-off voltage of the battery pack are respectively; v. of1(n) is the polarization voltage state of the last time step of the first phase. To further reduce the amount of computation, it is necessary to pair feasible fieldsAnd calculating the state space, and performing order reduction processing on the first-order RC model. Two boundary points [ soc ] at each time step in the first-stage prediction domain are determined as shown in FIG. 3min(k+1),v1,max(k+1)]And [ soc)max(k+1),v1,min(k+1)]：
Figure BDA0002865396380000081
Figure BDA0002865396380000082
After determining the boundary points of the feasible region at each time step, the equation of the approximate straight line of the feasible region in fig. 3 is calculated:
Figure BDA0002865396380000083
to control the reduced order error, the first stage predicts the domain duration N1Determined by the formula, N1Maximum n to satisfy the following:
Figure BDA0002865396380000084
wherein, deltarelThe relative tolerance is preset, which is preset to 20% in the embodiment; e is a natural constant. The first phase duration N corresponding to different polarization time constants can be obtained1The look-up table may be stored in practice and is operable to adaptively determine the duration of the first stage prediction domain based on the estimated polarization time constant of the first stage RC model.
Determining a feasible region boundary soc for each time step in the second stage prediction domain bymin(k) And socmax(k)，k＝N1+1,N1+2,…,N，
Figure BDA0002865396380000085
Figure BDA0002865396380000086
The solution according to the invention is further illustrated below by means of several examples.
Example 1
As shown in fig. 1, the embodiment provides a two-stage dual-model predictive control method for energy management of a hybrid electric vehicle, including:
s01: acquiring estimated SoC state and polarization voltage state v of current battery pack1And obtaining the internal resistance R of the battery pack0Internal polarization resistance R1Polarization time constant τ1And a prediction domain length N; wherein, the SoC state and the polarization voltage state v of the current battery pack1Estimated by a state observer, the polarization time constant tau1＝R1C1，C1Is a polarization capacitance.
S02: dividing a prediction domain into two continuous stages, constructing a first-stage RC model aiming at the first-stage prediction domain, and constructing a pure internal resistance model aiming at the second-stage prediction domain; based on first-order RC model and polarization time constant tau1Obtaining the first stage prediction domain length N1Further obtain the second stage prediction domain length N2(ii) a First stage prediction field length N1By equation (9) and polarization time constant τ1The length N of the prediction domain in the second stage is obtained by solving2＝N-N1。
S03: respectively acquiring a feasible domain boundary of each time step in the first-stage prediction domain and the second-stage prediction domain; the method specifically comprises the following steps: calculating the feasible region boundary [ soc ] of each time step in the first-stage prediction domain according to the formulas (7.1) and (7.2)min(k),v1,max(k)]And [ soc)max(k),v1,min(k)]，k＝1,2,…,N1Calculating the feasible region boundary soc of each time step in the second-stage prediction domain according to the formula (10)min(k) And socmax(k)，k＝N1+1,N1+2,…,N。
S04: respectively dispersing the state space based on the feasible domain boundary of each time step in the first-stage prediction domain and the second-stage prediction domain, and expressing the state point in the feasible domain of each time step obtained after dispersion as xi(k)。
The method specifically comprises the following steps: discretizing the state space according to the feasible domain boundary of each time step in the first-stage prediction domain and the second-stage prediction domain obtained in the step S03; for the first stage prediction domain, firstly dispersing the state variable SoC into
Figure BDA0002865396380000091
Figure BDA0002865396380000092
(k＝1,2,…,N1) Another state variable polarization voltage v1Based on
Figure BDA0002865396380000093
And formula (8) is directly discretized into
Figure BDA0002865396380000094
For the second stage prediction domain, dispersing the state variable SoC into
Figure BDA0002865396380000095
Figure BDA0002865396380000096
(k＝N1+1,N1+2,…,N)。
S05: acquiring all state points x from an initial state to a time step Ni(N) optimal feasible path, screening the path with optimal global cost and obtaining the optimal state point x corresponding to the path at the time step N*(N) and optimal control input P transitioning from time step N-1 to time step Ne *(N-1) and reversely recurrently obtaining the optimal control input P for transferring from the initial state to the time step 1e *(0)。
For better presentation, the following description is givenThe feasible domains obtained after the dispersion of each time step in the first-stage prediction domain and the second-stage prediction domain are uniformly expressed as state vectors
