Battery management system design method applied to direct-current photovoltaic micro-grid

Abstract

The invention provides a battery management system design method applied to a direct-current photovoltaic micro-grid. The system comprises a data acquisition and monitoring module, an intelligent algorithm analysis module and a dynamic balance management module. Parameters such as voltage, current and temperature of the battery pack are collected in real time through a sensor, data are processed and analyzed through big data analysis and a machine learning algorithm, and the health state and the residual life of the battery pack are predicted. And the system dynamically adjusts the charging and discharging states of each battery unit according to an analysis result, so that the over-charging and over-discharging phenomena are avoided, and the service life of the battery pack is prolonged. According to the intelligent battery management system, the operation efficiency and reliability of the photovoltaic micro-grid are improved, and remarkable economic and social benefits are achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of battery management systems, and in particular to a battery management system design method applied to a direct current photovoltaic microgrid. Background Art

[0002] With the rapid development of renewable energy, photovoltaic power generation has been widely used around the world. As a way to efficiently utilize solar energy, photovoltaic microgrids can achieve power self-sufficiency in a certain area. However, photovoltaic power generation is intermittent and volatile, which places high demands on power quality and grid stability. Therefore, battery energy storage systems have been widely used in photovoltaic microgrids to smooth power output, improve power quality, and enhance system stability. In the existing battery management system (BMS) of DC photovoltaic microgrids, the battery management system (BMS) is not only responsible for monitoring the operating status of the battery, but also needs to optimize energy management and improve system reliability.

[0003] Patent CN117423915A solves the battery balancing problem in a multi-battery environment by introducing a modular balancing circuit topology. It focuses on using a decentralized control approach to reduce balancing time and improve efficiency, however it lacks a robust strategy for different operating conditions. Patent CN116825354A proposes an adaptive BMS that uses a machine learning algorithm to predict the battery state under different conditions, aiming to improve the accuracy of the state of charge (SOC) estimation. Although it provides enhanced prediction capabilities, the computational complexity is relatively high, which affects real-time performance.

[0004] In order to solve the above problems and improve the operating efficiency and safety of photovoltaic microgrids, the present invention proposes a battery management system for DC photovoltaic microgrids. The present invention adopts a balancing strategy based on fuzzy control, which dynamically adjusts the balancing current according to different battery conditions, thereby improving the reliability and efficiency of the entire system. By using the extended Kalman filter (EKF) for SOC estimation, the filter strikes a balance between accuracy and computational efficiency, making its real-time performance more excellent. Based on high-precision battery status monitoring, advanced energy balance control and intelligent control strategies, the system realizes the intelligentization of battery management and the optimization of system integration, providing strong technical support for large-scale photovoltaic grid connection. Summary of the invention

[0005] The purpose of the present invention is to provide a battery management system design method for a DC photovoltaic microgrid, so as to solve the deficiencies of the existing battery management system in terms of performance monitoring, energy balance and intelligent control strategy, and improve the operating efficiency and safety of the photovoltaic microgrid.

[0006] The technical solution adopted by the present invention is: a battery management system design method applied to a DC photovoltaic microgrid, comprising the following steps:

[0007] S1. Energy management of the photovoltaic energy storage system is carried out. The output power of the photovoltaic array is divided into three stages. For the convenience of analysis, the temperature input variable of the photovoltaic array is considered to be constant. The light intensity input of the photovoltaic array is divided into the peak light stage, the valley light stage and the transition stage from peak to valley to simulate the output power change of the photovoltaic. The photovoltaic working mode is determined through the photovoltaic energy storage energy control strategy link.

[0008] S2. Accurate estimation of battery SOC is crucial for battery management systems and is a prerequisite for effective control of battery cells. A model-based SOC estimation algorithm is used to perform online estimation of battery cell SOC. The most basic model-based estimation algorithm is the Kalman filter algorithm, but this algorithm is not suitable for nonlinear equivalent models such as batteries. The present invention performs linear processing on the model to obtain an extended Kalman filter algorithm (EKF).

[0009] S3. In actual projects, a battery pack usually contains multiple battery cells connected in series or in parallel to increase the output voltage and output power. The present invention mainly discusses the balancing control of the battery cells connected in series in the battery pack. Therefore, the balancing circuit topology of the series battery pack is designed in this step. To improve the balancing efficiency, the present invention adopts a hierarchical series balancing topology.

[0010] S4. For the purpose of protecting the battery cells, the balancing strategy mentioned in step 3 adopts a fixed duty cycle, and limits the balancing current of the battery from the parameter design, so that during the balancing process of the battery cells, the balancing current peak on the inductor is always within the threshold. However, in this form, only the minimum value of the balancing current that different battery cells can accept can be used for control, and it is impossible to perform balancing control on different battery cells separately according to their conditions. Therefore, the present invention introduces a balancing control strategy based on fuzzy control.

[0011] S5. In actual photovoltaic DC microgrids, in order to increase the energy storage capacity of the energy storage system, the energy storage units are mostly connected to the power grid in a distributed manner. Therefore, in order to avoid overcharging or over-discharging of the distributed energy storage units of the photovoltaic DC microgrid under island operation, the present invention proposes an improved droop control strategy to balance the distributed energy storage of the photovoltaic DC microgrid.

[0012] Furthermore, step S1 is specifically implemented as follows:

[0013] Photovoltaic energy storage control structure such as Figure 1As shown, the photovoltaic energy storage system is energy managed, and the output power of the photovoltaic array is divided into three stages. For the convenience of analysis, the temperature input variable of the photovoltaic array is considered unchanged, and the light intensity input of the photovoltaic array is considered to be divided into the peak light stage, the valley light stage, and the transition stage from the peak to the valley to simulate the output power change of the photovoltaic. Through the photovoltaic energy storage energy control strategy link, the photovoltaic working mode is determined, and the PWM signal of the photovoltaic side DC-DC converter is output.

