CN110549914A - approximate optimal energy management method for daily operation of fuel cell tramcar - Google Patents

approximate optimal energy management method for daily operation of fuel cell tramcar Download PDF

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CN110549914A
CN110549914A CN201910835881.1A CN201910835881A CN110549914A CN 110549914 A CN110549914 A CN 110549914A CN 201910835881 A CN201910835881 A CN 201910835881A CN 110549914 A CN110549914 A CN 110549914A
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train
fuel cell
power
soc
interval
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CN110549914B (en
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陈维荣
张国瑞
李奇
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Southwest Jiaotong University
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Southwest Jiaotong University
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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
    • B60L58/00—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles
    • B60L58/30—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling fuel cells
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
    • B60L2200/00—Type of vehicles
    • B60L2200/26—Rail vehicles
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00—Road transport of goods or passengers
    • Y02T10/60—Other road transportation technologies with climate change mitigation effect
    • Y02T10/72—Electric energy management in electromobility
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T90/00—Enabling technologies or technologies with a potential or indirect contribution to GHG emissions mitigation
    • Y02T90/40—Application of hydrogen technology to transportation, e.g. using fuel cells

Abstract

The invention discloses an approximately optimal energy management method for daily operation of a fuel cell tramcar, which is characterized by obtaining SOC reduction standards of each adjacent interval of an energy storage system in the fuel cell tramcar according to target line information, station information and a train scheduling scheme; calculating the optimal power distribution relation of the train in each running interval under different load conditions by adopting an adaptive planning algorithm based on an extreme learning machine, wherein the optimal power distribution relation comprises a power-time curve and a power system SOC change curve, and storing the optimal power distribution relation to a vehicle automatic running system in a matrix form; and obtaining the optimal power distribution scheme of the next operation interval in the vehicle automatic operation system through a fuzzy control method. The invention enables the specific parameters of the energy management method for the actual operation of the train to be adaptively adjusted along with the change of the loading capacity, effectively reduces the energy consumption level of the train and improves the feasibility of adopting an off-line energy management method in the actual operation of the train.

Description

Approximate optimal energy management method for daily operation of fuel cell tramcar
Technical Field
The invention belongs to the technical field of fuel cell tramcars, and particularly relates to an approximately optimal energy management method for daily operation of a fuel cell tramcar.
background
the fuel cell has the advantages of high energy conversion efficiency and no pollution to the environment, so that the fuel cell hybrid power system taking the fuel cell as the core is greatly concerned in recent years and is applied to the field of rail transit, and a novel tramcar system, namely a fuel cell tramcar, is formed.
Due to the characteristic that the fuel cell tramcar has a fixed working condition, the energy management of the fuel cell tramcar can be designed in an off-line global optimization mode. Particularly, the energy management method designed based on ELM-ADP is approximately optimized in control effect in various online and offline methods. However, since the off-line energy management method is generally designed based on the power-time curve of the train, and the actually operating train only has a relatively fixed speed curve, the power-time curve may change greatly with the difference of the load capacity. Therefore, the optimal energy management method calculated off-line according to a certain fixed power-time curve often fails in on-line use, and the expected control effect is far from being achieved.
disclosure of Invention
In order to solve the problems, the invention provides an approximately optimal energy management method for daily operation of a fuel cell tramcar, which is a method combining offline optimization and online adjustment, so that specific parameters of the energy management method for actual operation of a train can be adaptively adjusted along with the change of load capacity, the energy consumption level of the train is effectively reduced, the problem of poor control effect of the original energy management method in practice is solved, and the feasibility of adopting the offline energy management method in the actual operation of the train is improved.
In order to achieve the purpose, the invention adopts the technical scheme that: a fuel cell tramcar daily operation approximate optimal energy management method comprises the following steps:
s100, acquiring state data of a target line by a fuel cell tramcar operation administration, wherein the state data comprises a train dispatching scheme, a single-train speed and position curve and vehicle parameter data;
s200, obtaining an SOC reduction standard of each adjacent interval of an energy storage system in the fuel cell tramcar according to the target line information, the station information and the train scheduling scheme, so that the train starts to run in a full-power state of the energy storage system every day and finishes running at the lowest power, and the actual hydrogen consumption level and the running cost of the train are reduced;
S300, establishing a tramcar dynamics model, and identifying a basic train resistance coefficient; based on the single train speed position curve, calculating the optimal power distribution relation of the train in each running interval under different load conditions, including a power time curve and a power system SOC change curve, of the train by adopting an adaptive planning algorithm based on an extreme learning machine, and storing the optimal power distribution relation to a vehicle automatic running system in a matrix form;
S400, when the train actually runs stops, acquiring the load data of the current train on line through a weight sensor arranged at the bottom of the train, and inputting the load data into the automatic train running system; obtaining an optimal power distribution scheme of a next operation interval in the vehicle automatic operation system through a fuzzy control method;
and S500, the vehicle automatic operation system controls the output of the fuel cell cascade DC/DC converter, the control of the output power of the fuel cell is indirectly realized, and the energy storage system supplements the output or recovers the braking power.
