CN113022385B - Parameter matching method for fuel cell lithium battery hybrid power system - Google Patents
Parameter matching method for fuel cell lithium battery hybrid power system Download PDFInfo
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- CN113022385B CN113022385B CN202110596547.2A CN202110596547A CN113022385B CN 113022385 B CN113022385 B CN 113022385B CN 202110596547 A CN202110596547 A CN 202110596547A CN 113022385 B CN113022385 B CN 113022385B
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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
- B60L58/40—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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- 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
- Y02T10/70—Energy storage systems for electromobility, e.g. batteries
Abstract
The invention provides a parameter matching method of a fuel cell lithium battery hybrid power system, S1, a multi-dimensional parameter matching optimization tensor space is established, and parameter matching optimization of the hybrid power system is carried out; s2, matching and optimizing three-dimensional variables in a tensor space by using the multi-dimensional parameters to serve as alternative parameter matching schemes to be evaluated; s3, optimizing a power distribution strategy for each alternative scheme aiming at characteristic working conditions based on a deep reinforcement learning algorithm; and S4, generating an optimal power distribution result according to each alternative scheme by an energy management strategy based on a reinforcement learning algorithm. The invention quantitatively considers the equivalent hydrogen consumption cost, the fuel cell operation aging loss cost and the power cell operation aging loss cost in real time in the optimization problem of the fuel cell energy management strategy, has comprehensive optimization target coverage, can reduce the calculation time and improve the efficiency.
Description
Technical Field
The invention belongs to the technical field of new energy, and particularly relates to a parameter matching method for a fuel cell lithium battery hybrid power system.
Background
Due to the gradual shortage of fossil energy, the proton exchange membrane fuel cell automobile uses renewable resource hydrogen, is more efficient and environment-friendly compared with the traditional engine, and is now a key research and development technology in the national energy field. However, the output characteristics of the fuel cell are weak and the dynamic response is slow, so that in order to meet the normal driving requirements of the automobile and enable the automobile to have the regenerative braking function, a power cell (such as a lithium battery) with high power density is generally required to be equipped to form a hybrid power system. The quality of the parameter matching of the fuel cell automobile hybrid power system will seriously affect the various performances of the whole automobile, such as dynamic property, economical efficiency and the like. In addition, how to distribute power between two power sources and how to make energy management policies coupled with the parameter matching problem also affects the effect of parameter matching. Around the above problems, in order to cooperatively deal with the static multi-source system size matching problem and the dynamic power allocation strategy design problem, a parameter matching method capable of fusing power allocation strategy optimization needs to be provided.
In the patent CN104071033A of li chi et al, a parameter matching optimization method for a fuel cell super capacitor hybrid locomotive adopts a high-speed group intelligent optimization algorithm to perform multi-objective optimization on the weight and volume indexes of a fuel cell/super capacitor hybrid system. The performance of the hybrid power system under the dynamic working condition is not considered in the optimization target of the method, the reduction of the service life loss of components is not considered in the optimization target, and the economy of the system cannot be comprehensively improved.
Hu et al, in the literature (multi-object energy management optimization and parameter sizing for improved fuel cell passenger car hybrid vehicle), proposed a multi-objective optimization of fuel cell passenger car hybrid power system economy and system durability, and further, optimized the full life cycle cost of the lithium cell by comprehensively considering the parameters and equivalent hydrogen consumption of the lithium cell. The energy management strategy used in the method needs to be based on a global optimization algorithm, the calculation amount is large, the time consumption is long, and the result has no potential of real-time application. The method does not take the influence of key power parameters of the fuel cell except the rated power and the power cell except the capacity on the economy of the whole vehicle into detailed consideration.
Disclosure of Invention
The current fuel cell passenger car hybrid power system parameter matching method only considers the indexes meeting static dynamic requirements, does not consider the influence of a dynamic power distribution strategy on a power system, and does not comprehensively consider the influence of running loss of power components on the economy of the whole car, and the obtained result cannot realize ideal economic effect. Aiming at specific vehicle types and working condition requirements, the invention aims to provide a parameter matching method and a system capable of generating an optimal power distribution strategy, the influence of key power parameters of a fuel cell except rated power and a power cell except capacity on the economy of the whole vehicle is taken into consideration in a detailed mode, and meanwhile, various factors such as hydrogen consumption and component loss are taken into consideration, so that the ideal economy of the whole vehicle can be realized by a parameter matching result.
