Self-adaptive control method for voltage threshold of energy storage converter

Abstract

The invention provides a voltage threshold adaptive control method for an energy storage converter, and the method comprises the steps: collecting the terminal voltage USC of a super capacitor and the DC bus voltage Udc of a traction system in real time, and obtaining the SOC of the super capacitor and the bus voltage Udc0 in a no-load state; fuzzy definition and fuzzy division are carried out on the super capacitor state of charge SOC and the traction bus voltage, and a membership function is set; dividing a fuzzy domain according to the threshold voltage output by the fuzzy controller, and constructing a rule base of the fuzzy controller; fuzzy reasoning is carried out by using a maximum-minimum reasoning method, and defuzzification is carried out by using a centroid method to obtain a threshold voltage value USCDischarge; performing control logic setting based on the threshold voltage, and dynamically adjusting the threshold value of the discharge voltage of the super capacitor; a polling frequency is set, the steps are polled, and the threshold value of the discharge voltage of the stage capacitor is updated in real time; according to the invention, precise control of the voltage of the super capacitor is realized, the voltage stability is ensured, and the system efficiency and reliability are improved.

Description

Technical Field

[0001] This invention relates to the field of power electronic energy feedback technology, and in particular to an adaptive control method for voltage threshold of energy storage converter. Background Technology

[0002] With the rapid development of urban rail transit, subways, as an efficient, fast, low-carbon, and environmentally friendly public transportation tool, are playing an increasingly important role in urban transportation systems. The traction system of subway trains is one of the most critical components in the entire subway operation, directly affecting the train's energy efficiency, stability, and operating costs. However, traditional subway power supply systems use 24-pulse rectification technology, allowing energy to flow in only one phase. With frequent train braking and acceleration, braking energy is fed back to the DC bus through the energy feedback system. Since a large amount of energy is generated during train braking, if it is not utilized effectively and promptly, this energy may be wasted or have a negative impact on the system: the bus voltage fluctuates drastically under traction and braking conditions, affecting the safe and reliable operation of the train converter. However, all train energy feedback DC buses sometimes face a mismatch between energy demand and supply. When the energy feedback from a braking train exceeds the traction demand of other trains, the voltage of the traction bus rises rapidly, leading to increased voltage fluctuations and affecting the train power supply throughout the entire section of the line.

[0003] To address these issues, energy storage devices such as supercapacitors are typically introduced into subway traction systems as temporary storage devices for braking energy. Supercapacitors possess rapid charging and discharging capabilities, enabling them to absorb large amounts of braking energy in a short time and release it when the bus power is insufficient. However, most existing supercapacitor control strategies are based on fixed charging and discharging voltage thresholds. This method cannot fully leverage the advantages of supercapacitors under dynamically changing traction bus voltages. When the traction bus voltage fluctuates, the fixed charging and discharging thresholds are difficult to adjust adaptively, resulting in low supercapacitor charging and discharging efficiency and a larger lower limit for voltage fluctuations. Furthermore, and more significantly, supercapacitors may even draw energy from the AC grid for charging instead of fully utilizing the energy generated by train braking, thus severely increasing the system's energy consumption.

[0004] Therefore, a control method capable of dynamically adapting to changes in traction bus voltage is urgently needed to optimize the charging and discharging strategy of supercapacitors, improve the utilization efficiency of braking energy, and ensure the stability of the traction system. This invention addresses this need by proposing an adaptive control method based on fuzzy control and an energy feedback voltage threshold. The supercapacitor's discharge voltage threshold is dynamically adjusted in real time to ensure that braking energy can be rationally and effectively fed back and stored. Simultaneously, this invention sets a relatively high supercapacitor charging voltage threshold to ensure that braking energy is preferentially consumed by other trains on the line and to reduce the possibility of the supercapacitor drawing energy from the AC grid. Through this adaptive control method, fluctuations in traction bus voltage are significantly reduced, system stability and energy utilization are improved, thus providing a strong guarantee for the efficient operation of the traction system. Summary of the Invention

