CN112140942B - Self-adaptive equivalent consumption minimized energy management method for fuel cell vehicle - Google Patents
Self-adaptive equivalent consumption minimized energy management method for fuel cell vehicle Download PDFInfo
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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
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- 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
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Abstract
The invention relates to a fuel cell automobile adaptive equivalent consumption minimized energy management method, and belongs to the technical field of new energy. The method comprises the steps of periodically updating an equivalent factor in an equivalent consumption minimization energy management strategy, and ensuring that the state of charge of the energy storage battery is changed towards the direction of converging the state of charge to a target reference value at the fastest speed in a short-term future time by using the updated equivalent factor to perform power distribution; forecasting the required power time sequence data of the vehicle in a short-term future time range by adopting an intelligent algorithm, and using the forecasted required power data for local optimization to determine a short-term optimal equivalent factor; based on an equivalent consumption minimization strategy, a local optimization method is used for self-adaptively adjusting equivalent factors of the energy storage battery, so that the charge state maintaining capacity of the energy storage battery is improved, and the fuel economy of a power system is guaranteed to the greatest extent. The invention guarantees the power system to have the approximately optimal fuel economy according to the approximately optimal power distribution proportion of the equivalent hydrogen consumption.
Description
Technical Field
The invention belongs to the technical field of new energy, and relates to a fuel cell automobile adaptive equivalent consumption minimized energy management method.
Background
The power system of a fuel cell vehicle generally comprises a fuel cell engine and an energy storage battery, and can be divided into an extended range type and a full power type according to the power grade of the fuel cell engine and the capacity difference of the energy storage battery. The full-power fuel cell automobile power system is provided with a high-power fuel cell engine and a small-capacity energy storage battery, has the advantages of compact structure, light weight, easiness in arrangement, no charging, long endurance and the like, and is the key direction of future development of fuel cell automobiles. For a full-power fuel cell hybrid electric vehicle integrated with a small-capacity energy storage cell, the electric energy margin of the energy storage cell must be kept within a reasonable range at any time so as to respond to the transient peak power demand of the vehicle in the driving process and store braking recovery energy which appears irregularly, so that the charge state maintaining capability of the energy storage cell is as important as the fuel economy of a power system. The state of charge maintaining capability of the energy storage battery is as follows: in the running process of the vehicle, the state of charge of the energy storage battery does not fluctuate violently or touch the upper limit and the lower limit of the state of charge of the energy storage battery along with the change of working conditions, and can be stably maintained near a set state of charge reference value or in a permissible range. An Equivalent Consumption Minimization Strategy (ECMS) is used as an instant optimization energy management method, has the characteristic of strong real-time performance, and adopts equivalent hydrogen consumption rate to represent the sum of the electric energy consumption rate of an energy storage battery and the actual hydrogen consumption rate of a fuel cell, and then the equivalent hydrogen consumption rate is minimized to extract the optimal reference power value of the fuel cell, so that the distribution of required power is completed, and the fuel economy of a power system is ensured to be close to the optimal. However, the equivalent factor of ECMS is usually a fixed value, and the maintenance of the state of charge of the energy storage battery cannot be achieved under different driving conditions. While the traditional adaptive ECMS can regularly adjust the equivalent factor according to the actual feedback value of the state of charge of the energy storage battery and converge the state of charge of the energy storage battery to a target reference value, the traditional adaptive ECMS has a limited adaptive effect due to the constant proportionality coefficient. The full-power fuel cell automobile self-adaptive equivalent consumption minimum energy management method based on the demand power prediction can perform local optimization according to the total automobile demand power predicted by the demand power prediction module in a short time in the future to determine a near-future optimal equivalent factor value, perform equivalent consumption minimum power distribution according to the obtained optimal equivalent factor, remarkably enhance the maintaining capability of the charge state of the energy storage battery, and simultaneously ensure the fuel economy of an approximately optimal power system.
Disclosure of Invention
In view of the above, the present invention aims to provide a fuel cell vehicle adaptive equivalent consumption minimized energy management method, which achieves the purposes of reducing computation time and obtaining accurate results, wherein the optimal solution is a global optimal solution within an allowable error.
