CN112918330B - Method for calculating optimal working state control strategy of fuel cell vehicle - Google Patents
Method for calculating optimal working state control strategy of fuel cell vehicle Download PDFInfo
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- B60L58/00—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles
- B60L58/40—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for controlling a combination of batteries and fuel cells
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Abstract
The invention provides a method for calculating an optimal working state control strategy of a fuel cell vehicle, which is established for the overall economy and the durability of a fuel cell of an extended-range fuel cell vehicle. According to the method, the start-stop state of the fuel cell is added as the state variable, and the low-power transition stage is added between the start-up state and the shut-down state of the fuel cell, so that the self-adaptive start-stop interval control of the fuel cell is realized, and the frequent start-stop of the fuel cell is avoided. And the fuel cell performance degradation index is used as the durability cost, the whole vehicle energy consumption is used as the economy cost, a multi-target joint cost function of the economy and the durability is constructed, and the joint optimization control of the economy and the durability is realized. And obtaining an energy management strategy with global optimal economy and durability through off-line calculation, and using the energy management strategy as a benchmarking and reference standard for the development of the energy management strategy of the real vehicle.
Description
Technical Field
The invention relates to the technical field of automobile power system control, in particular to a method for calculating an optimal working state control strategy of a fuel cell vehicle.
Background
As one of the most promising new energy vehicles, research on fuel cell vehicles is a major hot spot. Due to the characteristics of the fuel cell and the limitations of the current technology, the fuel cell for a vehicle is often combined with another power source to form a hybrid system of the vehicle. Therefore, in order to reasonably distribute the energy of the two power sources and improve the efficiency and the service life of the system, the research on the energy management strategy of the fuel cell vehicle has very practical value and urgent needs.
When the vehicle fuel cell is in a power fluctuation state, a start-stop change state, a low-power operation state (a state that the output power is lower than the idle power) and a high-power operation state (a state that the output power is higher than the rated power) during the running process of the vehicle, the performance of the vehicle fuel cell can be obviously degraded. Severe power fluctuations and frequent start-stop changes are the key influencing factors of the performance degradation of the fuel cell. Therefore, in the development process of the energy management strategy of the fuel cell vehicle, the durability of the vehicle fuel cell needs to be considered on the basis of ensuring the economy of the whole vehicle, the working state of the fuel cell is controlled, the performance degradation is avoided, and the service life of the fuel cell is prolonged.
In the current research, aiming at the power fluctuation of the fuel cell, the adopted means comprises setting the constraint condition of the output power change rate of the fuel cell; a low pass filter is provided to filter the required power, etc. Aiming at the start-stop change of the fuel cell, the adopted measures comprise setting the constraint condition of the lowest output power of the fuel cell so as to prohibit the fuel cell from being closed; establishing a target function of the start-stop change decline amount of the fuel cell, and indirectly reducing the start-stop times; and setting fixed fuel cell start-stop interval constraints to prevent frequent start-stop of the fuel cell and the like.
In the above research, for the start-stop problem of the fuel cell, a direct control mode based on engineering experience or an indirect control mode based on optimization calculation is mostly adopted, flexible adaptation to different driving conditions and system states is difficult to achieve, and the research of a multi-objective fuel cell start-stop control method comprehensively considering economy and durability is lacked.
Disclosure of Invention
The embodiment of the invention provides a method for calculating an optimal working state control strategy of a fuel cell vehicle, aiming at obtaining an offline global optimal strategy which can give consideration to both economy and durability.
In order to achieve the purpose, the invention adopts the following technical scheme.
A method for calculating a control strategy of an optimal working state of a fuel cell vehicle comprises the following steps:
s1, acquiring basic parameters, establishing simplified models of a fuel cell, a power cell and a whole vehicle based on the basic parameters, and acquiring the output power of the fuel cell and the SOC of the power cell at each moment;
s2, taking the output power of the fuel cell at each moment as a control variable and the SOC of the power cell as a first state variable to obtain a first state transition equation;
s3, taking the output power of the fuel cell at each moment as a second state variable, obtaining a second state transition equation and obtaining an accessible state of the output power variation amplitude of the fuel cell;
s4, determining switching logics of different start-stop state pieces of the fuel cell by taking the start-stop state of the fuel cell as a third state variable to obtain a third state transition equation;
s5, setting constraint conditions of a first state variable, a second state variable, power battery output power and fuel battery hydrogen consumption according to vehicle type power system parameters;
s6, obtaining power consumption of the power battery, hydrogen consumption of the fuel battery and a performance attenuation index of the fuel battery according to the output power of the power battery and the output power of the fuel battery, and constructing a comprehensive cost function;
s7, forward optimization is conducted on the whole reachable state space according to the first state transition equation, the second state transition equation, the third state transition equation and the comprehensive cost function, an optimal solution set containing all control sequences of different last states is obtained, and reverse solution is conducted on the optimal solution set, so that an optimal decision sequence is obtained.
