Intelligent energy scheduling method for energy storage and charging direct current coupling system

Abstract

The invention discloses an intelligent energy scheduling method for an energy storage and charging direct-current coupling system, and the method employs a bidirectional buck-boost intelligent switching control module, a duty ratio optimization algorithm module, an intelligent control circuit switching module, a mode switching judgment algorithm module, a safety interlocking protection module, and a parameter interaction fusion mechanism. The method is used for realizing collaborative optimization control among algorithm modules, and comprises the following steps: transmitting voltage stability monitoring parameters from a bidirectional buck-boost intelligent switching control module to a duty ratio optimization algorithm module; the duty ratio change rate and the control stability parameter are transmitted to the safety interlocking protection module and the mode switching judgment algorithm module from the duty ratio optimization algorithm module, and the safety state information is transmitted to the intelligent control circuit switching module from the safety interlocking protection module. According to the invention, flexible, efficient and safe charging of the electric vehicles on different voltage platforms by the energy storage battery is realized, the real-time performance, the accuracy and the reliability of the system are considered, and collaborative optimization control is realized through a parameter interaction mechanism.

Description

Technical Field

[0001] This invention relates to the fields of power electronics technology and intelligent control technology, and in particular to an intelligent energy dispatching method for a DC-coupled energy storage and charging system. Background Technology

[0002] With the rapid development of the new energy vehicle industry and the advancement of smart grid construction, energy storage and charging DC-DC coupling systems, as a core component of electric vehicle charging infrastructure, undertake crucial energy management functions. This system provides charging services for different types of electric vehicles through energy storage devices, serving as a core technological foundation for the popularization of new energy vehicles and the construction of smart grids.

[0003] However, currently available V2G products in the market have a core technical limitation in DC-coupled energy storage and charging systems: traditional V2G products use a single-stage step-down DC-DC module design, which only supports step-down charging of electric vehicle batteries by the energy storage battery, requiring the energy storage battery voltage to be higher than the electric vehicle battery voltage. This unidirectional step-down design cannot meet the charging needs of electric vehicles with different voltage platforms. In particular, when the energy storage battery voltage is lower than the electric vehicle battery voltage, the system cannot work, greatly limiting the system's applicability.

[0004] With the rapid development of electric vehicle technology, different brands and models of electric vehicles have adopted various voltage architectures: from the early 300V-400V low-voltage platform, to the current mainstream 600V medium-voltage platform, to the latest 800V high-voltage platform, and even the possible emergence of a 1000V ultra-high-voltage platform. This diversified development trend of voltage platforms has led to compatibility issues for traditional energy storage and charging systems. For example, when an energy storage system uses a 400V lithium iron phosphate battery pack and needs to charge an 800V electric vehicle, the traditional unidirectional buck system cannot provide charging services at all, and a dedicated boost converter must be configured, which not only increases the system cost and complexity but also reduces the overall energy conversion efficiency.

[0005] Specifically, this core technical problem has led to the following consequences: First, the system has poor adaptability and cannot achieve flexible charging across voltage platforms, severely limiting the application scope and economic benefits of energy storage devices; Second, in practical applications, due to voltage matching issues, multiple sets of charging equipment of different specifications are often required, increasing system investment costs by 40%-60%; Third, the energy conversion path is singular, lacking an intelligent energy scheduling mechanism, resulting in low overall system efficiency, generally only 85%-90%, far below the theoretical optimal efficiency; Fourth, during the buck-boost mode switching process, there is a lack of effective safety interlocking mechanisms and intelligent collaborative control, posing serious safety risks such as direct short circuits and insufficient system reliability.

[0006] Therefore, how to break through the technical limitations of traditional unidirectional step-down and realize the bidirectional step-up and step-down function of the DC coupling system for energy storage and charging, and achieve efficient and safe charging of multiple voltage platforms through intelligent control algorithms, has become a core technical problem that urgently needs to be solved for the industrial application of V2G technology. Summary of the Invention

[0007] The technical problem to be solved by this invention is: how to realize intelligent bidirectional buck-boost energy scheduling function in a DC-coupled energy storage and charging system, and overcome the technical limitations of unidirectional bucking in traditional V2G products through control algorithms and circuit topology design, so as to realize flexible, efficient and safe charging of electric vehicles with different voltage platforms by energy storage batteries, while taking into account the real-time performance, accuracy and reliability of the system, and achieve collaborative optimization control through parameter interaction mechanism between algorithms.

[0008] To address the aforementioned technical problems, this invention provides an intelligent energy scheduling method for a DC-DC coupled energy storage and charging system. This method includes modules such as a bidirectional buck-boost intelligent switching control module, a duty cycle optimization algorithm module, an intelligent control circuit switching module, a mode switching judgment algorithm module, a safety interlock protection module, and a parameter interaction and fusion mechanism. The core of the system lies in enabling a single DC-DC converter module to simultaneously perform boost and buck functions by executing the bidirectional buck-boost control algorithm and mode selection algorithm through the bidirectional buck-boost intelligent switching control module, and achieving collaborative optimization among the various algorithm modules through the parameter interaction and fusion mechanism.

[0009] The bidirectional buck-boost intelligent switching control module is the core of the system. It is responsible for intelligently selecting buck or boost mode based on real-time comparisons of the energy storage battery voltage and the electric vehicle battery voltage, enabling bidirectional energy transfer. This module uses mathematical modeling to achieve flexible charging of electric vehicles with different voltage platforms from the energy storage battery. It can automatically select the optimal operating mode based on voltage relationships, including buck, boost, and direct-through modes. Through mathematical modeling and optimization algorithms, it achieves millisecond-level mode switching and voltage matching. This module simultaneously outputs voltage stability indicators and mode switching status information to the duty cycle optimization algorithm module and the safety interlock protection module, realizing bidirectional information flow.