In order to achieve the purpose, the invention provides the following technical scheme:
a fuel cell vehicle adaptive equivalent consumption minimized energy management method, the method includes the following steps:
s1, taking n seconds as a fixed period, and collecting real-time finished automobile required power and the change rate thereof on a direct-current bus of a power system of a full-power type fuel cell automobile at the beginning moment of each period;
s2, inputting the real-time required power and the change rate data thereof collected in the step S1 into a power demand prediction module constructed by a machine learning algorithm, and predicting the time sequence data of the required power of the whole vehicle within n seconds from the beginning moment of the cycle to the future;
s3, inputting the near-future required power prediction data obtained in the step S2 into an ECMS equivalent factor local optimization module to obtain an optimal equivalent factor within n seconds in the future;
s4, calculating a required power distribution scheme within n seconds in the future according to the near-future optimal equivalent factor obtained in the step S3 by using the power distribution model based on the ECMS and established in the full-power fuel cell automobile power system controller;
and S5, according to the required power distribution scheme obtained in the step S4, the bottom layer DCDC converter of the power system of the full-power fuel cell automobile completes required power distribution within n seconds in the future.
Optionally, the execution method in the ECMS equivalent factor local optimization module is:
31. inputting the time sequence data of the required power of the whole vehicle within the future n seconds predicted by the power demand prediction module;
32. substituting the time sequence data of the required power of the whole vehicle input in the step 31 into an ECMS power distribution model developed based on a semi-empirical model of the power system, and solving the required power distribution proportion when the equivalent factor is set as a certain numerical value in the interval [ a, b ];
the ECMS power distribution model developed based on the semi-empirical model of the power system is concretely as follows:
optimizing an objective function:
P fc_net represents: fuel cell system parameterExamining the net power;
η fc_stack represents: fuel cell stack efficiency;
η fc_system represents: fuel cell system efficiency;
s represents: an ECMS equivalent factor;
P bat represents: energy storage battery power;
n cell represents: the number of fuel cell units;
f represents: faraday constant;
V fc_oc represents: fuel cell stack open circuit voltage;
R fc represents: fuel cell stack internal resistance;
R bat represents: internal resistance of the energy storage battery;
V oc represents: the open circuit voltage of the energy storage battery;
f 1 represents: a calibration functional relationship between the net power of the fuel cell system and the system efficiency;
f 2 represents: the calibration function relation between the net power of the fuel cell system and the efficiency of the electric pile;
P demand represents: the direct current bus requires power;
SOC represents: the state of charge of the energy storage battery;
SOC min represents: the lower limit value of the allowable operation range of the charge state of the energy storage battery;
SOC max represents: the upper limit value of the allowable operation range of the charge state of the energy storage battery;
P fc_net_min represents: a lower limit value of a permissible net power range of the fuel cell system;
P fc_net_max represents: the upper limit value of the allowable net power range of the fuel cell system;
P fc_net ' means: a fuel cell system power rate of change; .
33. Distributing the required power between the energy storage battery and the fuel cell system according to the required power distribution proportion obtained in the step 32, and solving a theoretical change track of the state of charge of the energy storage battery within n seconds in the future by combining with an energy storage battery semi-empirical model;
wherein, the energy storage battery semi-empirical model:
SOC int represents: the initial value of the charge state of the energy storage battery;
η coulomb represents: the energy storage battery coulomb efficiency;
I bat represents: an energy storage battery current;
Q max represents: the capacity of the energy storage battery;
V oc represents: the open circuit voltage of the energy storage battery;
R bat represents: internal resistance of the energy storage battery;
P bat represents: energy storage battery power;
f 3 represents: the calibration function relation of the charge state and the open-circuit voltage of the energy storage battery;
f 4 represents: the calibration function relation between the charge state and the internal resistance of the energy storage battery;
34. calculating the difference between the theoretical calculated value of the state of charge of the energy storage battery at the nth second in the future and a target reference value, and storing the difference value and the equivalent factor value at the moment into a temporary storage array;
35. judging whether the point-by-point search of the equivalent factors in the interval [ a, b ] is finished or not, if not, jumping to the step 36, otherwise, jumping to the step 37;
36. adding the last equivalent factor to the set search step length to form a new equivalent factor search value, and then jumping to step 32;
37. and searching the temporary storage array, outputting an equivalent factor which enables the difference between the theoretical calculation value of the state of charge of the energy storage battery and the target reference value at the nth second in the future to be minimum, and taking the equivalent factor as an optimal equivalent factor.