[0005] To address the shortcomings of existing technologies, the technical problem this invention aims to solve is to provide an adaptive control method for the voltage threshold of an energy storage converter. This method aims to improve the utilization efficiency of braking energy in the traction system, reduce traction bus voltage fluctuations, and ensure stable system operation. This invention introduces fuzzy control technology to dynamically adjust the discharge voltage threshold of the supercapacitor based on its state of charge (SOC) and the traction bus voltage. This not only reduces traction bus voltage fluctuations but also optimizes energy utilization and improves the reliability of the traction system.

[0006] To solve the above-mentioned technical problems, in a first aspect, the technical solution adopted by the present invention is an adaptive control method for voltage threshold of energy storage converter, comprising the following steps:

[0007] S1: Real-time acquisition of the supercapacitor's terminal voltage USC and the traction system's DC bus voltage Udc, to obtain the supercapacitor's state of charge SOC and the bus voltage Udc0 under no-load conditions.

[0008] S2: Define and classify the state of charge (SOC) of the supercapacitor and the voltage of the traction bus in a fuzzy manner, and set the membership function.

[0009] S3: Divide the fuzzy domain based on the threshold voltage output by the fuzzy controller and construct the rule base of the fuzzy controller;

[0010] S4: Use the maximum-minimum reasoning method for fuzzy reasoning and use the centroid method for defuzzification to obtain the threshold voltage value USC_Discharge;

[0011] S5: Based on the threshold voltage, the control logic is set to dynamically adjust the threshold value of the supercapacitor's discharge voltage;

[0012] S6: Set the polling frequency, poll steps S1-S5, and update the threshold value of the discharge voltage of the stage capacitor in real time.

[0013] Optionally, the State of Charge (SOC) of the supercapacitor is fuzzy-divided, and the SOC of the supercapacitor is defined as three fuzzy sets: when the fuzzy universe of discourse is 0≤SOC<0.7, the state is set as low (L); when the fuzzy universe of discourse is 0.6≤SOC<0.8, the state is set as medium (M); when the fuzzy universe of discourse is 0.7≤SOC<1, the state is set as high (H).

[0014] Optionally, for the three fuzzy sets, the membership function is set as follows:

[0015] -Low (L):

[0016] 0&\text{if}SOC<0\\\frac{SOC}{0.6}&\text{if}0\leq SOC<0.6\\\frac{0.7-SOC}{0.1}&\text{if}0.6\leq SOC<0.7\\0&\text{if}SOC\geq 0.7\end{cases}\]

[0017] -Mid (M): \[\mu_M(SOC)=\begin{cases}0&\text{if}SOC<0.6\\\frac{SOC-0.6}{0.1}&\text{if}0.6\leq SOC<0.7\\1&\text{if}0.7\leq SOC<0.8\\\frac{0.8-SOC}{0.1}&\text{if}0.8\leq SOC<0.9\\0&\text{if}SOC\geq 0.9\end{cases}\]

[0018] -High (H): \[\mu_H(SOC)=\begin{cases}0&\text{if}SOC<0.7\\\frac{SOC-0.7}{0.1}&\text{if}0.7\leq SOC<0.8\\1&\text{if}0.8\leq SOC<0.9\\\frac{1-SOC}{0.1}&\text{if}0.9\leq SOC<1\\0&\text{if}SOC\geq 1\end{cases}\].

[0019] Optionally, the fuzzy classification of the traction bus voltage includes: B1 (very low), B2 (low), B3 (medium), B4 (high), and B5 (very high); and corresponding membership functions are set.

[0020] Optionally, divide the fuzzy domain according to the threshold voltage output by the fuzzy controller, and construct the rule base of the fuzzy controller:

[0021] R1: If SOC is L and Udc is B1, then USC_Discharge is OP1;

[0022] R2: If SOC is M and Udc is B3, then USC_Discharge is OP2;

[0023] R3: If SOC is H and Udc is B5, then USC_Discharge is OP3.

