Battery system energy management strategy and system

Abstract

The embodiment of the invention provides a battery system energy management strategy and system, and relates to the technical field of battery energy management. The management strategy comprises the following steps: acquiring initial output power of a fuel cell, a lithium battery and a super capacitor; and obtaining the optimal output power of the fuel cell, the lithium battery and the super capacitor by adopting a particle swarm algorithm. According to the management strategy and system, a fuel cell, a super capacitor and a lithium battery are adopted for combined power supply, the advantages of the three power sources are fully played, fuel decline is limited by taking the minimum equivalent hydrogen consumption of the system and the fuel decline rate as targets, distribution of output power of the fuel cell and the auxiliary energy storage system is solved by using a particle swarm optimization algorithm, and the system reliability is improved. The advantages of the fuel cell, the super capacitor and the lithium battery are brought into full play, fuel cell recession and the influence of the fuel cell recession on the cost are considered, the service life of the fuel cell is prolonged, and the system economy is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of battery energy management, and in particular to a battery system energy management strategy and system. Background Art

[0002] Fuel cells, as the energy source of fuel cell vehicles, have insufficient instantaneous dynamic performance and need to be powered by auxiliary power sources. Common auxiliary power sources include power batteries and supercapacitors. In the prior art, fuel cells and power batteries are generally used for power supply, or fuel cells and supercapacitors are used for power supply. However, power batteries have the problem of limited instantaneous output power, and supercapacitors have the problem of low energy density, and neither can give full play to their respective advantages. Summary of the invention

[0003] The purpose of the embodiment of the present invention is to provide a battery system energy management strategy and system, which adopts a fuel cell, a super capacitor and a lithium battery for combined power supply, fully utilizes the advantages of the three power sources, takes the minimum system equivalent hydrogen consumption and the fuel decay rate as the goal, limits the fuel decay, uses a particle swarm optimization algorithm to solve the output power distribution of the fuel cell and the auxiliary energy storage system, fully utilizes the advantages of the three power sources of fuel cells, super capacitors and lithium batteries, takes into account the decay of the fuel cell and its impact on the cost, and improves the fuel cell life and system economy.

[0004] In order to achieve the above object, the present invention provides a battery system energy management strategy on one hand, the management strategy comprising:

[0005] Obtain the initial output power of fuel cells, lithium batteries and supercapacitors;

[0006] The particle swarm algorithm is used to obtain the optimal output power of the fuel cell, lithium battery and supercapacitor, wherein the particle swarm algorithm includes:

[0007] Obtain the instantaneous hydrogen consumption operating cost of the system of the current particle and the instantaneous operating cost of the fuel cell degradation;

[0008] According to formula (1), the instantaneous objective function of the current particle is obtained:

[0009] J(k)=min(J sys_H2 (k)+J life (k)), (1)

[0010] Among them, J(k) is the objective function, J sys_H2 (k) is the instantaneous hydrogen consumption operating cost of the system, J life (k) is the instantaneous operating cost of the fuel cell degradation.

[0011] Optionally, a particle swarm algorithm is used to obtain the optimal output power of the fuel cell, lithium battery and supercapacitor, including:

[0012] Initialize the maximum number of iterations and initial parameters of the particle swarm algorithm;

[0013] Calculate the fitness function value according to the initial parameters;

[0014] Determine whether the current fitness function value is less than or equal to the current single particle objective function value;

[0015] When it is determined that the current fitness function value is less than or equal to the objective function value of the current single particle, retaining the particle position of the previous stage;

[0016] When it is determined that the current fitness function value is greater than the objective function value of the current single particle, the current particle position is iterated.

[0017] Optionally, using a particle swarm algorithm to obtain the optimal output power of the fuel cell, lithium battery and supercapacitor also includes:

[0018] Determine whether the particle position iteration is completed;

[0019] In the case where it is determined that the particle position iteration is not completed, returning to the step of determining whether the current fitness function value is less than or equal to the objective function value of the current single particle;

[0020] When it is determined that the particle position iteration is completed, determining whether the objective function value converges during the iteration process;

[0021] When it is determined that the objective function value has not converged during the iteration process, returning to the step of initializing the maximum number of iterations and initial parameters of the particle swarm algorithm;

[0022] When it is determined that the objective function value converges during the iteration process, the output powers of the fuel cell, the lithium battery and the supercapacitor are output as the optimal output power.