further, in the step S200, according to a train dispatching scheme, calculating a total operating mileage L of the tramcar per train per day; and taking the available interval of the electric quantity of the energy storage system as x 1% and x 2%, and calculating the value of SOC reduction of each adjacent interval of the energy storage system of the tramcar based on statistical analysis of the road condition of the line.
further, the method for calculating the SOC reduction value of each adjacent area of the energy storage system of the tramcar through statistical analysis of the road condition of the line comprises the following steps:
the total mileage coefficient of the daily operation straight road section of the electric vehicle with the rail is as follows:
L＝L1+L2+L3+…+Ln；
the total mileage coefficient of the rail electric vehicle during daily operation and climbing is calculated as follows:
U＝β1×U1+β2×U2+β3×U3+…+βn×Un；
Calculating the total coefficient of mileage of the rail electric vehicle in downhill operation on a daily basis:
D＝ρ1 -1×D1+ρ2 -1×D2+ρ3 -1×D3+…+ρn -1×Dn；
The total mileage coefficient counted on the daily running curve of the rail electric vehicle is as follows:
R＝ω1 -1×R1+ω2 -1×R2+ω3 -1×R3+…+ωn -1×Rn；
Wherein L isn、Un、DnAnd RnRespectively representing the length of a straight road section, the length of a climbing road section, the length of a descending road section and the length of a curved road section in the nth running section; beta is an、ρnand ωnRespectively obtaining the average slope of a climbing road section, the average slope of a downhill road section and the average curvature radius of a curve road section in the nth operation section;
The reference value of the SOC value decreased in the nth section is:
ΔSOCref＝(Ln+β1×U1+ρ1 -1×D1+ω1 -1×R1)×(x2-x1)/(L+U+D+R)。
Further, in the step S300, performing dynamic modeling on the train based on a newton formula, and establishing a tramcar dynamic model; estimating a power time curve of the tramcar running in a single region based on a basic resistance formula and a speed position curve; the method for obtaining the resistance coefficients A, B and C in the basic resistance formula by adopting a fitting method based on Levenberg-Marquardt iteration specifically comprises the following steps:
performing a type test on the fuel cell tramcar to obtain experimental data of speed and power when the tramcar runs at a constant speed on a straight road section, wherein the train only overcomes basic resistance to do work at the moment;
reversely deducing the relation between the basic resistance and the speed according to a Newton formula and experimental data;
resistance coefficients A, B and C are identified by adopting a Levenberg-Marquardt-based iterative method, and the calculation method comprises the following steps:
For a fuel cell tram system: y ═ f (x, c);
Wherein y is a calculated value of the measured basic resistance, x is the speed of the train, and c is a parameter to be identified;
and has the following components: c (k +1) ═ c (k) + (J)TJ+λI)-1JTr(c(k))；
Wherein I is a unit matrix, J is a Jacobian matrix, lambda damping factor lambda is more than 0, r is a residual error, and I is the ith group of experimental data;
and has the following components:ri＝f(xi,c)-yi。
Further, in the step of calculating the optimal power distribution relation of the train in each operation interval under different load conditions, the operation interval is between every two adjacent stations of the train operation; the load conditions include m load conditions generated at fixed intervals from no load to full load.
