Battery energy storage system control method and equipment

Abstract

The invention relates to the technical field of battery energy storage, in particular to a battery energy storage system control method and equipment. According to the invention, only real-time input-output data generated in a system operation process is utilized to construct a BESS dynamic equivalent model, so that a controller thoroughly avoids dependence on internal parameters and a mechanism model of a battery, a Softplus nonlinear quantizer is designed and is combined with a dynamic encryption coding and decoding mechanism to form a novel quantitative encryption communication strategy, and the reliability of the system is improved. The stability and controllability of quantization errors in the whole dynamic input range are ensured, and eavesdropping attacks in the communication process can be effectively resisted; besides, the invention also provides a semi-independent dual-channel dynamic event triggering mechanism, on the basis of maintaining dual-channel autonomous triggering, the dual channels are forced to cooperatively trigger during input updating, and the real-time performance of time-varying pseudo partial derivative estimation data is ensured; meanwhile, a dynamic attenuation threshold is introduced to replace a fixed threshold so as to adjust a steady-state error. The control performance of the battery energy storage system is effectively improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of battery energy storage, and particularly relates to a battery energy storage system control method and device. BACKGROUND

[0002] Photovoltaic power generation promotes the rapid expansion of installed capacity due to its significant environmental benefits and continuously declining power generation costs. However, the inherent intermittent fluctuation characteristics of solar energy result in strong randomness of photovoltaic output, which not only threatens power supply continuity, but also may cause secondary problems such as power grid frequency fluctuation. Therefore, the battery energy storage system (BESS) is introduced, which realizes power smoothing, energy efficiency improvement and power supply reliability enhancement through energy storage / release.

[0003] In the battery energy storage system, photovoltaic components adopt the maximum power point tracking (MPPT) control strategy, and the BESS is used to smooth the power fluctuation caused by the random fluctuation characteristics of photovoltaic power generation. In the energy storage system, the storage battery is connected with the DC bus through a buck-boost converter. By adjusting the PWM switching duty cycle of the insulated gate bipolar transistor (IGBT) in the converter, the switching between the power absorption and release modes of the BESS can be realized, the inductor current of the converter can be accurately tracked, and finally the BESS can accurately match the photovoltaic output and the load demand. When the photovoltaic output exceeds the load demand, the energy storage device absorbs the excess power, and vice versa, the stored energy is released to make up for the power generation gap. In this energy conversion process, the bidirectional DC-DC converter is the core execution unit of the energy storage interface device, and its control performance directly affects the overall efficiency of the system.

[0004] To improve the performance of bidirectional DC-DC converter in renewable energy grid-connected, various control strategies have been researched and practiced in academia. Control strategies are mainly divided into model-based control methods and model-free control methods. Model-based control methods rely on accurate mathematical models of the system to achieve regulation, such as deadbeat control (DBC), model predictive control (MPC), and sliding mode control (SMC). These methods are concerned because they can quickly track dynamic load changes and maintain bus voltage stability. However, the DBC method relies on an accurate system model, and if the model deviates significantly from the actual working conditions, the control accuracy will decrease significantly. The MPC method requires solving an optimization problem every control cycle, which can cause control delays and increase the difficulty of implementation in actual engineering. The SMC method produces high-frequency chattering near the sliding surface due to the switching mechanism, which directly affects control performance, and like DBC and MPC, its control effect is highly dependent on the accuracy of the system model. The three types of control methods mentioned above are limited by the dependence on accurate system models and face the risk of performance degradation when parameters are mismatched.

[0005] Because model-based control methods are limited by the dependence on accurate system models, the lack of prior model accuracy can lead to system control performance degradation or even instability, especially in microgrids with high proportions of intermittent renewable energy. It is often extremely challenging to establish an accurate system model. Therefore, model-free control strategies are introduced. In the classic control architecture, proportional-integral (PI) controllers are widely used in bidirectional DC-DC converter current regulation due to their simple parameter tuning and structure. The PI controller generates a PWM duty cycle command by controlling the difference between the actual value and the reference value of the inductor current of the bidirectional DC-DC converter. However, PI controllers lack adaptive ability, and the parameters of PI control need to be pre-tuned and fixed, lacking adaptive ability, and have obvious limitations in complex systems with strong nonlinearity and time-varying parameter uncertainty.

[0006] To solve the parameter solidification problem of PI control, some studies use fuzzy logic control (FLC) to replace PI control. The deviation and rate of change of the deviation between the actual value and the reference value of the inductor current of the bidirectional DC-DC converter are taken as inputs, converted into fuzzy language variables through a preset membership function, and converted into a PWM duty cycle based on a preset fuzzy control rule library. This reduces the tidal oscillation time and frequency fluctuation amplitude. However, the performance of FLC is highly dependent on the completeness of the preset knowledge base. When the battery ages and the temperature changes, the preset knowledge base cannot adapt to changes in battery parameters. Moreover, the complexity of the rules increases exponentially with the increase in input dimensions, making it difficult to accurately control the PWM duty cycle of the IGBT in the bidirectional DC-DC converter. This results in inaccurate tracking of the reference current by the inductor current, distortion of signal transmission accuracy, and deviation of control instructions, thereby reducing the overall control accuracy. SUMMARY

[0007] To overcome the above technical problems, the present application provides a battery energy storage system control method, comprising:

[0008] To solve the above technical problems, the present application provides a battery energy storage system control method, comprising: The PPD estimation module stores the inductor current reconstruction signal of the bidirectional DC-DC converter at time t and its corresponding current reconstruction increment obtained by decoding the first decoder. The second decoder decodes the PWM duty cycle reconstruction value of the bidirectional DC-DC converter at time t. The second decoder decodes the PWM duty cycle reconstruction value of the bidirectional DC-DC converter at time t. t represents time t. Based on The increment of the inductor current reconstruction signal of the bidirectional DC-DC converter at time t, The PWM duty cycle reconstruction increment of the bidirectional DC-DC converter at time t, The time-varying pseudo partial derivative at time t, The time-varying pseudo partial derivative at time t is calculated. The time-varying pseudo partial derivative at time t is calculated. The time-varying pseudo partial derivative at time t, the inductor current reconstruction signal of the bidirectional DC-DC converter, and its corresponding current reconstruction increment, and the PWM duty cycle reconstruction value of the bidirectional DC-DC converter are sent to the controller. The time-varying pseudo partial derivative at time t, the inductor current reconstruction signal of the bidirectional DC-DC converter, and its corresponding current reconstruction increment, and the PWM duty cycle reconstruction value of the bidirectional DC-DC converter are sent to the controller. Controller based The time-varying pseudo-partial derivative at time t, the reconstructed inductor current signal of the bidirectional DC-DC converter and its corresponding current reconstruction increment, the reference signal of the inductor current of the bidirectional DC-DC converter and... Reconstructed PWM duty cycle value of the bidirectional DC-DC converter at any given time, calculated The PWM duty cycle of the bidirectional DC-DC converter is determined and transmitted to the second encoder. Second encoder pair After the PWM duty cycle of the bidirectional DC-DC converter is encoded, it is sent to the second decoder through the communication network. The second decoder then processes the PWM duty cycle. After decoding and reconstructing the quantized value of the PWM duty cycle of the bidirectional DC-DC converter at any given time, The reconstructed PWM duty cycle value of the bidirectional DC-DC converter is sent to the actuator at any given time. Actuator based on The PWM duty cycle reconstruction value of the bidirectional DC-DC converter is used to adjust the switching state of the bidirectional DC-DC converter, thereby realizing the charging and discharging control of the battery energy storage system.