Figure BDA0002865396380000097
(k-1, 2, …, N), feasible domain
Figure BDA0002865396380000098
The state points in are denoted xi(k) In that respect The method specifically comprises the following steps:
s051: uniformly dispersing the controlled variable in the constraint range of the controlled variable into
Figure BDA0002865396380000099
Is calculated at
Figure BDA00028653963800000910
Under the action of the action, the ith state point x can be transferred from the time step k to the step k +1iState set of (k +1)
Figure BDA00028653963800000911
Figure BDA00028653963800000912
In the formula, g () represents the inverse function of the state transition equation of the state variable SoC (i.e., formulas (2), (3.1));
s052: calculating from initial state to state point xiCost of feasible path of (k +1)
Figure BDA0002865396380000101
Figure BDA0002865396380000102
In the formula (I), the compound is shown in the specification,
Figure BDA0002865396380000103
to representLast time step state vector
Figure BDA0002865396380000104
To the state point xiThe cost of the transfer of (k +1),
Figure BDA0002865396380000105
state vector representing initial state to last time step
Figure BDA0002865396380000106
The optimal cost of (2); wherein
Figure BDA0002865396380000107
And
Figure BDA0002865396380000108
calculated by the following formula:
Figure BDA0002865396380000109
Figure BDA00028653963800001010
s053: screening from initial state to state point xiOptimal cost of feasible path of (k +1)
Figure BDA00028653963800001011
And a corresponding optimal control input for transitioning from the state at time step k to time step k +1
Figure BDA00028653963800001012
Figure BDA00028653963800001013
S054: repeating the above steps S051 to S053, and gradually recurring from the time step 1 to the time step N to obtainTo all state points x from initial state to time step Ni(N) optimal feasible path, screening the path with optimal global cost and obtaining the state point x corresponding to the path at the time step N*(N) and optimal control input P transitioning from time step N-1 to time step Ne *(N-1) and reversely recurrently obtaining the optimal control input P for transferring from the initial state to the time step 1e *(0)。
S06: with Pe *(0) Performing power distribution control as a target output power of the engine; the above steps S01 to S06 are repeated with scrolling in time steps. The implementation of the above control process is schematically shown in fig. 4.
In order to verify the control performance of the energy management control method provided by the invention, a power system of a series hybrid school bus is taken as an object, a Matlab/Simulink/Stateflow is used for building a MiL test platform, the method and a single Rint model (namely a pure internal resistance model) method are simulated under 6 city and suburban driving conditions respectively, the fuel consumption values of the two obtained methods are shown in the following table (Dual and Rint in the table respectively refer to the method provided by the invention and the Rint model method), and the relative percentage of the fuel consumption of the two methods is shown in FIG. 5. Since the final SoC values after each simulation are not completely equal (Δ socf ≠ 0), for fair comparison of the fuel consumption differences, the fuel consumption values in the table have been corrected according to the final SoC value differences. Because the difference of the SoC final value is small, the error brought by the correction does not influence the overall result of the fuel consumption comparison. In addition, in order to verify the control performance of the method under different vehicle speed prediction accuracies, the simulation test also sets two situations of accurate speed prediction and inaccurate speed prediction (constant speed in a prediction domain).
TABLE 1 fuel consumption table for two methods under different working conditions
Figure BDA0002865396380000111
As can be seen from fig. 5, the proposed method achieves lower fuel consumption when the discrete state points are sparse, and the fuel consumption difference between the two methods is 1.81% under the condition of inaccurate vehicle speed prediction. This shows that the proposed method is more robust to inaccuracies in the vehicle speed prediction. In addition, although the proposed method has no advantage in fuel consumption compared to the Rint model method when the discrete state points are encrypted to 200, the algorithm takes most of the sampling time (1 second) to complete the calculation in view of the single-step calculation time of the algorithm listed in the table, and such a high density of discrete state points obviously does not meet the requirement of real-time performance. Compared with a Rint model method, the method has higher engineering application value due to the fact that the proposed method has advantages under sparse discrete state points and real-time performance is also needed by the vehicle-mounted controller.