further, in the optimal power distribution relation between every two adjacent stations of the train under different load conditions is calculated by adopting a self-adaptive planning algorithm based on the extreme learning machine, the self-adaptive planning algorithm utilizes a function approximation structure and adopts a successive iteration method to approximate a performance index function and a control strategy in a dynamic planning equation, so that the optimal power distribution relation is gradually obtained by approximation;
the calculation process of the self-adaptive planning algorithm is represented by a recursion formula:
Wherein, x (J) is the current state of the system, x (J +1) refers to the state of the system at the next sampling time under the control decision u (J), p (x) (J), u (J) represents the instant penalty value function under the decision u (J) under the state x (J), and J (x (J +1)) represents the state value function under the state x (J + 1); to be provided withThe SOC of the storage battery in the energy storage system is used as a state quantity, and the reference value P of the output power of the fuel cell is usedFCFor the controlled variable, the state transition equation and the cost function are respectively:
wherein, CH2The equivalent hydrogen consumption of the system comprises two parts of instantaneous hydrogen consumption of a fuel cell and instantaneous equivalent hydrogen consumption of a lithium battery; j is the control stage in the interval, and k stages in total;
the constraints that the system needs to satisfy include:
PFC(j)+PBAT(j)＝Pload(j)，
PFCmin≤PFC(j)≤PFCmax，
PBATmin≤PBAT(j)≤PBATmax；
wherein, PFCAnd PBATPower for fuel cells and batteries; pFCminAnd PFCmaxThe minimum and maximum output power of the fuel cell are respectively; pBATminand PBATmaxThe minimum and maximum output power of the storage battery are respectively;
The system start and end states need to satisfy the following formula:
SOCinitial-SOCend＝ΔSOCref；
therein, SOCinitialand SOCendRespectively refers to the SOC value, delta SOC, of the energy storage system when the train starts and ends running in the sectionrefObtaining the reference value of the SOC value reduced in the nth section;
Similarly, for each load condition in n operating intervals, respectively adopting a self-adaptive planning algorithm to calculate the optimal power distribution relation of the interval with the lowest energy consumption, and obtaining n times m optimal power distribution relations in total, wherein each distribution relation comprises k blocksPreparing; the fuel cell reference power matrix obtained by the self-adaptive planning algorithm under each load condition is respectively named as PFC (Power factor correction) correspondingly1，PFC2，PFC3…PFCmAnd the matrix is a matrix of the running interval n multiplied by the decision step number k of each running interval, and the matrix is written into a vehicle automatic running system controller of the train.
further, an optimal power distribution scheme of a next operation interval is obtained in the vehicle automatic operation system through a fuzzy control method; meanwhile, in order to ensure that the SOC of the next interval can be reduced to the planned level, an interpolation method is adopted to form a preliminary power distribution scheme of the next interval; taking the current SOC and the offline calculated SOC as input, and generating a feedback regulating quantity by utilizing a PI controller to regulate a preliminary power distribution scheme in real time; and determining the output reference power of the fuel cell system to form a power distribution scheme.
Further, when the actually running train stops, the load data of the current train is acquired on line through a weight sensor arranged at the bottom of the train and then is input into the automatic vehicle running system; the load information of the vehicle is obtained from a weight sensor of the vehicle device, the weight sensor transmits a message containing the load information to the vehicle automatic operation system through the CAN bus in a fixed protocol, and the message is interpreted by the vehicle automatic operation system according to the protocol.
Further, the vehicle automatic operation system transmits the fuel cell output reference power value corresponding to the power distribution scheme to the DC/DC converter in a CAN communication mode according to a time sequence, controls the output power of the DC/DC converter through PWM modulation to indirectly control the output power of the fuel cell system, and the energy storage system supplements or recovers the braking power.
Further, the off-line design controller in the vehicle automatic operation system comprises an interpolation controller and a PI feedback controller, and is realized by adopting a method for controlling the DC/DC converter by the vehicle automatic operation system, and the method comprises the following steps:
Determining an input variable as the current load capacity, and outputting a preliminary power distribution curve of the next operation interval by using an interpolation method;
acquiring the current of a power system by using a current sensor, calculating the SOC of the power battery system in real time on line, using the SOC as the input quantity of PI control, and obtaining a feedback regulating quantity by using the operation of a PI controller to obtain an output reference power curve of the fuel battery in the next operating interval;
The vehicle automatic operation system communicates with the DC/DC converter controller, transmits the output reference power curve of the fuel cell in the next operation interval to the controller according to time sequence, and realizes the conversion of the fuel cell power by controlling the DC/DC converter.
the beneficial effects of the technical scheme are as follows:
the method combines offline optimization and online adjustment, so that specific parameters of an energy management method for the actual running of a train can be adaptively adjusted along with the change of the load capacity, a power distribution scheme can be adaptively adjusted along with the change of passenger flow in the daily running process of the fuel cell tramcar, and the adopted power distribution schemes are all the optimal energy management methods designed based on the lowest energy consumption target in the interval. The invention solves the problem of poor control effect caused by external working conditions when the global optimal energy management method based on offline design is used online.
the invention adopts the strategy of SOC reduction, utilizes the vehicle storage time at night to charge the vehicle, effectively replaces the consumption of partial hydrogen energy and reduces the operation cost. In addition, the numerical value of SOC reduction of each adjacent area of the tramcar energy storage system is calculated through statistical analysis of the road conditions of the line, the electric energy of the fuel cell tramcar is reasonably distributed, and the power performance and the electric power and electric quantity requirements of the train are fully guaranteed while the running cost is reduced.