[0009] Preferably, the sensor acquires the output of the battery energy storage system. The signal of the inductor current of the bidirectional DC-DC converter is sent to the output channel event triggering module. Output channel event triggering module based on The signal of the inductor current of the bidirectional DC-DC converter at any time and The absolute value of the difference between the inductor current reconstructed signals of the bidirectional DC-DC converter at any given time. First dynamic decay parameter at time The system continuously outputs the channel event trigger status and reference threshold to determine whether the dynamic event triggering conditions of the output channel are met; among which... Indicates the first The time when the dynamic event triggering conditions of the output channel are met is the distance from The most recent moment when the dynamic event triggering conditions of the output channel are met; If satisfied, then let ,Will The inductor current signal of the bidirectional DC-DC converter is sent to the first encoder at any time; the first encoder processes the received signal. The signal encoding and processing of the inductor current of the bidirectional DC-DC converter at any given time is sent to the first decoder via a communication network; wherein, For the first The moment when the dynamic event triggering conditions of the output channel are met; First decoder pair Decoding and reconstructing the first quantized value of the inductor current signal of the bidirectional DC-DC converter at any given time yields... The inductor current reconstruction signal of the bidirectional DC-DC converter is reconstructed at any time and sent to the PPD estimation module; If not satisfied, the PPD estimation module will... The inductor current signal of the bidirectional DC-DC converter at each moment and its corresponding current reconstruction increment are respectively used as The inductor current signal of the bidirectional DC-DC converter at any given time and its corresponding current reconstruction increment will be... The time-varying pseudopartial derivative at time t is used as The time-varying pseudopartial derivative at time t; Input channel event trigger reception The inductor current reconstruction signal of the bidirectional DC-DC converter at any time is based on The absolute value of the difference between the reconstructed inductor current signal and the reference inductor current signal of the bidirectional DC-DC converter at any time, the second dynamic attenuation parameter, the input channel event triggering status and the input reference threshold are used to determine whether the input channel dynamic event triggering condition is met. If satisfied, then let ,Will The time-varying pseudo-partial derivatives at time t and the reconstructed inductor current signal of the bidirectional DC-DC converter are sent to the controller, which calculates... The duty cycle of the PWM switching control signal of the bidirectional DC-DC converter at any given time; where... Indicates the first The moment when the dynamic event triggering conditions of the input channel are met; If not satisfied, the executor is based on The PWM duty cycle reconstruction value of the bidirectional DC-DC converter is used to adjust the switching state of the bidirectional DC-DC converter, thereby realizing the charging and discharging control of the battery energy storage system.

[0010] Preferably, the dynamic event triggering condition for the output channel is: , , in, Indicates the infimum, For the set of natural numbers, for The channel event trigger status is constantly output. The first scaling factor is... , for The state variable is output at any time, and its update formula is: , , for the input state variable at time k, is a first set constant, , , , is the signal of the inductor current of the bidirectional DC-DC converter at time k, is the reconstructed signal of the inductor current of the bidirectional DC-DC converter at time k, is an output reference threshold, is a first dynamic decay parameter at time k, , is a first dynamic decay factor, is a natural constant, is an absolute value.

[0011] Preferably, the input channel dynamic event triggering condition is: , , wherein, denotes the lower bound, is a set of natural numbers, is the input channel event triggering state at time k, is a second scaling coefficient, , is the input state variable at time k, whose update formula is: , , is the input state variable at time k, is a second set constant, , , , is the reference signal of the inductor current of the bidirectional DC-DC converter at time k, is the reconstructed signal of the inductor current of the bidirectional DC-DC converter at time k, is an input reference threshold, is a second dynamic decay parameter at time k, , is a second dynamic decay factor, is a natural constant, is an absolute value.

[0012] Preferably, the input state variable at time k is based on the input state variable at time k, the increment of the inductor current reconstruction signal of the bidirectional DC-DC converter at the time instant t, the increment of the PWM duty cycle reconstruction of the bidirectional DC-DC converter at the time instant t, the time-varying pseudo partial derivative at the time instant t, calculated as the time-varying pseudo partial derivative at the time instant t, calculated as , , wherein, is the time-varying pseudo partial derivative at the time instant t, , denotes the transpose, is the time-varying pseudo partial derivative at the time instant t, is the time-varying pseudo partial derivative corresponding to the inductor current of the bidirectional DC-DC converter, is the time-varying pseudo partial derivative corresponding to the PWM duty cycle of the bidirectional DC-DC converter, is a first step factor, is a first weight coefficient, , is the increment of the inductor current reconstruction signal of the bidirectional DC-DC converter at the time instant t, is the increment of the PWM duty cycle reconstruction of the bidirectional DC-DC converter at the time instant t, , is the PWM duty cycle reconstruction of the bidirectional DC-DC converter at the time instant t, is the PWM duty cycle reconstruction of the bidirectional DC-DC converter at the time instant t, is the transpose of, is the increment of the inductor current reconstruction signal of the bidirectional DC-DC converter at the time instant t, , is the inductor current reconstruction signal of the bidirectional DC-DC converter at the time instant t, is the inductor current reconstruction signal of the bidirectional DC-DC converter at the time instant t, is a positive number, is the sign function, denotes the norm.

[0013] Preferably, the controller is based on the time-varying pseudo partial derivative at the time instant t, the inductor current reconstruction signal of the bidirectional DC-DC converter, the reference signal of the inductor current of the bidirectional DC-DC converter and The PWM duty cycle reconstruction value of the bidirectional DC-DC converter at the moment t, is calculated The PWM duty cycle of the bidirectional DC-DC converter at the moment t, is calculated , wherein, The PWM duty cycle of the bidirectional DC-DC converter at the moment t, The PWM duty cycle reconstruction value of the bidirectional DC-DC converter at the moment t, , The time-varying pseudo partial derivative at the moment t, The time-varying pseudo partial derivative corresponding to the inductor current of the bidirectional DC-DC converter, The time-varying pseudo partial derivative corresponding to the PWM duty cycle of the bidirectional DC-DC converter, The second step factor, The third step factor, The increment of the inductor current reconstruction signal of the bidirectional DC-DC converter at the moment t, , The reference signal of the inductor current of the bidirectional DC-DC converter at the moment t, The inductor current reconstruction signal of the bidirectional DC-DC converter at the moment t, The absolute value.

[0014] Preferably, the encoder input signal at the moment t is processed by an encoder to obtain the quantized value of the encoder input signal at the moment t; wherein the encoder is a Softplus quantizer. The encoder input signal at the moment t is processed by a Softplus quantizer to obtain the quantized value of the encoder input signal at the moment t, and the formula is: The encoder input signal at the moment t is processed by a Softplus quantizer to obtain the quantized value of the encoder input signal at the moment t, and the formula is: , , wherein, The encoder input signal at the moment t, The quantized value of the encoder input signal at the moment t, The sign function, The absolute value, The scaling factor, , The natural constant, ​​​​​​​​​​​is a curvature adjustment factor, , is the quantization level of the encoder input signal at time is the absolute value of the amplitude of the quantization interval, is the lower bound of the absolute value of the amplitude of the quantization interval, is the upper bound of the absolute value of the amplitude of the quantization interval, is the time when the signal is input into the encoder for processing for the time, is the set boundary value, .

[0015] Preferably, the calculation formula of the quantization level of the encoder input signal at time , wherein, is the quantization level of the encoder input signal at time is the time when the signal is input into the encoder for processing for the time, is a scaling factor, , is a natural constant, is a curvature adjustment factor, , is the encoder input signal at time is a rounding up symbol, is the set boundary value, .