[0024] Optionally, use the centroid method for defuzzification to obtain the threshold voltage value USC_Discharge:

[0025] \[U_{SC_{Discharge}}=\frac{\sum_{i=1}^{n}U_{i}\cdot\mu_i(U_{i})}{\sum_{i=1}^{n}\mu_i(U_{i})}\]

[0026] where \(U_{i}\) is the \(i\)-th element in the output universe, and \(\mu_i(U_{i})\) is the corresponding membership degree.

[0027] Optionally, based on the threshold voltage, set the control logic, dynamically adjust the threshold value of the discharge voltage of the supercapacitor, and set the starting supercapacitor charging voltage USC_Charge = Udc0 + 80, based on the real-time DC bus voltage:

[0028] Charging logic: USC_Charge < Udc ≤ Udc_max, start charging the supercapacitor;

[0029] Discharge logic: Udc ≤ USC_Discharge, start discharging the supercapacitor;

[0030] Safety logic: Udc > Udc_max, the supercapacitor exits operation, and the bus voltage is reduced through the chopping resistor; after Udc drops to the safety threshold, start charging the supercapacitor again.

[0031] In a second aspect, the present invention also provides an electronic device, including:

[0032] One or more processors;

[0033] A memory; and one or more programs stored in the memory, the one or more programs including instructions for executing any of the above-mentioned energy storage converter voltage threshold adaptive control methods.

[0034] Thirdly, the present invention also provides a computer-readable storage medium comprising one or more programs executable by one or more processors of an electronic device, said one or more programs comprising instructions for performing any of the above-described adaptive control methods based on energy storage converter voltage thresholds.

[0035] Compared with the prior art, the technical solution of the present invention has the following beneficial effects:

[0036] 1. By using the state of charge (SOC) of the supercapacitor and the traction bus voltage as inputs to the fuzzy controller and constructing a fuzzy control rule base, the discharge voltage threshold can be adaptively adjusted, which can dynamically match the real-time operation requirements of the system. This not only ensures that the braking energy is preferentially used by other trains in the traction system, but also enhances the supercapacitor's ability to obtain energy from the load braking side, effectively improving the utilization rate of braking energy.

[0037] 2. By dynamically adjusting the discharge voltage threshold of the supercapacitor, flexible control can be achieved based on the real-time status of the traction bus voltage, thereby reducing voltage fluctuations. Smaller voltage fluctuations contribute to the stable operation of the traction system, avoiding the risks of shock or instability caused by drastic voltage changes. Attached Figure Description

[0038] Figure 1 A flowchart of an adaptive voltage threshold control method for an energy storage converter provided in an embodiment of the present invention;

[0039] Figure 2 This is a schematic diagram of an energy feedback system topology provided in an embodiment of the present invention;

[0040] Figure 3 A schematic diagram of the input membership function of a fuzzy controller provided in an embodiment of the present invention;

[0041] Figure 4 A schematic diagram of the output membership function of a fuzzy controller provided in an embodiment of the present invention;

[0042] Figure 5 This is a schematic diagram of a charge / discharge hysteresis control structure for an energy feedback device provided in an embodiment of the present invention. Detailed Implementation

[0043] Obviously, many modifications and variations made by those skilled in the art based on the spirit of this invention fall within the scope of protection of this invention.

[0044] Those skilled in the art will understand that, unless specifically stated otherwise, the singular forms “a,” “an,” “the,” and “the” used herein may also include the plural forms. It should be further understood that the term “comprising” as used in this specification means the presence of the stated features, integers, steps, operations, elements, and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof. It should be understood that when an element or component is referred to as “connected” to another element or component, it may be directly connected to the other element or component, or there may be intermediate elements or components. The term “and / or” as used herein includes any and all combinations of one or more of the associated listed items.