The method adopts a type test method, and fits the relation between the basic resistance and the running speed of the train based on a Levenberg-Marquardt (Levenberg-Marquardt) iteration method, so as to determine the basic resistance coefficient A, B, C. Compared with other methods, the Levenberg-Marquardt iteration method has the advantages that the amount of utilized measurement data is small, fitting errors can be reduced, and engineering practicability is high.
the invention adopts the self-adaptive programming algorithm based on the extreme learning machine to carry out offline energy management calculation on the fuel battery tramcar, and can solve any power distribution situation which possibly occurs due to the inherent advantages of the algorithm, so the method is widely accepted as an energy management method with approximately optimal control effect, and has the advantages of high learning speed and good generalization performance. The self-adaptive planning algorithm based on the extreme learning machine is used for performing offline energy management calculation on the tramcar, so that the total energy consumption of the train can be effectively reduced, and the overall operation efficiency of the train hybrid power system is improved.
Drawings
FIG. 1 is a schematic flow chart of a fuel cell tramcar daily operation near-optimal energy management method of the present invention;
FIG. 2 is a schematic diagram of a control structure of a fuel cell tramcar system according to an embodiment of the present invention;
fig. 3 is an electrical schematic diagram of a fuel cell tram system in an embodiment of the invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described with reference to the accompanying drawings.
In this embodiment, referring to fig. 1, the present invention provides a method for approximately optimal energy management of daily operation of a fuel cell tramcar, comprising the steps of:
S100, acquiring state data of a target line by a fuel cell tramcar operation administration, wherein the state data comprises a train dispatching scheme, a single-train speed and position curve and vehicle parameter data;
S200, obtaining an SOC reduction standard of each adjacent interval of an energy storage system in the fuel cell tramcar according to the target line information, the station information and the train scheduling scheme, so that the train starts to run in a full-power state of the energy storage system every day and finishes running at the lowest power, and the actual hydrogen consumption level and the running cost of the train are reduced;
S300, establishing a tramcar dynamics model, and identifying a basic train resistance coefficient; based on the single train speed position curve, calculating the optimal power distribution relation of the train in each running interval under different load conditions, including a power time curve and a power system SOC change curve, of the train by adopting an adaptive planning algorithm based on an extreme learning machine, and storing the optimal power distribution relation to a vehicle automatic running system in a matrix form;
s400, when the train actually runs stops, acquiring the load data of the current train on line through a weight sensor arranged at the bottom of the train, and inputting the load data into the automatic train running system; obtaining an optimal power distribution scheme of a next operation interval in the vehicle automatic operation system through a fuzzy control method;
and S500, the vehicle automatic operation system controls the output of the fuel cell cascade DC/DC converter, the control of the output power of the fuel cell is indirectly realized, and the energy storage system supplements the output or recovers the braking power.
As an optimization scheme of the above embodiment, in the step S200, the total operating mileage L of the tramcar per train per day is calculated according to a train scheduling scheme; and taking the available interval of the electric quantity of the energy storage system as x 1% and x 2%, and calculating the value of SOC reduction of each adjacent interval of the energy storage system of the tramcar based on statistical analysis of the road condition of the line.