[0010] The duty cycle optimization algorithm module is responsible for calculating the optimal duty cycle in buck and boost modes, ensuring that the output voltage accurately matches the target voltage. This module includes buck mode duty cycle calculation units and boost mode duty cycle calculation units. By monitoring voltage parameters and load status in real time, it dynamically adjusts the duty cycle value to achieve high-precision voltage control. The module also integrates a duty cycle change rate monitoring function, providing real-time feedback information for the system's dynamic response. This module transmits duty cycle change rate information and control stability parameters to the safety interlock protection module, while simultaneously receiving voltage stability indicators from the bidirectional buck-boost intelligent switching control module, forming a closed-loop parameter interaction mechanism.

[0011] The intelligent control circuit switching module controls the on / off states of four switching devices to achieve flexible switching between buck and boost modes, and realizes intelligent reconfiguration of the circuit topology through combination control of switching devices. This module adopts a four-switch topology to achieve bidirectional buck-boost functionality from a single DC-DC module. The module dynamically adjusts the switching sequence based on safety status information provided by the safety interlock protection module to ensure the safety of the switching process.

[0012] The mode switching judgment algorithm module is used to dynamically determine the operating mode of the system based on voltage comparison and hysteresis logic, including intelligent selection of buck mode, boost mode, and standby mode. This module avoids frequent mode switching through hysteresis control, ensuring stable system operation. This module receives control stability parameters from the duty cycle optimization algorithm module to dynamically adjust the hysteresis threshold, achieving adaptive mode switching control.

[0013] The safety interlock protection module ensures the safe mutual exclusion control of the four switching devices, preventing short-circuit faults in the same bridge arm caused by simultaneous conduction of switches on the upper and lower arms. This module, through dual control of hardware-level safety logic and software protection algorithms, guarantees safe operation of the system under any operating condition. It receives duty cycle change rate information from the duty cycle optimization algorithm module and dynamically adjusts the protection threshold and response time based on the magnitude of the change rate, achieving adaptive safety protection. Simultaneously, it feeds back safety status information to the intelligent control circuit switching module to ensure the safety of the switching process.

[0014] The parameter interaction and fusion mechanism is the core invention of this work, enabling collaborative optimization among various algorithm modules. This mechanism includes the transfer of voltage stability monitoring parameters from the bidirectional buck-boost intelligent switching control module to the duty cycle optimization algorithm module; the transfer of duty cycle change rate and control stability parameters from the duty cycle optimization algorithm module to the safety interlock protection module and the mode switching judgment algorithm module; and the transfer of safety status information from the safety interlock protection module to the intelligent control circuit switching module. Through this multi-directional parameter interaction, the system can achieve global optimized control, significantly improving overall performance.

[0015] In summary, the present invention has the following beneficial effects: 1. A technological breakthrough has been achieved in bidirectional buck-boost functionality. Through intelligent switching of circuit topology, a single DC-DC module can simultaneously perform both boost and buck functions, overcoming the technical limitation of traditional V2G systems that can only perform buck charging. This significantly expands the applicability of energy storage systems and supports flexible charging of electric vehicles with a wide voltage range.

[0016] 2. It has excellent control precision and dynamic response performance, improved voltage control precision, and shortened mode switching time, which can meet the high charging quality requirements of modern electric vehicles.

[0017] 3. Through collaborative optimization, the system efficiency is improved compared with traditional solutions, resulting in significant energy-saving effects.

[0018] 4. Significantly improved safety and reliability: Through multiple safety mechanisms and fault-tolerant design, the system's reliability is enhanced, ensuring the safe and stable operation of the system.

[0019] 5. The collaborative optimization control achieved through the parameter interaction and fusion mechanism enables the various algorithm modules to cooperate with each other, optimize the global performance, and significantly improve the overall system performance compared to when each algorithm works independently. Attached Figure Description

[0020] Figure 1 This is a system architecture diagram of the intelligent energy dispatching method for the DC-coupled energy storage and charging system of the present invention; Figure 2 This is a flowchart illustrating the implementation of the bidirectional boost / buck intelligent switching control algorithm of the present invention. Figure 3 This is a flowchart illustrating the calculation process of the duty cycle optimization algorithm of this invention. Figure 4 This is a flowchart illustrating the logic of the mode switching judgment algorithm of this invention. Figure 5 This is a diagram showing the switching state and architecture of the intelligent control circuit switching module of the present invention. Figure 6 This is the control logic diagram of the safety interlock protection module of the present invention; Figure 7 This is a schematic diagram of the information flow of the parameter interaction and fusion mechanism of the present invention. Detailed Implementation

[0021] The present invention will now be described in further detail with reference to the accompanying drawings and specific embodiments.

[0022] like Figure 1 and Figure 2 As shown, the bidirectional buck-boost intelligent switching control algorithm of this invention is the core technology of the system. This algorithm, through mathematical modeling, enables flexible charging of electric vehicles with different voltage platforms by the energy storage battery, and achieves collaborative optimization with other algorithm modules through a parameter interaction mechanism.

[0023] When the energy storage battery voltage is higher than the electric vehicle battery voltage, the system automatically selects buck mode. In buck mode, the output voltage control equation is V. out =V batt ×D. Where V out This indicates the actual output voltage of the DC-DC module, measured in volts. Its value directly determines the voltage transmitted to the electric vehicle battery, typically ranging from 200V to 800V, depending on the voltage requirements of the electric vehicle. battThe output voltage of the energy storage battery is expressed in volts and typically varies between 300V and 600V. The specific value depends on the battery's state of charge, temperature conditions, and battery type. D represents the duty cycle, a dimensionless parameter with a value limited to 0 to 1. It is the core variable in the control algorithm; adjusting the value of D allows for precise control of the output voltage.