The invention has the beneficial effects that:
1. and adaptively adjusting equivalent factors in an equivalent consumption minimization energy management strategy in a periodic updating mode, minimizing the difference between a theoretical calculated value of the state of charge of the energy storage battery at the nth second in the future and a target reference value to serve as an optimization target in each period, performing local optimization, and searching and determining the optimal equivalent factor in the nth second in the future. The mechanism greatly enhances the maintenance capability of the charge state of the energy storage battery, can still meet the requirement of the sustainability of the charge state of the energy storage battery of the full-power fuel cell automobile under the changing working condition, has strong robustness and has the effect superior to the traditional equivalent consumption minimized energy management strategy;
2. the parameters needing to be calibrated in the method only comprise an equivalent factor updating period n, an equivalent factor searching interval [ a, b ] and a searching step length thereof, and only need to be slightly adjusted according to the difference of an actual vehicle type, so that the calibration difficulty is low, the realization is easy, and the universality is strong;
3. the updating mode of the equivalent factor in the method is realized based on optimization, namely, an optimal value is searched in the search interval [ a, b ]. In the traditional method, the equivalent factor is adjusted by proportional feedback according to the difference between the actual feedback quantity of the state of charge of the energy storage battery and the target reference quantity, and the increment of the updated new equivalent factor relative to the last equivalent factor is limited due to improper setting of the proportional factor, so that the self-adaptive adjustment speed is slow and the self-adaptive adjustment effect is poor. In contrast, the adaptive adjustment speed of the equivalent factor is higher, and the control effect on the state of charge of the energy storage battery is better.
Additional advantages, objects, and features of the invention will be set forth in part in the description which follows and in part will become apparent to those having ordinary skill in the art upon examination of the following or may be learned from practice of the invention. The objectives and other advantages of the invention may be realized and attained by the means of the instrumentalities and combinations particularly pointed out hereinafter.
Drawings
For the purposes of promoting a better understanding of the objects, aspects and advantages of the invention, reference will now be made to the following detailed description taken in conjunction with the accompanying drawings in which:
FIG. 1 is a general framework of the energy management method of the present invention;
FIG. 2 is a flow chart of the local equivalence factor optimization module according to the present invention;
fig. 3 is an exemplary diagram of the adaptive effect of the equivalent factor and the state of charge maintaining capability of the energy storage battery obtained by the present invention.
Detailed Description
The embodiments of the present invention are described below with reference to specific embodiments, and other advantages and effects of the present invention will be easily understood by those skilled in the art from the disclosure of the present specification. The invention is capable of other and different embodiments and of being practiced or of being carried out in various ways, and its several details are capable of modification in various respects, all without departing from the spirit and scope of the present invention. It should be noted that the drawings provided in the following embodiments are only for illustrating the basic idea of the present invention in a schematic way, and the features in the following embodiments and examples may be combined with each other without conflict.
Wherein the showings are for the purpose of illustration only and not for the purpose of limiting the invention, shown in the drawings are schematic representations and not in the form of actual drawings; to better illustrate the embodiments of the present invention, some parts of the drawings may be omitted, enlarged or reduced, and do not represent the size of an actual product; it will be understood by those skilled in the art that certain well-known structures in the drawings and descriptions thereof may be omitted.
The same or similar reference numerals in the drawings of the embodiments of the present invention correspond to the same or similar components; in the description of the present invention, it should be understood that if there is an orientation or positional relationship indicated by terms such as "upper", "lower", "left", "right", "front", "rear", etc., based on the orientation or positional relationship shown in the drawings, it is only for convenience of description and simplification of description, but it is not an indication or suggestion that the referred device or element must have a specific orientation, be constructed in a specific orientation, and be operated, and therefore, the terms describing the positional relationship in the drawings are only used for illustrative purposes, and are not to be construed as limiting the present invention, and the specific meaning of the terms may be understood by those skilled in the art according to specific situations.
Aiming at the energy management problem of a full-power fuel cell automobile, aiming at enhancing the charge state maintaining capability of an energy storage cell and ensuring the fuel economy, the invention provides a novel method for managing the self-adaptive equivalent consumption minimum energy of the fuel cell automobile based on demand power prediction. The energy management method determines the optimal equivalent factor of an equivalent consumption minimization strategy which can enable the state of charge of the energy storage battery to be rapidly converged to a target reference value by utilizing local optimization according to the predicted required power of the vehicle in the short-term future time. By using the method to periodically update the equivalent factor, the state of charge maintaining capability of the energy storage battery can be obviously improved when the equivalent consumption minimization strategy is used for power distribution, and simultaneously the fuel economy of an approximately optimal power system is achieved.
