Vehicle hybrid power system, energy management method thereof, medium and vehicle

Abstract

The invention relates to the technical field of fuel cells, and discloses a vehicle hybrid power system and an energy management method thereof, a medium and a vehicle, the vehicle hybrid power system is composed of a fuel cell and a power cell, the fuel cell and the power cell simultaneously generate electric energy to drive a motor, and the power cell plays a role in supplementing and recycling the electric energy. According to the method, the fuel cell works in a high-efficiency interval and a long-life interval as much as possible, multi-target optimization is performed on the efficiency and the life of the fuel cell based on a multi-target particle swarm optimization algorithm, and the fuel cell works at a high-efficiency and long-life comprehensive optimization working point.

Description

Technical Field

[0001] The present invention relates to the technical field of fuel cells, and in particular to a vehicle fuel cell power battery hybrid power system and an energy management method, medium and vehicle thereof. Background Art

[0002] With the development of the hydrogen energy industry, fuel cell vehicles, as a key area of ​​hydrogen energy application, provide an important direction for the transformation and development of my country's automobile industry. Fuel cell vehicles use hydrogen as the main power source and have the following advantages: 1) Zero or near zero carbon emissions, no CO, CO2, NO x 1) It uses hydrogen as the power source, and its driving range and charging speed are better than pure electric vehicles. 2) It avoids the constraints of the Carnot cycle and has better energy conversion efficiency and output efficiency.

[0003] The fuel cell system supplies energy through electrochemical reactions. The control and regulation of its air and hydrogen supply systems are highly nonlinear, and the response time of mechanical moving parts such as air compressors is long. When the vehicle is driving under complex driving conditions, the power output of the power system often changes greatly. Therefore, vehicles using fuel cells as the sole power source often have shortcomings such as poor dynamic response, poor power performance, and inability to recover braking energy. Frequent changes in power can easily cause damage to the fuel cell and affect the life of the fuel cell. Therefore, in order to improve the dynamic performance and economic performance of the system, fuel cell vehicles are usually hybrid systems, and power batteries (PB) or supercapacitors (SC) are often used as energy storage devices.

[0004] Fuel cells generate electricity to drive motors through electrochemical reactions between hydrogen and air. Power batteries can be charged by external power sources and can be used to drive motors alone or to assist fuel cells in driving motors. When the power battery's state of charge (SOC) is high, the power battery is used to drive the motor first; when the SOC is low, the fuel cell is mainly used to drive the motor, with the power battery as an auxiliary. Fuel cells generate electricity to drive motors. When the required power increases, the power battery also provides electricity to drive the motor to ensure the vehicle's response speed. When the vehicle brakes, the power battery can recover the energy of the vehicle's braking; when the power battery's SOC is below the limit, the fuel cell can provide electricity for the power battery.

[0005] For a vehicle fuel cell / power battery hybrid power system, the key is how to reasonably allocate the power and efficiency of each power device to improve the performance and efficiency of the vehicle, reduce performance degradation, and extend the service life of the fuel cell. For example, a Chinese invention patent application with publication number CN117022058A discloses an adaptive energy management method, device, medium and equipment, wherein the method includes: obtaining a high-efficiency interval sent by a fuel cell controller, and adaptively allocating the output power of the power battery system and the fuel cell system according to the high-efficiency interval; wherein, when the fuel cell controller detects that the characteristic curve of the fuel cell stack has changed, the high-efficiency interval of the fuel cell system is recalculated, and the high-efficiency interval is the power output interval corresponding to the efficiency in the characteristic curve when it is higher than the first threshold. Although the invention application can adaptively allocate the output power of the fuel cell system and the power battery system according to the high-efficiency interval, when the high-efficiency interval of the fuel cell is not necessarily a high-life interval, it is difficult to achieve both high efficiency and long life. Summary of the invention

[0006] The purpose of the present invention is to provide a high-efficiency and long-life vehicle hybrid power system energy management method, in order to solve one or more technical problems existing in the prior art, and at least provide a beneficial option or create conditions.

[0007] To achieve the above objectives, the present invention adopts the following technical solutions.