To sum up, the invention provides a two-stage and two-model predictive control method for energy management of a hybrid electric vehicle, which divides the whole predictive domain into two continuous stages in the time domain: in the first stage prediction domain, a first-order RC model is applied to obtain more accurate power constraint; in the second stage prediction domain, a low-order Rint model is applied to ensure the calculation efficiency; meanwhile, the equivalent order reduction processing within the error allowable range is carried out on the first-order RC model so as to further reduce the calculation cost; the capability of meeting the battery power constraint can be improved on the premise of ensuring the calculation efficiency. Compared with the traditional Rint model-based method, the scheme can more reasonably process the battery power constraint on the premise of not increasing the calculation burden, thereby obtaining better fuel economy than the traditional method under the condition of sparse control grid division, and further improving the fuel economy of the hybrid electric vehicle when being applied to a real vehicle.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims (7)

1. A two-stage double-model predictive control method for energy management of a hybrid electric vehicle is characterized by comprising the following steps:
obtaining an estimated current batterySoC state and polarization voltage state v of a packet1And obtaining the internal resistance R of the battery pack0Internal polarization resistance R1Polarization time constant τ1And a prediction domain length N;
dividing a prediction domain into two continuous stages, constructing a reduced-order first-order RC model aiming at the first-stage prediction domain, and constructing a pure internal resistance model (Rint model) aiming at the second-stage prediction domain; based on first-order RC model and polarization time constant tau1Obtaining the first stage prediction domain length N1Further obtain the second stage prediction domain length N2；
Respectively acquiring a feasible state boundary of each time step in a first-stage prediction domain and a second-stage prediction domain;
respectively dispersing the state space based on the feasible state boundary of each time step in the first-stage prediction domain and the second-stage prediction domain, and expressing the state point in the feasible region of each time step obtained after dispersion as xi(k) (ii) a Acquiring all state points x from initial state to last time step Ni(N) optimal feasible path, screening the path with optimal global cost and obtaining the optimal state point x corresponding to the path at the time step N*(N) and optimal control input P transitioning from time step N-1 to time step Ne *(N-1) and reversely recurrently obtaining the optimal control input P for transferring from the initial state to the time step 1e *(0)；
With Pe *(0) Performing power distribution control as the target output power of the engine at the current moment; the above steps are repeated with a rolling motion of time steps.
2. The energy management control method for hybrid electric vehicle according to claim 1, wherein the first-stage prediction domain length N is obtained based on the first-stage RC model and the polarization time constant τ1Further obtain the second stage prediction domain length N2The method specifically comprises the following steps:
the maximum n is solved that satisfies the following formula,
Figure FDA0002865396370000011
in the formula, the polarization time constant τ1＝R1C1，C1Is a polarization capacitor; deltarelIs a preset relative tolerance; e is a natural constant;
the maximum N obtained by solving is the length N of the prediction domain of the first stage1Second stage prediction field length N2＝N-N1。
3. The energy management control method of claim 1, wherein the obtaining of the feasible region boundary of each time step in the first-stage prediction region and the second-stage prediction region respectively comprises:
solving for the feasible region boundary [ soc ] of each time step in the first-stage prediction domain by the following formulamin(k),v1,max(k)]And [ soc)max(k),v1,min(k)]，k＝1,2,…,N1，
Figure FDA0002865396370000021
Figure FDA0002865396370000022
Figure FDA0002865396370000023
Figure FDA0002865396370000024
Wherein i (k) is calculated by the following formula,
Figure FDA0002865396370000025
solving the feasible region boundary soc of each time step in the second-stage prediction domain by the following formulamin(k) And socmax(k)，k＝N1+1,N1+2,…,N，
Figure FDA0002865396370000026
Figure FDA0002865396370000027
Figure FDA0002865396370000028
In the above formula, k represents a time step, SoC (k) represents a SoC state of the battery; qnomRepresents a rated capacity of the battery; i (k) represents a current; voc(soc (k)) represents the open-circuit voltage of the battery; v. of1(k) Represents the cell polarization voltage; pdmd(k) Representing the required power of the bus; pe(k) Representing engine power; r0(soc (k)) represents the ohmic internal resistance of the cell.
