Control method, device, computer program product and vehicle for a traction battery system

Abstract

The application provides a control method and device of a power battery system, a computer program product and a vehicle, fuel cells and accumulators of the power battery system are used to output electric energy to provide power, the method comprises the following steps: training corresponding fuzzy controllers in multiple aging states respectively, so that the fuzzy controllers control the output power of the fuel cells and the accumulators to maximize the service life of the fuel cells and the accumulators, the aging state is a state under different aging degree combinations of the fuel cells and the accumulators; determining a target fuzzy controller according to a fuzzy controller corresponding to an aging state closest to the aging degree of the power battery system; inputting the current system demand power of the power battery system and the current battery capacity of the accumulator into the target fuzzy controller to obtain the output power of the fuel cells and the output power of the accumulator, so as to control the power distribution of the fuel cells and the accumulators, and the problem of poor durability of the power battery system in the prior art is solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power battery control, in particular to a control method and device of a power battery system, a computer program product and a vehicle. BACKGROUND

[0002] The driving system powered by hydrogen through fuel cells is playing a significant role in the decarbonization energy transformation in the field of transportation, and hydrogen-powered fuel cell vehicles are becoming an important direction for the sustainable development of the future automobile industry. In the field of transportation, including road vehicles, hydrogen expands electricization to high-load cycle applications, such as passenger cars, long-distance vehicles, special vehicles, and trains and ships, etc. Through re-electrification, the chemical energy in hydrogen is converted into electrical energy, which has the advantages of good environmental performance, high conversion efficiency, short refueling time, long cruising range, etc. Fuel cell vehicles usually use a hybrid electric drive system of "fuel cell / power battery + motor". The fuel cell and the power battery serve as the main and auxiliary power sources to provide electrical energy for the motor. Compared with other types of fuel cells, proton exchange membrane fuel cells have a good application prospect in the field of transportation due to their high power density, fast start-up speed, and low operating temperature. Although current fuel cell technology has achieved industrialization breakthroughs and the system integration capability has been greatly enhanced, the vehicle fuel cell system usually exhibits distributed, dynamic, time-varying, multi-physical domain, and multi-time and space scale characteristics due to the strong coupling relationship and frequent changes of gas, water, and electricity in the system and the complex working conditions. There are problems such as poor durability and high failure rate. Developing a long-life fuel cell system is one of the important challenges currently faced by the fuel cell vehicle field.

[0003] Fuel cell system control refers to the control strategy of the system supervision level of power distribution of the multi-power source hybrid power system. It works with the low-level controller of the system. The low-level controller obtains the instructions of the energy management strategy and then acts on the DC / DC converter, DC / AC inverter, and electric motor, etc. to realize the optimal operation of the system. The advantages and disadvantages of the control method directly affect the reliability, maneuverability, and economy of the fuel cell vehicle. In the control of the fuel cell system, fuel economy is usually the primary goal, and durability, safety, and other goals are supplemented. The physical parameter constraints of the system are considered, and the optimal power distribution point between the multi-power sources is finally solved. The existing technology usually establishes a performance degradation model of the fuel cell and uses it as a rule or optimization item of the objective function of the control strategy to improve the durability of the fuel cell and reduce its efficiency degradation. The existing technology provides a hydrogen fuel cell vehicle energy management method considering thermal safety and durability. Through a reinforcement learning control method established by a deep neural network, real-time energy optimization management and distribution of a hydrogen fuel cell hybrid electric vehicle are realized, thereby achieving balanced management, reducing system fuel consumption, prolonging system life, and improving system durability.

[0004] The prior art has the following technical defects:

[0005] 1) The existing fuel cell system for vehicles has low efficiency and limited durability optimization:

[0006] The existing fuel cell control method for vehicles lacks monitoring of the actual operating state of the fuel cell. The performance degradation of the fuel cell is combined with existing models and control strategies, or boundary conditions are added in the rule design to delay the degradation of the fuel cell, and the research focus is concentrated on the real-time optimization algorithm design of the control method. However, due to the lack of accurate judgment of the performance of the fuel cell, the control method is difficult to achieve its optimality. In addition, the performance degradation process of the fuel cell is usually considered on the time scale of hours, while the control method is considered on a smaller time scale. If the state monitoring method of the fuel cell is directly combined with the optimization-based control method, not only will the computational burden be greatly increased, but the uncertainty of the prediction algorithm will also greatly affect the effectiveness of the control method, making it difficult to achieve the purpose of improving the durability of the system.

[0007] 2) The existing control strategy for fuel cells for vehicles has poor adaptability:

[0008] The existing control strategy for fuel cells for vehicles is not flexible enough, lacks accurate state estimation and intelligent decision-making technology, and is difficult to adapt to changing use conditions and environments, such as temperature changes, humidity changes, load fluctuations, etc. It is difficult to effectively predict and prevent potential failures, affecting the performance and life of the fuel cell, and thus affecting the reliability and long-term durability of the fuel cell system. SUMMARY

[0009] The main purpose of the present application is to provide a control method, device, computer program product and vehicle for a power battery system, to at least solve the problem of poor durability of the power battery system in the prior art.

[0010] In order to achieve the above object, according to one aspect of the present application, a control method of a power battery system is provided, fuel cells and accumulators of the power battery system are both used to output electric energy to provide power, the method comprises: training corresponding fuzzy controllers in multiple aging states respectively, so that the fuzzy controllers control output power of the fuel cells and the accumulators to maximize service life of the fuel cells and the accumulators, the aging states are states under different aging degree combinations of the fuel cells and the accumulators; determining a target fuzzy controller according to the fuzzy controller corresponding to the aging state closest to the aging degree of the power battery system; inputting current system demand power of the power battery system and current battery capacity of the accumulators into the target fuzzy controller to obtain output power of the fuel cells and output power of the accumulators, so as to control power distribution of the fuel cells and the accumulators.

[0011] Optionally, inputting the current system demand power of the power battery system and the current battery capacity of the accumulators into the target fuzzy controller to obtain the output power of the fuel cells and the output power of the accumulators comprises: obtaining the current system demand power and the current battery capacity; dividing a plurality of first fuzzy sets according to a value range of the current system demand power, dividing a plurality of second fuzzy sets according to a value range of the current battery capacity, and dividing a plurality of third fuzzy sets according to a value range of the output power of the fuel cells; inputting the current system demand power into a system demand power membership function to obtain a plurality of first memberships, the plurality of first memberships are memberships of the current system demand power to each of the first fuzzy sets respectively; inputting the current battery capacity into a battery capacity membership function to obtain a plurality of second memberships, the plurality of second memberships are memberships of the current battery capacity to each of the second fuzzy sets respectively, the demand power membership function and the battery capacity membership function are both trapezoidal membership functions; calculating a plurality of third memberships according to fuzzy rules, the plurality of first memberships and the plurality of second memberships, the plurality of third memberships are memberships of the output power of the fuel cells to each of the third fuzzy sets respectively, the fuzzy rules are mapping relationships between the first fuzzy sets, the second fuzzy sets and the third fuzzy sets; calculating the output power of the fuel cells by using a centroid method according to the plurality of third memberships and centroids of each of the third fuzzy sets; calculating a difference value between the current system demand power and the output power of the fuel cells to obtain the output power of the accumulators.

[0012] Optionally, the third membership degrees are calculated according to the fuzzy rule, the first membership degrees and the second membership degrees, comprising: a querying step of querying the combination of the first fuzzy set and the second fuzzy set corresponding to any one of the third fuzzy sets according to the fuzzy rule; a first calculating step of calculating the membership degree component corresponding to the combination of the first fuzzy set and the second fuzzy set, the membership degree component being the product of the first membership degree and the second membership degree corresponding to the combination of the first fuzzy set and the second fuzzy set; a second calculating step of calculating the sum of the membership degree components corresponding to all the combinations of the first fuzzy set and the second fuzzy set to obtain the third membership degree corresponding to the third fuzzy set; and repeating the querying step, the first calculating step and the second calculating step at least once in turn until the third membership degrees corresponding to all the third fuzzy sets are obtained.