Preferably, step S1 comprises:
s11, obtaining the output power and the corresponding efficiency of the fuel cell at each moment through a fuel cell system efficiency-fuel cell system power output characteristic curve;
s12, the power battery system is equivalent to a series circuit of a power supply and internal resistance, and a formula for simulating the dynamic process of end voltage and SOC (state of charge) of the power battery system in the charging and discharging process is obtained
V B ＝V OC -i B ·R B (1) And
the state of charge SOC is used for representing the residual capacity of the power battery
Is shown by the ampere-hour method
s13, establishing a vehicle longitudinal dynamics model based on a vehicle running dynamics equation to obtain the stress of the vehicle in the running process, wherein the expression is
F t ＝F r +F a +F g +F j (5) And
s14, obtaining a power balance equation in the vehicle running process according to the expressions (1) to (6)
Preferably, step S2 comprises:
s21, constructing a first state transition equation by taking the output power of the fuel cell at each moment as a control variable u and the SOC of the power cell as a first state variable x
SOC k+1 ＝SOC k A + Δ SOC (9); in the formula, SOC k Represents the SOC value at the time k, Δ SOC represents the SOC variation from the time k to the time k +1, and the value of the control variable u is expressed by the following formula
SOC k+1 ＝f(SOC k ,P fc,k ) (11)。
Preferably, step S3 comprises:
s31, taking the output power of the fuel cell at each moment as a second state variable, and constructing a second state transition equation
△P fc,min ≤△P fc <△P fc,max (14) Is obtained in which Δ P fc,min Representing the maximum load reduction rate, deltaP, of the output power of the fuel cell fc,max Representing the maximum load-lifting rate of the output power of the fuel cell, and the unit kW/s;
s32 is obtained according to formulae (13) and (14)
State transition range of
S33, obtaining the output power of the fuel cell according to the double-state variable relation of each moment
Preferably, step S4 comprises:
s41, dividing the starting and stopping states of the fuel cell based on the output power of the fuel cell at each moment, and enabling different output power decisions to correspond to different starting and stopping states; the start-stop states of the fuel cell comprise full shut-down, low-power transition and full start-up;
s42, analyzing the starting and stopping change process of the fuel cell, defining the switching logic among different starting and stopping states of the fuel cell, and obtaining a third state transfer equation
phase k+1 ＝h(phase k +P fc,k ) (17); in the formula, h represents the switching relation of the starting and stopping states of the fuel cell;
and S43, recording the data of the working state of the fuel cell at each decision moment.
Preferably, the constraints of the first state variable, the second state variable, the power cell output power and the fuel cell hydrogen consumption comprise:
in the formula, P fc For the output of power, P, of the fuel cell batt The power is output by the power battery,
is the fuel cell hydrogen consumption.
Preferably, step S6 includes:
s61, constructing a comprehensive cost function according to the economical efficiency of the vehicle and the durability of the fuel cell
J＝ω eco ·J eco +ω du ·J du (19) (ii) a In the formula, wherein, ω eco And ω du Weight coefficients representing economic cost and durability cost, J eco And J du Respectively representing an economic cost and a fuel cell durability cost;
s62 passing type
s63 passing formula
J du ＝ω load-change ·J load-change +ω start-stop ·J start-stop +ω heavy-load ·J heavy-load +ω idle ·J idle (23) Obtaining a fuel cell durability penalty; in the formula, J represents the cost of life decay of the fuel cell caused by four operating states in the formula, and ω represents the weight corresponding to the four costs in the formula.
Preferably, the correction coefficient of the power battery SOC is obtained by the following process:
s621, the power battery SOC is standardized to obtain a standardization coefficient phi,
s622, obtaining correction coefficient of power battery SOC according to equation (21)
S(φ)＝1-aφ 3 +bφ 4 (22) (ii) a Wherein (a, b) takes the value of 3,2.
Preferably, step S7 includes:
s71, acquiring an SOC state at a certain moment in an accessible state of the output power variation amplitude of the fuel cell;
s72, acquiring a corresponding start-stop state according to the SOC state at a certain moment;
s73, acquiring a control variable corresponding to the start-stop state at a certain moment;
s74, acquiring the comprehensive cost and the corresponding start-stop state at the next moment according to the control variable at the certain moment;
s75, judging whether the comprehensive cost at the next moment is lower, if so, recording the comprehensive cost, the corresponding start-stop state, the SOC state and the output power of the fuel cell at the next moment, and then executing the substep S76; otherwise, performing substep S76;
s76, judging whether the traversal of the control variable is finished or not, and if so, executing a substep S77; otherwise, stepping the control variable, and returning to execute the substep S73;
s77, judging whether the traversal aiming at the start-stop state is finished or not, if so, executing a substep S78; otherwise, returning to execute substep S71;
s78, judging whether the traversal aiming at the working condition is finished or not, if so, executing a substep S79; otherwise, returning to execute the substep S71;
s79, based on the optimal solution sets of different states at all times, an optimal decision sequence is obtained.
According to the technical scheme provided by the embodiment of the invention, the method for calculating the optimal working state control strategy of the fuel cell vehicle is established aiming at the whole vehicle economy and the fuel cell durability of the extended-range fuel cell vehicle. According to the method, the start-stop state of the fuel cell is added as the state variable, and the low-power transition stage is added between the start-up state and the shut-down state of the fuel cell, so that the self-adaptive start-stop interval control of the fuel cell is realized, and the frequent start-stop of the fuel cell is avoided. And the fuel cell performance degradation index is used as the durability cost, the whole vehicle energy consumption is used as the economy cost, a multi-target joint cost function of the economy and the durability is constructed, and the joint optimization control of the economy and the durability is realized. And obtaining an energy management strategy with global optimal economy and durability through off-line calculation, and using the energy management strategy as a benchmarking and reference standard for the development of the energy management strategy of the real vehicle.
Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
FIG. 1 is a processing flow chart of a method for calculating an optimal operating state control strategy of a fuel cell vehicle according to the present invention;
FIG. 2 is a schematic diagram of an actual state transition process in a calculation method of an optimal operating state control strategy of a fuel cell vehicle according to the present invention;
FIG. 3 is a schematic diagram of a power battery SOC correction function of a calculation method of a fuel cell vehicle optimal working state control strategy according to the present invention;
FIG. 4 is a schematic diagram of a forward optimization of a classical dynamic programming strategy;
FIG. 5 is a schematic diagram of a process flow of forward optimization of a classical dynamic programming strategy;
FIG. 6 is a schematic diagram of a forward optimization processing flow of a calculation method for an optimal operating state control strategy of a fuel cell vehicle according to the present invention;
FIG. 7 is a speed profile of the NEDC cycle regime;
FIG. 8 is a schematic diagram of SOC traces of different end states of a strategy obtained in a test embodiment of a calculation method for a control strategy for an optimal operating state of a fuel cell vehicle according to the present invention;
fig. 9 is a schematic diagram of the output power of the fuel cell when the SOC end state is 0.2 in the embodiment of the calculation method of the optimal operating state control strategy of the fuel cell vehicle provided by the present invention;
fig. 10 is a schematic diagram of the output power of the fuel cell when the SOC end state is 0.3 in the embodiment of the calculation method of the optimal operating state control strategy of the fuel cell vehicle according to the present invention;
fig. 11 is a schematic diagram of the output power of the fuel cell when the SOC end state is 0.4 in the embodiment of the calculation method of the optimal operating state control strategy of the fuel cell vehicle provided by the present invention;
fig. 12 is a schematic diagram of the output power of the fuel cell when the SOC end state is 0.5 in the embodiment of the calculation method of the optimal operating state control strategy of the fuel cell vehicle according to the present invention;
fig. 13 is a schematic diagram of a simulation result of a power battery output power variation trajectory when an SOC end state is 0.4 in an embodiment of a calculation method of an optimal operating state control strategy of a fuel cell vehicle according to the present invention;
fig. 14 is a schematic diagram of a simulation result of a power battery SOC variation trajectory when an SOC end state is 0.4 in an embodiment of a calculation method of an optimal operating state control strategy of a fuel cell vehicle according to the present invention;
fig. 15 is a schematic diagram of a simulation result of a fuel cell output power variation trajectory when an SOC end state is 0.4 in an embodiment of a calculation method of an optimal operating state control strategy of a fuel cell vehicle according to the present invention;
fig. 16 is a schematic diagram of a simulation result of a hydrogen equivalent energy consumption variation trajectory when the SOC end state is 0.4 in an embodiment of a calculation method of an optimal operating state control strategy of a fuel cell vehicle according to the present invention;
FIG. 17 is a schematic diagram comparing SOC traces of three strategies in an embodiment of a method for calculating a control strategy for an optimal operating state of a fuel cell vehicle according to the present invention;
FIG. 18 is a schematic diagram comparing equivalent hydrogen consumption curves of three strategies in an embodiment of a method for calculating an optimal operating state control strategy of a fuel cell vehicle according to the present invention;
FIG. 19 is a schematic diagram comparing output power curves of fuel cells of a first strategy in an embodiment of a method for calculating an optimal operating state control strategy of a fuel cell vehicle according to the present invention;
FIG. 20 is a schematic diagram comparing output power curves of fuel cells of a second strategy in an embodiment of a method for calculating an optimal operating state control strategy of a fuel cell vehicle according to the present invention;
fig. 21 is a schematic diagram comparing output power curves of fuel cells of a third strategy in an embodiment of a method for calculating an optimal operating state control strategy of a fuel cell vehicle according to the present invention.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the accompanying drawings are illustrative only for the purpose of explaining the present invention, and are not to be construed as limiting the present invention.
As used herein, the singular forms "a", "an", "the" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises" and/or "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. It will be understood that when an element is referred to as being "connected" or "coupled" to another element, it can be directly connected or coupled to the other element or intervening elements may also be present. Further, "connected" or "coupled" as used herein may include wirelessly connected or coupled. As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed items.
It will be understood by those skilled in the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the prior art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
For the convenience of understanding the embodiments of the present invention, the following description will be further explained by taking several specific embodiments as examples in conjunction with the drawings, and the embodiments are not to be construed as limiting the embodiments of the present invention.
Aiming at the situation that flexible adaptation to different driving conditions and system states is difficult to realize in the starting and stopping control of the current vehicle fuel cell and a multi-target starting and stopping control method of the fuel cell comprehensively considering the economical efficiency and the durability is lacked. The invention aims to provide an optimal strategy calculation method for a fuel cell vehicle, which is based on a dynamic programming algorithm, manages and controls various working states causing performance attenuation of the vehicle fuel cell by setting a multi-state variable and a comprehensive cost function, and calculates to obtain an offline global optimal strategy capable of giving consideration to both economy and durability.