The parameter matching method for the fuel cell lithium battery hybrid power system comprises the following steps:
s1, setting up a multi-dimensional parameter matching optimization tensor space by combining respective dynamic characteristics of the fuel cell and the lithium battery, and respectively using the output power variation threshold of the fuel cell
Per unit value of maximum power of hybrid power system
And maximum charge-discharge rate of the battery
Performing parameter matching optimization on the hybrid power system for optimizing variables;
s2, optimizing each three-dimensional variable in tensor space by multi-dimensional parameter matching
Namely, the alternative parameter matching scheme to be evaluated;
s3, optimizing a power distribution strategy for each alternative scheme aiming at characteristic working conditions based on a deep reinforcement learning algorithm;
s4, according to the optimal power distribution result generated by the energy management strategy based on the reinforcement learning algorithm according to each alternative scheme, quantifying the average running cost of the fuel cell passenger car under the characteristic working condition: the sum of the equivalent hydrogen consumption cost, the fuel cell operation loss cost and the power cell operation loss cost;
obtaining the optimal parameter matching scheme three-dimensional variable minimizing the average running cost by using the cost analysis result
And (4) value taking, calculating the battery capacity reversely, and obtaining the optimal energy management strategy of deep reinforcement learning corresponding to the scheme for real-time control.
Further, step S1 specifically includes the following steps:
based on a specific vehicle model, determining the rated power of the fuel cell according to the steady-state high-power output requirement when the passenger vehicle is cruising at the highest speed,
mass of the whole vehicle
Correlation;
according to the peak power demand under the characteristic working condition cycle
Maximum vehicle speed required power
Maximum required power for climbing slope
Maximum acceleration required power
Determining the maximum total power standard value of the power source
Wherein, in the step (A),
are the mass of the whole vehicle
A function of (a);
maximum power of hybrid power system
The sum of the rated power of the fuel cell and the maximum charging and discharging power of the power cell; maximum power of hybrid power system
Per unit value of
Using the maximum total power standard value
Is a standard;
optimizing tensor space by three-dimensional variables using multi-dimensional parameter matching
And (4) forming. And establishing an alternative parameter matching scheme in the optimized tensor space according to the constraint range of each variable.
Step S2, specifically comprising the following steps:
dynamically optimizing a power distribution strategy according to characteristic working conditions based on a deep reinforcement learning algorithm aiming at each alternative parameter matching scheme; the three indexes of hydrogen consumption, fuel cell operation loss cost and power cell operation loss cost under a certain working condition are minimized as optimization targets, in order to coordinate different optimization targets, the minimum system operation cost is realized, and all indexes are quantized into operation cost to be considered comprehensively;
the equivalent hydrogen consumption cost is calculated according to the hydrogen price, the hydrogen consumption of the fuel cell and the equivalent hydrogen consumption model of the power cell. Calculating the running loss cost of the fuel cell according to the cost of the fuel cell and a decline model;
the fuel cell degradation model can quantify the degradation degree in real time according to the operation condition of the fuel cell;
the running loss cost of the power battery is calculated according to the initial investment cost of the power battery and a regression model of the power battery, wherein the initial investment cost of the power battery is a function of a three-dimensional variable of a parameter matching scheme; the power battery recession model can quantify the recession degree in real time according to the battery running condition;
and combining the index quantized values of the three aspects into a reinforcement learning reward function according to a certain weight coefficient.
The parameter matching optimization method for the fuel cell super-capacitor hybrid power locomotive provided by the invention has the beneficial effects that:
1. the invention carries out real-time quantitative consideration on the equivalent hydrogen consumption cost, the fuel cell operation aging loss cost and the power cell operation aging loss cost in the fuel cell energy management strategy optimization problem for the first time, and the optimization target covers comprehensively;
2. the invention can be suitable for various working conditions and occasions, and is not limited to passenger cars;
3. the invention simultaneously provides an optimal parameter matching result and a corresponding energy management strategy, and can realize real-time application;
4. the energy management strategy optimization method based on the deep reinforcement learning algorithm is used, and compared with the traditional global optimization algorithm, the method can reduce the calculation time and improve the efficiency.
Drawings
FIG. 1 is a flow chart of the method of the present invention;
FIG. 2 is a graph of output power versus hydrogen consumption efficiency for a fuel cell system according to the present invention;
FIG. 3 is a schematic diagram of a multi-dimensional parameter matching optimization tensor space and different three-dimensional optimization variables of the present invention;
FIG. 4 is a graph of an embodiment;
FIG. 5 is a fuel cell power distribution under an energy management strategy optimized by a deep reinforcement learning algorithm according to an embodiment;
fig. 6 is a fuel cell power distribution without using the energy management strategy of the present invention.
Detailed Description
The specific technical scheme of the invention is described by combining the embodiment.