[0013] Optionally, the third membership degrees are calculated according to the fuzzy rule, the first membership degrees and the second membership degrees, comprising: a querying step of querying the combination of the first fuzzy set and the second fuzzy set corresponding to any one of the third fuzzy sets according to the fuzzy rule; a first calculating step of calculating the membership degree component corresponding to the combination of the first fuzzy set and the second fuzzy set, the membership degree component being the product of the first membership degree and the second membership degree corresponding to the combination of the first fuzzy set and the second fuzzy set; a second calculating step of calculating the sum of the membership degree components corresponding to all the combinations of the first fuzzy set and the second fuzzy set to obtain the third membership degree corresponding to the third fuzzy set; and repeating the querying step, the first calculating step and the second calculating step at least once in turn until the third membership degrees corresponding to all the third fuzzy sets are obtained. The output power P of the fuel cell is calculated fc * wherein P fci is the centroid of the ith third fuzzy set, μ Ai (P fc ) is the third membership degree corresponding to the ith third fuzzy set, and n is the number of the third fuzzy sets.

[0014] Optionally, the fuzzy controllers corresponding to the plurality of aging states are trained respectively, so that the fuzzy controllers control the output power of the fuel cell and the battery to maximize the life of the fuel cell and the battery, comprising: a step of establishing an initial fuzzy controller corresponding to a target aging state, the target aging state being any one of the aging states; a step of inputting the current system demand power and the current battery power into the initial fuzzy controller to obtain a first output power of the fuel cell and a second output power of the battery; a step of obtaining a current output voltage of the fuel cell and a current battery capacity of the battery under the condition that the fuel cell is controlled to operate at the first output power and the battery is controlled to operate at the second output power for a predetermined time length; a step of estimating the life of the fuel cell using a state estimation algorithm and the current output voltage, and estimating the life of the battery using the state estimation algorithm and the current battery capacity; a step of adjusting the parameters of the initial fuzzy controller, the current system demand power and the current battery power until any one of the current system demand power and any one of the current battery power input into the initial fuzzy controller satisfies that a first life difference and a second life difference are both less than a predetermined threshold, the initial fuzzy controller is determined as the fuzzy controller corresponding to the target aging state, the first life difference is the life difference of the fuel cell before and after the fuzzy control of the initial fuzzy controller, and the second life difference is the life difference of the battery before and after the fuzzy control of the initial fuzzy controller; the steps of establishing, inputting, obtaining, estimating and adjusting are repeated at least once in sequence until the fuzzy controllers corresponding to all the aging states are obtained.

[0015] Optionally, the life of the fuel cell is estimated using a state estimation algorithm and the current output voltage, and the life of the battery is estimated using the state estimation algorithm and the current battery capacity, comprising: a first estimation step of calculating an output voltage at a next time according to the current output voltage by a state estimation algorithm; a second estimation step of calculating a battery capacity at the next time according to the current battery capacity by the state estimation algorithm; repeating the first estimation step until the output voltage at the next time is equal to a life termination voltage, and calculating the life of the fuel cell according to the next time and a current time, and repeating the second estimation step until the battery capacity at the next time is equal to a life termination capacity, and calculating the life of the battery according to the next time and the current time.

[0016] Optionally, the fuzzy controller corresponding to the aging state closest to the aging degree of the power battery system is determined as the target fuzzy controller, comprising: setting the credibility of each fuzzy controller at a plurality of reference aging degrees of the fuel cell; determining the fuzzy controller with the maximum credibility as the optimal fuzzy controller corresponding to each reference aging degree; in the case that the aging degree of the fuel cell is between two reference aging degrees, combining the credibility of the optimal fuzzy controllers corresponding to the two reference aging degrees by DS theory to obtain the normalized credibility of the two optimal fuzzy controllers, so that the sum of the two normalized credibilities is 1; performing weighted average on the two optimal fuzzy controllers corresponding to the two normalized credibilities to obtain the target fuzzy controller, so that the output of the target fuzzy controller is the weighted average of the outputs of the two optimal fuzzy controllers.

[0017] According to another aspect of the present application, a control device of a power battery system is provided, the fuel cell and the storage battery of the power battery system are both used to output power, the device comprises: a training unit, configured to train a corresponding fuzzy controller under a plurality of aging states respectively, so that the fuzzy controller controls the output power of the fuel cell and the storage battery to maximize the life of the fuel cell and the storage battery, the aging state is a state under different aging degree combinations of the fuel cell and the storage battery; a determination unit, configured to determine the fuzzy controller corresponding to the aging state closest to the aging degree of the power battery system as a target fuzzy controller; an input unit, configured to input the current system demand power of the power battery system and the current battery power of the storage battery into the target fuzzy controller to obtain the output power of the fuel cell and the output power of the storage battery, so as to control the power distribution of the fuel cell and the storage battery.

[0018] According to still another aspect of the present application, a computer program product is provided, comprising computer programs / instructions, which, when executed by a processor, implement any of the methods described above.

[0019] According to still another aspect of the present application, a vehicle is provided, comprising: a power battery system, one or more processors, a memory, and one or more programs, wherein the fuel cell and the storage battery of the power battery system are both used to output power, the one or more programs are stored in the memory and configured to be executed by the one or more processors, and the one or more programs comprise instructions for executing any of the methods described above.

[0020] With the technical solution of the present application, the control method of the power battery system considers that the power control strategies of the fuel cell and the storage battery in the aging state have different influences on the service life of the fuel cell and the storage battery, so corresponding fuzzy controllers are trained in various aging states, and the control strategy is optimized in real time through a suitable fuzzy controller to maximize the service life of the fuel cell and the storage battery, thereby solving the poor durability problem of the power battery system in the prior art. BRIEF DESCRIPTION OF DRAWINGS

[0021] Figure 1 A hardware structure block diagram of a mobile terminal for executing a control method of a power battery system according to an embodiment of the present application is shown;

[0022] Figure 2 A flowchart of a control method of a power battery system according to an embodiment of the present application is shown;

[0023] Figure 3 A curve diagram of a required power membership function and a battery power membership function according to an embodiment of the present application is shown;

[0024] Figure 4 A schematic diagram of a fuel cell-lithium battery power system power distribution real-time optimization control result according to an embodiment of the present application is shown;

[0025] Figure 5 A schematic diagram of the aging degree of a fuel cell and a lithium battery according to an embodiment of the present application is shown;

[0026] Figure 6 A schematic diagram of a fuel cell service life prediction result according to an embodiment of the present application is shown;

[0027] Figure 7 A schematic diagram of a lithium battery service life prediction result according to an embodiment of the present application is shown;

[0028] Figure 8 A schematic diagram of a fuzzy controller fusion result according to an embodiment of the present application is shown;

[0029] Figure 9 A structure block diagram of a control device of a power battery system according to an embodiment of the present application is shown.

[0030] Among the above drawings, the following reference signs are included:

[0031] 102, processor; 104, memory; 106, transmission device; 108, input and output device. DETAILED DESCRIPTION

[0032] It should be noted that the embodiments and features of the embodiments in the present application can be combined with each other in the case of no conflict. The technical solutions in the embodiments of the present application will be described in detail below with reference to the drawings and in combination with the embodiments.

[0033] In order for those skilled in the art to better understand the scheme of the present application, the technical solutions in the embodiments of the present application will be described clearly and completely below in combination with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor should belong to the scope of protection of the present application.

[0034] It should be noted that the terms "first", "second" and the like in the specification and claims of the present application and the above-described drawings are used to distinguish similar objects, and do not necessarily indicate a specific order or a chronological sequence. It should be understood that the data thus used can be interchanged under appropriate circumstances, so that the embodiments of the present application described herein can be implemented. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product or device that includes a series of steps or units does not have to be limited to those steps or units clearly listed, but can include other steps or units not clearly listed or inherent to the process, method, product or device.

[0035] For the convenience of description, the following describes some nouns or terms related to the embodiments of the present application:

[0036] DS theory (Dempster-Shafer envidence theory): a complete theory for dealing with uncertainty problems.

[0037] As introduced in the background, the power battery system in the prior art has poor durability. To solve the technical problem, the embodiments of the present application provide a control method, device, computer program product and vehicle for a power battery system.

[0038] The technical solutions in the embodiments of the present application will be described clearly and completely below in combination with the drawings in the embodiments of the present application.