[0023] Optionally, obtaining the instantaneous hydrogen consumption operating cost of the current particle system and the instantaneous operating cost of fuel cell degradation includes:

[0024] According to formula (2), the instantaneous hydrogen consumption operating cost of the system is determined:

[0025] J sys_H2 (k) = m sys (k)p H2 , (2)

[0026] Among them, J sys_H2 (k) is the instantaneous hydrogen consumption operating cost of the system, msys (k) is the instantaneous equivalent hydrogen consumption of the three-source hybrid system calculated for the kth time, p H2 is the unit price of hydrogen per kilogram.

[0027] Optionally, obtaining the instantaneous hydrogen consumption operating cost of the current particle system and the instantaneous operating cost of fuel cell degradation also includes:

[0028] The instantaneous operating cost of the fuel cell degradation is determined according to formula (3):

[0029]

[0030] Among them, J life (k) is the instantaneous operating cost of the fuel cell degradation, D loss_end is the total loss at the end of the fuel cell life, D loss (k) is the fuel cell degradation rate calculated for the kth time, P fc_rate is the fuel cell rated power.

[0031] Optionally, calculating the fitness function value includes:

[0032] According to formula (4), the fitness function value is calculated:

[0033]

[0034] Among them, J is the fitness function value, x(t) is the state variable, u(t) is the decision variable, t 0 is the initial time, t 1 is the end time, and t is the current time.

[0035] Optionally, iterating the current particle position comprises:

[0036] Update the particle position according to formula (5) and formula (6),

[0037]

[0038] in, is the particle velocity of particle i in the k+1th iteration, w is the inertia weight, is the particle velocity of particle i during the kth iteration, is the particle position of particle i in the kth iteration, is the particle position of particle i in the k+1th iteration, c 1 is the cognitive learning factor, c 2 is the social learning factor, r 1 、r 2 is a random parameter, is the optimal solution of particle i in the kth iteration, xgbestk is the optimal solution of all particles in the kth iteration process, P fc is the supercapacitor output power, P bat Output power for lithium batteries.

[0039] Optionally, iterating the current particle position further includes:

[0040] According to formula (7), the constraints of the iterative current particle position are determined.

[0041]

[0042] Among them, P fcmin is the minimum output power of the supercapacitor, P fcmax is the maximum output power of the supercapacitor, P batmin is the minimum output power of lithium battery, P batmax is the maximum output power of lithium battery, P sc is the supercapacitor output power, P load The vehicle requires power.

[0043] On the other hand, the present invention further provides a battery system energy management system, the management system comprising a processor for executing any of the management strategies described above.

[0044] Through the above technical solution, the present invention provides a battery system energy management strategy and system, which obtains the initial output power of fuel cells, lithium batteries and supercapacitors and uses a particle swarm algorithm to obtain the optimal output power of fuel cells, lithium batteries and supercapacitors. The management strategy and system adopts fuel cells, supercapacitors and lithium batteries for joint power supply, giving full play to the advantages of the three power sources, with the minimum system equivalent hydrogen consumption and fuel decay rate as the goal, restricting fuel decay, using a particle swarm optimization algorithm to solve the distribution of fuel cell and auxiliary energy storage system output power, giving full play to the advantages of the three power sources of fuel cells, supercapacitors and lithium batteries, and considering the decay of fuel cells and their impact on costs, improving the life of fuel cells and system economy.

[0045] Other features and advantages of the embodiments of the present invention will be described in detail in the subsequent detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] The accompanying drawings are used to provide a further understanding of the embodiments of the present invention and constitute a part of the specification. Together with the following specific implementations, they are used to explain the embodiments of the present invention, but do not constitute a limitation on the embodiments of the present invention. In the accompanying drawings:

[0047] Figure 1 is a flow chart of a battery system energy management strategy according to an embodiment of the present invention;

[0048] Figure 2 is a flow chart of obtaining optimal output power according to an embodiment of the present invention;

[0049] Figure 3 is a flow chart of obtaining operating costs according to one embodiment of the present invention;

[0050] Figure 4 is an iterative particle position flow chart of one embodiment of the present invention. DETAILED DESCRIPTION

[0051] The specific implementation of the embodiment of the present invention is described in detail below in conjunction with the accompanying drawings. It should be understood that the specific implementation described here is only used to illustrate and explain the embodiment of the present invention, and is not used to limit the embodiment of the present invention.