[0014] When the output power of the photovoltaic array is not enough to meet the load demand, the energy storage unit is needed to supplement the photovoltaic power shortage to the DC microgrid. At this time, the DC-DC converter on the energy storage side should work in the Boost working mode, and the power is transmitted from the battery to the DC bus; when the photovoltaic output power is still surplus after meeting the load demand, it is necessary to absorb the surplus power according to the energy storage situation. At this time, the DC-DC converter on the energy storage side works in the Buck working mode, and the power is transmitted from the DC bus to the battery. If the energy storage unit does not have the conditions to absorb power, such as when the battery is about to be fully charged or the battery fails, the photovoltaic control mode should be switched to constant voltage and constant current dual closed-loop control, so that the photovoltaic can carry the load alone.

[0015] Further, step S2 is specifically implemented as follows:

[0016] Since the battery model is nonlinear, the Kalman filter algorithm is no longer suitable for estimating the battery model. Therefore, the battery SOC is estimated through the variant EKF of the Kalman filter algorithm. In the process, the nonlinear system needs to be linearized. The state expression of the nonlinear system is as follows:

[0017]

[0018] In the formula, f(X n ,U n ) is the state equation of the nonlinear system, g(X n ,U n ) is the state observation equation of the nonlinear system, W n is the nonlinear system noise, V n For nonlinear system state observation noise, we need to Perform a first-order Taylor transform at:

[0019]

[0020] Assume the state transition matrix of the system Assume the state observation matrix Substituting equations (2) and (3) into equation (1), the linearized system equation is:

[0021]

[0022] The Thevenin equivalent model of the battery is selected as the battery SOC estimation model. According to Kirchhoff's current law, it can be obtained:

[0023]

[0024] Where I is the current across the battery equivalent model, and U is the polarization voltage of the battery.

[0025] The battery SOC calculation formula is obtained according to the ampere-hour integration method:

[0026]

[0027] Among them, SOC n is the SOC value of the battery at time n, η n is the charging efficiency of the battery, T is the sampling time, C N is the battery capacity, I n-1 is the current value at time n-1.

[0028] Take the battery's [U, SOC] T As the state variable of the system, take the current I as the state input variable and the terminal voltage U t As the state observation variable of the system, the SOC estimation algorithm matrix based on EKF is calculated:

[0029]

[0030] By extending the Kalman filter algorithm, a model-based SOC estimation method, the working characteristics of the internal equivalent circuit model of the battery can be better combined, and a more accurate SOC value can be obtained. Combined with the estimation matrix of formula (7), the recursive steps of the EKF are as follows:

[0031] Predict the system's prior state estimate and prior state covariance matrix based on the initial state value:

[0032]

[0033] Calculate the Kalman gain matrix:

[0034]

[0035] Update the posterior state estimate and the posterior state covariance matrix:

[0036]

[0037] Further, step S3 is specifically implemented as follows:

[0038] like Figure 2As shown, taking 8 battery cells as an example, the present invention adopts the form of layered balancing of battery groups. Every two batteries in the first layer constitute a minimum balancing unit. Every two minimum balancing units in the second layer constitute a second-layer balancing unit. Two second-layer balancing units constitute a third-layer balancing unit. If there are more balancing units, multi-layer balancing can be constructed by analogy. The above constitutes the balancing topology circuit of the present invention.

[0039] The balancing strategy adopted by the present invention is to perform hierarchical balancing on the basis of a hierarchical topology circuit, and to control the switching of the switch tube of the balancing unit by comparing the difference in the battery SOC value at both ends of the inductor in each balancing unit. The switch tubes of the balancing units at different layers are switched independently of each other, and the balancing ends when the battery SOC difference in the battery pack is less than the threshold value set in advance, and the balancing circuit stops balancing. Through the hierarchical structure, non-adjacent battery cells can exchange energy, which can greatly improve the balancing efficiency of the battery pack.

[0040] Further, step S4 is specifically implemented as follows:

[0041] Fuzzy control does not require mathematical modeling of the controlled model. It is mainly composed of fuzzification, fuzzy reasoning, and defuzzification. The variables input to the fuzzy controller will be fuzzified, and fuzzy output will be obtained in the fuzzy reasoning link, and finally the specific output will be obtained through defuzzification.

[0042] The fuzzy controller of the present invention adopts the fuzzy control module provided in MATLAB, and the input variable selects the average value of the battery pack SOC The SOC difference ΔSOC between adjacent battery cells in the battery pack ij , the output variable selects the switch duty cycle D of the balancing circuit. Set the input The domain of discourse is (0,100), ΔSOC ij The domain of is (0,20), and the domain of output D is (0,0.45). Set the fuzzy subsets of input and output to VS, S, M, L, VL.

[0043] After the input data is fuzzified, it needs to be processed through a fuzzy reasoning link to obtain output data. The fuzzy rules of the fuzzy reasoning link are formulated by the present invention based on experience as shown in Table 1.

[0044] Table 1 Fuzzy rules table

[0045]

[0046]

[0047] The balancing strategy under the above fuzzy rule control is: when the SOC difference is very small, a smaller current is used for balancing; when the SOC difference is large but the SOC average value is small or large, a moderate current is used for balancing; when the SOC difference is large and the SOC average value is moderate, a larger current is used for balancing; when the SOC difference is moderate, a moderate current is used for balancing.

[0048] The balancing strategy formulated in this way can not only adapt to the balancing needs of battery cells under different SOC states, but also protect battery cells with larger and smaller SOCs to prevent overcharge or over-discharge of battery cells in the battery pack during the balancing process. At the same time, trickle balancing is adopted near the end of battery balancing, avoiding over-balancing of battery cells and increasing the service life of the battery pack.