[0045] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0046] The technical solution of the present invention will be described in detail below with reference to the accompanying drawings. A method for adaptive voltage threshold control of an energy storage converter, such as... Figure 1 As shown, fuzzy definitions are derived based on the state of charge (SOC) of the supercapacitor and the voltage of the traction bus. Fuzzy partitioning is performed based on the threshold voltage of the fuzzy controller output. A rule base for the fuzzy controller is established, and the relationship between the input and output variables of the fuzzy controller is obtained. The threshold value of the supercapacitor's discharge voltage is then dynamically adjusted. The specific steps are as follows:

[0047] See appendix Figure 2 This is a schematic diagram of the topology of the energy feedback system, where energy can only flow in one direction and cannot be fed back to the AC grid. First, the state of charge (SOC) of the supercapacitor and the traction bus voltage are fuzzy defined. The terminal voltage USC and charging / discharging current of the supercapacitor are collected in real time, and the SOC of the supercapacitor is calculated. The DC bus voltage Udc of the traction system and its change process are collected in real time, and the bus voltage Udc0 under no-load conditions is recorded.

[0048] Fuzzy definition: State of charge (SOC): The charging level of the supercapacitor, calculated by real-time acquisition of the supercapacitor's terminal voltage (USC); Traction bus voltage (Udc): The DC bus voltage of the traction system is sampled and recorded as Udc0 under no-load conditions.

[0049] See appendix Figure 3The supercapacitor SOC input to the fuzzy controller is fuzzy partitioned, and the state of SOC is defined as three fuzzy sets as follows: when the fuzzy universe of discourse is 0 ≤ SOC < 0.7, the state is set to low (L); when the fuzzy universe of discourse is 0.6 ≤ SOC < 0.8, the state is set to medium (M); when the fuzzy universe of discourse is 0.7 ≤ SOC < 1, the state is set to high (H). The corresponding membership functions are further set as follows:

[0050] -Low (L):

[0051] 0&\text{if}SOC<0\\\frac{SOC}{0.6}&\text{if}0\leq SOC<0.6\\\frac{0.7-SOC}{0.1}&\text{if}0.6\leq SOC<0.7\\0&\text{if}SOC\geq 0.7\end{cases}\]

[0052] -Mid (M): \[\mu_M(SOC)=\begin{cases}0&\text{if}SOC<0.6\\\frac{SOC-0.6}{0.1}&\text{if}0.6\leq SOC<0.7\\1&\text{if}0.7\leq SOC<0.8\\\frac{0.8-SOC}{0.1}&\text{if}0.8\leq SOC<0.9\\0&\text{if}SOC\geq 0.9\end{cases}\]

[0053] -High (H): \[\mu_H(SOC)=\begin{cases}0&\text{if}SOC<0.7\\\frac{SOC-0.7}{0.1}&\text{if}0.7\leq SOC<0.8\\1&\text{if}0.8\leq SOC<0.9\\\frac{1-SOC}{0.1}&\text{if}0.9\leq SOC<1\\0&\text{if}SOC\geq 1\end{cases}\].

[0054] Similarly, the traction bus voltage range and bus voltage levels are fuzzily defined as B1 to B5, and the corresponding membership functions are further set as follows:

[0055] -B1 (Very Low):

[0056] \[\mu_{B1}(U_{dc})=\begin{cases}

[0057] 1 & if \(U_{dc} \leq U_{dc0}+\Delta U_{1}\)

[0058] \(\frac{U_{dc0}+\Delta U_{2}-U_{dc}}{\Delta U_{2}-\Delta U_{1}}\) & if \(U_{dc0}+\Delta U_{1}<U_{dc} \leq U_{dc0}+\Delta U_{2}\)

[0059] 0 & if \(U_{dc}>U_{dc0}+\Delta U_{2}\)

[0060] \end{cases}\];

[0061] - B2 (low):

[0062] \(\mu_{B2}(U_{dc})=\begin{cases}\)