[0052] like Figure 1 The figure shows a flow chart of a battery system energy management strategy according to an embodiment of the present invention. Figure 1 In this case, the management strategy may include:

[0053] In step S1, the initial output power of the fuel cell, the lithium battery and the supercapacitor is obtained;

[0054] In step S2, a particle swarm algorithm is used to obtain the optimal output power of the fuel cell, the lithium battery and the supercapacitor, wherein the particle swarm algorithm includes:

[0055] Obtain the instantaneous hydrogen consumption operating cost of the system of the current particle and the instantaneous operating cost of the fuel cell degradation;

[0056] According to formula (1), the instantaneous objective function of the current particle is obtained:

[0057] J(k)=min(J sys_H2 (k)+J life (k)), (1)

[0058] Among them, J(k) is the objective function, J sys_H2 (k) is the instantaneous hydrogen consumption operating cost of the system, J life (k) is the instantaneous operating cost of the fuel cell degradation.

[0059] In this Figure 1In the method shown, step S1 can be used to obtain the initial output power of the fuel cell, lithium battery and super capacitor. Step S2 can be used to obtain the optimal output power of the fuel cell, lithium battery and super capacitor using a particle swarm algorithm, wherein the particle swarm algorithm includes obtaining the instantaneous hydrogen consumption operating cost of the current particle system and the instantaneous operating cost of the fuel cell decay, and obtaining the instantaneous objective function of the current particle according to formula (1). In one example of the present invention, step S2 may include the following: Figure 2 The methods shown, such as Figure 2 is a flowchart of obtaining the optimal output power according to an embodiment of the present invention. Figure 2 In the step S2, the step S2 may further include the following steps:

[0060] In step S10, the maximum number of iterations and initial parameters of the particle swarm algorithm are initialized;

[0061] In step S11, the fitness function value is calculated according to the initial parameters;

[0062] In step S12, it is determined whether the current fitness function value is less than or equal to the objective function value of the current single particle;

[0063] In step S13, when it is determined that the current fitness function value is less than or equal to the objective function value of the current single particle, the particle position of the previous stage is retained;

[0064] In step S14, when it is determined that the current fitness function value is greater than the objective function value of the current single particle, the current particle position is iterated.

[0065] In step S15, it is determined whether the particle position iteration is completed;

[0066] In step S16, when it is determined that the particle position iteration is not completed, return to the step of determining whether the current fitness function value is less than or equal to the objective function value of the current single particle;

[0067] In step S17, when it is determined that the particle position iteration is completed, it is determined whether the objective function value converges during the iteration process;

[0068] In step S18, when it is determined that the objective function value has not converged during the iteration process, the process returns to the step of initializing the maximum number of iterations and initial parameters of the particle swarm algorithm;

[0069] In step S19, when it is determined that the objective function value converges during the iteration process, the output power of the fuel cell, the lithium battery and the super capacitor is output as the optimal output power.

[0070] Step S10 can be used to initialize the maximum number of iterations and initial parameters of the particle swarm algorithm. Step S11 can be used to calculate the fitness function value according to the initial parameters. Among them, the specific method for calculating the fitness function value can be a variety of methods known to those skilled in the art. In one example of the present invention, the fitness function value can be calculated according to formula (4):

[0071] According to formula (4), the fitness function value is calculated:

[0072]

[0073] Among them, J is the fitness function value, x(t) is the state variable, u(t) is the decision variable, t 0 is the initial time, t 1 is the end time, and t is the current time.

[0074] Step S12 can be used to determine whether the current fitness function value is less than or equal to the current single particle's objective function value. Step S13 can be used to retain the particle position of the previous stage when it is determined that the current fitness function value is less than or equal to the current single particle's objective function value. Step S14 can be used to iterate the current particle position when it is determined that the current fitness function value is greater than the current single particle's objective function value. Among them, the specific method for iterating the current particle position can be a variety of methods known to those skilled in the art. In one example of the present invention, step S14 may include the following: Figure 4 The method shown in this Figure 4 In the step S14, the step S14 may further include the following steps:

[0075] In step S30, the particle position is updated according to formula (5) and formula (6),

[0076]

[0077] in, is the particle velocity of particle i in the k+1th iteration, w is the inertia weight, is the particle velocity of particle i during the kth iteration, is the particle position of particle i in the kth iteration, is the particle position of particle i in the k+1th iteration, c 1 is the cognitive learning factor, c 2 is the social learning factor, r 1 、r 2 is a random parameter, is the optimal solution of particle i in the kth iteration, xgbest k is the optimal solution of all particles in the kth iteration process, P fcis the supercapacitor output power, P bat Output power for lithium batteries.