[0049] Use the fuzzy control configuration tool in MATLAB to verify the above fuzzy control strategy. Figure 3 It is a three-dimensional relationship diagram of input and output under the above fuzzy rules. It can be seen from the figure that when the SOC difference is large, the SOC average value adopts large current balancing when it is moderate, and the balancing current gradually decreases in other cases. The control strategy is basically consistent with the balancing demand.

[0050] Further, step S5 is specifically implemented as follows:

[0051] In traditional droop control, the droop coefficient is not only a constant but also limited in value. When the initial SOC values ​​of the energy storage batteries are not equal, it may lead to uneven power distribution of the energy storage units. In addition, during the use of the energy storage batteries, the capacity and specifications of the energy storage units may be inconsistent. As time goes by, the inconsistency between the energy storage batteries will increase, causing their SOC differences to become increasingly larger, which may cause the energy storage batteries to be overcharged or over-discharged, which is not only not conducive to the stable operation of the DC microgrid system, but also greatly reduces the service life of the battery. Therefore, in order to achieve balanced control between DC microgrid energy storage units, the present invention proposes an improved adaptive droop control strategy.

[0052] The present invention adopts an adaptive droop coefficient based on the SOC of the energy storage battery to realize adaptive droop control. When the energy storage unit transmits power to the DC bus, the energy storage battery unit with a large SOC should output a higher power, while the energy storage battery unit with a small SOC should output a lower power. Similarly, when the energy storage unit absorbs power from the DC bus, the energy storage battery unit with a small SOC should absorb a higher power, while the energy storage battery unit with a large SOC should absorb a lower power. The droop coefficient should be designed accordingly. It can be obtained from formula (11) that the current of the energy storage battery unit is inversely proportional to the size of the droop coefficient. When the bus voltage remains stable, the power allocated to the energy storage battery unit is proportional to the current at both ends of them.

[0053]

[0054] On this basis, the adaptive droop coefficient based on the SOC of the energy storage battery is analyzed and designed. The balancing efficiency of the SOC of the energy storage battery is related to the selected droop coefficient. The larger the difference of the droop coefficients, the larger or smaller the ratio, and the faster the SOC balancing speed of the energy storage battery. It can be concluded from formula (12) that the selection of the droop coefficient has a certain limit, so the difference of the droop coefficient cannot be infinitely increased to speed up the balancing efficiency, and the adaptive droop coefficient based on SOC also needs to take the limit into account. Therefore, the present invention designs an adaptive droop coefficient based on SOC according to the properties of the inverse tangent function.

[0055] The adaptive droop coefficient of the present invention is designed as:

[0056]

[0057] Among them, R0 is the initial droop coefficient, SOC i is the SOC value of the i-th energy storage battery, SOC avg is the average value of the SOC of the energy storage battery, and the calculation formula is as shown in formula (13). h is the speed regulation factor. By controlling the size of the speed regulation factor h, the balanced efficiency of the SOC of the energy storage battery can be changed.

[0058]

[0059] Taking the adaptive droop coefficient of the energy storage unit during discharge as an example for analysis, the droop coefficient during discharge is mainly related to the initial droop coefficient R0, the speed regulation factor h and the SOC avg / SOC i If R0=2, we can get the droop coefficient and SOC under different speed regulation factors. avg / SOC i relationship.

[0060] The adaptive droop coefficient used in the present invention always changes in the interval (0,4), satisfying the requirements of formula (14).

[0061]

[0062] The droop coefficient curves with different h values ​​are analyzed. In order to effectively achieve balance, SOC avg / SOC i The energy storage battery unit with a SOC less than 1 requires a smaller droop coefficient, and the SOC avg / SOC i The energy storage battery unit with a value greater than 1 requires a larger droop coefficient. The larger h is, the smaller the droop coefficient curve is at SOC avg / SOC i=1, the greater the change range, the more it can meet the equilibrium requirements. However, when h is too large, the equilibrium strategy is too aggressive and the droop coefficient will be avg / SOC i =1 repeatedly jumps around, and the large change of the droop coefficient will cause the bus voltage to oscillate and the current on the energy storage battery side to fluctuate. Therefore, h should be selected as a moderate value. After comprehensive consideration, the speed regulation factor h of the present invention is taken as 200.

[0063] The premise of achieving SOC balancing of energy storage batteries through adaptive droop control is to ensure the stability of the bus voltage. The droop coefficient is equivalent to virtual impedance in the circuit, and the addition of virtual impedance will cause a certain drop in the voltage of the DC bus, which is also explained in equation (14). Therefore, the droop coefficient must be limited. The offset of the bus voltage is:

[0064] ΔU bus =i dci R di (15)

[0065] Therefore, based on the traditional droop control, the present invention introduces voltage compensation control, and the reference value of the bus voltage U ref With the actual value U dc The difference between the bus voltage and the bus voltage compensation value Δu is generated through the PI control loop. dci , to compensate for the voltage drop of the busbar. The overall control block diagram of the strategy proposed in this invention is as follows Figure 4 As shown, U dci is the output voltage of the i-th energy storage battery, Li is the inductance of the DC-DC converter on the i-th energy storage battery side, I Li is the inductor current of the DC-DC converter on the i-th energy storage battery side, i dci is the output current of the i-th energy storage battery DC-DC converter, R di is the droop coefficient of the i-th energy storage unit, R linei The line impedance between the i-th energy storage unit and the DC bus. U bus is the actual value of DC bus voltage, U ref DC bus voltage reference value, U refi is the output voltage reference value of the DC-DC converter on the i-th energy storage battery side, I ref is the output current reference value of the i-th energy storage battery DC-DC converter.