[0016] Preferably, the input signal of the encoder at time is differentially encrypted for preprocessing, to obtain the encrypted signal at time , and the formula is: , the encrypted signal at time is encoded by the encoder for processing, to obtain the quantization value and the quantization level of the encrypted signal at time ; the quantization value and the quantization level of the encrypted signal at time are sent to the decoder through a communication network; the decoder decodes and reconstructs the quantization value of the encrypted signal at time based on the received quantization value and the quantization level of the encrypted signal at time , to obtain quantization recovery signal of the encrypted signal at the time point, quantization recovery signal of the input signal of the encoder at the time point, , , wherein, is the quantization recovery signal of the encrypted signal at the time point, quantization recovery signal of the encrypted signal at the time point, is the quantization recovery signal of the encrypted signal at the time point, quantization recovery signal of the encrypted signal at the time point, is the time point at which the signal is input into the encoder for the n-th time, is the time point at which the signal is input into the encoder for the n-th time, is the time point at which the signal is input into the encoder for the n-th time, is the time point at which the signal is input into the encoder for the n-th time, is the set bounded encrypted signal at the time point, , , is the quantization value of the encrypted signal at the time point. The application also provides a battery energy storage system control device, comprising:

[0017] a PPD estimation module, which stores the quantization recovery signal of the encrypted signal at the time point obtained by decoding of the first decoder, a current reconstruction signal of the bidirectional DC-DC converter at the time point and a current reconstruction increment corresponding to the current reconstruction signal, a PWM duty cycle reconstruction value of the bidirectional DC-DC converter at the time point obtained by decoding of the second decoder; wherein, indicates the time point; based on the current reconstruction increment of the current reconstruction signal of the bidirectional DC-DC converter at the time point, the PWM duty cycle reconstruction increment of the bidirectional DC-DC converter at the time point, a time-varying pseudo partial derivative at the time point, and a time-varying pseudo partial derivative at the time point is calculated. the time-varying pseudo partial derivative at the time point, the current reconstruction signal of the bidirectional DC-DC converter at the time point and the current reconstruction increment corresponding to the current reconstruction signal, and the PWM duty cycle reconstruction value of the bidirectional DC-DC converter are sent to a controller; the controller calculates based on the time-varying pseudo partial derivative at the time point, the current reconstruction signal of the bidirectional DC-DC converter at the time point and the current reconstruction increment corresponding to the current reconstruction signal, a reference signal of the current of the bidirectional DC-DC converter and the PWM duty cycle reconstruction value of the bidirectional DC-DC converter at the time point. the controller calculates ​​​The PWM duty cycle of the bidirectional DC-DC converter at the moment is encoded and transmitted to the second encoder; The second encoder decodes the quantized value of the PWM duty cycle of the bidirectional DC-DC converter at the moment and reconstructs the PWM duty cycle of the bidirectional DC-DC converter at the moment; The PWM duty cycle of the bidirectional DC-DC converter at the moment is encoded and transmitted to the second encoder; The second encoder decodes the quantized value of the PWM duty cycle of the bidirectional DC-DC converter at the moment and reconstructs the PWM duty cycle of the bidirectional DC-DC converter at the moment; The PWM duty cycle of the bidirectional DC-DC converter at the moment is encoded and transmitted to the second encoder; The PWM duty cycle of the bidirectional DC-DC converter at the moment is encoded and transmitted to the second encoder; The PWM duty cycle of the bidirectional DC-DC converter at the moment is encoded and transmitted to the second encoder; The PWM duty cycle of the bidirectional DC-DC converter at the moment is encoded and transmitted to the second encoder;

[0018] The above technical solutions of the present application have the following beneficial effects compared with the prior art: The battery energy storage system control method and device of the present application are based on the nonlinear dynamic characteristics of the battery energy storage system, and only the incremental value of the inductor current of the bidirectional DC-DC converter at the moment, The PWM duty cycle of the bidirectional DC-DC converter at the moment is encoded and transmitted to the second encoder; The PWM duty cycle of the bidirectional DC-DC converter at the moment is encoded and transmitted to the second encoder; The PWM duty cycle of the bidirectional DC-DC converter at the moment is encoded and transmitted to the second encoder; The PWM duty cycle of the bidirectional DC-DC converter at the moment is encoded and transmitted to the second encoder; The PWM duty cycle of the bidirectional DC-DC converter at the moment is encoded and transmitted to the second encoder; The PWM duty cycle of the bidirectional DC-DC converter at the moment is encoded and transmitted to the second encoder;

[0019] The present application constructs a semi-independent dual-channel dynamic triggering mechanism, and the present application also designs an output channel event triggering module and an input channel event triggering module. The absolute value of the difference between the inductor current at the moment and the recovery signal at the moment satisfies the dynamic triggering condition, The absolute value of the difference between the inductor current at the moment and the recovery signal at the moment satisfies the dynamic triggering condition, The absolute value of the difference between the inductor current at the moment and the recovery signal at the moment satisfies the dynamic triggering condition, The absolute value of the difference between the inductive current recovery signal and the reference signal at the moment meets the input channel trigger condition The pseudo partial derivative at the moment is time-varying, otherwise, the pseudo partial derivative at the last trigger moment is used The pseudo partial derivative at the moment is time-varying, which effectively reduces the computing resources of the control system, and on the basis of maintaining the autonomous triggering of the double channels, the double channels are forced to trigger cooperatively when the input is updated, the real-time inductive current signal transmitted by the output channel is based on the difference between the recovery signal calculated by the input channel and the reference signal, and the new duty cycle command generated after the input channel triggers will inversely affect the inductive current change of the output channel, and the cooperative relationship can avoid system abnormalities caused by single-channel failure. Moreover, a dynamic attenuation threshold and a dynamic state quantity are introduced to construct an adaptive trigger threshold, so that the input channel and the output channel can adapt to the system state, while maintaining the accuracy of the dynamic data model, the communication frequency is reduced as much as possible, and the control efficiency of the battery energy storage system is further improved.

[0020] In order to solve the inherent defect that the existing uniform quantization adopts a fixed quantization interval, and the quantization interval of the large signal region increases exponentially with the signal amplitude, the Softplus quantizer is designed, a gradual change relationship from nonlinearity to linearity is established, the low error characteristic of the quantizer is inherited in the small signal region ( ) when the input signal of the encoder is small, the absolute value of the quantization level is also small, so that the quantization interval of the small signal region is narrower, more quantization levels can be allocated, and the resolution is greatly improved, and the bounded error advantage of uniform quantization is maintained in the large signal region ( ). When the absolute value of the quantization level increases, the quantization interval is approximately a fixed value. This gradual linear characteristic keeps the quantization interval of the large signal region stable, ensures that the large signal can be allocated a "interval controllable" quantization interval even if the amplitude difference is large, and makes positive and negative signals symmetrically quantized through the sign function to avoid the quantization deviation of positive and negative small signals. The scaling factor can adjust the span of the quantization interval as a whole, the whole range of the quantization interval is expanded as a whole when the scaling factor is increased, which is suitable for large dynamic signals; when the scaling factor is reduced, the interval is reduced, which is suitable for high-precision scenarios. The curvature adjustment factor can adjust the inflection point position of the transition from nonlinearity to linearity, the inflection point moves to the left when the curvature adjustment factor increases, and the fine quantization range of the small signal region expands; when the curvature adjustment factor decreases, the linear quantization range of the large signal region is advanced. Through the cooperative adjustment of the scaling factor and the curvature adjustment factor, the significant characteristic of stable and controllable quantization error in the whole dynamic range can be realized, the communication data is effectively compressed, and through the design of encrypted transmission, the encoder input signal is encrypted and preprocessed by using the pre-shared encrypted signal, which can effectively resist eavesdropping attacks in the communication process, and the communication efficiency and transmission security of the battery energy storage system charging and discharging control are considered. Attached Figure Description

[0021] To make the content of this invention easier to understand, the invention will be further described in detail below with reference to specific embodiments and accompanying drawings, wherein: Figure 1 This is a structural diagram of a photovoltaic grid-connected system with a battery energy storage system.

[0022] Figure 2 This is a flowchart illustrating a battery energy storage system control method according to the present invention.

[0023] Figure 3 This is a schematic diagram of a data encryption encoding-decoding mechanism.

[0024] Figure 4 The illumination and temperature settings for the photovoltaic grid-connected system in Example 2 are as follows. Figure 4 (a) in the figure represents the illumination setting for the photovoltaic grid-connected system. Figure 4 (b) in the figure represents the temperature setting of the photovoltaic grid-connected system.

[0025] Figure 5 This is a schematic diagram of the actual inductor current output and the reference trajectory.