[0039] The method embodiments provided in the embodiments of the present application can be executed in a mobile terminal, a computer terminal or a similar computing device. Taking the case of running on a mobile terminal, Figure 1 is a hardware structure block diagram of a mobile terminal of a control method for a power battery system according to an embodiment of the present application. As Figure 1 shown, the mobile terminal can include one or more Figure 1Only one is shown) a processor 102 (the processor 102 may include but is not limited to a microprocessor MCU or a programmable logic device FPGA and other processing devices) and a memory 104 for storing data, wherein the mobile terminal may also include a transmission device 106 and an input and output device 108 for communication functions. It will be understood by those skilled in the art that Figure 1 The structure shown is only for illustration and does not limit the structure of the mobile terminal. Figure 1 More or fewer components than shown, or with Figure 1 Different configurations shown.

[0040] The memory 104 can be used to store computer programs, such as software programs and modules of application software, such as the computer program corresponding to the device information display method in the embodiment of the present invention. The processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, that is, implementing the above-mentioned method. The memory 104 may include a high-speed random access memory and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include a memory remotely located relative to the processor 102, and these remote memories can be connected to the mobile terminal via a network. Examples of the above-mentioned networks include but are not limited to the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof. The transmission device 106 is used to receive or send data via a network. Specific examples of the above-mentioned network may include a wireless network provided by the mobile terminal's communication provider. In one example, the transmission device 106 includes a network adapter (Network Interface Controller, abbreviated as NIC), which can be connected to other network devices via a base station to communicate with the Internet. In one example, the transmission device 106 may be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0041] In this embodiment, a control method for a power battery system running on a mobile terminal, a computer terminal or a similar computing device is provided. The fuel cell and the storage battery of the power battery system are both used to output electrical energy to provide power. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0042] Figure 2 FIG. 1 is a flow chart of a control method for a power battery system according to an embodiment of the present application. Figure 2As shown, the method comprises the following steps:

[0043] Step S201, training corresponding fuzzy controllers under multiple aging states respectively, so that the fuzzy controllers control the output power of the fuel cell and the battery to maximize the life of the fuel cell and the battery, and the aging state is the state under different aging degree combinations of the fuel cell and the battery;

[0044] Specifically, since the power distribution of the fuel cell and the battery has different effects on the battery life under different aging states, the corresponding fuzzy controllers are trained under multiple aging states, the life of the fuel cell and the battery, and the fuzzy controller intelligently decides to control the power distribution of the fuel cell and the battery, thereby maximizing the life of the fuel cell and the battery.

[0045] Step S202, determining the fuzzy controller corresponding to the aging state closest to the aging degree of the power battery system as the target fuzzy controller;

[0046] Specifically, according to the aging degree of the power battery system, the fuzzy controller corresponding to the aging state closest to the aging degree is determined as the target fuzzy controller.

[0047] Step S203, inputting the current system demand power of the power battery system and the current battery capacity of the battery into the target fuzzy controller to obtain the output power of the fuel cell and the output power of the battery, so as to control the power distribution of the fuel cell and the battery.

[0048] Specifically, the target fuzzy controller determines the output power of the fuel cell and the battery according to the current system demand power of the power battery system and the current battery capacity of the battery, controls the power distribution of the fuel cell and the battery, and simultaneously meets the current system demand power, thereby maximizing the life of the fuel cell and the battery.

[0049] In the control method of the power battery system, the power control strategy of the fuel cell and the battery under the aging state has different effects on the life of the fuel cell and the battery, so the corresponding fuzzy controllers are trained under multiple aging states, the real-time optimization control strategy is realized through the appropriate fuzzy controller, the life of the fuel cell and the battery is maximized, and the problem of poor durability of the power battery system in the prior art is solved.

[0050] In order to meet the power demand, in an optional implementation, the step S203 comprises:

[0051] Step S2031, obtaining the current system demand power and the current battery capacity.

[0052] Step S2032, divide a plurality of first fuzzy sets according to the range of the current system demand power, divide a plurality of second fuzzy sets according to the range of the current battery power, and divide a plurality of third fuzzy sets according to the range of the output power of the fuel cell;

[0053] Step S2033, input the current system demand power into a system demand power membership function to obtain a plurality of first memberships, the plurality of first memberships respectively being the membership of the current system demand power to each of the first fuzzy sets, and input the current battery power into a battery power membership function to obtain a plurality of second memberships, the plurality of second memberships respectively being the membership of the current battery power to each of the second fuzzy sets, the demand power membership function and the battery power membership function both being trapezoidal membership functions;

[0054] Step S2034, calculate a plurality of third memberships according to a fuzzy rule, the plurality of first memberships, and the second memberships, the plurality of third memberships respectively being the membership of the output power of the fuel cell to each of the third fuzzy sets, the fuzzy rule being the mapping relationship between the first fuzzy sets, the second fuzzy sets, and the third fuzzy sets;

[0055] Step S2035, calculate the output power of the fuel cell by using a centroid method according to the plurality of third memberships and the centroid of each of the third fuzzy sets;

[0056] Step S2036, calculate the difference between the current system demand power and the output power of the fuel cell to obtain the output power of the battery.

[0057] In the embodiment, a fuzzy controller is used to realize real-time optimization control of a short-time-scale system. First, fuzzy sets and membership functions of input and output variables are defined. The input variables are system demand power P demand and battery power SOC, which can be divided into several first fuzzy sets and several second fuzzy sets, i.e., "very low", "low", "medium", "high", and "very high". The output variable is the output power P fc of the fuel cell, and the third fuzzy set is divided into "very low", "low", "medium", "high", and "very high". For SOC, a trapezoidal membership function is used, and the parameters are as follows: "very low": SOC=[0, 0, 10, 20]%, "low": SOC=[10, 20, 30, 40]%, "medium": SOC=[30, 40, 60, 70]%, "high": SOC=[60, 70, 80, 90]%, and "very high": SOC=[80, 90, 100, 100]%. For P demand and Pfc , after normalization, a similar method is used to define the membership function: "very low": P demand =[0,0,10,20]%, "low": P demand =[10,20,30,40]%, "medium": P demand =[30,40,60,70]%, "high": P demand =[60,70,80,90]%, "very high": P demand =[80,90,100,100]%, "very low": P fc =[0,0,10,20]%, "low": P fc =[10,20,30,40]%, "medium": P fc =[30,40,60,70]%, "high": P fc =[60,70,80,90]%, "very high": P fc =[80,90,100,100]%, the curve diagrams of the above demand power membership function and the above battery power membership function are as follows: Figure 3 As shown, a series of fuzzy rules are formulated to calculate different SOC and P demand Next P fc The output value of the fuel cell can be calculated using the centroid method based on the multiple third membership degrees and the centroids of the third fuzzy sets. The difference between the current system demand power and the output power of the fuel cell is calculated to obtain the output power of the battery. The real-time optimization control result of the power distribution of the fuel cell-lithium battery power system on a certain driving cycle implemented by the fuzzy controller is as follows: Figure 4 As shown. The system power P is calculated by the fuzzy controller. demand The distribution results show that the fuzzy controller can effectively control the output power of the fuel cell, maintain it at a relatively stable level, and effectively maintain the life of the fuel cell. The power of the lithium battery changes with the demand power, and the response performance is good.

[0058] In order to calculate the third membership degree corresponding to each third fuzzy set, in an optional implementation, the above step S2034 includes:

[0059] Step S20341, a query step, querying the combination of the first fuzzy set and the second fuzzy set corresponding to any one of the third fuzzy sets according to the fuzzy rules;

[0060] Step S20342, a first calculating step, calculates a membership degree component corresponding to a combination of the first fuzzy set and the second fuzzy set, which is a product of the first membership degree and the second membership degree corresponding to the combination of the first fuzzy set and the second fuzzy set;

[0061] Step S20343, a second calculating step, calculates a sum of all the membership degree components corresponding to the combination of the first fuzzy set and the second fuzzy set, to obtain the third membership degree corresponding to the third fuzzy set;

[0062] Step S20344, the querying step, the first calculating step and the second calculating step are repeated in sequence at least once until the third membership degree corresponding to all the third fuzzy sets is obtained.

[0063] In the embodiment, the fuzzy rules are shown in Table 1. Taking the third fuzzy set "very low" as an example, the combination of the first fuzzy set and the second fuzzy set corresponding to the third fuzzy set "very low" includes the combination of the first fuzzy set "very low" and the second fuzzy set "very low" and the combination of the first fuzzy set "very low" and the second fuzzy set "low". The system required power P demand The product of the first membership degree of the first fuzzy set "very low" and the second membership degree of the second fuzzy set "very low" corresponding to the battery SOC is a membership degree component corresponding to one combination, and the system required power P demand The product of the first membership degree of the first fuzzy set "very low" and the second membership degree of the second fuzzy set "low" corresponding to the battery SOC is another membership degree component corresponding to one combination, and the sum of the two membership degree components is the output power P fc The third membership degree of the third fuzzy set "very low".