[0024] The formula for calculating voltage stability monitoring parameters is V. stability =1-|V out -V EV | / V EV Among them, V stability This represents a voltage stability index, a dimensionless parameter with a value range of 0 to 1. A higher value indicates more precise voltage matching. (V) out This represents the actual output voltage, V. EV This represents the target voltage of the electric vehicle. This parameter is passed as interactive information to the duty cycle optimization algorithm module for fine-tuning of the duty cycle.

[0025] The current balance equation in buck mode is I batt =I EV / D. Where I batt This indicates the output current of the energy storage battery, measured in amperes. It reflects the magnitude of the current the battery outputs to the system, typically ranging from 10A to 100A, with the specific value depending on the charging power requirements and battery characteristics. EV This represents the charging current of an electric vehicle battery, measured in amperes. It is determined by the charging requirements of the electric vehicle and the battery management system, and generally varies between 20A and 200A. This equation reflects the current conversion relationship in buck mode, where the input current is less than the output current, and the current amplification factor is precisely equal to 1 / D, ensuring accurate current control and efficient energy transfer.

[0026] The power conservation equation for buck mode is P batt =P EV =V batt ×I batt =V EV ×I EV Among them, P batt This indicates the output power of the energy storage battery, measured in watts. It reflects the power level that the energy storage battery outputs to the system, typically ranging from 5kW to 50kW. EV This represents the input power of an electric vehicle's battery, measured in watts (W), which is the actual charging power received by the electric vehicle. V EV This indicates the voltage of an electric vehicle battery, measured in volts.

[0027] In an ideal situation, without considering system losses, the input power equals the output power.

[0028] When the energy storage battery voltage is lower than the electric vehicle battery voltage, the system automatically switches to boost mode. The output voltage control equation for boost mode is V. out =V batt / (1-D). In this equation, the denominator (1-D) serves to achieve the voltage boost function. When D approaches 1, the boost factor tends towards infinity. However, in practical applications, the value of D is constrained by system stability and is generally limited to within 0.8 to ensure stable system operation and avoid system instability caused by excessive voltage boost. The current balance equation for boost mode is I. batt =I EV ×(1-D). This equation shows that in boost mode, the input current is greater than the output current, and the current reduction factor is (1-D), which is completely consistent with the basic principle of boost conversion. Boost mode also follows the power conservation equation P batt =P EV =V batt ×I batt =V EV ×I EV This ensures power balance and energy conservation during energy transmission.

[0029] The formula for calculating mode switching status information is Mode state =α×V ratio +β×P ratio +γ×T switch Mode state This represents a dimensionless parameter indicating the mode switching status, reflecting the complexity and stability of mode switching. V ratio =V EV / V batt P represents the voltage ratio. ratio =P EV / P rated T represents the power ratio. switch This indicates the switching time, in seconds. α, β, and γ are weighting coefficients, typically set to 0.5, 0.3, and 0.2 respectively. This status information is transmitted to the safety interlock protection module for dynamic adjustment of the protection strategy.

[0030] like Figure 3 As shown, the duty cycle optimization algorithm is the core algorithm for achieving accurate voltage matching. This algorithm includes two core calculation modules, which handle duty cycle optimization in buck and boost modes respectively, and receive voltage stability indicators through a parameter interaction mechanism to achieve adaptive optimization.

[0031] The formula for calculating the duty cycle in buck mode is D. buck =V EV / V batt ×(1+K correction ×(1-V stabilityD) buck This represents the optimal duty cycle in buck mode. It is a dimensionless parameter, ranging from 0 to 1, and directly determines the output voltage. (V) EV This indicates the target voltage of the electric vehicle battery, measured in volts. It is determined by the electric vehicle's battery management system based on charging requirements and is generally within the range of 200V to 800V. batt This indicates the current output voltage of the energy storage battery, measured in volts (k). correction This represents the correction factor, typically 0.05, V. stability This refers to the voltage stability indicator received from the bidirectional buck-boost intelligent switching control module. This formula, by introducing voltage stability feedback, achieves adaptive duty cycle adjustment, ensuring that the output voltage accurately matches the target voltage.

[0032] The formula for calculating the duty cycle in boost mode is D. boost =(1-V batt / V EV )×(1+K correction ×(1-V stability D) boost This represents the optimal duty cycle in boost mode. It is a dimensionless parameter with a value range of 0 to 0.8, with an upper limit of 0.8 for system stability considerations. This formula is based on the fundamental working principle of a boost converter, achieving voltage boost by adjusting the duty cycle and dynamically correcting it according to voltage stability indicators.

[0033] The formula for monitoring the duty cycle change rate is dD / dt=(D current -D previous ) / Δt. Where D current D represents the duty cycle at the current moment, dimensionless. previous Δt represents the duty cycle value of the previous control cycle, dimensionless, and represents the control cycle time in seconds, typically 10 microseconds, ensuring the real-time performance and accuracy of the algorithm. dD / dt represents the duty cycle change rate in seconds. This parameter reflects the dynamic response characteristics of the system and is passed as an interactive parameter to the safety interlock protection module for dynamically adjusting the protection threshold.

[0034] The formula for calculating the control stability parameter is Stability. control =1-|dD / dt| / dD max Among them, Stability control This represents a control stability index, a dimensionless parameter ranging from 0 to 1. A larger value indicates more stable control. max This indicates the maximum permissible duty cycle change rate, typically 1000s. -1This parameter is passed to the mode switching judgment algorithm module to dynamically adjust the hysteresis threshold and ensure the stability of mode switching.

[0035] The duty cycle optimization iterative formula is: D optimized =D calculated +K feedback Error voltage +K derivative ×dError / dt. Where D optimized D represents the optimized duty cycle. calculated K represents the basic calculated value. feedback This is the feedback gain coefficient, typically with a value of 0.1. (Error) voltage =V EV -V out K represents the voltage error. derivative The differential gain coefficient is typically 0.01, and dError / dt represents the rate of change of voltage error. This iterative formula implements an adaptive control strategy, ensuring precise adjustment of the duty cycle.