[0026] Figure 6 This is a schematic diagram of the tracking error of the inductor current.

[0027] Figure 7 This is a schematic diagram of the reconstructed PWM duty cycle values ​​of the bidirectional DC-DC converter at different times.

[0028] Figure 8 This is a diagram illustrating the triggering time of a dual-channel dynamic event. Figure 8 (a) in the diagram represents the moment when the dynamic event triggering condition of the input channel is met. Figure 8 (b) in the diagram represents the moment when the dynamic event triggering conditions of the output channel are met.

[0029] Figure 9 This is a schematic diagram of sensitivity analysis of dual-channel dynamic event triggering thresholds. Figure 9 (a) in the diagram illustrates the sensitivity analysis of the dynamic event triggering threshold of the input channel. Figure 9 (b) in the diagram is a schematic of the sensitivity analysis of the dynamic event triggering threshold of the output channel. Detailed Implementation

[0030] The present invention will be further described below with reference to the accompanying drawings and specific embodiments, so that those skilled in the art can better understand and implement the present invention. However, the embodiments described are not intended to limit the present invention.

[0031] like Figure 1 As shown, Figure 1This diagram illustrates the structure of a grid-connected photovoltaic (PV) system with a battery energy storage system. In this system, the PV modules employ a Maximum Power Point Tracking (MPPT) control strategy, while the battery energy storage system smooths out power fluctuations caused by the random volatility of PV power generation. Within the energy storage system, the battery is connected to the DC bus via a buck-boost converter. The insulated-gate bipolar transistors (IGBTs) in the converter are controlled. and The switch status allows for the switching of BESS operating modes.

[0032] Existing methods for adjusting PWM duty cycle, such as DBC, MPC, and SMC, heavily rely on accurate system mathematical models. Under complex operating conditions with highly random photovoltaic output and time-varying system parameters, model mismatch can easily lead to degraded control performance or even system instability. Traditional PI controllers lack adaptive capabilities and are limited in their adjustment effect in nonlinear, time-varying systems; while FLC performance depends on the completeness of a pre-set knowledge base, requiring extensive prior experience in the design process and exhibiting insufficient generalization ability. Existing technologies provide a basic framework for BESS control, but have not effectively implemented data-driven control of BESS. To address the above technical problems, this invention provides a battery energy storage system control method, as follows: This embodiment provides a battery energy storage system control method, including: like Figure 2 As shown, Figure 2 This is a flowchart illustrating a battery energy storage system control method according to the present invention.

[0033] Step S1: The sensor acquires the output of the battery energy storage system. The signal of the inductor current of the bidirectional DC-DC converter is sent to the output channel event triggering module. Step S2: The output channel event triggering module is based on The signal of the inductor current of the bidirectional DC-DC converter at any time and The absolute value of the difference between the inductor current reconstructed signals of the bidirectional DC-DC converter at any given time. First dynamic decay parameter at time The system continuously outputs the channel event trigger status and reference threshold to determine whether the dynamic event triggering conditions of the output channel are met; whereby the output channel event trigger time is defined as... , Indicates the first The time when the dynamic event triggering conditions of the output channel are met is the distance from The most recent moment when the dynamic event triggering conditions of the output channel were met. ; The dynamic event triggering conditions for the output channel are:

[0034]

[0035] in, Indicates the infimum, For the set of natural numbers, for The channel event trigger status is constantly output. The first scaling factor is... , for The state variable is output at any time, and its update formula is: , , for Output state variables at all times. As the first set constant, , , , for The signal of the inductor current of the bidirectional DC-DC converter at constant time. for Reconstructing the inductor current signal of the bidirectional DC-DC converter at any time. To output the reference threshold, for The first dynamic decay parameter at any given time. , As the first dynamic decay factor, It is a natural constant. It is an absolute value.

[0036] Step S3: If satisfied, then let ,Will The inductor current signal of the bidirectional DC-DC converter is sent to the first encoder at any time; the first encoder processes the received signal. The signal encoding and processing of the inductor current of the bidirectional DC-DC converter at any given time is sent to the first decoder via a communication network; wherein, For the first The moment when the dynamic event triggering conditions of the output channel are met; First decoder pair Decoding and reconstructing the first quantized value of the inductor current signal of the bidirectional DC-DC converter at any given time yields... The inductor current reconstruction signal of the bidirectional DC-DC converter is reconstructed at any time and sent to the PPD estimation module; The PPD estimation module stores the data decoded by the first decoder. The inductor current reconstruction signal of the bidirectional DC-DC converter at the moment and the corresponding current reconstruction increment of the bidirectional DC-DC converter at the moment, the second decoder decodes to obtain The PWM duty cycle reconstruction value of the bidirectional DC-DC converter at the moment; wherein, Indicates the moment; Based on The moment and The increment of the inductor current reconstruction signal of the bidirectional DC-DC converter at the moment, The PWM duty cycle reconstruction increment of the bidirectional DC-DC converter at the moment, The time-varying pseudo partial derivative at the moment, and the calculation The time-varying pseudo partial derivative at the moment, and the formula is: , , Wherein, For The time-varying pseudo partial derivative at the moment, , Indicates transposition, For The time-varying pseudo partial derivative at the moment, The time-varying pseudo partial derivative corresponding to the inductor current of the bidirectional DC-DC converter, The time-varying pseudo partial derivative corresponding to the PWM duty cycle of the bidirectional DC-DC converter, The first step factor, The first weight coefficient, , For The increment of the inductor current reconstruction signal of the bidirectional DC-DC converter at the moment, For The PWM duty cycle reconstruction value increment of the bidirectional DC-DC converter at the moment, , For The PWM duty cycle reconstruction value of the bidirectional DC-DC converter at the moment, For The PWM duty cycle reconstruction value of the bidirectional DC-DC converter at the moment, For The transpose of, For The increment of the inductor current reconstruction signal of the bidirectional DC-DC converter at the moment, , For The inductor current reconstruction signal of the bidirectional DC-DC converter at the moment, For The inductor current reconstruction signal of the bidirectional DC-DC converter at the moment, For a positive number, is a symbol function, denotes a norm.

[0037] wherein, is a reset algorithm, aiming to make the PPD estimation algorithm in the control scheme have stronger ability to track the parameters of the event.

[0038] If the input-output dual-channel dynamic event triggering mechanism is not introduced, the PPD estimation module is updated periodically, and the update formula is: , Step S4: if not satisfied, the PPD estimation module will the inductor current signal of the bidirectional DC-DC converter at the moment and its corresponding current reconstruction increment are taken as the inductor current signal of the bidirectional DC-DC converter at the moment and its corresponding current reconstruction increment are taken as the time-varying pseudo partial derivative at the moment is taken as the time-varying pseudo partial derivative at the moment; Step S5: the input channel event trigger receives the time-varying pseudo partial derivative at the moment output by the PPD estimation module the time-varying pseudo partial derivative at the moment, the inductor current reconstruction signal of the bidirectional DC-DC converter, based on the absolute value of the difference between the inductor current reconstruction signal of the bidirectional DC-DC converter at the moment and the reference signal of the inductor current of the bidirectional DC-DC converter, the second dynamic attenuation parameter, the input channel event trigger state and the input reference threshold, judges whether the input channel dynamic event triggering condition is satisfied; The input channel dynamic event triggering condition is: , , wherein, denotes the lower bound, is a natural number set, is the input channel event trigger state at the moment, is a second scaling coefficient, , is the input state variable at the moment, and the update formula is: , , is the input state variable at the moment, is a second set constant, , , , is the reference signal of the inductor current of the bidirectional DC-DC converter at the moment,​ is the time-varying pseudo partial derivative at the time instant, is the input reference threshold, is the second dynamic attenuation parameter at the time instant, , is the second dynamic attenuation factor, is the natural constant, is the absolute value.