[0064] Table 1

[0065]

[0066] In order to obtain the output power of the fuel cell, in an alternative embodiment, the step S2035 includes:

[0067] Step S20351, using to calculate the output power P fc * of the fuel cell, wherein P fci is the centroid of the i-th third fuzzy set, μ Ai (P fc ) is the third membership degree corresponding to the i-th third fuzzy set, and n is the number of the third fuzzy sets.

[0068] In the above embodiments, in actual operation, the maximum value of the membership functions of all activated rules is output as the aggregation result, and the fuzzy output set is converted into a definite output value through defuzzification. In the present application, the Centroid Method is adopted: wherein P fci is the centroid of the i-th third fuzzy set, μ Ai (P fc ) is the third membership corresponding to the i-th third fuzzy set, and n is the number of the third fuzzy sets. Accordingly, the fuzzy controller can adjust the output power P fc of the fuel cell under different operating conditions to meet the system demand.

[0069] In order to maximize the battery life, in an alternative embodiment, the step S201 comprises:

[0070] Step S2011, a step of establishing an initial fuzzy controller corresponding to a target aging state, wherein the target aging state is any one of the above aging states;

[0071] Step S2012, an input step of inputting the current system demand power and the current battery power into the initial fuzzy controller to obtain a first output power of the fuel cell and a second output power of the battery;

[0072] Step S2013, an acquisition step of acquiring the current output voltage of the fuel cell and the current battery capacity of the battery under the condition that the fuel cell is controlled to operate at the first output power and the battery is controlled to operate at the second output power for a predetermined time period;

[0073] Step S2014, an estimation step of estimating the life of the fuel cell using a state estimation algorithm and the current output voltage, and estimating the life of the battery using the state estimation algorithm and the current battery capacity;

[0074] Step S2015, an adjustment step of adjusting the parameters of the initial fuzzy controller, the current system demand power, and the current battery power until any one of the current system demand power and any one of the current battery power input into the initial fuzzy controller satisfies that the first life difference and the second life difference are both less than a predetermined threshold, determining the initial fuzzy controller as the fuzzy controller corresponding to the target aging state, wherein the first life difference is the difference in the life of the fuel cell before and after the fuzzy control by the initial fuzzy controller, and the second life difference is the difference in the life of the battery before and after the fuzzy control by the initial fuzzy controller;

[0075] Step S2016, the above-mentioned establishment step, the above-mentioned input step, the above-mentioned acquisition step, the above-mentioned estimation step and the above-mentioned adjustment step are repeated at least once in turn until the above-mentioned fuzzy controller corresponding to all the above-mentioned aging states is obtained.

[0076] In the above-mentioned embodiment, it is assumed that the fuel cell output voltage drop to 90% of the initial value is the end-of-life value, and the lithium battery capacity drop to 70% of the initial value is the end-of-life value, and the aging degree is as shown in Figure 5 Five kinds of aging state combinations are defined: 1, fuel cell not aged and lithium battery not aged, 2, fuel cell low aging and lithium battery not aged, 3, fuel cell low aging and lithium battery low aging, 4, fuel cell high aging and lithium battery low aging, 5, fuel cell high aging and lithium battery high aging, five fuzzy controllers are trained for the above-mentioned five kinds of aging state of the fuel cell system operation mode, according to the life prediction of the multi-power source, the power distribution result is optimized in real time, so that the control strategy of the trained fuzzy controller can maximize the life and energy efficiency of the whole system while meeting the power demand.

[0077] In order to accurately predict the battery life, in an optional embodiment, the above-mentioned step S2014 comprises:

[0078] Step S20141, a first estimation step, calculating the output voltage at the next time by a state estimation algorithm according to the above-mentioned current output voltage;

[0079] Step S20142, a second estimation step, calculating the battery capacity at the next time by the above-mentioned state estimation algorithm according to the above-mentioned current battery capacity;

[0080] Step S20143, repeating the above-mentioned first estimation step until the output voltage at the next time is equal to the end-of-life voltage, calculating the life of the fuel cell according to the next time and the current time, repeating the above-mentioned second estimation step until the battery capacity at the next time is equal to the end-of-life capacity, calculating the life of the battery according to the next time and the current time.

[0081] In the above-mentioned embodiment, the state estimation method based on the observer is used to estimate and predict the aging state of the fuel cell and the battery (lithium battery) in the system. First, a battery state model is constructed, the output voltage of the fuel cell is used as the state variable, and the battery capacity of the lithium battery is used as the state variable: Based on the model, the state equation and the measurement equation are constructed in the state space: where X is the state variable, i.e. the fuel cell output voltage or the lithium battery capacity, and w and 0 represent the Gaussian white noise. Further, the observer model is used to estimate and predict the variables in the state equation online, including three steps of state value prediction, gain calculation and state value update, as follows: 1) state value prediction: 2) gain calculation: 3) state value update: is the estimated value of the state variable at the kth period, and are the prior state estimate value and the error covariance estimate value, respectively, K k is the feedback gain, R and Q are the noise matrices, y k is the observation value of the state variable, F is the state transition matrix, and H is the conversion matrix from the state value to the observation value. Based on the historical data, the life prediction results of the fuel cell and the lithium battery calculated by the above method are obtained, the uncertainty of the prediction results is obtained by running the program multiple times, the prediction results of different prediction start times are plotted in the figure by using the box plot, the life prediction result of the fuel cell is shown in Figure 6 , and the life prediction result of the lithium battery is shown in Figure 7 .

[0082] In order to select a suitable fuzzy controller, in an optional implementation, the step S202 includes:

[0083] Step S2021, setting the credibility of each fuzzy controller under a plurality of reference aging degrees of the fuel cell;

[0084] Step S2022, determining the fuzzy controller with the maximum credibility as the optimal fuzzy controller corresponding to each reference aging degree;

[0085] Step S2023, in the case that the aging degree of the fuel cell is between two reference aging degrees, combining the credibility of the optimal fuzzy controllers corresponding to the two reference aging degrees by using the DS theory to obtain the normalized credibility of the two optimal fuzzy controllers, so that the sum of the two normalized credibilities is 1;

[0086] Step S2024, performing weighted average on the two optimal fuzzy controllers corresponding to the two normalized credibilities to obtain the target fuzzy controller, so that the output of the target fuzzy controller is the weighted average value of the outputs of the two optimal fuzzy controllers.

[0087] In the above-mentioned embodiments, five fuzzy controllers are defined for the five aging states of the fuel cell system, and the control decisions are given according to the inputs and rule base. The membership function parameters of the fuel cell output power are represented by i values, that is, i values are used to describe the membership function of the fuel cell output power. A fuzzy inference engine based on a Gaussian function is constructed to calculate the basic probability value of the fuel cell aging value (obtained by an observer) corresponding to each fuzzy controller under actual conditions, which reflects the confidence factor (CF) of the decision of each controller. The inputs of the fuzzy inference engine are three aging states (non-aging, low aging, and high aging), and the outputs are the confidence factors corresponding to the five fuzzy controllers. For example, for some random aging states in the online running process, the confidence factors corresponding to the five fuzzy controllers are shown in Table 2. The confidence factors of different controllers are combined to form a new confidence function by combining the D-S data fusion theory. This method is completed by weighted average of all possible intersections. For example, the following formula represents the calculation method of combining the confidence factors of two fuzzy controllers B and C into A where K is a normalization constant. Normalization: The combined results are normalized to ensure that the sum of all confidence factors is 1. The outputs of multiple fuzzy controllers are intelligently fused by using the D-S theory to improve the robustness and accuracy of the overall control system, and to handle the inconsistent power allocation optimization points provided by multiple fuzzy controllers under different aging states. The fusion results of two fuzzy controllers under a certain aging state of the fuel cell system by using the intelligent decision method are shown in Table 2. Figure 8

[0088] Table 2

[0089]

[0090]

[0091] It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0092] ​The embodiment of the present application further provides a control device of a power battery system. It should be noted that the control device of the power battery system of the embodiment of the present application can be used to execute the control method for the power battery system provided by the embodiment of the present application. The device is used to realize the above-mentioned embodiment and preferred embodiment, and the description has been made and will not be repeated. As used below, the term "module" can be a combination of software and / or hardware that realizes a predetermined function. Although the device described in the following embodiment is preferably realized in software, the realization of hardware or a combination of software and hardware is also possible and conceived.