[0008] A method for managing an energy management of a hybrid vehicle system is used to manage a hybrid vehicle system having a fuel cell and a power battery in parallel output, and comprises the following steps: 1) pre-calibrating the efficiency of the fuel cell according to the fuel cell efficiency curve to confirm the high energy conversion efficiency range of the fuel cell; 2) pre-calibrating the life characteristics of the fuel cell to confirm the high life range of the fuel cell; 3) pre-setting the efficiency and life priority of the fuel cell, first determining the working point position according to the priority, and then dynamically adjusting the comprehensive optimization working point of the fuel cell according to the current density, flow, pressure and temperature of the fuel cell when the fuel cell is working; 4) during the operation of the vehicle, making the working point of the fuel cell fall at the comprehensive optimization working point.

[0009] More preferably, the comprehensive optimization working point is obtained based on a multi-objective particle swarm optimization algorithm, which performs multi-objective optimization on the efficiency and life of the fuel cell, finds the optimal solution in the Pareto solution set, and thus makes the fuel cell work at a comprehensive optimization working point of high efficiency and long life.

[0010] More preferably, if the fuel cell cannot meet the power required by the vehicle at the comprehensive optimization working point, the power required is supplemented by the power battery.

[0011] More preferably, when the power battery cannot supplement the power required by the system, the output power of the fuel cell is increased, and the comprehensive optimization working point is determined and dynamically adjusted again.

[0012] More preferably, if the power of the fuel cell at the comprehensive optimal working point exceeds the power required by the system, the power battery is charged.

[0013] More preferably, the method for determining the comprehensive optimization working point is: 1) setting the number of iterations T, the number of population POP particles n, and initializing the position information X of each particle i =[X iI ,X iF ,X ip ,X iT ], the velocity information V of each particle i =[V iI ,V iF ,V ip ,V iT ]; 2) Calculate the objective function value of each particle , ; 3) According to the Pareto dominance relationship, select the non-inferior solution set and put it into the external archive set REP; 4) In the external archive set REP, use the grid division method and each target value of each individual to establish a hypercube coordinate system, and the coordinates of each individual in the archive set REP can be obtained accordingly; 5) Initialize the optimal P of each individual in the first generation BEST (i) Position, P BEST (i) = POP (i), in the external archive set REP, select the global optimal particle REP (h) according to the coordinates of the individual in the hypercube coordinate system; 6) according to the optimal P of each particle BEST (i) and the global optimal particle REP (h), using the formula + Update the speed of each particle; 7) Use the formula Update the position of each particle; 8) Calculate the objective function value of each particle, and select the contemporary non-inferior solution set NOD to update REP; 9) Select the global optimal individual REP (h) from the updated external archive set REP; 10) Repeat steps 6)-9), when t is equal to T, output the current REP (h), which is the optimal operating point that comprehensively considers the efficiency and life of the fuel cell.

[0014] Among them, T and n are natural numbers, X i represents the position of the ith particle, V i represents the velocity of the ith particle; represents the output power function of the fuel cell system, Represents the output power function of the battery stack, , Represents the life function of the battery, I, F, P, T are the stack current density, the stack cathode and anode air intake flow, the stack cathode and anode air intake pressure, and the stack operating temperature, respectively.

[0015] Where w is the inertia weight coefficient, c1 and c2 are acceleration factors greater than zero, and r1 and r2 are random numbers between [0,1]; and are the speed and position information of each particle at iteration t times; and are the velocity and position information of each particle at iteration t-1 respectively; is the iteration step length.

[0016] More preferably, the energy management method of a vehicle hybrid power system also includes a step of judging the power battery power level. When the power battery power level is higher than a first threshold, the power battery is used for driving; when the power battery power level is lower than a second threshold, the fuel cell is used for driving preferentially, and the power battery is used as an auxiliary. The first threshold is greater than the second threshold, and the specific values ​​of the first threshold and the second threshold can be determined through experience or experiments.

[0017] On the other hand, the present invention also provides a hybrid vehicle system, which includes: a power battery; a fuel cell; a memory, wherein the memory stores a program or an instruction; and a processor, wherein the processor is respectively connected to the memory, the power battery and the fuel cell, and when the processor executes the program or the instruction, it implements the energy management method of the hybrid vehicle system as described above.