[0039] Step S6: if the condition is satisfied, then let the time-varying pseudo partial derivative at the time instant is sent to the controller, and the controller calculates the PWM duty cycle of the bidirectional DC-DC converter at the time instant; wherein, represents the time instant at which the input channel dynamic event trigger condition is satisfied for the first time, which is transmitted to the second encoder; wherein, the input channel event trigger time instant is defined as , represents the time instant at which the input channel dynamic event trigger condition is satisfied for the first time; represents the time instant at which the input channel dynamic event trigger condition is satisfied for the first time; represents the time instant at which the input channel dynamic event trigger condition is satisfied for the first time; The calculation formula of the PWM duty cycle of the bidirectional DC-DC converter at the time instant is: , wherein, is the PWM duty cycle of the bidirectional DC-DC converter at the time instant, is the PWM duty cycle reconstruction value of the bidirectional DC-DC converter at the time instant, , is the time-varying pseudo partial derivative at the time instant, is the time-varying pseudo partial derivative corresponding to the inductor current of the bidirectional DC-DC converter, is the time-varying pseudo partial derivative corresponding to the PWM duty cycle of the bidirectional DC-DC converter, is the second step factor, is the third step factor, is the increment of the inductor current reconstruction signal of the bidirectional DC-DC converter at the time instant, , is the reference signal of the inductor current of the bidirectional DC-DC converter at the time instant, is the inductor current reconstruction signal of the bidirectional DC-DC converter at the time instant, ​​​​​​​​is an absolute value.

[0040] The second encoder encodes The PWM duty cycle of the bidirectional DC-DC converter at the moment is encoded and sent to the second decoder through the communication network. The quantized value of the PWM duty cycle of the bidirectional DC-DC converter at the moment is decoded and reconstructed. The reconstructed value of the PWM duty cycle of the bidirectional DC-DC converter at the moment is sent to the actuator. The actuator adjusts the switching state of the bidirectional DC-DC converter based on The PWM duty cycle of the bidirectional DC-DC converter at the moment is reconstructed.

[0041] Step S7: If not, the actuator adjusts the switching state of the bidirectional DC-DC converter based on The PWM duty cycle of the bidirectional DC-DC converter at the moment is reconstructed.

[0042] The above input-output dual-channel dynamic event triggering mechanism is semi-independent. At the next moment triggered by the input channel, the output channel needs to send output data for estimating the PPD of the dynamic data model. When the input channel is not triggered, the output channel judges whether to transmit according to the change of the output signal. A dynamic attenuation parameter Not only can signal distortion be avoided when the system output approaches a stable state, but also The accumulation during the hold period is reduced, so that It is more likely to be triggered when it exceeds the preset range .

[0043] For the nonlinear dynamic characteristics of the BESS, the present application adopts a full format dynamic linearization (FFDL) method to construct a dynamic data model thereof: , Wherein, is The inductor current signal (system output variable) of the bidirectional DC-DC converter at the moment, is The PWM duty cycle reconstructed value (system input variable) of the bidirectional DC-DC converter at the moment, is The inductor current signal of the bidirectional DC-DC converter at the moment, is The increment of the inductor current signal of the bidirectional DC-DC converter at the moment, , for The inductor current signal of the bidirectional DC-DC converter at constant time. for The increment of the reconstructed PWM duty cycle value of the bidirectional DC-DC converter at any given time. , and These are the time-varying pseudo-partial derivatives of the corresponding variables.

[0044] This dynamic model is designed for controllers and avoids the dependence on precise information of system parameters required by traditional mechanistic modeling. It can be built using only the input-output data generated during operation.

[0045] In this embodiment, preferably, the following is used: The time encoder input signal is processed by encoder encoding to obtain... The quantized value of the input signal of the time encoder; the encoder is a first encoder or a second encoder, wherein both the first encoder and the second encoder are Softplus quantizers; Will The time encoder input signal is passed through a Softplus quantizer to obtain... The quantization value of the time encoder input signal is given by the formula: , , in, for The time encoder input signal, for The quantized value of the encoder input signal. For symbolic functions, For absolute values, Scaling factor , It is a natural constant. For curvature adjustment factor, , for The quantization level of the input signal to the time encoder. For the first The absolute value of the amplitude of each quantization interval. For the first The lower bound of the absolute value of the amplitude of each quantization interval. For the first The upper bound of the absolute value of the amplitude of each quantization interval. For the first The next time the signal is input into the encoder for processing. To set boundary values, .

[0046] In this embodiment, optionally, . in, The formula for calculating the quantization level of the input signal of the time encoder is: , in, for The quantization level of the input signal to the time encoder. For the first The next time the signal is input into the encoder for processing. Scaling factor , It is a natural constant. For curvature adjustment factor, , for The time encoder input signal, The rounding up symbol, To set boundary values, .

[0047] This design establishes a gradual transition from nonlinear to linear relationships in the small signal region (…). →0) Inherits the low-error characteristics of the quantizer, in the large signal region ( →∞) Maintain the bounded error advantage of uniform quantization and achieve the significant characteristic of stable and controllable quantization error across the entire dynamic range.

[0048] like Figure 3 As shown, Figure 3 This is a schematic diagram of the data encryption encoding-decoding mechanism. The difference encryption preprocessing is an optional module, used in series with the Softplus quantizer. When encryption is not enabled, Softplus quantization is used directly.

[0049] In this embodiment, preferably, the following is used: The input signal of the time encoder undergoes differential encryption preprocessing to obtain... The encrypted signal is given by the following formula: , Will The encrypted signal is then encoded by an encoder to obtain... The quantization value and quantization level of the signal after encryption at any time; Will The quantized value and quantization level of the encrypted signal are sent to the decoder via the communication network. The decoder is based on the received The quantization value and quantization level of the encrypted signal at any given time. After the quantized value of the encrypted signal at time t is decoded and reconstructed, the system state is iteratively updated by the following formula and finally output the quantized recovered signal of the encrypted signal at time t as the quantized recovered signal of the input signal of the encoder at time t, the formula is wherein, is the quantized recovered signal of the encrypted signal at time t is the quantized recovered signal of the encrypted signal at time t is the time at which the signal is input into the encoder for processing for the n-th time is the time at which the signal is input into the encoder for processing for the n-th time is the bounded encrypted signal preset in the encoder and the decoder at time t , is the quantized value of the encrypted signal at time t. The encoding-decoding scheme can achieve encrypted transmission while compressing the transmission data, and the error before and after transmission is smaller compared to the scheme using a pair of quantizer and uniform quantizer.

[0050] The communication restriction problem caused by network bandwidth constraints is an important bottleneck for the safe and stable operation of microgrid systems, and resource-saving energy storage control strategies need to be developed. Event-triggered control (ETC) and quantization technology have been widely recognized in the academic community as effective methods for reducing data traffic, alleviating network transmission pressure, and not losing control performance. ETC reduces channel occupancy by transmitting data only when the parameter deviation exceeds the preset threshold by designing a trigger condition; while quantization converts the transmitted data into a piecewise constant signal using a specific algorithm, effectively compressing the data.

[0051] The communication restriction problem caused by network bandwidth constraints is an important bottleneck for the safe and stable operation of microgrid systems, and resource-saving energy storage control strategies need to be developed. Event-triggered control (ETC) and quantization technology have been widely recognized in the academic community as effective methods for reducing data traffic, alleviating network transmission pressure, and not losing control performance. ETC reduces channel occupancy by transmitting data only when the parameter deviation exceeds the preset threshold by designing a trigger condition; while quantization converts the transmitted data into a piecewise constant signal using a specific algorithm, effectively compressing the data.

[0052] ​​​​​​​​At the application layer of ETC, some research proposes a security control method based on ETC, which effectively suppresses the injection attack while optimizing the communication resources. Some research develops a single-channel dynamic event-triggered (DET) strategy based on model-free adaptive control, which is applied to the load frequency control of multi-region interconnected power grid. In the application of quantization technology, the quantization SMC method proposed by some research enhances the recovery ability of the system under sudden fault, while the uniform quantizer is introduced into the secondary control of the inverter microgrid to reduce the communication load.