[0036] like Figure 4 As shown, the mode switching judgment algorithm is responsible for dynamically deciding the operating mode of the system based on voltage comparison and hysteresis logic. The core of this algorithm is to avoid frequent mode switching through hysteresis control, ensuring stable system operation, and to receive control stability parameters through a parameter interaction mechanism to achieve adaptive hysteresis threshold adjustment.

[0037] The formula for calculating the adaptive hysteresis threshold is: V hys,adaptive =V hys,base ×(1+K adapt ×(1-Stability control V hys,adaptive This represents the adaptive hysteresis threshold, in volts (V). hys,base This represents the basic hysteresis threshold, typically 15V, K. adapt This represents the adaptive adjustment coefficient, typically 0.3, and the stability factor. control These are control stability parameters received from the duty cycle optimization algorithm module. When control stability is poor, the system automatically increases the hysteresis threshold, reduces the mode switching frequency, and improves system stability.

[0038] The mode selection logic is executed according to the following rules: when V batt >V EV +V hys,adaptive When selecting buck mode, when V batt <V EV -V hys,adaptive Select boost mode when needed, otherwise select standby mode. Vbatt This indicates the voltage of the energy storage battery, measured in volts, and is monitored in real time using a high-precision voltage sensor. (V) EV This indicates the battery voltage of an electric vehicle, measured in volts, and is provided by the electric vehicle via the CAN bus or other communication protocols.

[0039] The formula for evaluating mode stability is Mode stability =1-N switch / N total Mode stability The N represents the model stability index, a dimensionless parameter with a value range of 0 to 1. switch N represents the number of mode switches within a specific time window. total This represents the total number of samples. This metric is used to evaluate the stability of the mode switching algorithm; when stability is low, the system will further increase the hysteresis threshold.

[0040] The formula for calculating the switchover preparation time is T. prepare =T base +K prepare ×|V batt -V EV | / V rated Among them, T prepare The time T represents the mode switching preparation time in seconds. base K represents the basic setup time, typically 1 microsecond. prepare This represents the time adjustment factor, typically 0.1 microseconds per volt, V. rated This indicates the system's rated voltage, typically 600V. This timeframe is used to ensure sufficient preparation before mode switching, thereby improving the success rate of the switchover.

[0041] like Figure 5 As shown, the intelligent control circuit switching module adopts a four-switch topology, achieving flexible switching between buck and boost modes by controlling the on / off states of the four switching devices. The core advantage of this module lies in realizing bidirectional buck-boost functionality from a single DC-DC module and achieving adaptive timing control by receiving safety status information.

[0042] The switching state control follows these rules: In buck mode, S1=1, S2=1, S3=0, S4=0; in boost mode, S1=0, S2=0, S3=1, S4=1. Here, S1, S2, S3, and S4 represent the control signals for the four switching devices, with values ​​of 0 or 1, where 0 indicates the switch is open and 1 indicates the switch is closed. In buck mode, switches S1 and S2 are closed to form a buck converter topology. The energy storage battery charges the inductor through switch S1 and freewheels through switch S2, achieving a buck function where the output voltage is lower than the input voltage. In boost mode, switches S3 and S4 are closed to form a boost converter topology. The energy storage battery charges the inductor through switch S3 and transfers energy to the load through switch S4, achieving a boost function where the output voltage is higher than the input voltage.

[0043] The formula for calculating the adaptive dead time is T. dead,adaptive =T dead,base ×(1+K safety ×(1-Safety status )). Among them, T dead,adaptive T represents the adaptive dead time, in seconds. dead,base K represents the base dead time, typically 2 microseconds. safety This represents the safety adjustment factor, typically 0.5. status This refers to the safety status information received from the safety interlock protection module. When the safety status is poor, the system automatically extends the dead time to ensure the safety of the handover process.

[0044] The switching timing optimization formula is T sequence =T dead,adaptive +T rise +T fall Among them, T sequence T represents the total switching timing time. rise Indicates the rise time of the switch, typically 0.5 microseconds, T. fall This indicates the switch fall time, typically 0.3 microseconds. This timing sequence ensures safe and reliable switching of the switching devices.

[0045] The formula for calculating switching efficiency is η switch =1-E loss,switch / E total Where η switch E represents switching efficiency, a dimensionless parameter. loss,switch This represents the energy loss during the switching process, measured in joules (E). total This represents the total transmitted energy, measured in joules. This efficiency metric is used to evaluate the performance of the switching algorithm.

[0046] like Figure 6As shown, the safety interlock protection module is the core element for ensuring the safe operation of the system. This module, through dual control of hardware-level safety logic and software protection algorithms, ensures safe operation of the system under any operating condition, and receives duty cycle change rate information through a parameter interaction mechanism to achieve adaptive safety protection.

[0047] The basic safety interlock condition is expressed as Safety Check =NOT (S1AND S3) AND NOT (S2AND S4). Among them Safety Check This represents the result of a basic safety check. It is a Boolean variable. When the value is true, it indicates that the switch is safe. When the value is false, it indicates that there is a safety hazard and the protection mechanism needs to be triggered immediately.

[0048] S1, S2, S3, and S4 represent the control signals for four switching devices, with values ​​of 0 or 1. NOT represents a logical NOT operation, and AND represents a logical AND operation. The core function of this formula is to ensure that S1 and S3 cannot be turned on simultaneously, and S2 and S4 cannot be turned on simultaneously, effectively preventing shoot-through short-circuit faults caused by the simultaneous conduction of upper and lower switches on the same bridge arm.

[0049] The adaptive protection threshold calculation formula is as follows: Threshold adaptive =Threshold base ×(1+K threshold ×|dD / dt| / dD max ).

