Energy control strategy of a flywheel battery hybrid power system

Abstract

The present invention discloses an energy control strategy for a composite power supply system of a flywheel battery, comprising: building a fuzzy rule base and acquiring energy fuzzy data; performing fuzzy reasoning on the energy fuzzy data based on the fuzzy rule base to obtain a power distribution fuzzy result; clarifying the power distribution fuzzy result; and controlling the energy of the composite power supply system through the clarified result.

Description

Technical Field

[0001] The present invention relates to the technical field of battery energy control, and in particular to an energy control strategy for a composite power supply system of a flywheel battery. Background Art

[0002] Pure electric vehicles are equipped only with batteries. They consume no fuel and produce no tailpipe emissions, making them truly zero-emission, zero-pollution vehicles. However, due to the immaturity of pure electric vehicle battery technology, many technical indicators still fall short of expectations. In particular, high prices and short cycle life indirectly increase the manufacturing cost of pure electric vehicles. Low energy density also reduces the distance a pure electric vehicle can travel on a single charge, so pure electric vehicles are far from meeting expectations in terms of driving range and charging time. Under existing technological conditions, there is still significant room for improvement in the power and endurance of pure electric vehicles. A hybrid power system combining flywheel batteries and lithium-ion batteries can be installed in electric vehicles to improve their performance. For this hybrid power system, there is an urgent need for an energy control strategy that effectively utilizes flywheel batteries to reduce the impact of high-power output on lithium-ion batteries. Summary of the Invention

[0003] In order to solve the problems existing in the above-mentioned prior art, the present invention provides an energy control strategy for a composite power supply system of a flywheel battery, which can reasonably distribute the required power from the power bus to the flywheel battery and the lithium-ion battery, and reduce the impact of high power output on the lithium-ion battery under climbing and acceleration conditions.

[0004] In order to achieve the above technical objectives, the present invention provides the following technical solutions:

[0005] An energy control strategy for a flywheel battery composite power system includes:

[0006] A fuzzy rule base is constructed and energy data is obtained, the energy data is fuzzified to obtain fuzzy energy data; based on the fuzzy rule base, fuzzy reasoning is performed on the fuzzy energy data to obtain fuzzy results of power distribution, and the energy of the composite power system is controlled through the clarified results.

[0007] Optionally, the energy data includes flywheel battery SOE data, lithium-ion battery SOC data and drive motor required power data;

[0008] Optionally, the fuzzy energy data is transformed from the actual domain to the fuzzy domain through domain transformation, and the transformation result is calculated through a corresponding membership function to obtain the fuzzy energy data.

[0009] Optionally, the process of building a fuzzy rule base includes:

[0010] Construct input variables and output variables, wherein the input variables are the flywheel battery SOE, the lithium-ion battery SOC and the required power of the drive motor, and the output variable is the flywheel battery power allocation coefficient; set corresponding fuzzy language values ​​based on the input variables and output variables; and construct a fuzzy rule base based on the fuzzy language values.

[0011] Optionally, the fuzzy rule base is divided into a fuzzy rule base under driving conditions and a fuzzy rule base under braking conditions according to operating conditions.

[0012] Optionally, the process of performing fuzzy reasoning on the energy fuzzy data includes:

[0013] The operating conditions of the energy fuzzy data are judged, and according to the judgment result, the type of the fuzzy rule base is selected, and the fuzzy language value of the output variable corresponding to the energy fuzzy data is searched in the corresponding fuzzy rule base to obtain the power allocation fuzzy result.

[0014] Optionally, the fuzzy result of power allocation is clarified by using a corresponding membership function to obtain a clarified result.

[0015] Optionally, the process of controlling the energy of the composite power system through the clarification result includes: adjusting the output power of the flywheel battery and the output power of the lithium-ion battery according to the clarification result to achieve control of the energy of the composite power system.

[0016] The present invention has the following technical effects:

[0017] The present invention uses the above technical solution to reasonably distribute the required power from the power bus to the flywheel battery and the lithium-ion battery, so as to give full play to the flywheel battery's protective effect of "peak shaving and valley filling" on the lithium-ion battery current, and reduce the impact of high power output on the lithium-ion battery under climbing and acceleration conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0019] Figure 1 A schematic diagram of a fuzzy logic control structure provided by an embodiment of the present invention;

[0020] Figure 2 A schematic diagram of the membership function corresponding to the required power of the motor under driving conditions provided by an embodiment of the present invention;

[0021] Figure 3A schematic diagram of the membership function corresponding to the SOC of a lithium-ion battery under driving conditions provided by an embodiment of the present invention;

[0022] Figure 4 A schematic diagram of the membership function corresponding to the SOE of the flywheel battery under driving conditions provided by an embodiment of the present invention;

[0023] Figure 5 A schematic diagram of the membership function corresponding to the power distribution coefficient under driving conditions provided by an embodiment of the present invention;