[0066] The specific control process is as follows: first, obtain the SOC parameters of the energy storage system, and then obtain the corresponding droop coefficient R of the energy storage battery according to the adaptive droop coefficient equation designed by formula (12): di The bus voltage reference value is different from the actual bus voltage value, and the bus voltage compensation value Δu is output through the PI controller. dci, the droop coefficient R output by the adaptive equalization control link di The output current i of the DC-DC converter side of the energy storage battery dci Multiply and then introduce the bus voltage reference value U ref And bus voltage compensation Δu dci , together constitute the output voltage reference value of the DC-DC converter on the energy storage battery side, as shown in formula (16). Then, the PWM duty cycle signal is output through the energy storage battery voltage and current dual-loop PI control link, and then the PWM modulator outputs the PWM signal of the DC-DC converter, thereby realizing adaptive droop control of the energy storage battery and achieving the purpose of balancing SOC.

[0067] U refi =U ref +Δu dci -i dci R di (16)

[0068] The beneficial effects of the present invention are mainly reflected in the following aspects:

[0069] 1. Improve the accuracy of battery status monitoring: The model-based SOC estimation algorithm, combined with the extended Kalman filter algorithm (EKF), can more accurately monitor the battery's state of charge (SOC), improve the monitoring accuracy of the battery management system, and ensure efficient use of the battery.

[0070] 2. Enhance energy balance efficiency: Through the hierarchical balancing topology circuit design and the balancing strategy based on fuzzy control, the battery cells in the series battery pack can be effectively balanced to avoid overcharging or over-discharging of the battery, improve the overall balancing efficiency of the battery pack, and extend the battery life.

[0071] 3. Improved droop control strategy: The proposed improved droop control strategy can effectively avoid overcharging or over-discharging of distributed energy storage units under island operation, realize balanced control of distributed energy storage units in photovoltaic DC microgrids, and improve the safety and stability of the energy storage system.

[0072] 4. System integration optimization: Through the optimized design of the battery management unit and system controller, the efficient integration of photovoltaic power generation system, energy storage system and DC microgrid can be achieved, the overall efficiency and stability of the system can be improved, and the operation requirements under various working conditions can be met.

[0073] The battery management system provided by the present invention has significant advantages in battery status monitoring, energy balance, communication reliability, intelligent control strategy and system integration. It can effectively improve the operating efficiency and safety of DC photovoltaic microgrids and provide strong technical support for large-scale photovoltaic grid-connected. BRIEF DESCRIPTION OF THE DRAWINGS

[0074] Figure 1 It is the control block diagram of photovoltaic energy storage;

[0075] Figure 2 It is a hierarchical balanced topology diagram;

[0076] Figure 3 Fuzzy control input-output relationship diagram;

[0077] Figure 4 It is the overall control block diagram of the improved adaptive droop control. DETAILED DESCRIPTION

[0078] The present invention is described in detail below with reference to the accompanying drawings and specific embodiments.

[0079] The technical solution adopted by the present invention is a battery management system design method applied to a DC photovoltaic microgrid, and the specific implementation technical solution is:

[0080] Furthermore, step S1 is specifically implemented as follows:

[0081] Photovoltaic energy storage control structure such as Figure 1 As shown, the photovoltaic energy storage system is energy managed, and the output power of the photovoltaic array is divided into three stages. For the convenience of analysis, the temperature input variable of the photovoltaic array is considered unchanged, and the light intensity input of the photovoltaic array is considered to be divided into the peak light stage, the valley light stage, and the transition stage from the peak to the valley to simulate the output power change of the photovoltaic. Through the photovoltaic energy storage energy control strategy link, the photovoltaic working mode is determined, and the PWM signal of the photovoltaic side DC-DC converter is output.

[0082] When the output power of the photovoltaic array is not enough to meet the load demand, the energy storage unit is needed to supplement the photovoltaic power shortage to the DC microgrid. At this time, the DC-DC converter on the energy storage side should work in the Boost working mode, and the power is transmitted from the battery to the DC bus; when the photovoltaic output power is still surplus after meeting the load demand, it is necessary to absorb the surplus power according to the energy storage situation. At this time, the DC-DC converter on the energy storage side works in the Buck working mode, and the power is transmitted from the DC bus to the battery. If the energy storage unit does not have the conditions to absorb power, such as when the battery is about to be fully charged or the battery fails, the photovoltaic control mode should be switched to constant voltage and constant current dual closed-loop control, so that the photovoltaic can carry the load alone.

[0083] Further, step S2 is specifically implemented as follows:

[0084] Since the battery model is nonlinear, the Kalman filter algorithm is no longer suitable for estimating the battery model. Therefore, the battery SOC is estimated through the variant EKF of the Kalman filter algorithm. In the process, the nonlinear system needs to be linearized. The state expression of the nonlinear system is as follows:

[0085]

[0086] In the formula, f(X n ,U n ) is the state equation of the nonlinear system, g(X n ,U n ) is the state observation equation of the nonlinear system, W n is the nonlinear system noise, V n For nonlinear system state observation noise, we need to Perform a first-order Taylor transform at:

[0087]

[0088] Assume the state transition matrix of the system Assume the state observation matrix Substituting equations (2) and (3) into equation (1), the linearized system equation is:

[0089]

[0090] The Thevenin equivalent model of the battery is selected as the battery SOC estimation model. According to Kirchhoff's current law, it can be obtained:

[0091]

[0092] Where I is the current across the battery equivalent model, and U is the polarization voltage of the battery.

[0093] The battery SOC calculation formula is obtained according to the ampere-hour integration method:

[0094]

[0095] Among them, SOC n is the SOC value of the battery at time n, η n is the charging efficiency of the battery, T is the sampling time, C N is the battery capacity, I n-1 is the current value at time n-1.