[0053] However, the existing schemes mostly use uniform quantization or quantization methods, but the existing quantization methods have obvious technical shortcomings: uniform quantization has the inherent defect of insufficient small signal resolution, and quantization will produce significant accuracy loss when dealing with large dynamic range signals, making it difficult to balance the control accuracy requirements under wide working conditions. Therefore, designing a new quantization method to break through the above limitations and realizing its collaborative optimization application with DET constitutes another innovative motivation of the present application.

[0054] And the current ETC mainly focuses on a single data transmission channel, and does not fully consider the multi-channel DET strategy. In addition, insufficient attention is paid to the security protection during data transmission, making it difficult to ensure the security of the transmitted data.

[0055] Therefore, the Softplus nonlinear quantizer is designed creatively and combined with the dynamic encryption and decoding mechanism to form a new type of quantization encryption communication strategy. The gradual design of the Softplus quantizer from nonlinearity to linearity ensures the stability and controllability of the quantization error in the entire dynamic input range, and its comprehensive performance is significantly better than that of a single quantization or uniform quantization scheme. The encoding and decoding mechanism uses pre-shared encrypted signals to encrypt the input data. This design effectively resists eavesdropping attacks during communication, and cleverly balances communication efficiency and transmission security. Unlike traditional quantization transmission mechanisms that use quantization and uniform quantization, the present application first creates a Softplus quantizer and gives the corresponding dynamic encoding and decoding mechanism, which is the unique feature of the present application. This combination not only improves the communication efficiency, but also significantly enhances the security of the data.

[0056] The application also proposes a semi-independent double-channel dynamic event triggering mechanism, which maintains the autonomous triggering of the double channels and forcibly triggers the double channels in coordination when input updates to ensure the real-time performance of PPD estimation data; meanwhile, a dynamic attenuation threshold is introduced to replace the fixed threshold to adjust the steady-state error. A dynamic attenuation parameter and a dynamic state quantity are particularly introduced to construct an adaptive triggering threshold. Unlike the existing fixed threshold static event triggering or single-channel triggering scheme, the present scheme has a unique semi-independent double-channel dynamic triggering mechanism: the core is to design a coordinated triggering logic for the input channel and the output channel to maintain the accuracy of the dynamic data model while reducing the number of communications as much as possible; a dynamic attenuation parameter and a dynamic state quantity are particularly introduced to construct an adaptive triggering threshold, which is different from the triggering strategy in the existing scheme.

[0057] The battery energy storage system control method proposed by the application is a typical model-free control method. Model-free adaptive control (MFAC) is widely used and researched due to its significant effect in nonlinear systems. MFAC does not require an accurate system model or rely on professional knowledge in the field, and at the same time, it shows the adaptive ability to time-varying parameters. In addition, the low computational complexity of MFAC can effectively meet the real-time control requirements of DC-DC converters. These significant advantages are the main motivation for the application of MFAC control architecture in BESS.

[0058] The application combines the aforementioned three core technologies to realize sparse data-driven FFDL model updating based on double-channel dynamic event triggering, and realizes secure transmission by combining Softplus quantization encryption, forming a new data-driven control method for BESS. This method not only improves the control performance, but also significantly enhances the data security and communication efficiency. The control architecture proposed by the present scheme has no precedent in the field of BESS control by comprehensively using the above-mentioned technologies.

[0059] The boundedness of and tracking error is proved to verify the stability of the system. When the parameters satisfy , , , , and the parameters of the encoding-decoding mechanism and the double-channel dynamic event triggering mechanism are set appropriately, the system can successfully perform the tracking task, and the tracking error is bounded.

[0060] Based on embodiment one, embodiment two is applied in the BESS matched with the photovoltaic grid-connected system. The system configuration includes a 136kW photovoltaic array (1000 A 500Ah lithium battery pack and a buck-boost converter, with a grid power of 100kW, were deployed using a data-driven control framework based on an embedded industrial PC. During testing and verification, the illumination and photovoltaic equipment temperature settings were as follows: Figure 4 As shown, Figure 4 The illumination and temperature settings for the photovoltaic grid-connected system in Example 2 are as follows: Figure 4 (a) in the figure represents the illumination setting for the photovoltaic grid-connected system. Figure 4 (b) in the figure represents the temperature setting of the photovoltaic grid-connected system.

[0061] During operation, the inductor current of the buck-boost converter With switch duty cycle As a real-time input-output data source, sensor data and control inputs are transmitted via a Softplus quantizer and a dynamic encryption / decoding mechanism. A semi-independent dual-channel event-triggered mechanism is used to reduce communication load. The control scheme parameters are configured as follows: Time of adoption: ; Softplus quantizer: , ; Encrypted signal: ; Dynamic event triggering mechanism: , , , ; Controller: , , , , , .

[0062] MPPT algorithm and BESS in Access the system at any time. Indicates the time.

[0063] like Figure 5 As shown, Figure 5 This diagram illustrates the actual inductor current output and the reference trajectory; the corresponding tracking error is shown below. Figure 6 , Figure 6 This diagram illustrates the tracking error of the inductor current. Under stable illumination conditions, the system achieved stable tracking and maintained its tracking performance even when the illumination suddenly changed. (Excludes...) After analyzing the data, the mean absolute error (MAE) was calculated to be 0.48A and the root mean square error (RMSE) was 0.72A.

[0064] As shown in Figure 7 , the PWM duty cycle reconstruction values of the bidirectional DC-DC converter at different times are shown. Figure 7 Figure 8 As shown in Figure 8 (a), the time satisfying the input channel dynamic event triggering condition is shown. Figure 8 As shown in

[0065] As shown in Figure 7 , the actual control input of the BESS is shown. Figure 8 (a) and Figure 8 (b) show the triggering intervals of the input channel and the output channel, respectively. The results show that when the time is in the interval to , the sharp change of solar irradiance causes the reference current to change, and the input and output channels need to be frequently triggered to achieve trajectory tracking. After excluding the initial stage , the triggering times of the input channel and the output channel are 1,925 and 2,626, respectively, corresponding to 49.35% and 67.32% of the data volume in the time-triggered control mode. These results show that the proposed method can effectively reduce the number of data transmissions.

[0066] As shown in Figure 9 , the threshold sensitivity analysis of the dual-channel dynamic event triggering is shown. Figure 9 As shown in Figure 9 (a), the threshold sensitivity analysis of the input channel dynamic event triggering is shown. Figure 9 As shown in Figure 9 , the threshold parameters and influence the performance of event triggering, which is reflected in the control input triggering rate (CITR), system output triggering rate (SOTR), MAE, and RMSE indicators. The overall results show that as the value of or increases, the system tracking performance gradually decreases.

[0067] As shown in Figure 9 (a), under the condition of fixed , when , the input channel and the output channel are fully triggered at each sampling time. When , CITR increases with ​The increase significantly reduces the tracking accuracy, while the tracking precision gradually deteriorates. During this stage, the output channel trigger is primarily used for PPD parameter updates, and its SOTR decrease trend is synchronized with CITR. It is worth noting that when... At that time, the rate of CITR decline slowed down while the tracking performance deteriorated more severely, and SOTR remained at a high level. Figure 9 (b) It can be seen that, in Under fixed conditions, increase This will lead to a decrease in SOTR. This means that the controller acquires data less frequently, thus reducing control efficiency, and also requires the input channels to be triggered more frequently.