4. The energy management control method of claim 3, wherein discretizing the state space based on the feasible region boundary of each time step in the first-stage prediction domain and the second-stage prediction domain respectively comprises:
discretizing the state space based on the feasible region boundaries for each time step within the first stage prediction domain comprises:
based on the determined feasible region boundary of each time step in the first-stage prediction domain, obtaining an approximate linear equation of the feasible region of each time step in the first-stage prediction domain:
Figure FDA0002865396370000029
firstly, dispersing a state variable SoC into
Figure FDA00028653963700000210
Another state variable polarization voltage v1Based on
Figure FDA00028653963700000211
And the above-mentioned approximate linear equation of feasible domain of each time step is directly dispersed into
Figure FDA00028653963700000212
Discretizing the state space based on the feasible region boundaries for each time step within the second-stage prediction domain comprises:
discretizing state variable SoC into
Figure FDA0002865396370000031
5. The energy management control method for hybrid electric vehicle according to claim 4, wherein the obtaining of all state points x from the initial state to the last time step Ni(N) optimal feasible path, screening the path with optimal global cost and obtaining the optimal state point x corresponding to the path at the time step N*(N) and optimal control input P transitioning from time step N-1 to time step Ne *(N-1) and reversely recurrently obtaining the optimal control input P for transferring from the initial state to the time step 1e *(0) The method specifically comprises the following steps:
the feasible domains obtained after the dispersion of each time step in the first-stage prediction domain and the second-stage prediction domain are uniformly expressed as state vectors
Figure FDA0002865396370000032
Feasible region
Figure FDA0002865396370000033
The ith inner state point is denoted as xi(k)；
Uniformly dispersing the controlled variable in the constraint range of the controlled variable into
Figure FDA0002865396370000034
Is calculated at
Figure FDA0002865396370000035
Under action, the single state point x can be transferred from a time step k to a step k +1iState set of (k +1)
Figure FDA0002865396370000036
Figure FDA0002865396370000037
In the formula, g () represents an inverse function of a state variable SoC state transition equation, which in the first stage and the second stage respectively is:
Figure FDA0002865396370000038
Figure FDA0002865396370000039
calculating from initial state to state point xiCost of feasible path of (k +1)
Figure FDA00028653963700000310
Figure FDA00028653963700000311
In the formula (I), the compound is shown in the specification,
Figure FDA00028653963700000312
representing last time step state vector
Figure FDA00028653963700000313
To the state point xiThe cost of the transfer of (k +1),
Figure FDA00028653963700000314
state vector representing initial state to last time step
Figure FDA00028653963700000315
The optimal cost of (2); wherein
Figure FDA00028653963700000316
And
Figure FDA00028653963700000317
calculated by the following formula:
Figure FDA00028653963700000318
Figure FDA00028653963700000319
in the formula etae(Pe(k) ) is an optimal efficiency curve for the hybrid system; qlhvIs the low heat value of the fuel oil; zeta is a SoC track deviation penalty coefficient; SoC _ ref (k) is the SoC reference trace, preferably may be 0.5;
screening from initial state to state point xiOptimal cost of feasible path of (k +1)
Figure FDA0002865396370000041
And obtains the corresponding optimal control input for the transition from the state of time step k to time step k +1
Figure FDA0002865396370000042
Figure FDA0002865396370000043
Repeating the above process to obtain all state points x from the initial state to the time step Ni(N) optimal feasible path, screening the path with optimal global cost and obtaining the optimal state point corresponding to the path at the time step N
Figure FDA0002865396370000044
And an optimal control input P for transitioning from time step N-1 to time step Ne *(N-1) and reversely recurrently obtaining the optimal control input P for transferring from the initial state to the time step 1e *(0)。
6. The hybrid vehicle energy management control method according to any one of claims 1 to 5, characterized in that the following state and control constraints due to physical limitations and safety are satisfied throughout the prediction domain:
soclb≤soc(k)≤sochb
Pe,min≤Pe(k)≤Pe,max
ΔPe,min≤Pe(k+1)-Pe(k)≤ΔPe,max
Pb,min(k)≤Pb(k)≤Pb,max(k)
wherein, soclbAnd sochbIs SoC minimum and maximum; pb,minAnd Pb,maxThe minimum and maximum instantaneous charge and discharge power of the battery pack is mainly restricted by the terminal voltage of the charge and discharge; pe,minAnd Pe,maxIs the minimum and maximum output power of the engine; delta Pe,minAnd Δ Pe,maxIs the maximum allowable ramp down and ramp up rate of the engine.
7. The hybrid vehicle energy management control method according to claim 6,
for the first-order RC model corresponding to the first-stage prediction domain, the voltage v is polarized1Is a state variable, therefore Pb,minAnd Pb,maxThe represented peak power constraint can be directly translated into a voltage constraint, calculated as:
Vlow≤Voc(soc(k))-i(k)R0(soc(k))-v1(k)≤Vhigh
for the Rint model corresponding to the second stage prediction domain, Pb,minAnd Pb,maxThe calculation is as follows:
Figure FDA0002865396370000045
Figure FDA0002865396370000051
wherein, VlowAnd VhighThe discharge cut-off voltage and the charge cut-off voltage of the battery pack are respectively; v. of1(n) is the polarization voltage state of the last time step of the first phase.
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