[0096] Take the battery's [U, SOC] T As the state variable of the system, take the current I as the state input variable and the terminal voltage U t As the state observation variable of the system, the SOC estimation algorithm matrix based on EKF is calculated:

[0097]

[0098] By extending the Kalman filter algorithm, a model-based SOC estimation method, the working characteristics of the internal equivalent circuit model of the battery can be better combined, and a more accurate SOC value can be obtained. Combined with the estimation matrix of formula (7), the recursive steps of the EKF are as follows:

[0099] Predict the system's prior state estimate and prior state covariance matrix based on the initial state value:

[0100]

[0101] Calculate the Kalman gain matrix:

[0102]

[0103] Update the posterior state estimate and the posterior state covariance matrix:

[0104]

[0105] Further, step S3 is specifically implemented as follows:

[0106] like Figure 2 As shown, taking 8 battery cells as an example, the present invention adopts the form of layered balancing of battery groups. Every two batteries in the first layer constitute a minimum balancing unit. Every two minimum balancing units in the second layer constitute a second-layer balancing unit. Two second-layer balancing units constitute a third-layer balancing unit. If there are more balancing units, multi-layer balancing can be constructed by analogy. The above constitutes the balancing topology circuit of the present invention.

[0107] The balancing strategy adopted by the present invention is to perform hierarchical balancing on the basis of a hierarchical topology circuit, and to control the switching of the switch tube of the balancing unit by comparing the difference in the battery SOC value at both ends of the inductor in each balancing unit. The switch tubes of the balancing units at different layers are switched independently of each other, and the balancing ends when the battery SOC difference in the battery pack is less than the threshold value set in advance, and the balancing circuit stops balancing. Through the hierarchical structure, non-adjacent battery cells can exchange energy, which can greatly improve the balancing efficiency of the battery pack.

[0108] Further, step S4 is specifically implemented as follows:

[0109] Fuzzy control does not require mathematical modeling of the controlled model. It is mainly composed of fuzzification, fuzzy reasoning, and defuzzification. The variables input to the fuzzy controller will be fuzzified, and fuzzy output will be obtained in the fuzzy reasoning link, and finally the specific output will be obtained through defuzzification.

[0110] The fuzzy controller of the present invention adopts the fuzzy control module provided in MATLAB, and the input variable selects the average value of the battery pack SOC The SOC difference ΔSOC between adjacent battery cells in the battery pack ij , the output variable selects the switch duty cycle D of the balancing circuit. Set the input The domain of discourse is (0,100), ΔSOC ij The domain of is (0,20), and the domain of output D is (0,0.45). Set the fuzzy subsets of input and output to VS, S, M, L, VL.

[0111] After the input data is fuzzified, it needs to be processed through a fuzzy reasoning link to obtain output data. The fuzzy rules of the fuzzy reasoning link are formulated by the present invention based on experience as shown in Table 1.

[0112] Table 1 Fuzzy rules table

[0113]

[0114]

[0115] The balancing strategy under the above fuzzy rule control is: when the SOC difference is very small, a smaller current is used for balancing; when the SOC difference is large but the SOC average value is small or large, a moderate current is used for balancing; when the SOC difference is large and the SOC average value is moderate, a larger current is used for balancing; when the SOC difference is moderate, a moderate current is used for balancing.

[0116] The balancing strategy formulated in this way can not only adapt to the balancing needs of battery cells under different SOC states, but also protect battery cells with larger and smaller SOCs to prevent overcharge or over-discharge of battery cells in the battery pack during the balancing process. At the same time, trickle balancing is adopted near the end of battery balancing, avoiding over-balancing of battery cells and increasing the service life of the battery pack.

[0117] Use the fuzzy control configuration tool in MATLAB to verify the above fuzzy control strategy. Figure 3 It is a three-dimensional relationship diagram of input and output under the above fuzzy rules. It can be seen from the figure that when the SOC difference is large, the SOC average value adopts large current balancing when it is moderate, and the balancing current gradually decreases in other cases. The control strategy is basically consistent with the balancing demand.

[0118] Further, step S5 is specifically implemented as follows:

[0119] In traditional droop control, the droop coefficient is not only a constant but also limited in value. When the initial SOC values ​​of the energy storage batteries are not equal, it may lead to uneven power distribution of the energy storage units. In addition, during the use of the energy storage batteries, the capacity and specifications of the energy storage units may be inconsistent. As time goes by, the inconsistency between the energy storage batteries will increase, causing their SOC differences to become increasingly larger, which may cause the energy storage batteries to be overcharged or over-discharged, which is not only not conducive to the stable operation of the DC microgrid system, but also greatly reduces the service life of the battery. Therefore, in order to achieve balanced control between DC microgrid energy storage units, the present invention proposes an improved adaptive droop control strategy.

[0120] The present invention adopts an adaptive droop coefficient based on the SOC of the energy storage battery to realize adaptive droop control. When the energy storage unit transmits power to the DC bus, the energy storage battery unit with a large SOC should output a higher power, while the energy storage battery unit with a small SOC should output a lower power. Similarly, when the energy storage unit absorbs power from the DC bus, the energy storage battery unit with a small SOC should absorb a higher power, while the energy storage battery unit with a large SOC should absorb a lower power. The droop coefficient should be designed accordingly. It can be obtained from formula (11) that the current of the energy storage battery unit is inversely proportional to the size of the droop coefficient. When the bus voltage remains stable, the power allocated to the energy storage battery unit is proportional to the current at both ends of them.

[0121]

[0122] On this basis, the adaptive droop coefficient based on the SOC of the energy storage battery is analyzed and designed. The balancing efficiency of the SOC of the energy storage battery is related to the selected droop coefficient. The larger the difference of the droop coefficients, the larger or smaller the ratio, and the faster the SOC balancing speed of the energy storage battery. It can be concluded from formula (12) that the selection of the droop coefficient has a certain limit, so the difference of the droop coefficient cannot be infinitely increased to speed up the balancing efficiency, and the adaptive droop coefficient based on SOC also needs to take the limit into account. Therefore, the present invention designs an adaptive droop coefficient based on SOC according to the properties of the inverse tangent function.