[0018] On the other hand, the present invention further provides a vehicle, comprising: a load, and the vehicle hybrid power system as described above, wherein the vehicle hybrid power system is connected to the load, and the vehicle hybrid power system is used to supply energy to the load.

[0019] On the other hand, the present invention further provides a readable storage medium having a program or instruction stored thereon, wherein when the program or instruction is executed by a processor, the energy management method of a hybrid vehicle system as described above is executed.

[0020] Compared with the prior art, the present invention has at least the following beneficial effects.

[0021] The energy management method of the vehicle hybrid power system of the present invention fully considers the efficiency and life of the fuel cell, improves the energy conversion efficiency while taking into account the life of the fuel cell, extends the service life of the power system, and also improves the dynamic response and power performance of the vehicle, which is beneficial to the use of power batteries to recover kinetic energy and improves energy utilization. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 Schematic diagram of a fuel cell / power battery hybrid power system for a vehicle in an embodiment of the present invention.

[0023] Figure 2 Schematic diagram of the energy management strategy of a vehicle fuel cell / power battery hybrid power system in an embodiment of the present invention.

[0024] Figure 3 This is a control algorithm logic diagram of a vehicle fuel cell / power battery hybrid power system in an embodiment of the present invention. DETAILED DESCRIPTION

[0025] The following is a further description of the specific implementation of the present invention in conjunction with the drawings of the specification, so that the technical solution and its beneficial effects of the present invention are clearer and more explicit. The following description of the embodiments with reference to the drawings is exemplary and intended to explain the present invention, but cannot be understood as limiting the present invention.

[0026] Additional aspects and advantages of the present invention will become apparent from the following description or may be learned by practice of the present invention. Example

[0027] Figure 1 This is a schematic diagram of the structure of a fuel cell / power battery hybrid system for a vehicle, including a fuel cell and a power battery and their control methods and strategies. The fuel cell generates electricity through an electrochemical reaction between hydrogen and air to drive the motor. The power battery can be charged by an external power source and can be used to drive the motor alone or to assist the fuel cell in driving the motor together.

[0028] The output power of the fuel cell stack, the consumption of the BOP system and the life of the fuel cell are all nonlinearly related to the current density, flow rate, pressure and temperature. That is, the output power of the system is Output SYS =f1(I,F,P,T), the output power of the battery stack is Output FC =f2(I,F,P,T), the battery life is Life=f3(I,F,P,T), ; Among them, I, F, P, and T are the stack current density, the stack cathode and anode air intake flow, the stack cathode and anode air intake pressure, and the stack operating temperature, respectively, and are the decision variables in this optimization control.

[0029] When the above-mentioned fuel cell / power battery hybrid vehicle is in operation, its working principle is as follows.

[0030] In addition to meeting the power requirements for vehicle operation, energy conversion efficiency must be considered. According to the fuel cell efficiency curve, the fuel cell is calibrated in advance to find the range with high energy conversion efficiency. During vehicle operation, the working condition of the fuel cell is kept in the range with high fuel cell efficiency, about 50%, while the power battery itself has high efficiency, greater than 90%. The control optimization goal is: .

[0031]

[0032] .

[0033] Wherein, cons is the target output power of the fuel cell system, and x is the preset threshold value of allowable power fluctuation.

[0034] Furthermore, if the fuel cell cannot meet the power required by the vehicle when operating in the high-efficiency range, the power battery will make up for the required power to allow the fuel cell to operate in the high-efficiency range as much as possible.

[0035] Furthermore, while ensuring the power and efficiency of the fuel cell operation, the life of the fuel cell is further considered. The life characteristics of the fuel cell are calibrated in advance to confirm the high life area of ​​the fuel cell material and design. In this area, the fuel cell performance degradation rate is relatively slow. The fuel cell operating point is kept in the high life area as much as possible to extend the service life of the fuel cell. That is, the control optimization target is: .

[0036] For a fuel cell system, the high efficiency range and the high life range do not necessarily overlap, so the priority of efficiency and life is preset according to actual needs (the priority can be determined based on experience or experiments) to determine the appropriate position of the working point. When the fuel cell is working, the fuel cell working point is adjusted accordingly according to the working conditions to achieve dynamic adjustment of the fuel cell working point. That is, the joint optimization goal of the control is: .

[0037]

[0038] .