Referring to fig. 1, the method for calculating the optimal operating state control strategy of the fuel cell vehicle provided by the invention comprises the following steps:
s1, acquiring basic parameters, establishing simplified models of a fuel cell, a power cell and a whole vehicle based on the basic parameters, and acquiring the output power of the fuel cell and the SOC of the power cell at each moment;
s2, taking the output power of the fuel cell at each moment as a control variable and the SOC of the power cell as a first state variable to obtain a first state transition equation;
s3, taking the output power of the fuel cell at each moment as a second state variable, obtaining a second state transition equation and obtaining an accessible state of the output power variation amplitude of the fuel cell;
s4, determining switching logics of different start-stop state pieces of the fuel cell by taking the start-stop state of the fuel cell as a third state variable to obtain a third state transition equation;
s5, setting constraint conditions of a first state variable, a second state variable, power battery output power and fuel battery hydrogen consumption according to vehicle type power system parameters;
s6, obtaining power consumption of the power battery, hydrogen consumption of the fuel battery and a performance attenuation index of the fuel battery according to the output power of the power battery and the output power of the fuel battery, and constructing a comprehensive cost function;
s7, forward optimization is conducted on the whole reachable state space according to the first state transition equation, the second state transition equation, the third state transition equation and the comprehensive cost function, an optimal solution set containing all control sequences of different last states is obtained, and reverse solution is conducted on the optimal solution set, so that an optimal decision sequence is obtained.
In a preferred embodiment provided by the present invention, step S1 specifically includes the following sub-steps:
s11, obtaining the output power and the corresponding efficiency of the fuel cell at each moment through a fuel cell system efficiency-fuel cell system power output characteristic curve;
s12, the power battery system is equivalent to a series circuit of a power supply and internal resistance, and a formula for simulating the dynamic process of end voltage and SOC (state of charge) of the power battery system in the charging and discharging process is obtained
V B ＝V OC -i B ·R B (1) And
the state of charge SOC is used for representing the residual capacity of the power battery
Shows that, in the internal resistance model, it is by ampere-hour method
s13, establishing a vehicle longitudinal dynamics model based on a vehicle running dynamics equation to obtain the stress of the vehicle in the running process, wherein the expression is
F t ＝F r +F a +F g +F j (5) And
s14, obtaining a power balance equation in the vehicle running process according to the expressions (1) to (6)
Further, step S2 specifically includes:
s21, constructing a first state transition equation by taking the output power of the fuel cell at each moment as a control variable u and the SOC of the power cell as a first state variable x
SOC k+1 ＝SOC k +△SOC (9)；
In the formula, SOC k Represents the SOC value at the time k, Δ SOC represents the SOC variation from the time k to the time k + 1, and the value of the control variable u is represented by the following formula
s22, synthesizing the models, and converting the first state transition equation to obtain
SOC k+1 ＝f(SOC k ,P fc,k )(11)，
I.e., x (k + 1) = x (k, u (k)) (12).
It can be seen that this is a typical markov problem, i.e. the state x (k + 1) at the next time depends only on the state x (k) at the current time and the decision u (k) at the current time, and has no direct relation to all other information in the past and in the future. The expression (12) represents the characteristics that the state variables in the dynamic programming algorithm must meet, that is, only the variables that meet the characteristics described by the expression (12) can be used as the state variables for dynamic programming. Subsequently, the first state transition equation "expression (9)" in which the SOC can be obtained from expressions (1) to (4) and expression (10) can be expressed in the form of expression (11), that is, in the form of expression (12), so that it is proved that the SOC can be used as one state variable.
In the embodiment provided by the invention, the step S3 is used for keeping the variation of the output power of the fuel cell within the range of taking the maximum load-up rate as the upper limit and taking the maximum load-down rate as the lower limit when the state of the fuel cell is transferred each time through restricting the state transfer condition, eliminating the unreachable state with the too large variation amplitude of the output power of the fuel cell, fundamentally avoiding the possibility of severe load variation of the fuel cell, and the state transfer process is shown in fig. 2. The specific process comprises the following steps:
s31, taking the output power of the fuel cell at each moment as a second state variable, and constructing a second state transition equation
△P fc,min ≤△P fc <△P fc,max (14) Is obtained in which Δ P fc,min Representing the maximum load reduction rate, deltaP, of the output power of the fuel cell fc,max Representing the maximum load-lifting rate of the output power of the fuel cell, and the unit kW/s;
s32 according to formulae (13) and (14)
The state transition range of (A) is a dynamic region as shown in the following formula
In S33, since the output of the fuel cell is also a control variable, the following relationship is obtained
In addition, since the FC output power is simultaneously used as a control variable and a state variable in the algorithm, even if the FC output power is a dual-state variable at present, the dimension increasing of a state space is not needed, and the dimension disaster possibly caused by the increase of the state variables is avoided.
In the embodiment provided by the invention, the start-stop state of the fuel cell is taken as the third state variable in the step S4, and a low-power transition stage is added between the start state and the stop state of the fuel cell, so that the performance degradation caused by frequent start-stop is avoided, and the start-stop interval can be adaptively controlled by combining the state of the fuel cell, the state of the power cell and the running condition.