the method for calculating the value of SOC reduction of each adjacent region of the tramcar energy storage system through statistical analysis of the road condition of the line comprises the following steps:
the total mileage coefficient of the daily operation straight road section of the electric vehicle with the rail is as follows:
L＝L1+L2+L3+…+Ln；
the total mileage coefficient of the rail electric vehicle during daily operation and climbing is calculated as follows:
U＝β1×U1+β2×U2+β3×U3+…+βn×Un；
Calculating the total coefficient of mileage of the rail electric vehicle in downhill operation on a daily basis:
D＝ρ1 -1×D1+ρ2 -1×D2+ρ3 -1×D3+…+ρn -1×Dn；
the total mileage coefficient counted on the daily running curve of the rail electric vehicle is as follows:
R＝ω1 -1×R1+ω2 -1×R2+ω3 -1×R3+…+ωn -1×Rn；
Wherein L isn、Un、DnAnd RnRespectively representing the length of a straight road section, the length of a climbing road section, the length of a descending road section and the length of a curved road section in the nth running section; beta is an、ρnand ωnRespectively obtaining the average slope of a climbing road section, the average slope of a downhill road section and the average curvature radius of a curve road section in the nth operation section;
the reference value of the SOC value decreased in the nth section is:
ΔSOCref＝(Ln+β1×U1+ρ1 -1×D1+ω1 -1×R1)×(x2-x1)/(L+U+D+R)。
as an optimization scheme of the above embodiment, in step S300, a dynamic modeling is performed on the train based on a newton formula, and a tramcar dynamic model is established; estimating a power time curve of the tramcar running in a single region based on a basic resistance formula and a speed position curve; the method for obtaining the resistance coefficients A, B and C in the basic resistance formula by adopting a fitting method based on Levenberg-Marquardt iteration specifically comprises the following steps:
Performing a type test on the fuel cell tramcar to obtain experimental data of speed and power when the tramcar runs at a constant speed on a straight road section, wherein the train only overcomes basic resistance to do work at the moment;
Reversely deducing the relation between the basic resistance and the speed according to a Newton formula and experimental data;
Resistance coefficients A, B and C are identified by adopting a Levenberg-Marquardt-based iterative method, and the calculation method comprises the following steps:
for a fuel cell tram system: y ═ f (x, c);
Wherein y is a calculated value of the measured basic resistance, x is the speed of the train, and c is a parameter to be identified;
and has the following components: c (k +1) ═ c (k) + (J)TJ+λI)-1JTr(c(k))；
wherein I is a unit matrix, J is a Jacobian matrix, lambda damping factor lambda is more than 0, r is a residual error, and I is the ith group of experimental data;
and has the following components:ri＝f(xi,c)-yi。
As an optimization scheme of the embodiment, in the calculating of the optimal power distribution relationship of the train in each operation interval under different load conditions, the operation interval is between every two adjacent stations of the train in operation; the load conditions include m load conditions generated at fixed intervals from no load to full load.
calculating the optimal power distribution relationship between every two adjacent stations of the train under different load conditions by adopting a self-adaptive planning algorithm based on an extreme learning machine, wherein the self-adaptive planning algorithm approximates a performance index function and a control strategy in a dynamic planning equation by utilizing a function approximation structure and adopting a successive iteration method, so that the optimal power distribution relationship is gradually approximated and obtained;
the calculation process of the self-adaptive planning algorithm is represented by a recursion formula:
Wherein, x (J) is the current state of the system, x (J +1) refers to the state of the system at the next sampling time under the control decision u (J), p (x) (J), u (J) represents the instant penalty value function under the decision u (J) under the state x (J), and J (x (J +1)) represents the state value function under the state x (J + 1); using SOC of storage battery in energy storage system as state quantity and fuel cell to output powerreference value PFCfor the controlled variable, the state transition equation and the cost function are respectively:
wherein, CH2The equivalent hydrogen consumption of the system comprises two parts of instantaneous hydrogen consumption of a fuel cell and instantaneous equivalent hydrogen consumption of a lithium battery; j is the control stage in the interval, and k stages in total;
The constraints that the system needs to satisfy include:
PFC(j)+PBAT(j)＝Pload(j)，
PFCmin≤PFC(j)≤PFCmax，
PBATmin≤PBAT(j)≤PBATmax；
Wherein, PFCAnd PBATPower for fuel cells and batteries; pFCminand PFCmaxThe minimum and maximum output power of the fuel cell are respectively; pBATminand PBATmaxThe minimum and maximum output power of the storage battery are respectively;
The system start and end states need to satisfy the following formula:
SOCinitial-SOCend＝ΔSOCref；
therein, SOCinitialand SOCendrespectively refers to the SOC value, delta SOC, of the energy storage system when the train starts and ends running in the sectionrefObtaining the reference value of the SOC value reduced in the nth section;
Similarly, for each load condition in n operating intervals, respectively adopting a self-adaptive planning algorithm to calculate the optimal power distribution relation of the interval with the lowest energy consumption, and obtaining n times m optimal power distribution relations in total, wherein each distribution relation comprises k steps of decision; obtained by adaptive planning algorithm under each load conditionThe fuel cell reference power matrixes are respectively named PFC correspondingly1，PFC2，PFC3…PFCmAnd the matrix is a matrix of the running interval n multiplied by the decision step number k of each running interval, and the matrix is written into a vehicle automatic running system controller of the train.
as an optimization scheme of the above embodiment, an optimal power distribution scheme of a next operation interval is obtained in the vehicle automatic operation system by a fuzzy control method; meanwhile, in order to ensure that the SOC of the next interval can be reduced to the planned level, an interpolation method is adopted to form a preliminary power distribution scheme of the next interval; taking the current SOC and the offline calculated SOC as input, and generating a feedback regulating quantity by utilizing a PI controller to regulate a preliminary power distribution scheme in real time; and determining the output reference power of the fuel cell system to form a power distribution scheme.