[0078] In step S31, the constraint condition for iterating the current particle position is determined according to formula (7):

[0079]

[0080] Among them, P fcmin is the minimum output power of the supercapacitor, P fcmax is the maximum output power of the supercapacitor, P batmin is the minimum output power of lithium battery, P batmax is the maximum output power of lithium battery, P sc is the supercapacitor output power, P load The vehicle requires power.

[0081] Step S15 can be used to determine whether the iteration of the particle position is completed. Step S16 can be used to return to the step of determining whether the current fitness function value is less than or equal to the objective function value of the current single particle when it is determined that the iteration of the particle position is not completed. Step S17 can be used to determine whether the objective function value converges during the iteration process when it is determined that the iteration of the particle position is completed. Step S18 can be used to return to the step of initializing the maximum number of iterations and initial parameters of the particle swarm algorithm when it is determined that the objective function value does not converge during the iteration process. Step S19 can be used to output the output power of the fuel cell, lithium battery and supercapacitor as the optimal output power when it is determined that the objective function value converges during the iteration process.

[0082] In step S2, considering that the hydrogen consumption and fuel cell decay rate are indicators of different attributes, the fuel cell decay is converted into operating cost and combined with the hydrogen consumption cost to form an instantaneous objective function, and then the particle swarm algorithm is used to solve the optimal output power and obtain the optimal operating cost. The specific method of obtaining the operating cost can be various forms known to those skilled in the art. In one example of the present invention, obtaining the operating cost may include: Figure 3 The method shown in this Figure 3 The steps may further include:

[0083] In step S20, the instantaneous hydrogen consumption operating cost of the system is determined according to formula (2):

[0084] J sys_H2 (k) = m sys (k)p H2 , (2)

[0085] Among them, J sys_H2 (k) is the instantaneous hydrogen consumption operating cost of the system, msys (k) is the instantaneous equivalent hydrogen consumption of the three-source hybrid system calculated for the kth time, P H2 is the unit price of hydrogen per kilogram.

[0086] In step S21, the fuel cell degradation instantaneous operating cost is determined according to formula (3):

[0087]

[0088] Among them, J life (k) is the instantaneous operating cost of the fuel cell degradation, D loss_end is the total loss at the end of the fuel cell life, D loss (k) is the fuel cell degradation rate calculated for the kth time, P fc_rate is the fuel cell rated power.

[0089] On the other hand, the present invention further provides a battery system energy management system, the management system comprising a processor for executing any of the management strategies described above.

[0090] Through the above technical solution, the present invention provides a battery system energy management strategy and system, which obtains the initial output power of fuel cells, lithium batteries and supercapacitors and uses a particle swarm algorithm to obtain the optimal output power of fuel cells, lithium batteries and supercapacitors. The management strategy and system adopts fuel cells, supercapacitors and lithium batteries for joint power supply, giving full play to the advantages of the three power sources, with the minimum system equivalent hydrogen consumption and fuel decay rate as the goal, restricting fuel decay, using a particle swarm optimization algorithm to solve the distribution of fuel cell and auxiliary energy storage system output power, giving full play to the advantages of the three power sources of fuel cells, supercapacitors and lithium batteries, and considering battery decay and its impact on cost, improving the life of fuel cells and system economy.

[0091] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0092] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0093] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0094] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0095] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0096] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0097] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0098] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0099] The above are only embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included within the scope of the claims of the present application.