[0039] like Figure 3 As shown in the figure, taking into account various constraints, the fuel cell efficiency and life are optimized based on the multi-objective particle swarm optimization algorithm, and the optimal solution is found in the Pareto solution set, so that the fuel cell works at the optimal working point of high efficiency and long life.

[0040] The specific algorithm is as follows: 1) Set the number of iterations T, the number of POP particles in the population n, and initialize the position information X of each particle i =[XiI ,X iF ,X ip ,X iT ], the velocity information V of each particle i =[V iI ,V iF ,V ip ,V iT ]; 2) Calculate the objective function value of each particle , ; 3) According to the Pareto dominance relationship, select the non-inferior solution set and put it into the external archive set REP; 4) In the external archive set REP, use the grid division method and each target value of each individual to establish a hypercube coordinate system, and the coordinates of each individual in the archive set REP can be obtained accordingly; 5) Initialize the optimal P of each individual in the first generation BEST (i) Position, P BEST (i) = POP (i), in the external archive set REP, select the global optimal particle REP (h) according to the coordinates of the individual in the hypercube coordinate system; 6) according to the optimal P of each particle BEST (i) and the global optimal particle REP (h), using the formula + Update the speed of each particle; 7) Use the formula Update the position of each particle; where w is the inertia weight coefficient, c1 and c2 are acceleration factors greater than zero, and r1 and r2 are random numbers between [0,1]; and are the speed and position information of each particle at iteration t times; and are the velocity and position information of each particle at iteration t-1 respectively; is the iteration step; 8) calculate the objective function value of each particle, and select the contemporary non-inferior solution set NOD to update REP; 9) select the global optimal individual REP (h) from the updated external archive set REP; 10) repeat steps 6)-9), when t is equal to T, output the current REP (h), which is the optimal operating point that comprehensively considers the efficiency and life of the original fuel cell, and calculate the efficiency and life at this time.

[0041] Furthermore, if the fuel cell cannot meet the power demand when working at this working point, it will be supplemented by the power battery. For example, when the required fuel cell power is 60kW, and the optimal working point obtained by optimization is 58kW, the insufficient 2kW is caused by the power battery. When the power battery still cannot meet the power required by the system, it is necessary to increase the output power of the fuel cell and perform the next round of dynamic adjustment optimization of the working point. For example, if the optimal fuel cell power in the optimal life and power range is 62kW, the extra 2kW will be used to supplement the power of the power battery.

[0042] Furthermore, the energy management method of a vehicle hybrid power system also includes a power battery power judgment step. When the power battery power is higher than a first threshold, the power battery is used for driving; when the power battery power is lower than a second threshold, the fuel cell is used for driving first, and the power battery is used as an auxiliary. The first threshold is greater than the second threshold, and the specific values ​​of the first threshold and the second threshold can be determined through experience or experiments.

[0043] It should also be noted that in the description of this specification, any process or method description in the flowchart or otherwise described herein may be understood to represent a module, fragment or portion of a code comprising one or more executable instructions for implementing steps of a specific logical function or process, and the scope of the preferred embodiments of the present invention includes alternative implementations, in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present invention belong.

[0044] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute the instructions), or in combination with these instruction execution systems, devices or apparatuses. For the purpose of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in combination with these instruction execution systems, devices or apparatuses. More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk box (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium and then editing, interpreting or processing in other suitable ways if necessary, and then stored in a computer memory.

[0045] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0046] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0047] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0048] Although the embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and are not to be construed as limitations of the present invention. A person skilled in the art may change, modify, replace and vary the above embodiments within the scope of the present invention.

Claims

1. An energy management method for a vehicle hybrid power system, used to manage a vehicle hybrid power system having a fuel cell and a power battery in parallel output, characterized in that: The following steps are involved: 1) According to the fuel cell efficiency curve, the efficiency of the fuel cell is calibrated in advance to confirm the high energy conversion efficiency range of the fuel cell; 2) Pre-calibrate the life characteristics of the fuel cell and confirm the high life range of the fuel cell; 3) Preset the efficiency and life priority of the fuel cell, first determine the working point position according to the priority, and then dynamically adjust the comprehensive optimization working point of the fuel cell according to the current density, flow, pressure and temperature of the fuel cell during operation; 4) During vehicle operation, the operating point of the fuel cell is set to the comprehensive optimized operating point.