And dividing the start-stop states of the fuel cell based on the output power of the fuel cell, and enabling different output power decisions to correspond to different start-stop states. And then analyzing the starting and stopping change process of the fuel cell, and defining the switching logic among different starting and stopping states of the fuel cell to obtain a state transition equation of the fuel cell.
TABLE 1 Fuel cell Start-stop State switching logic
The switching logic between the start-stop states of the fuel cell and the output power variation corresponding to different states are defined in table 1. Prohibiting direct switching between the fully activated and fully deactivated states of the fuel cell necessitates a buffering of low power transition phases. In the fully-off state, the fuel cell stops operating and the output power is zero. In the fully activated state, the fuel cell output power is greater than its idle power. The output power of the fuel cell is greater than zero and less than idle power during the low power transient state. Because the performance of the fuel cell is degraded when the fuel cell is operated under the idle speed, and the time requirement in the state is as short as possible, when the fuel cell is controlled to be in the low-power transition state, the output power of the fuel cell keeps changing in the same direction (increasing or decreasing) as the previous state, and the change amplitude keeps the maximum load change rate (the maximum load increase rate or the maximum load decrease rate), so that the fuel cell is enabled to leave the low-power operation as soon as possible.
Due to the low power transient state, a state transition calculation is performed when a fully activated fuel cell can be selected to be shut down. And on the premise of meeting the dynamic requirement of the current working condition, comparing cost function values caused by two decisions of closing the fuel cell and keeping the fuel cell on. The fuel cell is turned off only when the performance degradation penalty of entering a low power transient state and turning it off is less than the energy consumption penalty of keeping the fuel cell on. Therefore, the self-adaptive start-stop interval control of the fuel cell is realized.
By the method, the effect of preventing the fuel cell from being started and stopped frequently is achieved. It can be found that the determination of the start-stop state of the fuel cell at each moment is obtained based on the start-stop state at the previous moment and the output power of the fuel cell, as follows:
phase k+1 ＝h(phase k +P fc,k ) (17)
and h represents the switching relation of the start-stop state of the fuel cell. Meanwhile, when the fuel cell is in different start-stop states, the working state data of the fuel cell at each decision moment is recorded, so that a comprehensive cost function can be conveniently established subsequently.
After the start-stop state of the fuel cell is set as the state variable, the number of the state variables of the dynamic programming algorithm is changed into three, and the state space is also increased from two dimensions to three dimensions.
In the preferred embodiment of the present invention, in step S5, the following variables are set according to the vehicle model power system parameters
Constraint conditions are as follows:
the above formula defines the fuel cell output power P fc Power battery output power P batt Power cell SOC and fuel cell hydrogen consumption
In (c) is used.
Further, the function of constructing the comprehensive cost function in step S6 is that the value of the cost function is used to constrain the variables to keep within the range defined by the above condition on one hand, and is used to optimize by comparing the costs of different control variable sequences in the optimization process on the other hand. The method specifically comprises the following steps:
s61, constructing the comprehensive cost function according to the economical efficiency of the vehicle and the durability of the fuel cell
J＝ω eco ·J eco +ω du ·J du (19) (ii) a In the formula, wherein, ω eco And omega du Weight coefficients, J, representing economic and durability costs, respectively eco And J du Respectively representing an economic cost and a fuel cell durability cost;
s62 passing type
s63, for the durability cost of the fuel cell, calculating the operating states of the four fuel cells respectively, and obtaining a formula
J du ＝ω load-change ·J load-change +ω start-stop ·J start-stop +ω heavy-load ·J heavy-load +ω idle ·J idle (23) Obtaining; in the formula, J represents the cost of life decay of the fuel cell caused by four operating states in the formula, and ω represents the weight corresponding to the four costs in the formula. The values of the weighting factors are shown in table 2.
TABLE 2 comprehensive cost function weight coefficient values
The weight of the economy and the durability of the fuel cell belong to empirical coefficients, and the weight of the working states of the four fuel cells is selected from experimental data of autocorrelation research.
In sub-step S62, the correction coefficient is calculated by the following function:
firstly, the SOC is normalized to obtain a normalization coefficient phi.
Where φ is a value between plus or minus 1, representing the degree to which the current SOC deviates from the midpoint of the SOC variation interval, the closer its absolute value is to 1, the further the deviation is, and the closer to zero, the less the deviation is.
Obtaining a penalty function S of power battery power consumption:
S(φ)＝1-aφ 3 +bφ 4 (22)
wherein (a, b) takes on the value (3,2). The effect of this correction function is: when the SOC is larger than, equal to or smaller than the middle point of the SOC change interval, the function respectively outputs a value smaller than, equal to or larger than 1; when the SOC is near the middle point of the SOC change interval, the function value changes smoothly; when the SOC deviates far from the midpoint of the SOC change interval, the function value quickly reaches the set maximum (small) value. The functional image is shown in fig. 3.
In the preferred embodiment provided by the present invention, step S7 is to perform forward optimization of the dynamic programming algorithm, calculate all control sequences in the whole feasible state space from the starting time to the ending time, perform optimization based on the cost function, obtain optimal solution sets in different end states, perform inverse solution, perform inverse push from the ending time, and screen and take out the optimal decision sequence corresponding to the specified end state. Wherein forward optimization is a core step of the whole algorithm.