[0068] This third embodiment provides a battery energy storage system control device, including: The PPD estimation module stores the PPD values ​​obtained from the first decoder. The inductor current reconstruction signal of the bidirectional DC-DC converter at any given time and its corresponding current reconstruction increment are obtained by the second decoder. The reconstructed PWM duty cycle value of the bidirectional DC-DC converter at any given time; where... Indicates time; based on Time and The increment of the inductor current reconstruction signal of the bidirectional DC-DC converter at any time Incremental PWM duty cycle reconstruction of bidirectional DC-DC converter Calculate the time-varying pseudopartial derivative at time t. The time-varying pseudopartial derivative at time t; Will The time-varying pseudo-partial derivatives at time t, the reconstructed inductor current signal of the bidirectional DC-DC converter and its corresponding current reconstruction increment, and the reconstructed PWM duty cycle value of the bidirectional DC-DC converter are sent to the controller. Controller, based on The time-varying pseudo-partial derivative at time t, the reconstructed inductor current signal of the bidirectional DC-DC converter and its corresponding current reconstruction increment, the reference signal of the inductor current of the bidirectional DC-DC converter and... Reconstructed PWM duty cycle value of the bidirectional DC-DC converter at any given time, calculated The PWM duty cycle of the bidirectional DC-DC converter is determined and transmitted to the second encoder. The second encoder, for After the PWM duty cycle of the bidirectional DC-DC converter is encoded, it is sent to the second decoder through the communication network; The second decoder, for After decoding and reconstructing the quantized value of the PWM duty cycle of the bidirectional DC-DC converter at any given time, The PWM duty cycle reconstruction value of the bidirectional DC-DC converter at the moment is sent to the actuator; The actuator adjusts the switching state of the bidirectional DC-DC converter based on The PWM duty cycle reconstruction value of the bidirectional DC-DC converter at the moment, adjusts the switching state of the bidirectional DC-DC converter, and realizes the charge and discharge control of the battery energy storage system.

[0069] Those skilled in the art will understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROMs, optical storage, etc.) containing computer-usable program code.

[0070] The present application is described with reference to flowcharts and / or block diagrams according to the methods, devices (systems), and computer program products of the embodiments of the present application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and the combination of flows and / or blocks in the flowcharts and / or block diagrams can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing apparatus to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing apparatus produce a device that implements the functions specified in the flowcharts and / or block diagrams. Figure 1 The functions specified in one or more flows and / or blocks Figure 1 The functions specified in one or more flows and / or blocks

[0071] These computer program instructions can also be stored in a computer-readable memory that can direct the computer or other programmable data processing apparatus to work in a specific manner, so that the instructions stored in the computer-readable memory produce a manufactured product including instruction devices that implement the functions specified in the flowcharts and / or block diagrams. Figure 1 The functions specified in one or more flows and / or blocks Figure 1 The functions specified in one or more flows and / or blocks

[0072] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus, so that a series of operation steps are performed on the computer or other programmable data processing apparatus to produce a computer-implemented process, so that the instructions executed on the computer or other programmable data processing apparatus provide a process for implementing the functions specified in the flowcharts and / or block diagrams. Figure 1 The functions specified in one or more flows and / or blocks Figure 1 The functions specified in one or more flows and / or blocks

[0073] Obviously, the above embodiments are merely example for clearly illustrating, and are not limitation to the embodiments. Based on the above description, other different forms of changes or variations can be made by those skilled in the art. Here, all the embodiments are not required to be enumerated, and the obvious changes or variations derived from the above are still within the protection scope of the present application.

Claims

1. A battery energy storage system control method, characterized by, Comprising: The PPD estimation module stores the first decoder-decoded The inductor current reconstruction signal of the bidirectional DC-DC converter at the moment and the corresponding current reconstruction increment, the second decoder-decoded The PWM duty cycle reconstruction value of the bidirectional DC-DC converter at the moment; wherein, Indicates the moment; based on the time instant and the time instant, the increment of the inductor current reconstruction signal of the bidirectional DC-DC converter, the time instant, the increment of the PWM duty cycle reconstruction of the bidirectional DC-DC converter, the time-varying pseudo partial derivative at the time instant, the calculation the time-varying pseudo partial derivative at the time instant; The The time-varying pseudo partial derivative of the moment, the bidirectional DC-DC converter inductor current reconstruction signal and its corresponding current reconstruction increment, and the PWM duty cycle reconstruction value of the bidirectional DC-DC converter are sent to the controller. The controller is based on the time-varying pseudo partial derivative of the time, the bidirectional DC-DC converter inductor current reconstruction signal and its corresponding current reconstruction increment, the reference signal of the bidirectional DC-DC converter inductor current and the PWM duty cycle reconstruction value of the bidirectional DC-DC converter at the time, and calculates the PWM duty cycle of the bidirectional DC-DC converter at the time, and transmits it to the second encoder; The second encoder is connected to the communication network The PWM duty cycle of the bidirectional DC-DC converter at the moment is encoded and sent to the second decoder through the communication network. The quantized value of the PWM duty cycle of the bidirectional DC-DC converter at the moment is decoded and reconstructed. The reconstructed value of the PWM duty cycle of the bidirectional DC-DC converter at the moment is sent to the actuator. The actuator is based on The PWM duty cycle reconstruction value of the bidirectional DC-DC converter at the moment, the switching state of the bidirectional DC-DC converter is adjusted, and the charge and discharge control of the battery energy storage system is realized.

2. The battery energy storage system control method of claim 1, wherein, The sensor acquires the output of the battery energy storage system The signal of the inductor current of the bidirectional DC-DC converter at the moment is sent to the output channel event trigger module. The output channel event trigger module is based on the absolute value of the difference between the signal of the inductor current of the bidirectional DC-DC converter at the moment and the reconstructed signal of the inductor current of the bidirectional DC-DC converter at the moment, the first dynamic attenuation parameter at the moment, the output channel event trigger state at the moment and the output reference threshold value, to determine whether the output channel dynamic event trigger condition is met; wherein, the moment at which the output channel dynamic event trigger condition is met for the first time is represented as T0, the moment at which the output channel dynamic event trigger condition is met for the last time is represented as Tn, and the moment at which the output channel dynamic event trigger condition is met for the nearest time is represented as Tn-1. If satisfied, then let The output channel dynamic event trigger condition is satisfied at the time t0. The inductor current signal of the bidirectional DC-DC converter at the time t0 is sent to the first encoder; the first encoder encodes the received The signal of the inductor current of the bidirectional DC-DC converter at the time t0 is encoded and processed, and then sent to the first decoder through a communication network; wherein, The first The time t0 at which the output channel dynamic event trigger condition is satisfied for the nth time. The first decoder is connected to the first quantizer and the second quantizer. The first quantizer decodes and reconstructs the first quantized value of the inductor current signal of the bidirectional DC-DC converter at the moment t, to obtain The second quantizer decodes and reconstructs the inductor current signal of the bidirectional DC-DC converter at the moment t and sends it to the PPD estimation module. If not, the PPD estimation module will The inductor current signal of the bidirectional DC-DC converter at the moment and its corresponding current reconstruction increment are respectively taken as The inductor current signal of the bidirectional DC-DC converter at the moment and its corresponding current reconstruction increment are respectively taken as The time-varying pseudo partial derivative at the moment is taken as The time-varying pseudo partial derivative at the moment is taken as The input channel event trigger receives The time instant bidirectional DC-DC converter inductor current reconstruction signal is based on The time instant bidirectional DC-DC converter inductor current reconstruction signal and the reference inductor current signal, the absolute value of the difference between them, the second dynamic attenuation parameter, the input channel event trigger state and the input reference threshold value, determine whether the input channel dynamic event trigger condition is met; If yes, let , and the time-varying pseudo partial derivative of the time, the bidirectional DC-DC converter inductor current reconstruction signal is sent to the controller, and the controller calculates the PWM switch control signal duty cycle of the bidirectional DC-DC converter at the time; wherein represents the first time that meets the input channel dynamic event trigger condition. If not satisfied, the actuator is based on The PWM duty cycle reconstruction value of the bidirectional DC-DC converter at the moment, the switching state of the bidirectional DC-DC converter is adjusted, and the charge and discharge control of the battery energy storage system is realized.