[0050] Threshold adaptive Threshold represents the adaptive protection threshold. base K represents the basic protection threshold. threshold This represents the threshold adjustment coefficient, typically 0.2. dD / dt is the duty cycle change rate received from the duty cycle optimization algorithm module. max This indicates the maximum permissible rate of change. When the duty cycle rate of change is large, the system automatically lowers the protection threshold and increases protection sensitivity.

[0051] The formula for safety status assessment is: Safety status =w1×Safety Check +w2×(1-|dD / dt| / dD max )+w3×Temperature factor Among them, Safety status This represents the comprehensive safety status index, a dimensionless parameter ranging from 0 to 1. w1, w2, and w3 are weighting coefficients, with values ​​of 0.5, 0.3, and 0.2 respectively. Temperature factorThis represents the temperature safety factor, with a value ranging from 0 to 1. This status information is transmitted to the intelligent control circuit switching module to adjust the switching timing.

[0052] The formula for calculating fault response time is: T response =T responsebase / (1+K response ×|dD / dt| / dD max ). Among them, T response T represents the fault response time in seconds. responsebase K represents the base response time, typically 10 microseconds. response This represents the response adjustment coefficient, typically with a value of 2. When the duty cycle change rate is large, the system automatically shortens the response time, increasing the protection speed.

[0053] The formula for calculating the intensity of protective action is: Protection intensity = Intensitybase ×(1+K intensity ×(1-Safety status )).

[0054] Protection intensity Indicates the intensity of the protective action; a dimensionless parameter. Intensitybase K represents the basic protection strength. intensity This represents the intensity adjustment coefficient, typically 0.8. When the safety condition is poor, the system automatically increases the intensity of the protection action.

[0055] like Figure 7 As shown, the parameter interaction and fusion mechanism is the core inventive point of this invention, realizing collaborative optimization among various algorithm modules. This mechanism establishes a multi-directional parameter interaction channel, enabling various algorithm modules to share key control information and achieve global optimization control.

[0056] The voltage stability information transmission equation is V stability,transfer =α1×V stability,raw +β1×V stability,filtered Among them, V stability,transfer V represents the voltage stability information passed to the duty cycle optimization algorithm module. stability,raw V represents the original voltage stability index. stability,filtered This represents the filtered voltage stability index, where α1 and β1 are fusion weighting coefficients, with values ​​of 0.7 and 0.3, respectively. This information transmission ensures the accuracy and real-time nature of the voltage stability data.

[0057] The equation for the information transfer of duty cycle change rate is dD dt,transfer =α2×dD dt,current +β2×dDdt,average Where dD dt,transfer This represents the duty cycle change rate information transmitted to the safety interlock protection module, dD dt,current dD represents the rate of change of the current duty cycle. dt,average This represents the rate of change of the moving average duty cycle, with α2 and β2 being fusion weighting coefficients, taking values ​​of 0.8 and 0.2 respectively. This information transmission provides accurate dynamic information for safety protection.

[0058] The control stability information transfer equation is: Stability control,transfer =α3×Stability control,instant +β3×Stability control,trend Among them, Stability control,transfer This represents the control stability information passed to the mode switching decision algorithm module. control,instant Indicates instantaneous control stability, Stability control,trend The stability trend is represented by α3 and β3, which are fusion weight coefficients with values ​​of 0.6 and 0.4, respectively.

[0059] The equation for transmitting security status information is: Safety status,transfer =α4×Safety status,hardware +β4×Safety status,software .

[0060] Safety status,transfer This indicates the safety status information transmitted to the intelligent control circuit switching module. status,hardware Indicates the security status at the hardware level, Safety status,software This represents the security status at the software level. α4 and β4 are fusion weight coefficients, with values ​​of 0.7 and 0.3, respectively.

[0061] The formula for evaluating the effectiveness of parameter interaction is: Interaction effectiveness =Σ(w i ×Performance improvement,i ).

[0062] Interaction effectiveness w represents an indicator of the effectiveness of parameter interaction. i Represents the weight of the i-th performance metric, Performance improvement,i This represents the degree of improvement in the i-th performance metric. This evaluation is used to verify the effectiveness of the parameter interaction mechanism.

[0063] The formula for calculating information fusion delay is: T fusion,delay =T communication +T processing +T synchronization Among them, T fusion,delay T represents the total delay of information fusion. communication T represents communication delay. processing Indicates processing delay, T synchronization This indicates the synchronization delay. This delay needs to be controlled at the microsecond level to ensure real-time performance.

[0064] System stability is a core indicator for ensuring smooth operation. The stability control parameter design adopted in this invention ensures stable operation of the system under various operating conditions and considers the impact of parameter interactions on stability.

[0065] The system transfer function is G(s) = V out (s) / D(s)=V batt ×K interaction / (1+s×L / R+s 2 ×L×C). Where G(s) represents the system's transfer function in the complex frequency domain, describing the dynamic relationship between the duty cycle input and the voltage output. K interaction This represents the parameter interaction influence factor, typically with a value of 1.05, reflecting the impact of the parameter interaction mechanism on the system gain. out D(s) represents the Laplace transform of the output voltage in the complex frequency domain, and D(s) represents the Laplace transform of the duty cycle in the complex frequency domain. batt The values ​​represent the battery voltage in volts and the DC gain of the transfer function. L represents the filter inductance in Henry, typically ranging from 100 microhenries to 1 millihenry, used to store magnetic field energy and smooth current fluctuations. R represents the equivalent resistance in ohms, including load resistance, line resistance, and the device's equivalent resistance. C represents the filter capacitance in Farads, typically ranging from 100 microfarads to 1000 microfarads, used to store electric field energy and smooth voltage fluctuations.

[0066] Considering the phase margin constraint for parameter interaction, let PM interactive =180°-∠G interactive (jω c ≥45°. Among them, PM interactive G represents the phase margin after considering parameter interaction. interactive ω represents the transfer function that includes interactive effects. c This represents the crossover frequency. The introduction of the parameter interaction mechanism has a positive effect on system stability, increasing the phase margin by approximately 5°.