The overall framework of the energy management method is shown in fig. 1, and the energy management method mainly comprises a full-power fuel cell automobile power system, a power demand prediction module, an ECMS equivalent factor local optimization module, a power distribution strategy established based on ECMS in a power system controller, and a DCDC converter at the bottom layer of the power system. The specific implementation logic is as follows:
1. taking n seconds as a fixed period, and acquiring real-time finished automobile required power and the change rate thereof on a direct current bus of a power system at the starting moment of each period;
2. inputting the real-time required power and the change rate data thereof collected in the step 1 into a power demand prediction module constructed by a machine learning algorithm, and predicting the time sequence data of the required power of the whole vehicle within n seconds from the beginning moment of the cycle to the future;
3. inputting the near-future required power prediction data obtained in the step 2 into an ECMS equivalent factor local optimization module to obtain an optimal equivalent factor within n seconds in the future;
4. calculating a required power distribution scheme within the future n seconds according to the near-future optimal equivalent factor obtained in the step 3 by using the power distribution model based on the ECMS established in the power system controller;
5. and (4) according to the required power distribution scheme obtained in the step (4), completing the required power distribution within the next n seconds by the DCDC converter at the bottom layer of the power system.
The specific execution flow (as shown in fig. 2) of the ECMS equivalence factor local optimization module in the overall framework is as follows:
31. inputting the time sequence data of the required power of the whole vehicle within the future n seconds predicted by the required power prediction module;
32. substituting the whole vehicle required power data input in the step 31 into an ECMS power distribution model developed based on a semi-empirical model of the power system, and solving a required power distribution proportion when an equivalent factor is set as a certain numerical value in the interval [ a, b ];
33. distributing the required power between the energy storage battery and the fuel cell system according to the distribution proportion obtained in the step 32, and solving a theoretical change track (calculated value) of the state of charge of the energy storage battery within n seconds in the future by combining with an energy storage battery semi-empirical model;
34. calculating the difference between the theoretical calculated value of the state of charge of the energy storage battery and a target reference value at the nth second in the future, and storing the difference value and the equivalent factor value at the moment into a temporary storage array;
35. judging whether the point-by-point search of the equivalent factors in the interval [ a, b ] is finished or not, if not, jumping to the step 36, otherwise, jumping to the step 37;
36. adding the last equivalent factor to the set search step length to form a new equivalent factor search value, and then jumping to step 32;
37. and searching the temporary storage array, outputting an equivalent factor which enables the difference between the theoretical calculated value of the state of charge of the energy storage battery and the target reference value to be minimum in the nth second in the future, and taking the equivalent factor as an optimal equivalent factor.
Examples
1. The energy management system hardware set-up of the invention is completed according to the overall framework shown in fig. 1;
2. compiling a power demand prediction module software program based on an intelligent algorithm;
3. writing a software program of the equivalent factor local optimization module according to the execution flow shown in FIG. 2;
4. establishing a semi-empirical model of a target vehicle power system, further establishing an ECMS-based power distribution model, and completing software program compiling of the part;
5. and integrating the software programs in the step 2-4 to debug the hardware in the ring. And taking the charge state maintaining capacity of the energy storage battery and the fuel economy of the power system as calibration optimization targets, and calibrating an equivalent factor updating period n, an equivalent factor searching interval [ a, b ] and an equivalent factor searching step length.
6. And writing the calibrated program into a controller of the power system of the whole vehicle, and carrying out real vehicle experimental verification. And verifying the obtained charge state data and equivalent hydrogen consumption data of the energy storage battery according to the working conditions of the actual vehicle, optimizing the calibration parameters and forming a final energy management software program. With the energy management method of the present invention, the equivalence factor adaptation effect and the energy storage battery state of charge maintenance effect as illustrated in fig. 3 should be achieved.