In the forward optimization process, each state variable x and control variable u constitutes a pair of coordinates defining a state at the current time, as shown in fig. 4. And when a single state has a plurality of path sources, comparing the cost values of different paths, reserving the smaller path, and enabling the path corresponding to the larger path to be an invalid path. In the figure, the triangular end points represent invalid paths, and the circular end points represent valid paths.
The forward optimization flow of the classical dynamic programming strategy is shown in fig. 5, which is a three-layer nested structure, and the forward optimization flow is optimized sequentially at each time of the current working condition, each state of the current time, and each control decision of the current state.
The reverse solution is based on the selected last state, and from the last time, namely the time T, according to the values of the three state variables at the time T, the position of the last state in the whole optimal solution set is determined, and the value of the state variable at the previous time, namely the time T-1, is read from the data recorded at the position (including the three state variables, one control variable and one cost function, and since the second state variable and the control variable are all the output power of the fuel cell, the total of four variables). And reading the state variable value at the T-2 moment according to the state variable value at the T-1 moment, and repeating the steps until the initial moment. An optimal decision sequence in the optimal solution set is thus obtained.
A three-state variable optimization flow chart of the dynamic programming strategy after the state variables and the cost function are improved is shown in fig. 6, which is a four-layer nested structure, and the operation of optimizing the start-stop state of each fuel cell in the current SOC state is added. The method specifically comprises the following substeps:
s71, acquiring an SOC state at a certain moment in an reachable state of the output power variation amplitude of the fuel cell;
s72, acquiring a corresponding start-stop state according to the SOC state at a certain moment;
s73, acquiring a control variable corresponding to the start-stop state at a certain moment;
s74, according to the control variable at a certain moment, obtaining the comprehensive cost and the corresponding start-stop state at the next moment;
s75, judging whether the comprehensive cost at the next moment is lower, if so, recording the comprehensive cost, the corresponding start-stop state, the SOC state and the output power of the fuel cell at the next moment, and then executing a substep S76; otherwise, performing substep S76;
s76, judging whether the traversal of the control variables is finished or not, if so, executing a substep S77; otherwise, stepping the control variable, and returning to execute the substep S73;
s77, judging whether the traversal aiming at the start-stop state is finished or not, if so, executing a substep S78; otherwise, returning to execute substep S71;
s78, judging whether the traversal aiming at the working condition is finished or not, if so, executing a substep S79; otherwise, returning to execute substep S71;
s79, based on the optimal solution sets of different states at all the moments, the optimal decision sequence is obtained.
After the forward optimization is finished, an optimal solution set can be obtained, wherein the solution set is a 4-dimensional state space (comprising three state variables, one control variable and a time sequence, and the second state variable and the control variable are all output power of the fuel cell, so that the four dimensions are total), and one position in the array corresponds to one state when the four variables are all determined. As can be seen from step S75, a state belongs to only one control sequence, and data of a time adjacent to the state in the control sequence is recorded in the state.
The invention also provides an embodiment for displaying the test by applying the method of the invention.
Taking the NEDC cycle condition as an example, a 100km endurance mileage (10 NEDC cycles) simulation test is performed to explain the calculation method provided by the invention, and the same condition test is performed by respectively using a hierarchical mode switching strategy and a classical dynamic planning strategy which takes the whole vehicle energy consumption as a unique cost function as comparison.
The calculation is carried out under different final state constraint conditions (10% -50%), and the simulation result shown in FIG. 8 is obtained. It can be seen that the SOC curves of the present invention in different end states can basically keep the general trend of slow drop. When the last state value is higher, the fluctuation of the SOC is compared regularly, and the fluctuation condition basically corresponds to the cycle working condition. In the first half of each cycle working condition, the required power of the whole vehicle is lower, the SOC of the power battery in the stage is in an ascending trend, and the required power of the second half is higher, and the SOC is also reduced immediately.
The reduction in the end state of SOC means that more use of the power battery is required during running. It can thus be seen that as the end state decreases, the regularity of the SOC fluctuation decreases, the charging time of the power battery during travel decreases, and the discharging time increases.
From fig. 9 to fig. 12, it can be found that the output power of the fuel cell calculated by the present invention can keep stable operation under different SOC end states, and the number of start-stop times is small. With the increase of the SOC end state, the time of the fuel cell in the shutdown state is shorter, and the start-stop change is smaller.
The analysis is also performed taking the SOC end state equal to 0.4 as an example. As can be seen from fig. 14 and 16, the electric energy consumption and the hydrogen energy consumption are generally close to constant speed variation, and as can be seen from fig. 13 and 15, the fuel cell basically maintains a constant power operating state with a low output level, and is responsible for driving the vehicle to run and charging the power battery when the required power is low, and the power battery bears the output under the conditions that the required power is large and the variation is severe during running.
The simulation test results of the strategy obtained by calculation, the hierarchical mode switching strategy and the classical dynamic planning strategy are comprehensively compared, as shown in fig. 17 and 18. As can be seen from the SOC track, the SOC changes of the two strategies based on the dynamic programming algorithm basically show a slow descending trend and are approximately changed at a constant speed, and the layered mode switching strategy shows a standard periodic change rule and cannot control the falling point of the SOC at the end of driving. The SOC variation range of the layered mode switching strategy is the largest in the three strategies, the strategy obtained by calculation is the second, and the classical dynamic programming strategy is the smallest.