[0093] The control device of the power battery system provided by the embodiment of the present application is introduced below. The fuel cell and the storage battery of the power battery system are both used to output power.

[0094] Figure 9 FIG. 1 is a structural block diagram of the control device of the power battery system according to the embodiment of the present application. As shown in FIG. 1, the device comprises: Figure 9

[0095] a training unit 10, configured to train a corresponding fuzzy controller under a plurality of aging states respectively, so that the fuzzy controller controls the output power of the fuel cell and the storage battery to realize the maximization of the life of the fuel cell and the storage battery, and the aging state is a state under a different aging degree combination of the fuel cell and the storage battery;

[0096] Specifically, since the power distribution of the fuel cell and the storage battery has different influences on the battery life under different aging states, the corresponding fuzzy controller is trained under a plurality of aging states respectively, so that the life of the fuel cell and the storage battery, and the intelligent decision of the fuzzy controller control the power distribution of the fuel cell and the storage battery, and the maximization of the life of the fuel cell and the storage battery is realized.

[0097] a determination unit 20, configured to determine the fuzzy controller corresponding to the aging state closest to the aging degree of the power battery system as a target fuzzy controller;

[0098] Specifically, the fuzzy controller corresponding to the aging state closest to the aging degree of the power battery system is determined as the target fuzzy controller according to the selection of the appropriate fuzzy controller according to the aging degree of the power battery system.

[0099] an input unit 30, configured to input the current system demand power of the power battery system and the current battery power of the storage battery into the target fuzzy controller, to obtain the output power of the fuel cell and the output power of the storage battery, so as to control the power distribution of the fuel cell and the storage battery.

[0100] ​Specifically, the target fuzzy controller determines the output power of the fuel cell and the battery according to the current system demand power of the power battery system and the current battery power of the battery, and controls the power distribution of the fuel cell and the battery, so that the maximum life of the fuel cell and the battery is achieved while the current system demand power is met.

[0101] The control device of the power battery system considers that the power control strategies of the fuel cell and the battery in different aging states have different effects on the life of the fuel cell and the battery, so corresponding fuzzy controllers are trained in different aging states, and the control strategy is optimized in real time by a suitable fuzzy controller to maximize the life of the fuel cell and the battery, thereby solving the poor durability problem of the power battery system in the prior art.

[0102] In order to meet the power demand, in an optional implementation, the input unit comprises:

[0103] The first acquisition module is configured to acquire the current system demand power and the current battery power.

[0104] The division module is configured to divide a plurality of first fuzzy sets according to the value range of the current system demand power, divide a plurality of second fuzzy sets according to the value range of the current battery power, and divide a plurality of third fuzzy sets according to the value range of the output power of the fuel cell.

[0105] The first calculation module is configured to input the current system demand power into a system demand power membership function to obtain a plurality of first memberships, the plurality of first memberships being the membership of the current system demand power to each of the first fuzzy sets, and input the current battery power into a battery power membership function to obtain a plurality of second memberships, the plurality of second memberships being the membership of the current battery power to each of the second fuzzy sets, the demand power membership function and the battery power membership function being trapezoidal membership functions.

[0106] The second calculation module is configured to calculate a plurality of third memberships according to fuzzy rules, the plurality of first memberships and the plurality of second memberships, the plurality of third memberships being the membership of the output power of the fuel cell to each of the third fuzzy sets, the fuzzy rules being the mapping relationship between the first fuzzy sets, the second fuzzy sets and the third fuzzy sets.

[0107] The third calculation module is configured to calculate the output power of the fuel cell by using a centroid method according to the plurality of third memberships and the centroid of each of the third fuzzy sets.

[0108] The fourth calculation module is configured to calculate the difference between the current system demand power and the output power of the fuel cell to obtain the output power of the battery.

[0109] In the above embodiment, the fuzzy controller is used to realize the real-time optimization control of the short time scale system. Firstly, the fuzzy sets and membership functions of the input and output variables are defined. The input variables are the system demand power P demand and the battery state of charge SOC, which can be divided into several first fuzzy sets and several second fuzzy sets, i.e. "very low", "low", "medium", "high" and "very high". The output variable is the output power P fc of the fuel cell, which is divided into "very low", "low", "medium", "high" and "very high" in the third fuzzy set. For SOC, the trapezoidal membership function is used to represent, and the parameters are as follows: "very low": SOC = [0, 0, 10, 20]%, "low": SOC = [10, 20, 30, 40]%, "medium": SOC = [30, 40, 60, 70]%, "high": SOC = [60, 70, 80, 90]%, "very high": SOC = [80, 90, 100, 100]%. For P demand and P fc , after normalization, the membership functions are defined in a similar way: "very low": P demand = [0, 0, 10, 20]%, "low": P demand = [10, 20, 30, 40]%, "medium": P demand = [30, 40, 60, 70]%, "high": P demand = [60, 70, 80, 90]%, "very high": P demand = [80, 90, 100, 100]%, "very low": P fc = [0, 0, 10, 20]%, "low": P fc = [10, 20, 30, 40]%, "medium": P fc = [30, 40, 60, 70]%, "high": P fc = [60, 70, 80, 90]%, "very high": P fc = [80, 90, 100, 100]%. The graphs of the above demand power membership functions and the above battery state of charge membership functions are shown in Figure 3 Fig. 2, and then a series of fuzzy rules are formulated to calculate the output values of P demand under different SOC and P fc . That is, the output power of the fuel cell can be calculated by the centroid method according to the centroid of the third membership and the centroid of the third fuzzy set; the difference between the current system demand power and the output power of the fuel cell is calculated to obtain the output power of the battery, and the real-time optimization control result of the fuel cell-lithium battery power system power distribution in a certain driving cycle based on the fuzzy controller is shown inFigure 4 The system demand power P demand The distribution results of the system demand power P

[0110] To calculate the third membership degree corresponding to each third fuzzy set, in an alternative embodiment, the second calculating module comprises:

[0111] The querying sub-module is configured to perform a querying step, and query the combination of the first fuzzy set and the second fuzzy set corresponding to any one of the third fuzzy sets according to the fuzzy rule;

[0112] The first calculating sub-module is configured to perform a first calculating step, and calculate the membership degree component corresponding to the combination of the first fuzzy set and the second fuzzy set, wherein the membership degree component is the product of the first membership degree and the second membership degree corresponding to the combination of the first fuzzy set and the second fuzzy set;

[0113] The second calculating sub-module is configured to perform a second calculating step, and calculate the sum of the membership degree components corresponding to all the combinations of the first fuzzy set and the second fuzzy set, to obtain the third membership degree corresponding to the third fuzzy set;

[0114] The repeating sub-module is configured to repeat the querying step, the first calculating step and the second calculating step at least once in turn, until the third membership degrees corresponding to all the third fuzzy sets are obtained.

[0115] In the above embodiment, the fuzzy rule is shown in Table 1, and taking the combination of the first fuzzy set “very low” and the second fuzzy set “very low” and the combination of the first fuzzy set “very low” and the second fuzzy set “low” as examples, the first membership degree of the first fuzzy set “very low” and the second membership degree of the second fuzzy set “very low” are multiplied to obtain a membership degree component corresponding to one combination, the first membership degree of the first fuzzy set “very low” and the second membership degree of the second fuzzy set “low” are multiplied to obtain a membership degree component corresponding to another combination, and the sum of the two membership degree components is the output power P demand of the fuel cell. demand of the fuel cell. fc of the fuel cell.

[0116] In order to obtain the definite output power of the fuel cell, in an alternative embodiment, the third calculation module comprises:

[0117] a third calculation sub-module, configured to employ to calculate the output power P of the fuel cell fc * wherein P fci is the centroid of the i-th third fuzzy set, μ Ai (P fc ) is the third membership degree corresponding to the i-th third fuzzy set, and n is the number of the third fuzzy sets.

[0118] In the above embodiment, in the actual operation process, the maximum value of the membership degree functions of all the activated rules is output as the aggregation result, and the fuzzy output set is converted into a definite output value through defuzzification. In the present application, the Centroid Method is employed: wherein P fci is the centroid of the i-th third fuzzy set, μ Ai (P fc ) is the third membership degree corresponding to the i-th third fuzzy set, and n is the number of the third fuzzy sets. Accordingly, the fuzzy controller can adjust the output power P fc of the fuel cell in different operating states to meet the system demand.