[0123] The adaptive droop coefficient of the present invention is designed as:

[0124]

[0125] Among them, R0 is the initial droop coefficient, SOC i is the SOC value of the i-th energy storage battery, SOC avg is the average value of the SOC of the energy storage battery, and the calculation formula is as shown in formula (13). h is the speed regulation factor. By controlling the size of the speed regulation factor h, the balanced efficiency of the SOC of the energy storage battery can be changed.

[0126]

[0127] Taking the adaptive droop coefficient of the energy storage unit during discharge as an example for analysis, the droop coefficient during discharge is mainly related to the initial droop coefficient R0, the speed regulation factor h and the SOC avg / SOC i If R0=2, we can get the droop coefficient and SOC under different speed regulation factors. avg / SOC i relationship.

[0128] The adaptive droop coefficient used in the present invention always changes in the interval (0,4), satisfying the requirements of formula (14).

[0129]

[0130] The droop coefficient curves with different h values ​​are analyzed. In order to effectively achieve balance, SOC avg / SOC i The energy storage battery unit with a SOC less than 1 requires a smaller droop coefficient, and the SOC avg / SOC i The energy storage battery unit with a value greater than 1 requires a larger droop coefficient. The larger h is, the smaller the droop coefficient curve is at SOC avg / SOC i =1, the greater the change range, the more it can meet the equilibrium requirements. However, when h is too large, the equilibrium strategy is too aggressive and the droop coefficient will be avg / SOC i =1 repeatedly jumps around, and the large change of the droop coefficient will cause the bus voltage to oscillate and the current on the energy storage battery side to fluctuate. Therefore, h should be selected as a moderate value. After comprehensive consideration, the speed regulation factor h of the present invention is taken as 200.

[0131] The premise of achieving SOC balancing of energy storage batteries through adaptive droop control is to ensure the stability of the bus voltage. The droop coefficient is equivalent to virtual impedance in the circuit, and the addition of virtual impedance will cause a certain drop in the voltage of the DC bus, which is also explained in equation (14). Therefore, the droop coefficient must be limited. The offset of the bus voltage is:

[0132] ΔU bus =i dci R di (31)

[0133] Therefore, based on the traditional droop control, the present invention introduces voltage compensation control, and the reference value of the bus voltage U ref With the actual value U dc The difference between the bus voltage and the bus voltage compensation value Δu is generated through the PI control loop. dci, to compensate for the voltage drop of the busbar. The overall control block diagram of the strategy proposed in this invention is as follows Figure 4 As shown, U dci is the output voltage of the i-th energy storage battery, Li is the inductance of the DC-DC converter on the i-th energy storage battery side, I Li is the inductor current of the DC-DC converter on the i-th energy storage battery side, i dci is the output current of the i-th energy storage battery DC-DC converter, R di is the droop coefficient of the i-th energy storage unit, R linei The line impedance between the i-th energy storage unit and the DC bus. U bus is the actual value of DC bus voltage, U ref DC bus voltage reference value, U refi is the output voltage reference value of the DC-DC converter on the i-th energy storage battery side, I ref is the output current reference value of the i-th energy storage battery DC-DC converter.

[0134] The specific control process is as follows: first, obtain the SOC parameters of the energy storage system, and then obtain the corresponding droop coefficient R of the energy storage battery according to the adaptive droop coefficient equation designed by formula (12): di The bus voltage reference value is different from the actual bus voltage value, and the bus voltage compensation value Δu is output through the PI controller. dci , the droop coefficient R output by the adaptive equalization control link di The output current i of the DC-DC converter side of the energy storage battery dci Multiply and then introduce the bus voltage reference value U ref And bus voltage compensation Δu dci , together constitute the output voltage reference value of the DC-DC converter on the energy storage battery side, as shown in formula (16). Then, the PWM duty cycle signal is output through the energy storage battery voltage and current dual-loop PI control link, and then the PWM modulator outputs the PWM signal of the DC-DC converter, thereby realizing adaptive droop control of the energy storage battery and achieving the purpose of balancing SOC.

[0135] U refi =U ref +Δu dci -i dci R di (32)

Claims

1. A battery management system design method for a DC photovoltaic microgrid, characterized in that: Follow the steps below to implement it: S1. Energy management of the photovoltaic energy storage system is carried out. The output power of the photovoltaic array is divided into three stages. For the convenience of analysis, the temperature input variable of the photovoltaic array is considered to be constant. The light intensity input of the photovoltaic array is divided into the peak light stage, the valley light stage and the transition stage from peak to valley to simulate the output power change of the photovoltaic. The photovoltaic working mode is determined through the photovoltaic energy storage energy control strategy link. S2. Accurate estimation of battery SOC is crucial for battery management systems and is a prerequisite for effective control of battery cells. A model-based SOC estimation algorithm is used to perform online estimation of battery cell SOC. The most basic model-based estimation algorithm is the Kalman filter algorithm, but this algorithm is not suitable for nonlinear equivalent models such as batteries. The present invention performs linear processing on the model to obtain an extended Kalman filter algorithm (EKF). S3. In actual projects, a battery pack usually contains multiple battery cells connected in series or in parallel to increase the output voltage and output power. The present invention mainly discusses the balancing control of the battery cells connected in series in the battery pack. Therefore, the balancing circuit topology of the series battery pack is designed in this step. To improve the balancing efficiency, the present invention adopts a hierarchical series balancing topology. S4. For the purpose of protecting the battery cells, the balancing strategy mentioned in step 3 adopts a fixed duty cycle, and limits the balancing current of the battery from the parameter design, so that during the balancing process of the battery cells, the balancing current peak on the inductor is always within the threshold. However, in this form, only the minimum value of the balancing current that different battery cells can accept can be used for control, and it is impossible to perform balancing control on different battery cells separately according to their conditions. Therefore, the present invention introduces a balancing control strategy based on fuzzy control. S5. In actual photovoltaic DC microgrids, in order to increase the energy storage capacity of the energy storage system, the energy storage units are mostly connected to the power grid in a distributed manner. Therefore, in order to avoid overcharging or over-discharging of the distributed energy storage units of the photovoltaic DC microgrid under island operation, the present invention proposes an improved droop control strategy to balance the distributed energy storage of the photovoltaic DC microgrid.