[0067] The gain margin constraint considering parameter interaction is GM. interactive =-20log|G interactive (jω180° )|≥8dB. Where GM interactive This indicates that the gain margin after considering parameter interaction is required to be no less than 8dB, which is higher than the 6dB requirement of traditional systems. This is because the parameter interaction mechanism enhances the stability of the system.

[0068] System efficiency optimization is a core aspect of improving overall performance. This invention achieves a significant improvement in system efficiency through efficiency calculation and optimization strategies, combined with a parameter interaction mechanism.

[0069] The system efficiency calculation formula considering parameter interaction is as follows: η interactive =(P out ×η interaction ) / P in =(V EV ×I EV ×η interaction ) / (V batt ×I batt ). Where η interactive η represents the system efficiency considering parameter interaction. interaction This represents the parameter interaction efficiency gain factor, typically with a value of 1.08, reflecting the efficiency improvement effect of collaborative optimization. out P represents output power. in This indicates the input power.

[0070] The parameter interaction loss compensation model is P loss,compensated =P switching +P conduction +P core -P interaction,benefit Among them, P loss,compensated P represents the total power loss after compensation. interaction,benefit This indicates a reduction in losses due to parameter interaction, typically 5-8% of the total losses.

[0071] The efficiency optimization objective function is: η target =max(η interactive (subject to V) stability ≥0.95, Safety status ≥0.98, Stability control ≥0.90. This objective function maximizes system efficiency while satisfying constraints on voltage stability, safe state, and control stability.

[0072] To verify the effectiveness of the present invention, an energy storage and charging system for an electric vehicle charging station is used as the main body, which includes intelligent charging management for electric vehicles with multiple voltage platforms, bidirectional boost and buck energy scheduling function, and collaborative optimization control of parameter interaction and fusion mechanism.

[0073] I. Test Scenario and System Parameter Settings The system configuration is as follows: 1.1 Basic System Configuration Main controller: 32-bit floating-point DSP processor TMS320F28377D, with a main frequency of 200MHz, featuring a dual-core architecture and supporting parameter interaction processing; Sampling frequency: voltage sampling 100kHz, current sampling 50kHz, switch signal monitoring 200kHz, parameter interaction frequency 10kHz; Memory: 512KB Flash memory, 256KB RAM, 32K BEEPROM for parameter storage, 16KB dedicated cache for parameter interaction; Communication interfaces: CAN bus 2.0B, RS485 communication, Ethernet interface, supports remote monitoring, internal high-speed parameter exchange bus; Voltage detection accuracy: ±0.05%, 16-bit resolution, range 0-1000V; Current detection accuracy: ±0.1%, 16-bit resolution, range 0-200A; Switching device: SiCMOSFET IPM module, on-resistance 5mΩ, switching frequency 50kHz; Filter inductor: 500μH, rated current 60A, core loss 2W, saturation current 80A; Filter capacitor: 470μF, rated voltage 1000V, ESR 10mΩ, ripple current 20A.

[0074] 1.2 Test Operating Parameters Energy storage battery pack: Lithium iron phosphate battery, rated voltage 665.6V, capacity 150Ah, SOC range 20%-95%; the rated voltage of a single cell is 3.2V, the voltage is about 3.34V when the battery is fully charged and at rest, and about 3.32V when the SOC is 75%.

[0075] Electric vehicle types: BYD Han EV (77kWh battery capacity) on 400V platform, Porsche Taycan (93.4kWh battery capacity) on 800V platform. Ambient temperature: 25℃, relative humidity: 60%, altitude: 100m; Load power range: 5kW-30kW, charging current 10A-50A, power factor 0.98; Test duration: 24 hours of continuous operation, recording 1440 data points, with sampling once per minute; Parameter interaction test: Monitor the parameter transfer effect and collaborative optimization performance between various algorithm modules.

[0076] II. Calculation of the Intelligent Switching Control Algorithm for Bidirectional Boosting and Boosting 2.1 Algorithm Parameter Settings Based on the actual working conditions, the algorithm parameters are set as follows: Energy storage battery voltage: V batt =690V (actual voltage at SOC=75%) Electric vehicle battery voltage range: V EV =200V-800V (Supports multiple voltage platforms); Duty cycle limit range: D min =0.05, D max =0.95 (safe operating range); Voltage control accuracy target: ±0.5% (high-precision control requirement); Control period: Δt = 20μs (corresponding to a 50kHz switching frequency); Mode switching hysteresis threshold: V hys =15V (to prevent frequent switching); Parameter interaction update cycle: T interaction =100μs (to ensure real-time performance).

[0077] 2.2 Data Acquisition Example At test time t=100s, the system collected the following real-time data: Energy storage battery voltage: V batt =690V (acquired via 16-bit ADC); Electric vehicle target voltage: V EV =400V (400V platform vehicle charging requirements). Load current: I load =25A (current charging current requirement for electric vehicles); Ambient temperature: T ambient =28℃ (actual temperature sensor reading); Battery temperature: T battery =32℃ (Battery Management System Feedback); Switching frequency: f sw =50000Hz (system set switching frequency).

[0078] 2.3 Calculation of Operating Point and Monitoring of Voltage Stability in Buck Mode Due to V batt =690V>V EV =400V, which meets the requirements for buck mode.

[0079] Step 1: Calculate the basic duty cycle D basic =V EV / V batt=400V / 690V=0.5797; Step 2: Calculate the output voltage V out =V batt ×D basic =690V × 0.5797 = 399.993V; Step 3: Calculation of voltage stability index V stability =1-|V out -V EV | / V EV =1-|399.993-400| / 400=1-0.0000175=0.999983.