[0119] In order to maximize the battery life, in an alternative embodiment, the training unit comprises:

[0120] a building module, configured to perform a building step of building an initial fuzzy controller corresponding to a target aging state, wherein the target aging state is any of the aging states;

[0121] an input module, configured to perform an input step of inputting the current system demand power and the current battery power into the initial fuzzy controller to obtain a first output power of the fuel cell and a second output power of the battery;

[0122] a second acquisition module, configured to perform an acquisition step of acquiring a current output voltage of the fuel cell and a current battery capacity of the battery under the condition that the fuel cell is controlled to operate at the first output power and the battery is controlled to operate at the second output power for a predetermined time length;

[0123] an estimation module, configured to perform an estimation step of estimating the life of the fuel cell by employing a state estimation algorithm and the current output voltage and estimating the life of the battery by employing the state estimation algorithm and the current battery capacity;

[0124] an adjusting module configured to perform an adjusting step of adjusting parameters of the initial fuzzy controller, the current system demand power, and the current battery capacity until any one of the current system demand power and any one of the current battery capacity input into the initial fuzzy controller satisfies that both the first life difference and the second life difference are less than a predetermined threshold value, the initial fuzzy controller is determined as the fuzzy controller corresponding to the target aging state, the first life difference is a life difference of the fuel cell before and after the initial fuzzy controller performs fuzzy control, and the second life difference is a life difference of the fuel cell before and after the initial fuzzy controller performs fuzzy control;

[0125] a repeating module configured to perform at least one time of repeating the establishing step, the inputting step, the obtaining step, the estimating step, and the adjusting step in sequence until the fuzzy controller corresponding to each of the aging states is obtained.

[0126] In the embodiment, it is assumed that 90% of the initial value of the fuel cell output voltage drop is the life end value, 70% of the initial value of the lithium battery capacity drop is the life end value, and the aging degree is defined as Figure 5 shown, five aging state combinations are defined: 1, no aging of the fuel cell and no aging of the lithium battery, 2, low aging of the fuel cell and no aging of the lithium battery, 3, low aging of the fuel cell and low aging of the lithium battery, 4, high aging of the fuel cell and low aging of the lithium battery, and 5, high aging of the fuel cell and high aging of the lithium battery. Five fuzzy controllers are trained for the fuel cell system operation modes in the above five aging states. According to the life prediction of the multi-power source, the power distribution result is optimized in real time, so that the control strategy of the trained fuzzy controller maximizes the life and energy efficiency of the overall system while meeting the power demand.

[0127] In order to accurately predict the battery life, in an optional embodiment, the estimating module comprises:

[0128] a first estimating submodule configured to perform a first estimating step of calculating the output voltage at the next time point by a state estimation algorithm according to the current output voltage;

[0129] a second estimating submodule configured to perform a second estimating step of calculating the battery capacity at the next time point by the state estimation algorithm according to the current battery capacity;

[0130] a repeating submodule configured to repeat the first estimating step until the output voltage at the next time point is equal to the life end voltage, and calculate the life of the fuel cell according to the next time point and the current time point, and repeat the second estimating step until the battery capacity at the next time point is equal to the life end capacity, and calculate the life of the battery according to the next time point and the current time point.

[0131] In the above embodiment, the state estimation method based on the observer estimates and predicts the aging states of the fuel cell and the battery (lithium battery) in the system online. First, a battery state model is constructed, in which the output voltage of the fuel cell is used as the state variable and the battery capacity of the lithium battery is used as the state variable: Based on the model, the state equation and the measurement equation are constructed in the state space: where X is the state variable, i.e., the output voltage of the fuel cell or the battery capacity of the lithium battery, and ω and θ represent Gaussian white noise. Further, the observer model is used to estimate and predict the variables in the state equation online, including three steps of state value prediction, gain calculation, and state value update, as follows: 1) state value prediction: 2) gain calculation: 3) state value update: is the estimated value of the state variable at the kth period, and are the prior state estimation value and the error covariance estimation value, respectively, K k is the feedback gain, R and Q are noise matrices, y k is the observation value of the state variable, F is the state transition matrix, and H is the conversion matrix from the state value to the observation value. Based on the historical data, the life prediction results of the fuel cell and the lithium battery calculated by the above method are obtained, the uncertainty of the prediction results is obtained by running the program multiple times, the prediction results of different prediction start times are plotted in the figure by using the box plot, the life prediction result of the fuel cell is shown in Figure 6 , and the life prediction result of the lithium battery is shown in Figure 7 .

[0132] In order to select a suitable fuzzy controller, in an optional embodiment, the determination unit includes:

[0133] a setting module configured to set the credibility of each fuzzy controller of the fuel cell at a plurality of reference aging degrees;

[0134] a determination module configured to determine the fuzzy controller with the maximum credibility as the optimal fuzzy controller corresponding to each reference aging degree;

[0135] a fifth calculation module configured to, when the aging degree of the fuel cell is between two reference aging degrees, combine the credibility of the optimal fuzzy controllers corresponding to the two reference aging degrees by using the DS theory to obtain the normalized credibility of the two optimal fuzzy controllers, so that the sum of the two normalized credibilities is 1;

[0136] The processing module is configured to perform weighted average on the two optimal fuzzy controllers corresponding to the two normalized confidences to obtain the target fuzzy controller, so that the output of the target fuzzy controller is a weighted average of the outputs of the two optimal fuzzy controllers.

[0137] In the above embodiment, five fuzzy controllers are defined for the fuel cell system operation modes in the above five aging states, and control decisions are given according to the inputs and rule base. The membership function parameter value of the fuel cell output power is represented by i, that is, i values are used to describe the membership degree function of the fuel cell output power, and a fuzzy inference engine based on Gaussian function is constructed. In actual situation, the basic probability value of the fuel cell aging value (obtained by observer prediction) corresponding to each fuzzy controller is calculated, which reflects the decision confidence factor (CF) of each controller. The inputs of the fuzzy inference engine are three aging states (non-aging, low aging, and high aging), and the outputs are the confidences corresponding to the five fuzzy controllers. For example, for some random aging state in the online running process, the confidences corresponding to the five fuzzy controllers are shown in Table 2. The confidences of different controllers are combined to form a new confidence function by combining the D-S data fusion theory. This method is completed by weighted average of all possible intersections. For example, the following formula represents the calculation method of combining the confidences of two fuzzy controllers B and C into A where K is a normalization constant. Normalization: The combined result is normalized to ensure that the sum of all confidences is 1. The outputs of multiple fuzzy controllers are intelligently fused by using the D-S theory to improve the robustness and accuracy of the overall control system, and to handle the case where the optimal power distribution points provided by multiple fuzzy controllers are inconsistent under different aging states. The fusion result of two fuzzy controllers under an aging state of the fuel cell system by using the intelligent decision method is shown in Figure 8 .

[0138] The control device of the power battery system includes a processor and a memory, and the units and the like are stored in the memory as program units. The corresponding functions are realized by executing the program units stored in the memory by the processor. The modules are located in the same processor; or the modules are located in different processors in any combination.

[0139] The processor includes a core, and the core retrieves the corresponding program unit from the memory. One or more cores can be set by adjusting the core parameters to solve the poor durability problem of the power battery system in the prior art.

[0140] The memory can include non-persistent memory in a computer readable medium, random access memory (RAM), and / or non-volatile memory such as read-only memory (ROM) or flash memory, including at least one memory chip.

[0141] The embodiment of the present application provides a computer readable storage medium, the computer readable storage medium comprises a stored program, wherein the program controls a device where the computer readable storage medium is located to execute the control method of the power battery system when the program runs.

[0142] Specifically, the control method of the power battery system comprises:

[0143] Step S201, training corresponding fuzzy controllers under a plurality of aging states respectively, so that the fuzzy controllers control the output power of the fuel cell and the storage battery to maximize the life of the fuel cell and the storage battery, and the aging state is a state under different aging degree combinations of the fuel cell and the storage battery;

[0144] Specifically, since the power distribution of the fuel cell and the storage battery has different effects on the battery life under different aging states, the corresponding fuzzy controllers are trained under a plurality of aging states, the life of the fuel cell and the storage battery, and the fuzzy controller intelligently decides to control the power distribution of the fuel cell and the storage battery, so as to maximize the life of the fuel cell and the storage battery.