2. A battery management system design method for a DC photovoltaic microgrid according to claim 1, characterized in that: The step S1 is specifically implemented as follows: When the output power of the photovoltaic array is not enough to meet the load demand, the energy storage unit is needed to supplement the photovoltaic power shortage to the DC microgrid. At this time, the DC-DC converter on the energy storage side should work in the Boost working mode, and the power is transmitted from the battery to the DC bus; when the photovoltaic output power is still surplus after meeting the load demand, it is necessary to absorb the surplus power according to the energy storage situation. At this time, the DC-DC converter on the energy storage side works in the Buck working mode, and the power is transmitted from the DC bus to the battery. If the energy storage unit does not have the conditions to absorb power, such as when the battery is about to be fully charged or the battery fails, the photovoltaic control mode should be switched to constant voltage and constant current dual closed-loop control, so that the photovoltaic can carry the load alone.

3. A battery management system design method for a DC photovoltaic microgrid according to claim 1, characterized in that: The step S2 is specifically implemented as follows: Since the battery model is nonlinear, the Kalman filter algorithm is no longer suitable for estimating the battery model. Therefore, the battery SOC is estimated through the variant EKF of the Kalman filter algorithm. In the process, the nonlinear system needs to be linearized. The state expression of the nonlinear system is as follows: In the formula, f(X n ,U n ) is the state equation of the nonlinear system, g(X n ,U n ) is the state observation equation of the nonlinear system, W n is the nonlinear system noise, V n For nonlinear system state observation noise, we need to Perform a first-order Taylor transform at: Assume the state transition matrix of the system Assume the state observation matrix Substituting equations (2) and (3) into equation (1), the linearized system equation is: The Thevenin equivalent model of the battery is selected as the battery SOC estimation model. According to Kirchhoff's current law, it can be obtained: Where I is the current across the battery equivalent model, and U is the polarization voltage of the battery. The battery SOC calculation formula is obtained according to the ampere-hour integration method: Among them, SOC n is the SOC value of the battery at time n, η n is the charging efficiency of the battery, T is the sampling time, C N is the battery capacity, I n-1 is the current value at time n-1. Take the battery's [U, SOC] T As the state variable of the system, take the current I as the state input variable and the terminal voltage U t As the state observation variable of the system, the SOC estimation algorithm matrix based on EKF is calculated: By extending the Kalman filter algorithm, a model-based SOC estimation method, the working characteristics of the internal equivalent circuit model of the battery can be better combined, and a more accurate SOC value can be obtained. Combined with the estimation matrix of formula (7), the recursive steps of the EKF are as follows: Predict the system's prior state estimate and prior state covariance matrix based on the initial state value: Calculate the Kalman gain matrix: Update the posterior state estimate and the posterior state covariance matrix:

4. A battery management system design method for a DC photovoltaic microgrid according to claim 1, characterized in that: The step S3 is specifically implemented as follows: The present invention adopts the form of layered balancing of battery groups. Every two batteries in the first layer constitute a minimum balancing unit. Every two minimum balancing units in the second layer constitute a second-layer balancing unit. Two second-layer balancing units constitute a third-layer balancing unit. If there are more balancing units, multi-layer balancing can be constructed by analogy. The above constitutes the balancing topology circuit of the present invention. The balancing strategy adopted by the present invention is to perform hierarchical balancing on the basis of a hierarchical topology circuit, and to control the switching of the switch tube of the balancing unit by comparing the difference in the battery SOC value at both ends of the inductor in each balancing unit. The switch tubes of the balancing units at different layers are switched independently of each other, and the balancing ends when the battery SOC difference in the battery pack is less than the threshold value set in advance, and the balancing circuit stops balancing. Through the hierarchical structure, non-adjacent battery cells can exchange energy, which can greatly improve the balancing efficiency of the battery pack.

5. A battery management system design method for a DC photovoltaic microgrid according to claim 1, characterized in that: The step S4 is specifically implemented as follows: Fuzzy control does not require mathematical modeling of the controlled model. It is mainly composed of fuzzification, fuzzy reasoning, and defuzzification. The variables input to the fuzzy controller will be fuzzified, and fuzzy output will be obtained in the fuzzy reasoning link, and finally the specific output will be obtained through defuzzification. The fuzzy controller of the present invention adopts the fuzzy control module provided in MATLAB, and the input variable selects the average value of the battery pack SOC The SOC difference ΔSOC between adjacent battery cells in the battery pack ij , the output variable selects the switch duty cycle D of the balancing circuit. Set the input The domain of discourse is (0,100), ΔSOC ij The domain of is (0,20), and the domain of output D is (0,0.45). Set the fuzzy subsets of input and output to VS, S, M, L, VL. The balancing strategy under the above fuzzy rule control is: when the SOC difference is very small, a smaller current is used for balancing; when the SOC difference is large but the SOC average value is small or large, a moderate current is used for balancing; when the SOC difference is large and the SOC average value is moderate, a larger current is used for balancing; when the SOC difference is moderate, a moderate current is used for balancing. The balancing strategy formulated in this way can not only adapt to the balancing needs of battery cells under different SOC states, but also protect battery cells with larger and smaller SOCs to prevent overcharge or over-discharge of battery cells in the battery pack during the balancing process. At the same time, trickle balancing is adopted near the end of battery balancing, avoiding over-balancing of battery cells and increasing the service life of the battery pack.