3. The battery energy storage system control method of claim 2, wherein, Output channel dynamic event trigger condition is: ， ， wherein, denotes the lower bound, is a natural number set, is the moment output channel event trigger state, is a first scaling coefficient, , is the moment output state variable, and its update formula is: , , is the moment output state variable, is a first set constant, , , , is the moment signal of the inductor current of the bidirectional DC-DC converter, is the moment reconstructed signal of the inductor current of the bidirectional DC-DC converter, is an output reference threshold value, is the moment first dynamic attenuation parameter, , is a first dynamic attenuation factor, is a natural constant, is an absolute value.

4. The battery energy storage system control method of claim 2, wherein, Input channel dynamic event trigger condition is: ， ， wherein, denotes the lower bound, is a natural number set, is the input channel event trigger state at the moment, is a second scaling coefficient, , is the input state variable at the moment, and its update formula is: , , is the input state variable at the moment, is a second setting constant, , , , is the reference signal of the inductor current of the bidirectional DC-DC converter at the moment, is the reconstructed signal of the inductor current of the bidirectional DC-DC converter at the moment, is an input reference threshold value, is the second dynamic attenuation parameter at the moment, , is a second dynamic attenuation factor, is a natural constant, is an absolute value.

5. The battery energy storage system control method of claim 2, wherein, The method comprises the following steps: The time instant and The time instant and The time instant and The time instant and The time instant and ， ， wherein is a time-varying pseudo partial derivative of the inductor current of the bidirectional DC-DC converter at the time instant , denotes a transpose, is a time-varying pseudo partial derivative of the inductor current of the bidirectional DC-DC converter at the time instant is a time-varying pseudo partial derivative of the inductor current of the bidirectional DC-DC converter corresponding to the PWM duty cycle, is a time-varying pseudo partial derivative of the PWM duty cycle of the bidirectional DC-DC converter corresponding to the inductor current, is a first step factor, is a first weight coefficient, , is an increment of the inductor current reconstruction signal of the bidirectional DC-DC converter at the time instant is an increment of the PWM duty cycle reconstruction value of the bidirectional DC-DC converter at the time instant , is a PWM duty cycle reconstruction value of the bidirectional DC-DC converter at the time instant is a PWM duty cycle reconstruction value of the bidirectional DC-DC converter at the time instant is a transpose of is an increment of the inductor current reconstruction signal of the bidirectional DC-DC converter at the time instant , is an inductor current reconstruction signal of the bidirectional DC-DC converter at the time instant is an inductor current reconstruction signal of the bidirectional DC-DC converter at the time instant is a positive number, is a sign function, denotes a norm.

6. The battery energy storage system control method of claim 2, wherein, The controller is based on a time-varying pseudo partial derivative of the time instant, a bidirectional DC-DC converter inductor current reconstruction signal, a reference signal of the bidirectional DC-DC converter inductor current, and a PWM duty cycle reconstruction value of the bidirectional DC-DC converter at the time instant, and calculates a PWM duty cycle of the bidirectional DC-DC converter at the time instant, comprising: ， wherein, is the PWM duty cycle of the bidirectional DC-DC converter at time is the PWM duty cycle reconstruction value of the bidirectional DC-DC converter at time , is the time-varying pseudo partial derivative at time is the time-varying pseudo partial derivative corresponding to the inductor current of the bidirectional DC-DC converter, is is is the increment of the inductor current reconstruction signal of the bidirectional DC-DC converter at time , is the reference signal of the inductor current of the bidirectional DC-DC converter at time is the inductor current reconstruction signal of the bidirectional DC-DC converter at time is 7. The battery energy storage system control method of claim 2, wherein, Will The time encoder input signal is processed by encoder encoding to obtain... The quantized value of the input signal to the time encoder; where the encoder is a Softplus quantizer; The time encoder input signal is passed through a Softplus quantizer to obtain quantized values of the time encoder input signal, given by ， ， in, for The time encoder input signal, for The quantized value of the encoder input signal. For symbolic functions, For absolute values, Scaling factor , It is a natural constant. For curvature adjustment factor, , for The quantization level of the input signal to the time encoder. For the first The absolute value of the amplitude of each quantization interval. For the first The lower bound of the absolute value of the amplitude of each quantization interval. For the first The upper bound of the absolute value of the amplitude of each quantization interval. For the first The next time the signal is input into the encoder for processing. To set boundary values, .

8. The battery energy storage system control method of claim 7, wherein, The formula for calculating the number of quantization levels of the time encoder input signal is: ， wherein is the number of quantization levels of the time encoder input signal, is the time at which the signal is input to the encoder for processing, is the time at which the signal is input to the encoder for processing, is a scaling factor, , is a natural constant, is a curvature adjustment factor, , is the time encoder input signal, is a ceiling symbol, is a set boundary value, .

9. The battery energy storage system control method of claim 7, wherein, The input signal of the time encoder is differentially encrypted and preprocessed to obtain The time-encrypted signal is obtained by the formula The time-encrypted signal is obtained by the formula ， The signal after the encryption processing at the time is encoded by an encoder to obtain The quantization value of the signal after the encryption processing at the time and the quantization number The quantization value of the signal after the encryption processing at the time and the quantization number Will The quantized value and quantization level of the encrypted signal are sent to the decoder via the communication network. The decoder decodes the received The quantized value of the encrypted signal at the time t is decoded to reconstruct the quantized value of the signal at the time t The quantized value of the encrypted signal at the time t is decoded to reconstruct the quantized value of the signal at the time t The quantized value of the encrypted signal at the time t is decoded to reconstruct the quantized value of the signal at the time t The quantized value of the encrypted signal at the time t is decoded to reconstruct the quantized value of the signal at the time t ， ， wherein is a quantized recovered signal of the encrypted signal at time is a quantized recovered signal of the encrypted signal at time is the time at which the signal is input to the encoder for processing for the first is the time at which the signal is input to the encoder for processing for the first is the time at which the signal is input to the encoder for processing for the first is the time at which the signal is input to the encoder for processing for the first is a set bounded encrypted signal at time , is a quantized value of the encrypted signal at time 10. A battery energy storage system control device, characterized by, Comprising: a PPD estimation module, which stores the first decoder decoding result a current reconstruction signal of the bidirectional DC-DC converter inductor at the moment and its corresponding current reconstruction increment, which is the second decoder decoding result a PWM duty cycle reconstruction value of the bidirectional DC-DC converter at the moment; wherein, represents the moment based on the time instant and the time instant, the increment of the inductor current reconstruction signal of the bidirectional DC-DC converter, the time instant, the increment of the PWM duty cycle reconstruction of the bidirectional DC-DC converter, the time-varying pseudo partial derivative at the time instant, the calculation the time-varying pseudo partial derivative at the time instant; The The time-varying pseudo partial derivative of the moment, the bidirectional DC-DC converter inductor current reconstruction signal and its corresponding current reconstruction increment, and the PWM duty cycle reconstruction value of the bidirectional DC-DC converter are sent to the controller. The controller is configured to a time-varying pseudo partial derivative of the time instant, a bidirectional DC-DC converter inductor current reconstruction signal and a corresponding current reconstruction increment thereof, a reference signal of the bidirectional DC-DC converter inductor current and a PWM duty cycle reconstruction value of the bidirectional DC-DC converter at the time instant, and calculate a PWM duty cycle of the bidirectional DC-DC converter at the time instant, and transmit the PWM duty cycle to the second encoder. The second encoder, to After the PWM duty cycle encoding process of the bidirectional DC-DC converter at the moment, it is sent to the second decoder through the communication network. The second decoder decodes the quantized value of the PWM duty cycle of the bidirectional DC-DC converter at the moment t. After the quantized value of the PWM duty cycle of the bidirectional DC-DC converter at the moment t is decoded and reconstructed, The reconstructed value of the PWM duty cycle of the bidirectional DC-DC converter at the moment t is sent to the actuator. An actuator, based on The PWM duty ratio reconstruction value of the bidirectional DC-DC converter at the moment is used to adjust the switching state of the bidirectional DC-DC converter, so as to realize the charge and discharge control of the battery energy storage system.

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