[0024] Figure 6 A schematic diagram of the membership function corresponding to the SOC of a lithium-ion battery under braking conditions provided by an embodiment of the present invention;

[0025] Figure 7 A schematic diagram of the membership function corresponding to the flywheel battery SOE under braking conditions provided by an embodiment of the present invention;

[0026] Figure 8 Schematic diagram of the membership function corresponding to the power distribution coefficient under braking conditions provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0027] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0028] In order to solve the problems existing in the prior art, the present invention provides the following solutions: Figure 1 The present invention provides an energy control strategy for a flywheel battery composite power system, and the above technical solution is explained in a specific real-time manner:

[0029] The present invention first determines the operating mode of the composite power supply. The required power for the drive motor changes with the driving conditions of the pure electric vehicle. Accordingly, the operating mode of the composite power supply varies under different driving conditions. Therefore, the present invention primarily designs the composite power supply to have the following three operating modes.

[0030] (1) Lithium-ion battery power supply mode: When a pure electric vehicle is running at a constant speed or light load, the power demand of the drive motor is relatively small. The power demand of the drive motor can be met by the lithium-ion battery alone, and no large current shock will be caused to the lithium-ion battery. Therefore, under this low power output condition, the auxiliary energy source flywheel battery is not required to provide driving energy. The main energy source lithium-ion battery alone can meet the energy demand of the pure electric vehicle, and no relevant strategy needs to be formulated. (2) Lithium-ion battery and flywheel battery power supply mode: When a pure electric vehicle is running in starting, acceleration or heavy load conditions, the power demand of the drive motor is relatively large. The power demand of the drive motor cannot be met by the lithium-ion battery alone. At this time, the auxiliary energy source flywheel battery needs to intervene and work together with the main energy source lithium-ion battery, and the flywheel battery is used to bear the peak power under this condition, thereby preventing the lithium-ion battery from the large current shock caused by the high power output. (3) Braking energy recovery mode: When a pure electric vehicle is running in a downhill or deceleration condition, the motor works in generator mode to convert braking energy into electrical energy to charge the composite power supply. When the composite power supply is charged, the flywheel battery absorbs electrical energy before the lithium-ion battery, and the lithium-ion battery is charged after the flywheel battery is fully charged.

[0031] Determine the goal of the energy control strategy: Before conducting in-depth research on the energy control strategy, the goal to be achieved by designing the energy control strategy should be clearly defined. Different goals will adapt to different control strategies. Therefore, the energy control strategy of the composite power system designed in this invention needs to meet the following goals: (1) It can reasonably allocate the required power from the power bus to the lithium-ion battery and the flywheel battery. When a pure electric vehicle is starting, climbing, and accelerating, the drive motor needs a large power output from the composite power supply to ensure the power of the vehicle. Therefore, under this condition, the energy control strategy should allocate most of the power output to the flywheel battery to reduce the high power output of the lithium-ion battery, thereby reducing the impact of the high current output on the lithium-ion battery. (2) Prioritize the energy recovered by braking to the flywheel battery. When a pure electric vehicle is braking under conditions such as downhill and deceleration, the energy generated by braking should be recovered by the flywheel battery before the lithium-ion battery. (3) Extend the cruising range of the pure electric vehicle. The addition of the flywheel battery as an auxiliary energy source is equivalent to adding a part of the driving energy to the original pure electric vehicle. Therefore, the designed energy control strategy should be able to achieve an improvement in cruising range.

[0032] Determine the category of energy control strategy: Fuzzy logic control strategy is a precise mathematical model based on engineering experience, knowledge, reasoning technology and control system state conditions, but does not rely on physical processes. The advantage of this control strategy is that it can achieve smooth transitions between various areas and states according to the set working area, that is, it can achieve smooth transitions between different fuzzy variables, and at the same time, it can also express some fuzzy concepts that cannot be determined by rules. Compared with the logic threshold control strategy, the fuzzy logic control strategy can give full play to the auxiliary role of the flywheel battery according to the power requirements of the vehicle cycle working conditions. Therefore, the present invention selects the fuzzy logic control strategy to allocate the required power of the drive motor.

[0033] Fuzzy logic control strategy: Fuzzy logic control mainly consists of four parts: fuzzification of input and output variables, fuzzy reasoning, fuzzy rule base, and clarity of output variables. The structure of fuzzy logic control is as follows: Figure 1 As shown in the figure, in the simulation of a pure electric vehicle powered by a hybrid flywheel battery and lithium-ion battery, the fuzzy logic control strategy is implemented using a fuzzy controller. Therefore, the fuzzy controller must be designed before simulation. Fuzzy Controller Design: Mamdani-type fuzzy controllers are often referred to as standard model controllers because fuzzy language is easy to understand and adjust. This paper selects the Mamdani-type fuzzy controller, which is widely used in the study of fuzzy logic control strategies for hybrid power sources.