The process of the invention is shown in figure 1 and comprises the following steps:
s1, the invention fully considers the dynamic characteristics of the fuel battery and the lithium battery, sets up the multi-dimensional parameter matching optimization tensor space, as shown in figure 2, respectivelyBy threshold of variation of output power of fuel cell
Per unit value of maximum power of hybrid power system
And maximum charge-discharge rate of the battery
In order to optimize variables, the method carries out deep and detailed parameter matching optimization of the hybrid power system, and comprises the following specific steps:
based on a specific vehicle model, determining the rated power of the fuel cell according to the steady-state high-power output requirement when the passenger vehicle is cruising at the highest speed,
mass of the whole vehicle
And (4) correlating.
According to the peak power demand under the characteristic working condition cycle
Maximum vehicle speed required power
Maximum required power for climbing slope
Maximum acceleration required power
Determining the maximum total power standard value of the power source
Wherein, in the step (A),
are the mass of the whole vehicle
As a function of (c).
Maximum power of hybrid power system
The sum of the rated power of the fuel cell and the maximum charging and discharging power of the power cell. Maximum power of hybrid power system
Per unit value of
Maximum total power standard value
Is a standard.
Optimizing tensor space by three-dimensional variables using multi-dimensional parameter matching
And (4) forming. And establishing an alternative parameter matching scheme in the optimized tensor space according to the constraint range of each variable.
S2, optimizing each three-dimensional variable in tensor space by multi-dimensional parameter matching
Namely the alternative parameter matching scheme to be evaluated. And dynamically optimizing a power distribution strategy according to the characteristic working condition based on a deep reinforcement learning algorithm aiming at each alternative parameter matching scheme. The method takes three indexes of minimizing hydrogen consumption, fuel cell operation loss cost and power cell operation loss cost under a certain working condition as optimization targets, and in order to coordinate different optimization targets and realize the minimum system operation cost, all indexes are quantized into operation cost to be considered comprehensively.
The equivalent hydrogen consumption cost is calculated according to the hydrogen price, the hydrogen consumption of the fuel cell and the equivalent hydrogen consumption model of the power cell. The fuel cell operation loss cost is calculated according to the fuel cell cost and the decline model.
The fuel cell degradation model can quantify the degradation degree in real time according to the operation condition (operation condition, working temperature and other factors) of the fuel cell.
The running loss cost of the power battery is calculated according to the initial investment cost of the power battery and a regression model of the power battery, wherein the initial investment cost of the power battery is a function of a three-dimensional variable of a parameter matching scheme; the power battery recession model can quantify the recession degree in real time according to the battery operation conditions (operation conditions, working temperature and other factors).
In the invention, in order to reflect a plurality of optimization targets, index quantized values in three aspects are combined into a reinforcement learning reward function according to a certain weight coefficient.
And S3, optimizing a power distribution strategy for each alternative scheme aiming at the characteristic working condition based on a deep reinforcement learning algorithm.
S4, according to the optimal power distribution result generated by the energy management strategy based on the reinforcement learning algorithm according to each alternative scheme, quantifying the average running cost of the fuel cell passenger car under the characteristic working condition: the sum of the equivalent hydrogen consumption cost, the fuel cell operating loss cost and the power cell operating loss cost.
Fig. 3 is a graph of output power versus hydrogen consumption efficiency of the fuel cell system.
Obtaining the optimal parameter matching scheme three-dimensional variable minimizing the average running cost by using the cost analysis result
And (4) value taking, calculating the battery capacity (Ah) reversely, and obtaining the optimal energy management strategy of deep reinforcement learning corresponding to the scheme for real-time control.
The embodiment is applied to a certain passenger car with the designed car weight of 14000kg, and the speed and acceleration curve of the working condition is shown in fig. 4 when the passenger car runs under a certain characteristic working condition. And training an energy management strategy based on a reinforcement learning algorithm aiming at optimization variables synthesized by power parameters such as variable quantity thresholds of output power of different fuel cells, per unit values of maximum power of a hybrid power system, maximum charge-discharge rate of the cells and the like.
And carrying out power distribution simulation tests under different optimization variables, quantifying equivalent hydrogen consumption, operation degradation cost of the fuel cell and operation degradation cost of the lithium battery of each scheme, and calculating the total operation cost. According to calculation, the lowest total operation cost can be realized when the output power variation threshold of the fuel cell is 10kw/s, the maximum power per unit value of the hybrid power system is 1.3, and the maximum charge-discharge rate of the battery is 5 according to the parameter matching scheme aiming at the vehicle type and the characteristic working condition.