As an optimization scheme of the embodiment, when the actually running train stops, the load data of the current train is acquired on line through a weight sensor arranged at the bottom of the train and then is input into the automatic vehicle running system; the load information of the vehicle is obtained from a weight sensor of the vehicle device, the weight sensor transmits a message containing the load information to the vehicle automatic operation system through the CAN bus in a fixed protocol, and the message is interpreted by the vehicle automatic operation system according to the protocol.
As an optimization scheme of the above embodiment, the vehicle automatic operation system transmits the fuel cell output reference power value corresponding to the power distribution scheme to the DC/DC converter in a CAN communication manner according to a time sequence, controls the output power of the DC/DC converter through PWM modulation to indirectly control the output power of the fuel cell system, and the energy storage system supplements or recovers the braking power.
the off-line design controller in the vehicle automatic operation system comprises an interpolation controller and a PI feedback controller, and is realized by adopting a method for controlling a DC/DC converter by the vehicle automatic operation system, and the method comprises the following steps:
Determining an input variable as the current load capacity, and outputting a preliminary power distribution curve of the next operation interval by using an interpolation method;
Acquiring the current of a power system by using a current sensor, calculating the SOC of the power battery system in real time on line, using the SOC as the input quantity of PI control, and obtaining a feedback regulating quantity by using the operation of a PI controller to obtain an output reference power curve of the fuel battery in the next operating interval;
The vehicle automatic operation system communicates with the DC/DC converter controller, transmits the output reference power curve of the fuel cell in the next operation interval to the controller according to time sequence, and realizes the conversion of the fuel cell power by controlling the DC/DC converter.
in an embodiment, as shown in fig. 2 and 3, the tramcar using the fuel cell comprises a fuel cell system, a unidirectional DC/DC converter, an energy storage system, a bidirectional DC/DC converter, a CAN communication bus, a train ATO controller and a series of current, voltage, air pressure and weight sensors. The fuel cell system is connected to the direct current bus through the unidirectional DC/DC converter, and the energy storage system is directly connected with the direct current bus through the bidirectional DC/DC converter. The CAN bus is connected with: the controller of the unidirectional DC/DC converter, the battery management system BMS of the energy storage system, the ATO controller of the train and the like can realize the interconnection communication of a plurality of trains. The unidirectional DC/DC converter comprises a power circuit part and a controller part, wherein the power circuit part is a direct-direct Boost converter circuit with a staggered parallel topological structure, the controller part is designed based on a DSP chip and mainly comprises a CAN communication circuit, an AD acquisition circuit, a conditioning circuit and a PWM modulation circuit. The controller receives the reference value of the output power of the fuel cell through the CAN communication circuit, reads the current and voltage value output by the fuel cell system through the AD acquisition circuit and the conditioning circuit, and controls the on-off of the switching tube through the PWM modulation circuit after the operation of an internal control algorithm, so that the effect of changing the output of the converter is realized. The bidirectional DC/DC converter includes a power circuit portion and a control circuit portion for stabilizing the bus voltage. The circuit structure and the control method are similar to those of a unidirectional DC/DC converter. The ATO controller of the train is designed based on a PLC controller, and the ATO has more functions to be realized. The method provided by the invention is mainly oriented to a hybrid power system, and the functions CAN be realized only by a CAN communication circuit at the periphery of a PLC controller. The PLC reads a signal transmitted to the bus by a weight sensor below the train when the station is stopped through the CAN communication circuit, obtains a time sequence signal of the output power reference value of the fuel cell in the next operation interval through internal operation, sends the time sequence signal to the CAN bus through the CAN communication circuit, and finally captures the time sequence signal by the unidirectional DC/DC converter controller.
the foregoing shows and describes the general principles and broad features of the present invention and advantages thereof. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, which are described in the specification and illustrated only to illustrate the principle of the present invention, but that various changes and modifications may be made therein without departing from the spirit and scope of the present invention, which fall within the scope of the invention as claimed. The scope of the invention is defined by the appended claims and equivalents thereof.