The hydrogen consumption change characteristics of the three strategies are similar to the SOC change, the hydrogen consumption of the two strategies based on the dynamic programming algorithm basically keeps uniform and slowly increasing, and the hydrogen consumption of the hierarchical mode switching strategy is concentrated in the time when the vehicle is in the range extending mode and is obviously higher than that of the other two strategies.
The three strategies in fig. 19-21 were analyzed in sequence. The fuel cell of the hierarchical mode switching strategy has few starting and stopping times and stable output after starting, but the stable output power is higher, so the output change amplitude of the fuel cell is extremely large at the starting moment.
The classic dynamic planning strategy only aims at economy and does not limit the fuel cell, so that the output power of the classic dynamic planning strategy changes very frequently, the starting and stopping times are very many, the classic dynamic planning strategy basically works as a main power source and does not meet the characteristics and requirements of the extended-range fuel cell passenger vehicle.
The strategy obtained by calculation effectively controls the power fluctuation and the starting and stopping times of the fuel cell after the variable load and the starting and stopping of the fuel cell are restrained, so that the fuel cell can keep a very stable state in the driving process, and various working states causing the performance attenuation of the fuel cell are avoided as much as possible. And because the stable output power of the fuel cell is low, the fuel cell can not cause great load change even at the moment of starting and stopping.
In summary, the calculation method for the optimal operating state control strategy of the fuel cell vehicle provided by the invention is based on a dynamic programming algorithm, takes the output power of the fuel cell at the current moment as a control variable, and takes the SOC of the power cell as a first state variable, so as to reflect the overall state change of the power system. And setting the output power of the fuel cell at the previous moment as a second state variable, and eliminating the state of severe power fluctuation by limiting the state transition range at the adjacent moment to reduce the variation amplitude of the output power of the fuel cell. The start-stop state of the fuel cell is set as a third state variable, an idle transition state is additionally arranged between the start and the stop of the fuel cell, so that the fuel cell must be buffered by the transition state when the start-stop change occurs, meanwhile, the strategy is recalculated in the transition state and the cost of the start-stop change is measured, and the frequent start-stop of the fuel cell is avoided. Finally, establishing a comprehensive cost function, and calculating comprehensive energy consumption comprising the hydrogen consumption of the fuel cell and the power consumption of the power cell to obtain an economic cost function; and recording the working state of the fuel cell at each moment and calculating the performance attenuation evaluation index of the fuel cell to obtain the durability cost function. And obtaining a comprehensive cost function by linearly weighting the economic cost and the durability cost, so that an optimization algorithm can select an output power distribution decision with a better comprehensive effect according to the energy consumption of the whole vehicle and the attenuation of the fuel cell in optimization calculation, thereby ensuring the economical efficiency and the durability of the vehicle running under the strategy. And finally, obtaining an energy management strategy with the overall optimal economy and durability through offline optimization calculation, and using the energy management strategy as a benchmarking and reference standard for the development of the energy management strategy of the real vehicle. The method provided by the invention has important significance for improving the economy and the durability of the fuel cell vehicle.
Those of ordinary skill in the art will understand that: the figures are merely schematic representations of one embodiment, and the blocks or flow diagrams in the figures are not necessarily required to practice the present invention.
From the above description of the embodiments, it is clear to those skilled in the art that the present invention can be implemented by software plus necessary general hardware platform. Based on such understanding, the technical solutions of the present invention may be embodied in the form of a software product, which may be stored in a storage medium, such as ROM/RAM, magnetic disk, optical disk, etc., and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the method according to the embodiments or some parts of the embodiments.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for apparatus or system embodiments, since they are substantially similar to method embodiments, they are described in relative terms, as long as they are described in partial descriptions of method embodiments. The above-described embodiments of the apparatus and system are merely illustrative, and the units described as separate parts may or may not be physically separate, and the parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (9)
1. A method for calculating a control strategy for the optimal working state of a fuel cell vehicle is characterized by comprising the following steps:
s1, acquiring basic parameters, establishing simplified models of a fuel cell, a power cell and a whole vehicle based on the basic parameters, and acquiring the output power of the fuel cell and the SOC of the power cell at each moment;
s2, taking the output power of the fuel cell at each moment as a control variable and the SOC of the power cell as a first state variable to obtain a first state transition equation;
s3, taking the output power of the fuel cell at each moment as a second state variable, obtaining a second state transition equation, and obtaining an accessible state of the output power variation amplitude of the fuel cell;
s4, determining switching logics of different start-stop state pieces of the fuel cell by taking the start-stop state of the fuel cell as a third state variable to obtain a third state transition equation;
s5, setting constraint conditions of the first state variable, the second state variable, the output power of the power battery and the hydrogen consumption of the fuel battery according to vehicle type power system parameters;
s6, obtaining power consumption of the power battery, hydrogen consumption of the fuel battery and a performance attenuation index of the fuel battery according to the output power of the power battery and the output power of the fuel battery, and constructing a comprehensive cost function;
s7, forward optimization is carried out on the whole reachable state space according to the first state transition equation, the second state transition equation, the third state transition equation and the comprehensive cost function, an optimal solution set containing all control sequences of different end states is obtained, and the optimal solution set is reversely solved, so that an optimal decision sequence is obtained.