[0080] Step 4: Calculation of Mode Switching Status Information V ratio =V EV / V batt =400 / 690=0.5797; P ratio =P EV / P rated =10000 / 30000=0.3333 (assuming rated power 30kW); T switch =3.2×10 -3 =0.0032s (actual switching time); Mode state =0.5×0.5797+0.3×0.3333+0.2×0.0032=0.2899+0.1000+0.0006=0.3905.

[0081] Step 5: Calculation of Current Relationships I batt =I EV / D basic =25A / 0.5797=43.14A; Power verification: P in =690V×43.14A=29.77kW, P out =400V×25A=10.00kW; Preliminary efficiency estimate: η basic =10.00 / 29.77=0.3360=33.60%.

[0082] 2.4 Calculation of Operating Point and Generation of Status Information in Boost Mode When V batt =690V, V EV When the voltage reaches 800V, the system switches to boost mode.

[0083] Step 1: Calculate the basic duty cycle D basic =1-(V batt / V EV )=1-(690V / 800V)=1-0.8625=0.1375.

[0084] Step 2: Output Voltage Calculation V out =V batt / (1-D basic )=690V / (1-0.1375)=690V / 0.8625=800.0V.

[0085] Step 3: Calculation of voltage stability index V stability =1-|V out -V EV | / V EV =1-|800.0-800| / 800=1.0.

[0086] Step 4: Calculation of Mode Switching Status Information V ratio =V EV / V batt =800 / 690=1.1594; P ratio =P EV / P rated =16000 / 30000=0.5333 (assuming charging power is 16kW); T switch =2.8×10 -3 =0.0028s (boost mode switching time); Mode state =0.5×1.1594+0.3×0.5333+0.2×0.0028=0.5797+0.1600+0.0006=0.7403.

[0087] Step 5: Calculation of Current Relationships I batt =I EV ×(1-D basic =20A × 0.8625 = 17.25A.

[0088] Power verification: P in =690V×17.25A=11.90kW, P out =800V×20A=16.00kW.

[0089] Note: The power imbalance here indicates that further parameter optimization is needed.

[0090] III. Duty Cycle Optimization Algorithm Calculation 3.1 Algorithm Parameter Settings Duty cycle calculation accuracy: ±0.001 (high-precision calculation requirement); Change rate monitoring period: 10μs (real-time monitoring requirement); Filtering time constant: τ = 100μs (noise suppression); Maximum rate of change limit: dD / dt max =1000s-1 (safety limit); Convergence criterion: |ΔD|<0.005 (convergence accuracy requirement); Correction factor: K correction =0.05 (adaptive correction coefficient).

[0091] 3.2 Precise Duty Cycle Calculation with Parameter Interaction Step 1: Receive voltage stability information from the bidirectional buck-boost intelligent switching control module: V stability =0.999983.

[0092] Step 2: Adaptive Duty Cycle Calculation in Buck Mode D buck =V EV / V batt ×(1+K correction ×(1-V stability )) =400 / 690×(1+0.05×(1-0.999983)) =0.5797×(1+0.05×0.000017)=0.5797×(1+0.00000085) =0.5797×1.00000085=0.579700.

[0093] Step 3: Calculation of duty cycle change rate D previous =0.5797 (previous period value); D current =0.579700 (current calculated value); dD / dt=(D current -D previous ) / Δt=(0.579700-0.5797) / (20×10 -6 )=0 / (20×10 -6 )=0s -1 .

[0094] Step 4: Calculation of control stability parameters Stabilitycontrol =1-|dD / dt| / dD max =1-|0| / 1000=1.0.

[0095] Step 5: Iterative calculation of duty cycle optimization Error voltage =V EV -V out =400-399.993=0.007; VK feedback =0.1, K derivative =0.01; dError / dt=(Error voltagecurrent -Error voltage,previous ) / Δt=(0.007-0.010) / (20×10 -6 ) = -0.003 / (20×10 -6 = -150V / s.

[0096] D optimized =D calculated +K feedback Error voltage +K derivative ×dError / dt=0.579700+0.1×0.007+0.01×(-150)=0.579700+0.0007-1.5=-0.9196 (Out of range, needs to be limited).

[0097] Because of D optimized Applications exceeding reasonable limits: D final =min(0.95,max(0.05,D optimized =0.05.

[0098] Since the error is very small, a fine-tuning strategy is adopted: D final =0.579700+0.0007=0.5804.

[0099] 3.3 Adaptive Duty Cycle Calculation in Boost Mode Step 1: Receive voltage stability information V stability =1.0 (ideal match for boost mode).

[0100] Step 2: Adaptive duty cycle calculation for boost mode D boost =(1-V batt / V EV )×(1+K correction ×(1-V stability))=(1 - 690 / 800)×(1 + 0.05×(1 - 1.0)) = 0.1375×(1 + 0.05×0) = 0.1375×1 = 0.1375。

[0101] Step 3: Calculation of duty cycle change rate Assume switching from buck mode to boost mode: D previous = 0.5804 (final value in buck mode); D current = 0.1375 (calculated value in boost mode); dD / dt = (0.1375 - 0.5804) / (100×10 -6 ) = -0.4429 / (100×10 -6 ) = -4429000 s -1 。

[0102] Step 4: Handling of change rate limit Since |dD / dt| = <4429000>> dD / dt max = 1000, step - by - step switching is required.

[0103] Switching step size: ΔD step = dD / dt max ×Δt = 1000×20×10 -6 = 0.02。

[0104] First step: D step,1 = 0.5804 - 0.02 = 0.5604; Multiple steps are required to complete the switching: n steps = |0.1375 - 0.5804| / 0.02 = 22.15 steps, take 23 steps.

[0105] IV. Mode switching judgment and parameter interaction collaborative calculation 4.1 Adaptive hysteresis threshold calculation Step 1: Receive control stability parameter Receive from the duty cycle optimization algorithm module: <000​​​​​​​​​​​​​​​​​​​​​​))=15×(1+0.3×(1-1.0))=15×(1+0.3×0)=15×1=15.0V.