Claims

1. A battery system energy management strategy, characterized in that: The management strategies include: Obtain the initial output power of fuel cells, lithium batteries and supercapacitors; The particle swarm algorithm is used to obtain the optimal output power of the fuel cell, lithium battery and supercapacitor, wherein the particle swarm algorithm includes: Obtain the instantaneous hydrogen consumption operating cost of the system of the current particle and the instantaneous operating cost of the fuel cell degradation; According to formula (1), the instantaneous objective function of the current particle is obtained: J(k)=min(J sys_H2 (k)+J life (k)), (1) Among them, J(k) is the objective function, J sys_H2 (k) is the instantaneous hydrogen consumption operating cost of the system, J life (k) is the instantaneous operating cost of the fuel cell degradation.

2. The management strategy according to claim 1, characterized in that: The particle swarm algorithm is used to obtain the optimal output power of the fuel cell, lithium battery and supercapacitor, including: Initialize the maximum number of iterations and initial parameters of the particle swarm algorithm; Calculate the fitness function value according to the initial parameters; Determine whether the current fitness function value is less than or equal to the current single particle objective function value; When it is determined that the current fitness function value is less than or equal to the objective function value of the current single particle, retaining the particle position of the previous stage; When it is determined that the current fitness function value is greater than the objective function value of the current single particle, the current particle position is iterated.

3. The management strategy according to claim 2, characterized in that: The particle swarm algorithm is used to obtain the optimal output power of the fuel cell, lithium battery and supercapacitor, and also includes: Determine whether the particle position iteration is completed; In the case where it is determined that the particle position iteration is not completed, returning to the step of determining whether the current fitness function value is less than or equal to the objective function value of the current single particle; When it is determined that the particle position iteration is completed, determining whether the objective function value converges during the iteration process; When it is determined that the objective function value has not converged during the iteration process, returning to the step of initializing the maximum number of iterations and initial parameters of the particle swarm algorithm; When it is determined that the objective function value converges during the iteration process, the output powers of the fuel cell, the lithium battery and the supercapacitor are output as the optimal output power.

4. The management strategy according to claim 1, characterized in that: Obtain the instantaneous hydrogen consumption operating cost of the current particle system and the instantaneous operating cost of fuel cell degradation, including: According to formula (2), the instantaneous hydrogen consumption operating cost of the system is determined: J sys_H2 (k)=m sys (k)p H2 , (2) Among them, J sys_H2 (k) is the instantaneous hydrogen consumption operating cost of the system, m sys (k) is the instantaneous equivalent hydrogen consumption of the three-source hybrid system calculated for the kth time, p H2 is the unit price of hydrogen per kilogram.

5. The management strategy according to claim 4, characterized in that: Obtain the instantaneous hydrogen consumption operating cost of the system and the instantaneous operating cost of fuel cell degradation of the current particle, including: The instantaneous operating cost of the fuel cell degradation is determined according to formula (3): Among them, J life (k) is the instantaneous operating cost of the fuel cell degradation, D loss_end is the total loss at the end of the fuel cell life, D loss (k) is the fuel cell degradation rate calculated for the kth time, P fc_rate is the fuel cell rated power.

6. The management strategy according to claim 2, characterized in that: Calculating the fitness function value includes: According to formula (4), the fitness function value is calculated: Among them, J is the fitness function value, x(t) is the state variable, u(t) is the decision variable, t0 is the initial time, t1 is the end time, and t is the current time.

7. The management strategy according to claim 2, characterized in that: Iterating the current particle position includes: Update the particle position according to formula (5) and formula (6), in, is the particle velocity of particle i in the k+1th iteration, w is the inertia weight, is the particle velocity of particle i during the kth iteration, is the particle position of particle i in the kth iteration, is the particle position of particle i in the k+1th iteration, c1 is the cognitive learning factor, c2 is the social learning factor, r1 and r2 are random parameters, is the optimal solution of particle i in the kth iteration, xgbest k is the optimal solution of all particles in the kth iteration process, P fc is the supercapacitor output power, P bat Output power for lithium batteries.

8. The management strategy according to claim 7, characterized in that: Iterating the current particle position also includes: According to formula (7), the constraints of the iterative current particle position are determined. Among them, P fcmin is the minimum output power of the supercapacitor, P fcmax is the maximum output power of the supercapacitor, P batmin is the minimum output power of lithium battery, P batmax is the maximum output power of lithium battery, P sc is the supercapacitor output power, P load The vehicle requires power.

9. A battery system energy management system, characterized in that: The management system comprises a processor for executing the management strategy according to any one of claims 1 to 8.

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