As shown in fig. 5 and fig. 6, under the matching parameters, compared with a system without the present invention, the operating efficiency of the fuel cell is more concentrated in the high-efficiency region, the actual equivalent hydrogen consumption of the fuel cell/lithium battery hybrid system is greatly reduced, the aging of the fuel cell and the lithium battery is also suppressed, and the service life is prolonged.
Claims (3)
1. The parameter matching method of the fuel cell lithium battery hybrid power system is characterized by comprising the following steps of:
s1, setting up a multi-dimensional parameter matching optimization tensor space by combining respective dynamic characteristics of the fuel cell and the lithium battery, and respectively using the output power variation threshold of the fuel cell
Per unit value of maximum power of hybrid power system
And the maximum charge-discharge rate of the lithium battery
Performing parameter matching optimization on the hybrid power system for optimizing variables;
s2, optimizing each three-dimensional variable in tensor space by multi-dimensional parameter matching
Namely, the alternative parameter matching scheme to be evaluated;
s3, optimizing a power distribution strategy for each alternative scheme aiming at characteristic working conditions based on a deep reinforcement learning algorithm;
s4, according to the optimal power distribution result generated by the energy management strategy based on the reinforcement learning algorithm according to each alternative scheme, quantifying the average running cost of the fuel cell passenger car under the characteristic working condition: the sum of the equivalent hydrogen consumption cost, the fuel cell operation loss cost and the lithium battery operation loss cost;
obtaining the optimal parameter matching scheme three-dimensional variable minimizing the average running cost by using the cost analysis result
And (4) value taking, calculating the capacity of the lithium battery reversely, and obtaining the optimal energy management strategy of deep reinforcement learning corresponding to the scheme for real-time control.
2. The parameter matching method for the fuel cell lithium battery hybrid power system as claimed in claim 1, wherein the step S1 specifically comprises the following steps:
based on specific vehicle type, according to the steady-state high-power output requirement of the passenger vehicle during the maximum speed cruise
The rated power of the fuel cell is determined,
mass of the whole vehicle
Correlation;
according to the peak power demand under the characteristic working condition cycle
Maximum vehicle speed required power
Maximum required power for climbing slope
Maximum acceleration required power
Determining the maximum total power standard value of the power source
Wherein, in the step (A),
,
,
,
are the mass of the whole vehicle
A function of (a);
maximum power of hybrid power system
The sum of the rated power of the fuel cell and the maximum charge-discharge power of the lithium battery; maximum power of hybrid power system
Per unit value of
Using the maximum total power standard value
Is a standard;
optimizing tensor space by three-dimensional variables using multi-dimensional parameter matching
Composition is carried out; and establishing an alternative parameter matching scheme in the optimized tensor space according to the constraint range of each variable.
3. The parameter matching method for the fuel cell lithium battery hybrid power system as claimed in claim 1, wherein the step S2 specifically comprises the following steps:
dynamically optimizing a power distribution strategy according to characteristic working conditions based on a deep reinforcement learning algorithm aiming at each alternative parameter matching scheme; the method comprises the following steps of minimizing three indexes of hydrogen consumption, fuel cell operation loss cost and lithium cell operation loss cost under a certain working condition as optimization targets, and in order to coordinate different optimization targets and realize the minimum system operation cost, quantifying each index into operation cost and taking overall consideration;
the equivalent hydrogen consumption cost is calculated according to the hydrogen price, the hydrogen consumption of the fuel cell and the equivalent hydrogen consumption model of the lithium battery; calculating the running loss cost of the fuel cell according to the cost of the fuel cell and a decline model;
the fuel cell degradation model can quantify the degradation degree in real time according to the operation condition of the fuel cell;
the lithium battery operation loss cost is calculated according to the initial investment cost of the lithium battery and a lithium battery recession model, wherein the initial investment cost of the lithium battery is a function of a three-dimensional variable of a parameter matching scheme; the lithium battery recession model can quantify the recession degree in real time according to the battery running condition;
and combining the index quantized values of the three aspects into a reinforcement learning reward function according to a certain weight coefficient.
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| CN113682203B (en) * | 2021-09-23 | 2023-02-07 | 西南交通大学 | Energy regulation and control method based on full life cycle state of fuel cell tramcar |
| CN114030392B (en) * | 2021-09-28 | 2023-05-09 | 西北工业大学 | Efficiency optimization energy management strategy for fuel cell-lithium battery hybrid power supply system |
| CN114889498B (en) * | 2022-05-07 | 2023-12-15 | 苏州市华昌能源科技有限公司 | Power optimization distribution method of hydrogen-electricity hybrid power system |
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