[0034] The design of Mamdani type fuzzy controller, i.e. fuzzy logic control strategy, mainly includes the following five parts:

[0035] (1) Input variables and output variables

[0036] Under ideal conditions, that is, without considering the power loss of energy transfer, the power relationship that needs to be satisfied between the drive motor and the lithium-ion battery and flywheel battery is shown in formula (4.1).

[0037] P m =P bat +P fly (4.1)

[0038] Where, P m is the power required to drive the motor, W; P bat Power provided to lithium-ion batteries, W; P fly Power supplied to the flywheel battery, W.

[0039] In order to more accurately express the power distribution of lithium-ion batteries and flywheel batteries, this paper introduces K f To express the proportion of the power provided by the flywheel battery to the power required by the drive motor, the power allocated to the flywheel battery can be expressed as:

[0040] P fly =K f ·P m (4.2)

[0041] Where K f The value range is [0,1].

[0042] Combining equations (4.1) and (4.2), we can also use K to express the power allocated to the lithium-ion battery. f Indicates that

[0043] P bat =(1-K f )·P m (4.3)

[0044] In addition, the charge state of the flywheel battery and the lithium-ion battery will affect the discharge efficiency of the battery. To ensure the charge and discharge efficiency of the two batteries, the charge state of both batteries needs to be considered when allocating the discharge power. m In addition, the flywheel battery SOE and lithium-ion battery SOC should be set as input variables. In summary, the input variables of the fuzzy controller are P m , SOE, SOC, the output variable is K f .

[0045] (2) Fuzzy set domain and fuzzy linguistic value

[0046] The input variables can only be recognized by the fuzzy controller after fuzzification. To achieve the fuzzification of input variables, it is necessary to transform the domain of the precise input variables. From the analysis of the characteristics of lithium-ion batteries and flywheel batteries, it can be seen that the SOC of lithium-ion batteries has good charge and discharge characteristics in the range of [0.2, 1], and the SOE of flywheel batteries has good energy characteristics in the range of [0, 1]. However, in order to avoid the flywheel rotor running at the lowest speed, the SOE can be controlled in the range of [0.1, 1]. In summary, the input variables SOC, SOE and the output variable K f The actual domain is consistent with the fuzzy domain, so the fuzzy domains of SOC and SOE can be set to [0.2, 1] and [0.1, 1] respectively, and K f The domain is set to [0,1].

[0047] When the car is in driving condition, the fuzzy controller has three input variables SOC, SOE and P m , according to P m , its fuzzy domain can be set to [0, 1]. When the vehicle is braking, the drive motor has no power demand, so the fuzzy controller only needs two input variables, SOC and SOE. The actual domain and fuzzy domain of each variable are shown in Table 1.

[0048] Table 1

[0049]

[0050] Fuzzy language values ​​are essential for formulating fuzzy rules. The conditional statements in fuzzy rules cannot recognize numerical language, so setting appropriate fuzzy language values ​​is crucial to ensuring the smooth operation of the fuzzy controller. The number of fuzzy language values ​​affects the efficiency and accuracy of the fuzzy controller. A greater number of fuzzy language values ​​results in higher control accuracy but lower computational speed. Therefore, the fuzzy language values ​​for each variable designed in this paper are shown in Table 2.

[0051] Table 2

[0052]

[0053]

[0054] In the table, NS—very small, S—small, M—medium, B—large, PB—very large, TL—very low, L—low, M—medium, and H—high.

[0055] (3) Quantization factor

[0056] As can be seen from Table 1, the input variable P m The actual domain of the fuzzy domain is inconsistent, so it is necessary to transform the actual domain to achieve the fuzzy target. According to the definition of the proportional factor, combined with the data in Table 1, when the drive motor is in the driving state, the required power is completed from the actual domain [0,P max ] to the fuzzy domain [0,1], it is necessary to introduce the quantization factor k1. The calculation formula of the quantization factor is:

[0057]

[0058] Where, P max is the peak power of the drive motor, kW.

[0059] (4) Fuzzy rules

[0060] The function of fuzzy rules is to output the exact power allocation coefficient K according to the state of the input variables. f , instructing the flywheel battery and lithium-ion battery to provide corresponding input and output power. During driving, the composite power supply provides output power, while during braking, it absorbs and regenerates braking power. Furthermore, establishing fuzzy rules is crucial in fuzzy controller design, so this aspect warrants special attention in design and analysis. Based on the energy control strategy objectives, the fuzzy rules for the fuzzy controller are designed as follows.