Claims (10)

1. A fuel cell tramcar daily operation approximate optimal energy management method is characterized by comprising the following steps:
s100, acquiring state data of a target line by a fuel cell tramcar operation administration, wherein the state data comprises a train dispatching scheme, a single-train speed and position curve and vehicle parameter data;
S200, obtaining an SOC reduction standard of each adjacent interval of an energy storage system in the fuel cell tramcar according to the target line information, the station information and the train scheduling scheme;
s300, establishing a tramcar dynamics model, and identifying a basic train resistance coefficient; based on the single train speed position curve, calculating the optimal power distribution relation of the train in each running interval under different load conditions, including a power time curve and a power system SOC change curve, of the train by adopting an adaptive planning algorithm based on an extreme learning machine, and storing the optimal power distribution relation to a vehicle automatic running system in a matrix form;
S400, when the train actually runs stops, acquiring the load data of the current train on line through a weight sensor arranged at the bottom of the train, and inputting the load data into the automatic train running system; obtaining an optimal power distribution scheme of a next operation interval in the vehicle automatic operation system through a fuzzy control method;
And S500, the vehicle automatic operation system controls the output of the fuel cell cascade DC/DC converter, the control of the output power of the fuel cell is indirectly realized, and the energy storage system supplements the output or recovers the braking power.
2. the method for approximately-optimal energy management of daily operation of the fuel cell tram according to claim 1, wherein in the step S200, the total operation mileage L of the tram per train per day is calculated according to a train scheduling scheme; and taking the available interval of the electric quantity of the energy storage system as x 1% and x 2%, and calculating the value of SOC reduction of each adjacent interval of the energy storage system of the tramcar based on statistical analysis of the road condition of the line.
3. The fuel cell tram daily operation near-optimal energy management method according to claim 2, wherein the value of SOC reduction of each adjacent interval of the tram energy storage system is calculated through statistical analysis of line road conditions, and the method comprises the following steps:
the total mileage coefficient of the daily operation straight road section of the electric vehicle with the rail is as follows:
L＝L1+L2+L3+…+Ln；
the total mileage coefficient of the rail electric vehicle during daily operation and climbing is calculated as follows:
U＝β1×U1+β2×U2+β3×U3+…+βn×Un；
calculating the total coefficient of mileage of the rail electric vehicle in downhill operation on a daily basis:
D＝ρ1 -1×D1+ρ2 -1×D2+ρ3 -1×D3+…+ρn -1×Dn；
the total mileage coefficient counted on the daily running curve of the rail electric vehicle is as follows:
R＝ω1 -1×R1+ω2 -1×R2+ω3 -1×R3+…+ωn -1×Rn；
wherein L isn、Un、Dnand Rnrespectively representing the length of a straight road section, the length of a climbing road section, the length of a descending road section and the length of a curved road section in the nth running section; beta is an、ρnand ωnrespectively obtaining the average slope of a climbing road section, the average slope of a downhill road section and the average curvature radius of a curve road section in the nth operation section;
The reference value of the SOC value decreased in the nth section is:
ΔSOCref＝(Ln+β1×U1+ρ1 -1×D1+ω1 -1×R1)×(x2-x1)/(L+U+D+R)。
4. the fuel cell tram daily operation approximate optimal energy management method according to claim 1, characterized in that in the step S300, a dynamic modeling is performed on the train based on a newton' S formula, and a tram dynamic model is established; estimating a power time curve of the tramcar running in a single region based on a basic resistance formula and a speed position curve; the method for obtaining the resistance coefficients A, B and C in the basic resistance formula by adopting a fitting method based on Levenberg-Marquardt iteration specifically comprises the following steps:
Performing a type test on the fuel cell tramcar to obtain experimental data of speed and power when the tramcar runs at a constant speed on a straight road section, wherein the train only overcomes basic resistance to do work at the moment;
Reversely deducing the relation between the basic resistance and the speed according to a Newton formula and experimental data;
resistance coefficients A, B and C are identified by adopting a Levenberg-Marquardt-based iterative method, and the calculation method comprises the following steps:
For a fuel cell tram system: y ═ f (x, c);
wherein y is a calculated value of the measured basic resistance, x is the speed of the train, and c is a parameter to be identified;
and has the following components: c (k +1) ═ c (k) + (J)TJ+λI)-1JTr(c(k))；
wherein I is a unit matrix, J is a Jacobian matrix, lambda damping factor lambda is more than 0, r is a residual error, and I is the ith group of experimental data;
and has the following components:ri＝f(xi,c)-yi。
5. The approximately optimal energy management method for daily operation of the fuel cell tramcar according to claim 1, characterized in that the optimal power distribution relation of the train in each operation interval under different loading conditions is calculated, wherein the operation interval is between every two adjacent stations when the train operates; the load conditions include m load conditions generated at fixed intervals from no load to full load.