2. The energy management method of a hybrid power system for a vehicle according to claim 1, characterized in that: The comprehensive optimization working point is obtained based on a multi-objective particle swarm optimization algorithm, which performs multi-objective optimization on the efficiency and life of the fuel cell, finds the optimal solution in the Pareto solution set, and then makes the fuel cell work at a comprehensive optimization working point with high efficiency and long life.

3. The energy management method of a hybrid power system for a vehicle according to claim 1 or 2, characterized in that: If the fuel cell cannot meet the power required by the vehicle at the comprehensive optimization working point, the power battery will make up the required power.

4. The energy management method of a hybrid power system for a vehicle according to claim 3, characterized in that: When the power battery cannot make up for the power required by the system, the output power of the fuel cell is increased, and the comprehensive optimization working point is determined and dynamically adjusted again.

5. The energy management method of a hybrid power system for a vehicle according to claim 1 or 2, characterized in that: If the power of the fuel cell at the comprehensive optimization working point exceeds the power required by the system, the power battery is charged.

6. The energy management method of a hybrid power system for a vehicle according to claim 1, characterized in that: The method for determining the comprehensive optimization working point is: 1) Set the number of iterations T, the number of POP particles in the population n, and initialize the position information X of each particle i =[X iI ,X iF ,X ip ,X iT ], the velocity information V of each particle i =[V iI ,V iF ,V ip ,V iT ]; 2) Calculate the objective function value of each particle , ; 3) According to the Pareto dominance relationship, select the non-inferior solution set and put it into the external archive set REP; 4) In the external archive set REP, a hypercube coordinate system is established with each target value of each individual using the grid division method, and the coordinates of each individual in the archive set REP can be obtained accordingly; 5) Initialize the optimal P of each individual in the first generation BEST (i) Position, P BEST (i) = POP(i), in the external archive set REP, the global optimal particle REP(h) is selected according to the coordinates of the individual in the hypercube coordinate system; 6) According to the optimal P of each particle BEST (i) and the global optimal particle REP (h), using the formula + Update each particle's velocity; Using the formula Update the position of each particle; Where w is the inertia weight coefficient, c1 and c2 are acceleration factors greater than zero, and r1 and r2 are random numbers between [0,1]; and are the speed and position information of each particle at iteration t times; and are the velocity and position information of each particle at iteration t-1 respectively; is the iteration step length; 8) Calculate the objective function value of each particle and select the contemporary non-inferior solution set NOD to update REP; 9) Select the global optimal individual REP (h) from the updated external archive set REP; 10) Repeat steps 6)-9), when t is equal to T, output the current REP (h), which is the optimal operating point considering the fuel cell efficiency and life; Among them, T and n are natural numbers, X i represents the position of the ith particle, V i represents the velocity of the ith particle; represents the output power function of the fuel cell system, Represents the output power function of the battery stack, , Represents the life function of the battery, I, F, P, T are the stack current density, the stack cathode and anode air intake flow, the stack cathode and anode air intake pressure, and the stack operating temperature, respectively.

7. The energy management method of a hybrid power system for a vehicle according to claim 1, characterized in that: The method includes a power battery power judgment step. When the power battery power is higher than a first threshold, the power battery is used for driving. When the power battery power is lower than a second threshold, the fuel cell is used for driving first, and the power battery is used as an auxiliary. The first threshold is greater than the second threshold.

8. A hybrid power system for a vehicle, characterized in that: include: Power battery; Fuel cells; A memory storing programs or instructions; A processor, wherein the processor is connected to the memory, the power battery and the fuel cell respectively, and when the processor executes the program or the instruction, an energy management method for a hybrid vehicle system as described in any one of claims 1 to 7 is implemented.

9. A vehicle, characterized in that: include: load; The vehicle hybrid power system as claimed in claim 8, wherein the vehicle hybrid power system is connected to the load, and the vehicle hybrid power system is used to supply energy to the load.

10. A readable storage medium having a program or instruction stored thereon, characterized in that: When the program or the instruction is executed by the processor, an energy management method for a hybrid vehicle system according to any one of claims 1 to 7 is performed.

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