6. A battery management system design method for a DC photovoltaic microgrid according to claim 1, characterized in that: The step S5 is specifically implemented as follows: In traditional droop control, the droop coefficient is not only a constant but also limited in value. When the initial SOC values ​​of the energy storage batteries are not equal, it may lead to uneven power distribution of the energy storage units. In addition, during the use of the energy storage batteries, the capacity and specifications of the energy storage units may be inconsistent. As time goes by, the inconsistency between the energy storage batteries will increase, causing their SOC differences to become increasingly larger, which may cause the energy storage batteries to be overcharged or over-discharged, which is not only not conducive to the stable operation of the DC microgrid system, but also greatly reduces the service life of the battery. Therefore, in order to achieve balanced control between DC microgrid energy storage units, the present invention proposes an improved adaptive droop control strategy. The present invention adopts an adaptive droop coefficient based on the SOC of the energy storage battery to realize adaptive droop control. When the energy storage unit transmits power to the DC bus, the energy storage battery unit with a large SOC should output a higher power, while the energy storage battery unit with a small SOC should output a lower power. Similarly, when the energy storage unit absorbs power from the DC bus, the energy storage battery unit with a small SOC should absorb a higher power, while the energy storage battery unit with a large SOC should absorb a lower power. The droop coefficient should be designed accordingly. It can be obtained from formula (11) that the current of the energy storage battery unit is inversely proportional to the size of the droop coefficient. When the bus voltage remains stable, the power allocated to the energy storage battery unit is proportional to the current at both ends of them. On this basis, the adaptive droop coefficient based on the SOC of the energy storage battery is analyzed and designed. The balancing efficiency of the SOC of the energy storage battery is related to the selected droop coefficient. The larger the difference of the droop coefficients, the larger or smaller the ratio, and the faster the SOC balancing speed of the energy storage battery. It can be concluded from formula (12) that the selection of the droop coefficient has a certain limit, so the difference of the droop coefficient cannot be infinitely increased to speed up the balancing efficiency, and the adaptive droop coefficient based on SOC also needs to take the limit into account. Therefore, the present invention designs an adaptive droop coefficient based on SOC according to the properties of the inverse tangent function. The adaptive droop coefficient of the present invention is designed as: Among them, R0 is the initial droop coefficient, SOC i is the SOC value of the i-th energy storage battery, SOC avg is the average value of the SOC of the energy storage battery, and the calculation formula is as shown in formula (13). h is the speed regulation factor. By controlling the size of the speed regulation factor h, the balanced efficiency of the SOC of the energy storage battery can be changed. Taking the adaptive droop coefficient of the energy storage unit during discharge as an example for analysis, the droop coefficient during discharge is mainly related to the initial droop coefficient R0, the speed regulation factor h and the SOC avg / SOC i If R0=2, we can get the droop coefficient and SOC under different speed regulation factors. avg / SOC i relationship. The adaptive droop coefficient used in the present invention always changes in the interval (0,4), satisfying the requirements of formula (14). The droop coefficient curves with different h values ​​are analyzed. In order to effectively achieve balance, SOC avg / SOC i The energy storage battery unit with a SOC less than 1 requires a smaller droop coefficient, and the SOC avg / SOC i The energy storage battery unit with a value greater than 1 requires a larger droop coefficient. The larger h is, the smaller the droop coefficient curve is at SOC avg / SOC i =1, the greater the change range, the more it can meet the equilibrium requirements. However, when h is too large, the equilibrium strategy is too aggressive and the droop coefficient will be avg / SOC i =1 repeatedly jumps around, and the large change of the droop coefficient will cause the bus voltage to oscillate and the current on the energy storage battery side to fluctuate. Therefore, h should be selected as a moderate value. After comprehensive consideration, the speed regulation factor h of the present invention is taken as 200. The premise of achieving SOC balancing of energy storage batteries through adaptive droop control is to ensure the stability of the bus voltage. The droop coefficient is equivalent to virtual impedance in the circuit, and the addition of virtual impedance will cause a certain drop in the voltage of the DC bus, which is also explained in equation (14). Therefore, the droop coefficient must be limited. The offset of the bus voltage is: ΔU bus =and dci R di (15) Therefore, based on the traditional droop control, the present invention introduces voltage compensation control, and the reference value of the bus voltage U ref With the actual value U dc The difference between the bus voltage and the bus voltage compensation value Δu is generated through the PI control loop. dci , to compensate for the voltage drop of the busbar. The specific control process is as follows: first, the SOC parameters of the energy storage system are obtained, and the corresponding droop coefficient R of the energy storage battery is obtained according to the adaptive droop coefficient equation designed by formula (12): di The bus voltage reference value is different from the actual bus voltage value, and the bus voltage compensation value Δu is output through the PI controller. dci , the droop coefficient R output by the adaptive equalization control link di The output current i of the DC-DC converter side of the energy storage battery dci Multiply and then introduce the bus voltage reference value U ref And bus voltage compensation Δu dci , together constitute the output voltage reference value of the DC-DC converter on the energy storage battery side, as shown in formula (16). Then, the PWM duty cycle signal is output through the energy storage battery voltage and current dual-loop PI control link, and then the PWM modulator outputs the PWM signal of the DC-DC converter, thereby realizing adaptive droop control of the energy storage battery and achieving the purpose of balancing SOC. AT refi =U ref +Δu dci -and dci R di (16)

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