[0145] Step S202, determining the fuzzy controller corresponding to the aging state closest to the aging degree of the power battery system as a target fuzzy controller according to the aging degree of the power battery system;

[0146] Specifically, according to the aging degree of the power battery system, the fuzzy controller corresponding to the aging state closest to the aging degree is determined as the target fuzzy controller.

[0147] Step S203, inputting the current system demand power of the power battery system and the current battery power of the storage battery into the target fuzzy controller to obtain the output power of the fuel cell and the output power of the storage battery, so as to control the power distribution of the fuel cell and the storage battery.

[0148] Specifically, the target fuzzy controller determines the output power of the fuel cell and the storage battery according to the current system demand power of the power battery system and the current battery power of the storage battery, controls the power distribution of the fuel cell and the storage battery, and can meet the maximization of the life of the fuel cell and the storage battery at the same time of the current system demand power.

[0149] The embodiment of the present application provides a processor, which is used for running a program, wherein the processor implements the control method of the power battery system when running the program.

[0150] Specifically, the control method of the power battery system comprises the following steps:

[0151] In step S201, corresponding fuzzy controllers are trained in multiple aging states, so that the fuzzy controllers control the output power of the fuel cell and the storage battery to maximize the service life of the fuel cell and the storage battery, and the aging states are states under different aging degree combinations of the fuel cell and the storage battery.

[0152] Specifically, since the power distribution of the fuel cell and the storage battery has different influences on the service life of the battery in different aging states, the corresponding fuzzy controllers are trained in multiple aging states, the service life of the fuel cell and the storage battery, and the fuzzy controller intelligently decides to control the power distribution of the fuel cell and the storage battery, thereby maximizing the service life of the fuel cell and the storage battery.

[0153] In step S202, the fuzzy controller corresponding to the aging state closest to the aging degree of the power battery system is determined as a target fuzzy controller.

[0154] Specifically, the fuzzy controller corresponding to the aging state closest to the aging degree of the power battery system is determined as the target fuzzy controller according to the aging degree of the power battery system.

[0155] In step S203, the current system demand power of the power battery system and the current battery capacity of the storage battery are input into the target fuzzy controller, so as to obtain the output power of the fuel cell and the output power of the storage battery, thereby controlling the power distribution of the fuel cell and the storage battery.

[0156] Specifically, the target fuzzy controller determines the output power of the fuel cell and the storage battery according to the current system demand power of the power battery system and the current battery capacity of the storage battery, thereby controlling the power distribution of the fuel cell and the storage battery, so as to maximize the service life of the fuel cell and the storage battery while meeting the current system demand power.

[0157] The embodiment of the present application provides a vehicle, which comprises a power battery system, a processor, a memory, and a program stored in the memory and capable of running on the processor, wherein the fuel cell and the storage battery of the power battery system are used for outputting electric energy to provide power, and the processor implements at least the following steps when running the program:

[0158] Step S201, training corresponding fuzzy controllers under multiple aging states respectively, so that the fuzzy controllers control the output power of the fuel cell and the battery to maximize the life of the fuel cell and the battery, and the aging states are states under different aging degree combinations of the fuel cell and the battery;

[0159] Specifically, since the power distribution of the fuel cell and the battery has different effects on the battery life under different aging states, the corresponding fuzzy controllers are trained under multiple aging states, the life of the fuel cell and the battery, and the fuzzy controller intelligently decides to control the power distribution of the fuel cell and the battery, thereby maximizing the life of the fuel cell and the battery.

[0160] Step S202, determining the fuzzy controller corresponding to the aging state closest to the aging degree of the power battery system as a target fuzzy controller;

[0161] Specifically, according to the aging degree of the power battery system, the fuzzy controller corresponding to the aging state closest to the aging degree is determined as the target fuzzy controller.

[0162] Step S203, inputting the current system demand power of the power battery system and the current battery capacity of the battery into the target fuzzy controller to obtain the output power of the fuel cell and the output power of the battery, so as to control the power distribution of the fuel cell and the battery.

[0163] Specifically, the target fuzzy controller determines the output power of the fuel cell and the battery according to the current system demand power of the power battery system and the current battery capacity of the battery, controls the power distribution of the fuel cell and the battery, and simultaneously meets the current system demand power, thereby maximizing the life of the fuel cell and the battery.

[0164] The application also provides a computer program product adapted to execute the program of initializing at least the following method steps when executed on a data processing device:

[0165] Step S201, training corresponding fuzzy controllers under multiple aging states respectively, so that the fuzzy controllers control the output power of the fuel cell and the battery to maximize the life of the fuel cell and the battery, and the aging states are states under different aging degree combinations of the fuel cell and the battery;

[0166] Specifically, since the power distribution of the fuel cell and the battery has different effects on the battery life in different aging states, the corresponding fuzzy controller is trained in each aging state, the life of the fuel cell and the battery is controlled by the intelligent decision of the fuzzy controller, and the maximization of the life of the fuel cell and the battery is realized.

[0167] In step S202, the fuzzy controller corresponding to the aging state closest to the aging degree of the power battery system is determined as the target fuzzy controller;

[0168] Specifically, the fuzzy controller corresponding to the aging state closest to the aging degree of the power battery system is determined as the target fuzzy controller.

[0169] In step S203, the current system demand power of the power battery system and the current battery capacity of the battery are input into the target fuzzy controller, and the output power of the fuel cell and the output power of the battery are obtained to control the power distribution of the fuel cell and the battery.

[0170] Specifically, the target fuzzy controller determines the output power of the fuel cell and the battery according to the current system demand power of the power battery system and the current battery capacity of the battery, controls the power distribution of the fuel cell and the battery, and realizes the maximization of the life of the fuel cell and the battery at the same time.

[0171] Obviously, those skilled in the art should understand that each module or each step of the present application can be realized by a general computing device, which can be concentrated on a single computing device or distributed on a network composed of multiple computing devices, and can be realized by program codes executable by the computing device, so that they can be stored in a storage device and executed by the computing device, and in some cases, the steps shown or described can be executed in different order, or they can be manufactured into each integrated circuit module, or multiple modules or steps can be manufactured into a single integrated circuit module. Therefore, the present application is not limited to any specific combination of hardware and software.

[0172] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can be in the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can be in the form of a computer program product implemented on one or more computer usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer usable program codes.

[0173] The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks. Figure 1 one or more flow or blocks Figure 1 means for functionally implementing the steps listed in the flowchart block or blocks.

[0174] These computer program instructions can also be stored in a computer readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer readable memory produce an article of manufacture including instructions which implement the function specified in the flowchart block or blocks. Figure 1 one or more flow or blocks Figure 1 means for functionally implementing the steps listed in the flowchart block or blocks.

[0175] The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks. Figure 1 one or more flow or blocks Figure 1 means for functionally implementing the steps listed in the flowchart block or blocks.

[0176] In one typical configuration, the computing device includes one or more processors (CPUs), input / output interfaces, network interfaces, and memory.

[0177] The memory can include non-persistent memory and / or volatile memory, such as random access memory (RAM) and / or cache memory, for storing instructions and data used and / or generated by the computing device. The memory can also include non-volatile memory, such as read-only memory (ROM), electrically programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), flash memory, or nonvolatile random access memory (RAM) (e.g., fault tolerant RAM), for storing instructions and data used and / or generated by the computing device. The memory can also include a storage device, such as a disk drive, hard drive, or flash storage, for storing instructions and data used and / or generated by the computing device. The memory can be embodied in an article of manufacture that includes one or more computer program instructions.

[0178] Computer-readable media includes permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology for information storage. 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 disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassette, magnetic tape disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information accessible to a computing device. According to the definition herein, computer-readable media does not include transitory media such as modulated data signals and carriers.

[0179] It should also be noted that the terms "comprising", "including", or any other variant thereof are intended to cover non-exclusive inclusion, so that processes, methods, articles or devices including a series of elements not only include those elements, but also include other elements not explicitly listed or inherent to such processes, methods, articles or devices. Without more limitations, the element defined by the statement "including a" does not exclude the presence of another identical element in the process, method, article or device including the element.