6. The fuel cell tramcar daily operation approximate optimal energy management method according to claim 5, characterized in that in calculating the optimal power distribution relationship between every two adjacent stations of the train under different load conditions by adopting an adaptive planning algorithm based on an extreme learning machine, the adaptive planning algorithm approximates a performance index function and a control strategy in a dynamic planning equation by using a function approximation structure and adopting a successive iteration method, thereby gradually approximating and obtaining the optimal power distribution relationship;
the calculation process of the self-adaptive planning algorithm is represented by a recursion formula:
where x (j) is the current state of the system, x (j +1) is the state of the system at the next sampling time under the control decision u (j), p (x (j), u(j) represents the immediate penalty function under the decision u (J) under the state x (J), and J (x (J +1)) represents the state value function under the state x (J + 1); taking the SOC of a storage battery in the energy storage system as a state quantity and taking the output power reference value P of the fuel cellFCFor the controlled variable, the state transition equation and the cost function are respectively:
wherein, CH2the equivalent hydrogen consumption of the system comprises two parts of instantaneous hydrogen consumption of a fuel cell and instantaneous equivalent hydrogen consumption of a lithium battery; j is the control stage in the interval, and k stages in total;
The constraints that the system needs to satisfy include:
PFC(j)+PBAT(j)＝Pload(j)，
PFCmin≤PFC(j)≤PFCmax，
PBATmin≤PBAT(j)≤PBATmax；
Wherein, PFCand PBATPower for fuel cells and batteries; pFCminand PFCmaxThe minimum and maximum output power of the fuel cell are respectively; pBATminand PBATmaxthe minimum and maximum output power of the storage battery are respectively;
the system start and end states need to satisfy the following formula:
SOCinitial-SOCend＝ΔSOCref；
Therein, SOCinitialand SOCendRespectively refers to the SOC value, delta SOC, of the energy storage system when the train starts and ends running in the sectionrefobtaining the reference value of the SOC value reduced in the nth section;
Similarly, the enabling calculation of each load condition under n operation intervals is respectively carried out by adopting an adaptive planning algorithmobtaining n times m optimal power distribution relations in total according to the optimal power distribution relation with the lowest consumption in the interval, wherein each distribution relation comprises k steps of decision; the fuel cell reference power matrix obtained by the self-adaptive planning algorithm under each load condition is respectively named as PFC (Power factor correction) correspondingly1，PFC2，PFC3…PFCmand the matrix is a matrix of the running interval n multiplied by the decision step number k of each running interval, and the matrix is written into a vehicle automatic running system controller of the train.
7. the fuel cell tram daily operation approximately optimal energy management method according to claim 1, characterized in that an optimal power distribution scheme of a next operation interval is obtained in the vehicle automatic operation system by a fuzzy control method; forming a preliminary power distribution scheme of the next interval by adopting an interpolation method; taking the current SOC and the offline calculated SOC as input, and generating a feedback regulating quantity by utilizing a PI controller to regulate a preliminary power distribution scheme in real time; and determining the output reference power of the fuel cell system to form a power distribution scheme.
8. The fuel cell tramcar daily operation approximately optimal energy management method as claimed in claim 1, characterized in that when an actually operating train stops, load data of the current train is acquired on line through a weight sensor arranged at the bottom of the train and then input into the automatic vehicle operation system; the load information of the vehicle is obtained from a weight sensor of the vehicle device, the weight sensor transmits a message containing the load information to the vehicle automatic operation system through the CAN bus in a fixed protocol, and the message is interpreted by the vehicle automatic operation system according to the protocol.
9. the fuel cell tramcar daily operation approximately-optimal energy management method according to claim 1, wherein the vehicle automatic operation system transmits the fuel cell output reference power value corresponding to the power distribution scheme to the DC/DC converter in a CAN communication mode according to a time sequence, the output power of the DC/DC converter is controlled through PWM modulation, so as to indirectly control the output power of the fuel cell system, and the energy storage system supplements or recovers braking power.
10. The fuel cell tramcar daily operation approximately optimal energy management method according to claim 9, wherein the off-line design controller comprises an interpolation controller and a PI feedback controller in the automatic vehicle operation system, and the method for controlling the DC/DC converter by using the automatic vehicle operation system is realized by the following steps:
determining an input variable as the current load capacity, and outputting a preliminary power distribution curve of the next operation interval by using an interpolation method;
Acquiring the current of a power system by using a current sensor, calculating the SOC of the power battery system in real time on line, using the SOC as the input quantity of PI control, and obtaining a feedback regulating quantity by using the operation of a PI controller to obtain an output reference power curve of the fuel battery in the next operating interval;
The vehicle automatic operation system communicates with the DC/DC converter controller, transmits the output reference power curve of the fuel cell in the next operation interval to the controller according to time sequence, and realizes the conversion of the fuel cell power by controlling the DC/DC converter.
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