[0061] 1) Driving Condition: When the flywheel battery SOE is low and the drive motor power demand is low, the lithium-ion battery provides all of the power output of the composite power supply. As the power demand increases, the lithium-ion battery still provides the majority of the power output, while the flywheel battery's power output proportion increases appropriately. When the flywheel battery SOE remains high and the drive motor power demand is low, the lithium-ion battery remains the primary source of power. When the power demand is high, the flywheel battery becomes the primary source of power. The fuzzy rule base developed for these driving conditions is shown in Table 3.

[0062] Table 3

[0063]

[0064] 2) Braking Condition: When the flywheel battery SOE and the lithium-ion battery SOC are low, regenerative braking energy first charges the flywheel battery, and if there is any remaining energy, the lithium-ion battery is then charged. Due to the flywheel battery's characteristic of supporting frequent charging and discharging, the flywheel battery absorbs regenerative braking energy before the lithium-ion battery throughout the braking process. Based on this, the fuzzy rule base for braking conditions is developed as shown in Table 4.

[0065] Table 4

[0066]

[0067] Adopting “ifP m is..., andSOCis..., andSOEis..., ThenK f The fuzzy rules are written into the Matlab fuzzy control toolbox using the "is..." language.

[0068] (5) Membership function

[0069] Membership is the degree of membership of each element point in the fuzzy domain mapped in the range of 0 to 1. The essence of the membership function is to indicate the degree of membership of a specific element. In the range of [0,1], the membership function can be set to any shape with a single peak. The main types of membership functions provided in the Matlab fuzzy control toolbox and commonly used in fuzzy control related research are: normal distribution type, triangle type and trapezoidal type. The stability and sensitivity of different types of membership functions are also different. The smoother the function curve, the better the control stability, and the lower the corresponding control sensitivity; conversely, the sharper the function curve, the higher the corresponding control sensitivity and the worse the control stability. This paper uses the membership function to complete the conversion of input variables from fuzzy domain to fuzzy language value, and uses the membership function to complete the defuzzification or clarification of output variables, and completes the driving conditions and the membership functions of each variable under the working conditions according to the above function types. Figure 2-8 shown.

[0070] This paper first analyzes the working mode of the composite power supply and clarifies the goal of the energy control strategy; then it selects fuzzy logic control to distribute the required power of the drive motor. In addition, a fuzzy control strategy simulation model is established through the design analysis of the fuzzy controller; finally, the construction of the composite power supply simulation model is completed by combining the power bus model, lithium-ion battery model, flywheel battery model and bidirectional DC / DC converter simulation model.

[0071] The basic principles, main features, and advantages of the present invention are shown and described above. Those skilled in the art should understand that the present invention is not limited to the foregoing embodiments. The foregoing embodiments and descriptions are merely illustrative of the principles of the present invention. Various changes and modifications may be made to the present invention without departing from the spirit and scope of the present invention. Such changes and modifications are intended to fall within the scope of the present invention. The scope of protection claimed in the present invention is defined by the appended claims and their equivalents.

Claims

1. An energy control strategy for a flywheel battery composite power system, characterized in that: include: Construct a fuzzy rule base and obtain energy data, fuzzify the energy data, and obtain fuzzy energy data; Based on the fuzzy rule base, fuzzy reasoning is performed on the fuzzy energy data to obtain a fuzzy result of power distribution, and the energy of the composite power system is controlled by clarifying the result; The process of building a fuzzy rule base includes: Constructing input variables and output variables, wherein the input variables are the flywheel battery SOE, the lithium-ion battery SOC, and the required power of the drive motor, and the output variable is the flywheel battery power allocation coefficient; setting corresponding fuzzy language values ​​based on the input variables and the output variables; and constructing a fuzzy rule base based on the fuzzy language values; The fuzzy rule base is divided into a fuzzy rule base under driving conditions and a fuzzy rule base under braking conditions according to the operating conditions; The fuzzy rule base formulated under the driving condition is as follows: The fuzzy rule base under the braking condition is formulated as follows: The energy data includes flywheel battery SOE data, lithium-ion battery SOC data and drive motor required power data; The fuzzy energy data is transformed from the actual domain to the fuzzy domain through the domain transformation, and the transformation result is calculated by the membership function to obtain the fuzzy energy data; The process of performing fuzzy reasoning on the energy fuzzy data includes: Determine the operating conditions of the energy fuzzy data, select the type of the fuzzy rule base according to the determination result, search the fuzzy language value of the output variable corresponding to the energy fuzzy data in the corresponding fuzzy rule base, and obtain the power allocation fuzzy result; Different types of membership functions are constructed through normal distribution, trapezoidal and triangular functions. The fuzzy results of power allocation are clarified through membership functions to obtain clarified results.

2. The energy control strategy of the flywheel battery composite power system according to claim 1 is characterized by: The process of controlling the energy of the composite power system by clarifying the results includes: According to the clarification results, the output power of the flywheel battery and the output power of the lithium-ion battery are adjusted to achieve energy control of the composite power system.

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