Finally, although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that various changes and modifications may be made therein without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (1)
1. A fuel cell vehicle adaptive equivalent consumption minimized energy management method is characterized in that: the method comprises the following steps:
s1, taking n seconds as a fixed period, and collecting real-time finished automobile required power and the change rate thereof on a direct-current bus of a full-power fuel cell automobile power system at the beginning of each period;
s2, inputting the real-time required power and the change rate data thereof collected in the step S1 into a power demand prediction module constructed by a machine learning algorithm, and predicting the time sequence data of the required power of the whole vehicle within n seconds from the beginning moment of the cycle to the future;
s3, inputting the near-future required power prediction data obtained in the step S2 into an ECMS equivalent factor local optimization module to obtain an optimal equivalent factor within n seconds in the future;
s4, calculating a required power distribution scheme within n seconds in the future according to the near-future optimal equivalent factor obtained in the step S3 by using the power distribution model based on the ECMS and established in the full-power fuel cell automobile power system controller;
s5, according to the required power distribution scheme obtained in the step S4, the DCDC converter at the bottom layer of the power system of the full-power fuel cell automobile is used for completing required power distribution within the next n seconds;
the execution method in the ECMS equivalent factor local optimization module comprises the following steps:
31. inputting the time sequence data of the required power of the whole vehicle within the future n seconds predicted by the power demand prediction module;
32. substituting the time sequence data of the required power of the whole vehicle input in the step 31 into an ECMS power distribution model developed based on a semi-empirical model of the power system, and solving the required power distribution proportion when the equivalent factor is set as a certain value in the interval [ a, b ];
the ECMS power distribution model developed based on the semi-empirical model of the power system is specifically as follows:
optimizing an objective function:
P fc_net represents: the fuel cell system reference net power;
η fc_stack represents: fuel cell stack efficiency;
η fc_system represents: fuel cell system efficiency;
s represents: an ECMS equivalent factor;
P bat represents: energy storage battery power;
n cell represents: the number of fuel cell units;
f represents: faraday constant;
V fc_oc represents: fuel cell stack open circuit voltage;
R fc represents: fuel cell stack internal resistance;
R bat represents: internal resistance of the energy storage battery;
V oc represents: the open circuit voltage of the energy storage battery;
f 1 represents: a calibration functional relationship between the net power of the fuel cell system and the system efficiency;
f 2 represents: the calibration function relation between the net power of the fuel cell system and the efficiency of the electric pile;
P demand represents: the direct current bus requires power;
SOC represents: the state of charge of the energy storage battery;
SOC min represents: the lower limit value of the allowable operation range of the charge state of the energy storage battery;
SOC max represents: the upper limit value of the allowable operation range of the charge state of the energy storage battery;
P fc_net_min represents: a lower limit value of a permissible net power range of the fuel cell system;
P fc_net_max represents: the upper limit value of the allowable net power range of the fuel cell system;
P fc_net ' means: a fuel cell system power rate of change;
33. distributing the required power between the energy storage battery and the fuel cell system according to the required power distribution proportion obtained in the step 32, and solving a theoretical change track of the state of charge of the energy storage battery within n seconds in the future by combining with an energy storage battery semi-empirical model;
wherein, the energy storage battery semi-empirical model:
SOC int represents: the initial value of the charge state of the energy storage battery;
η coulomb represents: the coulomb efficiency of the energy storage battery;
I bat represents: an energy storage battery current;
Q max represents: the capacity of the energy storage battery;
V oc represents: the open circuit voltage of the energy storage battery;
R bat represents: internal resistance of the energy storage battery;
P bat represents: energy storage battery power;
f 3 represents: the calibration function relation between the charge state of the energy storage battery and the open-circuit voltage;
f 4 represents: the calibration function relation between the charge state and the internal resistance of the energy storage battery;
34. calculating the difference between the theoretical calculated value of the state of charge of the energy storage battery at the nth second in the future and a target reference value, and storing the difference value and the equivalent factor value at the moment into a temporary storage array;
35. judging whether the point-by-point search of the equivalent factors in the interval [ a, b ] is finished or not, if not, jumping to the step 36, otherwise, jumping to the step 37;
36. adding the last equivalent factor to the set search step length to form a new equivalent factor search value, and then jumping to step 32;
37. and searching the temporary storage array, outputting an equivalent factor which enables the difference between the theoretical calculated value of the state of charge of the energy storage battery and the target reference value to be minimum in the nth second in the future, and taking the equivalent factor as an optimal equivalent factor.
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