[0063] 0 & if \(U_{dc} \leq U_{dc0}+\Delta U_{2}\)

[0064] \(\frac{U_{dc}-(U_{dc0}+\Delta U_{2})}{\Delta U_{3}-\Delta U_{2}}\) & if \(U_{dc0}+\Delta U_{2}<U_{dc} \leq U_{dc0}+\Delta U_{3}\)

[0065] 1 & if \(U_{dc}>U_{dc0}+\Delta U_{3}\)

[0066] \end{cases}\);

[0067] - B3 (medium):

[0068] \(\mu_{B3}(U_{dc})=\begin{cases}\)

[0069] 1 & if \(U_{dc} \leq U_{dc0}+\Delta U_{4}\)

[0070] \frac{U_{dc0}+\Delta U_{4}-U_{dc}}{\Delta U_{4}-\Delta U_{3}}&\text{if}U_{dc0}+\Delta U_{3}<U_{dc}\leq U_{dc0}+\Delta U_{4}\\

[0071] 0&\text{if}U_{dc}>U_{dc0}+\Delta U_{4}

[0072] \end{cases}\];

[0073] -B4 (High):

[0074] \[\mu_{B4}(U_{dc})=\begin{cases}

[0075] 0&\text{if}U_{dc}\leq U_{dc0}+\Delta U_{4}\\

[0076] \frac{U_{dc}-(U_{dc0}+\Delta U_{4})}{\Delta U_{5}-\Delta U_{4}}&\text{if}U_{dc0}+\Delta U_{4}<U_{dc}\leq U_{dc0}+\Delta U_{5}\\

[0077] 1&\text{if}U_{dc}>U_{dc0}+\Delta U_{5}

[0078] \end{cases}\];

[0079] -B5 (Very High):

[0080] \[\mu_{B5}(U_{dc})=\begin{cases}

[0081] 0&\text{if}U_{dc}\leq U_{dc0}+\Delta U_{5}\\

[0082] \frac{U_{dc}-(U_{dc0}+\Delta U_{5})}{U_{dc_{max}}-\Delta U_{5}}&\text{if}U_{dc0}+\Delta U_{5}<U_{dc}\leq U_{dc_{max}}\\

[0083] 1&\text{if}U_{dc}>U_{dc_{max}}

[0084] \end{cases}\];

[0085] Where U_{dc0} is the bus voltage under no-load conditions, and Delta U_{1}, Delta U_{2}, Delta U_{3}, Delta U_{4}, Delta U_{5} are the threshold values ​​of the voltage range, which need to be determined based on the actual system parameters.

[0086] See appendix Figure 4 The output voltage threshold value of the fuzzy controller is divided into three levels as the output variable: OP1, OP2, and OP3. Based on the operating experience of the energy feed system, the rule base of the fuzzy controller is obtained. The rule base of the fuzzy controller must meet the following rules: when the SOC state of the supercapacitor is high, it indicates that the supercapacitor is basically fully charged, and the discharge threshold value of the supercapacitor can be increased; when the SOC state of the supercapacitor is low, it indicates that its energy is insufficient, and its discharge threshold value should be appropriately reduced; when the supercapacitor is fully charged or discharged to the lower limit, it needs to be taken out of operation.

[0087] In some embodiments, a rule base for the fuzzy controller is constructed based on the operational experience of the energy feeder system:

[0088] R1: If SOC is L and Udc is B1, then USC_Discharge is OP1.

[0089] R2: If SOC is M and Udc is B3, then USC_Discharge is OP2.

[0090] R3: If SOC is H and Udc is B5, then USC_Discharge is OP3.

[0091] Detailed settings are as follows:

[0092] SOC / Udc B1 B2 B3 B4 B5 L OP1 OP1 OP1 OP2 OP2 M OP1 OP2 OP2 OP3 OP3 H OP2 OP2 OP3 OP3 OP3

[0093] Fuzzy reasoning: Using a max-min reasoning method, for each rule, if the preconditions are met, the rule is applied. The output is the minimum of the fuzzy set of all rule outputs.