2. The method according to claim 1, wherein step S1 comprises:
s11, obtaining the output power and the corresponding efficiency of the fuel cell at each moment through a fuel cell system efficiency-fuel cell system power output characteristic curve;
s12, enabling the power battery system to be equivalent to a series circuit of a power supply and internal resistance, and obtaining a dynamic process for simulating end voltage and SOC (state of charge) of the power battery system in the charging and discharging process
Formula V B ＝V OC -i B ·R B (1) And
the state of charge SOC is used for representing the residual capacity of the power battery
Is shown by the ampere-hour method
s13, establishing a vehicle longitudinal dynamics model based on a vehicle running dynamics equation to obtain the stress of the vehicle in the running process, wherein the expression is
F t ＝F r +F a +F g +F j (5) And
s14, obtaining a power balance equation in the vehicle driving process according to the formulas (1) to (6)
3. The method according to claim 2, wherein step S2 comprises:
s21, taking the output power of the fuel cell at each moment as a control variable u, taking the SOC of the power cell as a first state variable x, and constructing the first state transition equation
SOC k+1 ＝SOC k A + Δ SOC (9); in the formula, SOC k Represents the SOC value at the time k, Δ SOC represents the SOC variation from the time k to the time k +1, and the value of the control variable u is given by the equation
4. The method according to claim 3, wherein step S3 comprises:
s31, taking the output power of the fuel cell at each moment as a second state variable, and constructing a second state transition equation
△P fc,min ≤△P fc <△P fc,max (14) Is obtained in which Δ P fc,min Representing the maximum load reduction rate, deltaP, of the output power of the fuel cell fc,max Representing the maximum load rate of the output power of the fuel cell in kW/s;
s32 is obtained according to formulae (13) and (14)
State transition range of
S33, obtaining the output power of the fuel cell at each moment according to the double-state variable relation
5. The method according to claim 4, wherein step S4 comprises:
s41, dividing the starting and stopping states of the fuel cell based on the output power of the fuel cell at each moment, and enabling different output power decisions to correspond to different starting and stopping states; the start-stop states of the fuel cell comprise full shut-down, low-power transition and full start-up;
s42, analyzing the starting and stopping change process of the fuel cell, defining the switching logic among different starting and stopping states of the fuel cell, and obtaining the third state transfer equation
phase k+1 ＝h(phase k +P fc,k ) (17); in the formula, h represents the switching relation of the starting and stopping states of the fuel cell;
and S43, recording the data of the working state of the fuel cell at each decision moment.
6. The method of claim 5, wherein the constraints of the first state variable, the second state variable, power cell output power and fuel cell hydrogen consumption comprise:
in the formula, P fc For the output power of the fuel cell, P batt The power is output for the power battery,
is the fuel cell hydrogen consumption.
7. The method of claim 6, wherein step S6 comprises:
s61, constructing the comprehensive cost function according to the economical efficiency of the vehicle and the durability of the fuel cell
J＝ω eco ·J eco +ω du ·J du (19) (ii) a In the formula, wherein, ω is eco And ω du Respectively represent economic cost and enduranceWeight coefficient of permanent cost, J eco And J du Representing an economic penalty and a fuel cell durability penalty, respectively;
s62 passing type
s63 through type
J du ＝ω load-change ·J load-change +ω start-stop ·J start-stop +ω heavy-load ·J heavy-load +ω idle ·J idle (23) Obtaining the fuel cell durability penalty; in the formula, J represents the cost of life decay of the fuel cell caused by four operating states in the formula, and ω represents the weight corresponding to the four costs in the formula.
8. The method according to claim 6, characterized in that the correction factor for the power battery SOC is obtained by:
s621, the power battery SOC is standardized to obtain a standardization coefficient phi,
s622, obtaining correction coefficient of power battery SOC according to equation (21)
S(φ)＝1-aφ 3 +bφ 4 (22) (ii) a Wherein (a, b) takes the value of 3,2.
9. The method according to claim 1, wherein step S7 comprises:
s71, acquiring an SOC state at a certain moment in an reachable state of the output power variation amplitude of the fuel cell;
s72, acquiring a corresponding start-stop state according to the SOC state at a certain moment;
s73, acquiring a control variable corresponding to the start-stop state at a certain moment;
s74, according to the control variable at a certain moment, obtaining the comprehensive cost and the corresponding start-stop state at the next moment;
s75, judging whether the comprehensive cost at the next moment is lower, if so, recording the comprehensive cost, the corresponding start-stop state, the SOC state and the output power of the fuel cell at the next moment, and then executing the substep S76; otherwise, performing substep S76;
s76, judging whether the traversal of the control variable is finished or not, and if so, executing a substep S77; otherwise, stepping the control variable, and returning to execute the substep S73;
s77, judging whether the traversal aiming at the start-stop state is finished or not, if so, executing a substep S78; otherwise, returning to execute substep S71;
s78, judging whether the traversal aiming at the working condition is finished or not, if so, executing a substep S79; otherwise, returning to execute substep S71;
s79, obtaining the optimal decision sequence based on the optimal solution sets of different states at all times.
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