[0180] From the above description, it can be seen that the above-mentioned embodiments of the present application achieve the following technical effects:

[0181] 1) In the control method of the power battery system of the present application, the power control strategies of the fuel cell and the storage battery under the aging state have different effects on the service life of the fuel cell and the storage battery, so corresponding fuzzy controllers are trained under multiple aging states, and the control strategy is optimized in real time through the appropriate fuzzy controller, so as to maximize the service life of the fuel cell and the storage battery, and solve the poor durability problem of the power battery system in the prior art.

[0182] 2) In the control device of the power battery system of the present application, the power control strategies of the fuel cell and the storage battery under the aging state have different effects on the service life of the fuel cell and the storage battery, so corresponding fuzzy controllers are trained under multiple aging states, and the control strategy is optimized in real time through the appropriate fuzzy controller, so as to maximize the service life of the fuel cell and the storage battery, and solve the poor durability problem of the power battery system in the prior art.

[0183] The above descriptions are only the preferred embodiments of the present application, and are not intended to limit the present application. The present application can have various modifications and changes for those skilled in the art. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A control method of a power battery system, characterized by, The fuel cell and the battery of the power battery system are used to output power, and the method comprises: Training corresponding fuzzy controllers under different aging states, so that the fuzzy controllers control the output power of the fuel cell and the battery to maximize the life of the fuel cell and the battery, wherein the aging states are states under different aging degree combinations of the fuel cell and the battery; Determining the fuzzy controller corresponding to the aging state closest to the aging degree of the power battery system as a target fuzzy controller; Inputting the current system demand power of the power battery system and the current battery power of the battery into the target fuzzy controller to obtain the output power of the fuel cell and the output power of the battery, so as to control the power distribution of the fuel cell and the battery.

2. The method of claim 1, wherein, Inputting the current system demand power of the power battery system and the current battery power of the battery into the target fuzzy controller to obtain the output power of the fuel cell and the output power of the battery, comprises: Obtaining the current system demand power and the current battery power; Dividing a plurality of first fuzzy sets according to the value range of the current system demand power, a plurality of second fuzzy sets according to the value range of the current battery power, and a plurality of third fuzzy sets according to the value range of the output power of the fuel cell; Inputting the current system demand power into a system demand power membership function to obtain a plurality of first memberships, wherein each of the first memberships is the membership of the current system demand power to each of the first fuzzy sets, and inputting the current battery power into a battery power membership function to obtain a plurality of second memberships, wherein each of the second memberships is the membership of the current battery power to each of the second fuzzy sets, and the demand power membership function and the battery power membership function are both trapezoidal membership functions; According to the fuzzy rule, the plurality of first memberships and the plurality of second memberships, a plurality of third memberships are calculated, wherein each of the third memberships is the membership of the output power of the fuel cell to each of the third fuzzy sets, and the fuzzy rule is the mapping relationship between the first fuzzy sets, the second fuzzy sets and the third fuzzy sets; According to the plurality of third memberships and the centroid of each of the third fuzzy sets, the centroid method is used to calculate the output power of the fuel cell; The difference between the current system demand power and the output power of the fuel cell is calculated to obtain the output power of the battery.

3. The method of claim 2, wherein, According to the fuzzy rule, the plurality of first memberships and the plurality of second memberships, a plurality of third memberships are calculated, comprising: According to the fuzzy rule, the combination of the first fuzzy set and the second fuzzy set corresponding to any one of the third fuzzy sets is queried; In a first calculation step, the membership component corresponding to the combination of each of the first fuzzy sets and the second fuzzy sets is calculated, wherein the membership component is the product of the corresponding first membership and the corresponding second membership of the combination of the first fuzzy set and the second fuzzy set. a second calculation step of calculating a sum of membership degree components corresponding to combinations of all the first fuzzy sets and the second fuzzy sets to obtain the third membership degree corresponding to the third fuzzy set; repeating the query step, the first calculation step and the second calculation step in sequence at least once until the third membership degrees corresponding to all the third fuzzy sets are obtained.

4. The method of claim 2, wherein, calculating the output power of the fuel cell according to the plurality of third membership degrees and the centroid of each third fuzzy set by using a centroid method, comprising: Adopting calculating the output power of the fuel cell wherein, is the centroid of the ith third fuzzy set, is the third membership corresponding to the ith third fuzzy set, and n is the number of the third fuzzy sets.

5. The method according to any one of claims 1 to 4, characterized in that, training a corresponding fuzzy controller under each of a plurality of aging states so that the fuzzy controller controls the output power of the fuel cell and the battery to maximize the life of the fuel cell and the battery, comprising: a building step of building an initial fuzzy controller corresponding to a target aging state, the target aging state being any one of the aging states; an input step of inputting the current system demand power and the current battery power into the initial fuzzy controller to obtain a first output power of the fuel cell and a second output power of the battery; an obtaining step of obtaining a current output voltage of the fuel cell and a current battery capacity of the battery under the condition that the fuel cell is controlled to operate at the first output power and the battery is controlled to operate at the second output power for a predetermined time length; an estimating step of estimating the life of the fuel cell by using a state estimation algorithm and the current output voltage and estimating the life of the battery by using the state estimation algorithm and the current battery capacity; an adjusting step of adjusting the parameters of the initial fuzzy controller, the current system demand power and the current battery power until any one of the current system demand power and any one of the current battery power input into the initial fuzzy controller satisfies that a first life difference and a second life difference are both less than a predetermined threshold, the first life difference being a difference in the life of the fuel cell before and after the initial fuzzy controller performs fuzzy control, the second life difference being a difference in the life of the battery before and after the initial fuzzy controller performs fuzzy control, and the initial fuzzy controller being determined as the fuzzy controller corresponding to the target aging state; repeating the building step, the input step, the obtaining step, the estimating step and the adjusting step in sequence at least once until the fuzzy controllers corresponding to all the aging states are obtained.

6. The method of claim 5, wherein, estimating the life of the fuel cell by using a state estimation algorithm and the current output voltage and estimating the life of the battery by using the state estimation algorithm and the current battery capacity, comprising: a first estimating step of calculating an output voltage at a next time by a state estimation algorithm according to the current output voltage; a second estimating step of calculating a battery capacity at the next time by the state estimation algorithm according to the current battery capacity; The first estimating step is repeated until the output voltage at the next time equals the end-of-life voltage, and the life of the fuel cell is calculated according to the next time and the current time; the second estimating step is repeated until the battery capacity at the next time equals the end-of-life capacity, and the life of the battery is calculated according to the next time and the current time.

7. The method of claim 1, wherein, The fuzzy controller corresponding to the aging state closest to the aging degree of the power battery system is determined as the target fuzzy controller, including: Setting the credibility of each fuzzy controller at a plurality of reference aging degrees of the fuel cell; Determining the fuzzy controller with the maximum credibility as the optimal fuzzy controller corresponding to each reference aging degree; In the case that the aging degree of the fuel cell is between two reference aging degrees, combining the credibility of the optimal fuzzy controllers corresponding to the two reference aging degrees by DS theory to obtain the normalized credibility of the two optimal fuzzy controllers, so that the sum of the two normalized credibilities is 1; According to the two normalized credibilities, the two optimal fuzzy controllers are weighted and averaged to obtain the target fuzzy controller, so that the output of the target fuzzy controller is the weighted average of the outputs of the two optimal fuzzy controllers.

8. A control device of a power battery system, characterized in that, The fuel cell and the battery of the power battery system are both used to output electric energy to provide power, and the device includes: A training unit is configured to train the corresponding fuzzy controller under a plurality of aging states respectively, so that the fuzzy controller controls the output power of the fuel cell and the battery to maximize the life of the fuel cell and the battery, and the aging state is a state under different aging degree combinations of the fuel cell and the battery; A determination unit is configured to determine the fuzzy controller corresponding to the aging state closest to the aging degree of the power battery system as the target fuzzy controller; An input unit is configured to input the current system demand power of the power battery system and the current battery capacity of the battery into the target fuzzy controller to obtain the output power of the fuel cell and the output power of the battery, so as to control the power distribution of the fuel cell and the battery.

9. A computer program product comprising a computer program, characterized in that, The computer program is executed by a processor to implement the method of any one of claims 1-7.

10. A vehicle characterized by comprising: Including: A power battery system, one or more processors, a memory, and one or more programs, wherein the fuel cell and the battery of the power battery system are both used to output electric energy to provide power, the one or more programs are stored in the memory and configured to be executed by the one or more processors, and the one or more programs include instructions for performing the method of any one of claims 1-7.

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