[0094] Defuzzification: Defuzzification is performed using the centroid method. The calculation formula is as follows:

[0095] \[U_{SC_{Discharge}}=\frac{\sum_{i = 1}^{n}U_{i}\cdot\mu_i(U_{i})}{\sum_{i = 1}^{n}\mu_i(U_{i})}\]

[0096] Among them, \(U_{i}\) is the \(i\)-th element in the output universe, and \(\mu_i U_{i}\) is the corresponding membership degree.

[0097] The threshold voltage of the output fuzzy is defuzzified by the above centroid method to obtain the voltage threshold value \(USC_Discharge\), and the control logic is further set. See the appendix Figure 5 , set the starting voltage of the supercapacitor charging, that is, the upper limit of the voltage threshold. The threshold value for starting the supercapacitor charging remains unchanged, and \(USC_Charge = Udc0 + 80\); judge the DC bus voltage at any time:

[0098] Charging logic: When the DC bus voltage \(USC_Charge < Udc\leq Udc\_max\), start the supercapacitor charging;

[0099] Discharging logic: When the DC bus voltage \(Udc\leq USC_Discharge\), start the supercapacitor discharging;

[0100] Safety logic: When the DC bus voltage \(Udc > Udc\_max\), the bus voltage rises significantly. To ensure safe operation, the supercapacitor exits the operation, and the bus voltage is reduced through the chopper resistor; when the bus voltage is reduced to the safety threshold by the chopper resistor, then start the supercapacitor to continue charging.

[0101] Polling mechanism: Continuously poll the above steps to dynamically update the discharge voltage threshold value of the supercapacitor in real time;

[0102] Update frequency: Set the polling frequency according to the system response time and control accuracy requirements.

[0103] Parameter adjustment and optimization: Adjust the parameters in the membership function according to the actual operation data, such as adjusting the parameter settings of the B1 - B2 voltage level, to ensure that the division of the fuzzy set is more in line with the actual operation; adjust the rules in the fuzzy rule base according to the system performance to optimize the control effect.

[0104] System performance evaluation: Evaluate the stability of the system under different SOCs and bus voltages, evaluate the response time of the system to the change of the bus voltage, evaluate the energy consumption of the system, ensure the efficiency of energy management, and based on this, further feedback and adjust the settings of the SOC and Udc fuzzy domains.

[0105] Through the above steps, the application of fuzzy logic control in adaptive control of supercapacitor voltage threshold in rail transit can be implemented in detail, thereby achieving precise control of supercapacitor voltage, ensuring voltage stability, and improving system efficiency and reliability.

[0106] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since it corresponds to the method disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to in the method section.

[0107] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0108] Finally, it should be noted that in this document, relationships such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

Claims

1. An adaptive control method for voltage threshold of an energy storage converter, characterized in that, include: S1: Real-time acquisition of the supercapacitor's terminal voltage USC and the traction system's DC bus voltage Udc, to obtain the supercapacitor's state of charge SOC and the bus voltage Udc0 under no-load conditions. S2: Define and classify the state of charge (SOC) of the supercapacitor and the voltage of the traction bus in a fuzzy manner, and set the membership function. S3: Divide the fuzzy domain based on the threshold voltage output by the fuzzy controller and construct the rule base of the fuzzy controller; S4: Use the maximum-minimum reasoning method for fuzzy reasoning and use the centroid method for defuzzification to obtain the threshold voltage value USC_Discharge; S5: Based on the threshold voltage, the control logic is set to dynamically adjust the threshold value of the supercapacitor's discharge voltage; S6: Set the polling frequency, poll steps S1-S5, and update the threshold value of the discharge voltage of the stage capacitor in real time.

2. The adaptive voltage threshold control method for energy storage converter according to claim 1, characterized in that, The State of Charge (SOC) of the supercapacitor is fuzzy partitioned, and the SOC of the supercapacitor is defined as three fuzzy sets: when the fuzzy universe of discourse is 0≤SOC<0.7, the state is set as low (L); when the fuzzy universe of discourse is 0.6≤SOC<0.8, the state is set as medium (M); when the fuzzy universe of discourse is 0.7≤SOC<1, the state is set as high (H).

3. The adaptive voltage threshold control method for energy storage converter according to claim 2, characterized in that, For the three fuzzy sets mentioned above, the membership function is set as follows: -Low (L): 0&\text{if}SOC<0\\\frac{SOC}{0.6}&\text{if}0\leq SOC<0.6\\\frac{0.7-SOC}{0.1}&\text{if}0.6\leq SOC<0.7\\0&\text{if}SOC\geq 0.7\end{cases}\] -Mid (M): \[\mu_M(SOC)=\begin{cases}0&\text{if}SOC<0.6\\\frac{SOC-0.6}{0.1}&\text{if}0.6\leq SOC<0.7\\1&\text{if}0.7\leq SOC<0.8\\\frac{0.8-SOC}{0.1}&\text{if}0.8\leq SOC<0.9\\0&\text{if}SOC\geq 0.9\end{cases}\] -High (H): \[\mu_H(SOC)=\begin{cases}0&\text{if}SOC<0.7\\\frac{SOC -0.7}{0.1}&\text{if}0.7\leq SOC<0.8\\1&\text{if}0.8\leq SOC<0.9\\\frac{1-SOC}{0.1}&\text{if}0.9\leq SOC<1\\0&\text{if}SOC\geq 1\end{cases}\].

4. The adaptive voltage threshold control method for energy storage converter according to claim 1, characterized in that, The fuzzy division of the traction bus voltage includes: B1 (very low), B2 (low), B3 (medium), B4 (high), B5 (very high); and the corresponding membership functions are set.

5. The adaptive voltage threshold control method for energy storage converter according to claim 1, characterized in that, The fuzzy domain is divided according to the threshold voltage output by the fuzzy controller, and the rule base of the fuzzy controller is constructed. R1: If SOC is L and Udc is B1, then USC_Discharge is OP1; R2: If SOC is M and Udc is B3, then USC_Discharge is OP2; R3: If SOC is H and Udc is B5, then USC_Discharge is OP3.

6. The adaptive voltage threshold control method for energy storage converter according to claim 1, characterized in that, The centroid method is used for defuzzification to obtain the threshold voltage value USC_Discharge. \[U_{SC_{Discharge}}=\frac{\sum_{i=1}^{n}U_{i}\cdot\mu_i(U_{i})}{\sum_{i=1}^{n}\mu_i(U_{i})}\] Where, U_{i} is the i-th element in the output universe, and mu_i U_{i} is the corresponding membership degree.

7. The adaptive voltage threshold control method for energy storage converter according to claim 1, characterized in that, Based on the above threshold voltage, the control logic is set, the threshold value of the discharge voltage of the super capacitor is dynamically adjusted, and the starting super capacitor charging voltage USC_Charge = Udc0 + 80 is set. Based on the real-time DC bus voltage, it is set as follows: Charging logic: USC_Charge < Udc ≤ Udc_max, start charging the super capacitor; Discharging logic: Udc ≤ USC_Discharge, start discharging the super capacitor; Safety logic: Udc > Udc_max, the super capacitor exits the operation, and the bus voltage is reduced through the chopping resistor; after Udc is reduced to the safety threshold, start charging the super capacitor again.

8. An electronic device, characterized in that, It includes: One or more processors; A memory; And one or more programs stored in the memory, the one or more programs include instructions for executing the energy storage converter voltage threshold adaptive control method according to any one of claims 1-7.

9. A computer-readable storage medium, characterized in that, It includes one or more programs for execution by one or more processors of the power supply device, the one or more programs include instructions for executing the energy storage converter voltage threshold adaptive control method according to any one of claims 1-7.

Images