[0108] Step 3: Execution of mode selection logic V batt =690V, V EV =400V.

[0109] Judgment condition: V batt >V EV +V hys,adaptive 690>400+15.0=415.0 690>415.0 is true, select buck mode.

[0110] Step 4: Mode Stability Assessment Assuming that the number of mode switching times N is within 1000 sampling periods. switch =1, N total =1000; Mode stability =1-N switch / N total =1 - 1 / 1000 = 0.999.

[0111] 4.2 Calculation of switchover preparation time Step 1: Switch to preparation time calculation T base =1×10 -6 s (basic preparation time); K prepare =0.1×10 -6 s / V (time adjustment factor); V rated =665.6V (system rated voltage); T prepare =T base +K prepare ×|V batt -V EV | / V rated =1×10 -6 +0.1×10 -6 ×|690-400| / 665.6=1×10 -6 +0.1×10 -6 ×290 / 665.6=1×10 -6 +0.1×10 -6 ×0.4356=1×10 -6 +0.04356×10 -6 =1.04356×10 -6 s.

[0112] V. Safety Interlock Protection Algorithm and Parameter Interaction Calculation 5.1 Adaptive Protection Threshold Calculation Step 1: Receive duty cycle change rate information from the duty cycle optimization algorithm module: dD / dt=0s -1 (Stable operating status).

[0113] Step 2: Adaptive protection threshold calculation Threshold base =1.0 (basic protection threshold). K threshold =0.2 (threshold adjustment coefficient); dD max =1000s -1 (Maximum allowable rate of change); Threshold adaptive =Threshold base ×(1+K threshold ×|dD / dt| / dD max =1.0×(1+0.2×|0| / 1000)=1.0×(1+0)=1.0.

[0114] Step 3: Safety Status Assessment Temperature factor =0.95 (temperature safety factor, temperature is normal); Safety Check =1 (Switch state is safe); w1=0.5, w2=0.3, w3=0.2 (weighting coefficients); Safety status =w1×Safety Check +w2×(1-|dD / dt| / dD max )+w3×Temperature factor =0.5×1+0.3×(1-0 / 1000)+0.2×0.95=0.5+0.3×1+0.19=0.5+0.3+0.19=0.99.

[0115] Step 4: Fault Response Time Calculation T response,base =10×10 -6 s (base response time); K response =2 (response adjustment coefficient); T response =T response,base / (1+K response ×|dD / dt| / dD max ) =10×10 -6 / (1+2×0 / 1000)=10×10 -6 / 1=10×10 -6 s.

[0116] 5.2 Adaptive Timing Calculation for Intelligent Control Circuit Switching Step 1: Receive safety status information from the safety interlock protection module: Safety status =0.99.

[0117] Step 2: Adaptive Dead Time Calculation T dead,base =2×10 -6 s (basic dead time); K safety =0.5 (safety adjustment coefficient).

[0118] T dead,adaptive =T dead,base ×(1+K safety ×(1-Safety status ))=2×10 -6 ×(1+0.5×(1-0.99))=2×10 -6 ×(1+0.5×0.01)=2×10 -6 ×(1+0.005)=2×10 -6 ×1.005=2.01×10 -6 s.

[0119] Step 3: Optimize switching timing T rise =0.5×10 -6 s (switch rise time); T fall =0.3×10 -6 s (switch descent time); T sequence =T dead,adaptive +T rise +T fall =2.01×10 -6 +0.5×10 -6 +0.3×10 -6 =2.81×10 -6 s.

[0120] VI. Parameter Interaction and Fusion Mechanism Calculation 6.1 Parameter Interaction, Information Transmission, and Calculation Step 1: Transmission of voltage stability information V stability,raw=0.999983 (original index); V stability,filtered =0.999985 (filtered index); α1=0.7, β1=0.3 (fusion weight coefficients); V stability,transfer =α1×V stability,raw +β1×V stability,filtered =0.7×0.999983+0.3×0.999985=0.6999881+0.2999955=0.9999836.

[0121] Step 2: Transmission of duty cycle change rate information dD dt,current =0s -1 (Current rate of change); dD dt,average =0.01s -1 (Moving average rate of change); α²=0.8, β²=0.2 (fusion weighting coefficients); dD dt,transfer =α2×dD dt,current +β2×dD dt,average =0.8×0+0.2×0.01=0+0.002=0.002s -1 .

[0122] Step 3: Control the transmission of stability information Stability control,instant =1.0 (instantaneous stability); Stability control,trend =0.9998 (stability trend); α3=0.6, β3=0.4 (fusion weighting coefficients); Stability control,transfer =α3×Stability control,instant +β3×Stability control,trend =0.6×1.0+0.4×0.9998=0.6+0.39992=0.99992.

[0123] Step 4: Transmission of security status information Safety status,hardware =0.996 (hardware security status); Safety status,software =0.988 (Software security status); α4=0.7, β4=0.3 (fusion weighting coefficients); Safety status,transfer =α4×Safety status,hardware +β4×Safety status,software =0.7×0.996+0.3×0.988=0.6972+0.2964=0.9936.

[0124] 6.2 Evaluation of the effectiveness of parameter interaction Step 1: Setting Performance Metric Weights w1=0.3 (voltage control accuracy weight); w2=0.25 (system efficiency weight); w3=0.2 (response speed weight); w4=0.15 (safety weight); w5=0.1 (stability weight).

[0125] Step 2: Calculation of Performance Improvement Performance improvement,1 =0.89 (voltage control accuracy improved by 89%, normalized to 0.89); Performance improvement,2 =0.06 (System efficiency improved by 6%, normalized to 0.06); Performance improvement,3 =0.15 (response speed improved by 15%, normalized to 0.15); Performance improvement,4 =0.025 (Security improvement of 2.5%, normalized to 0.025); Performance improvement,5 =0.08 (stability improved by 8%, normalized to 0.08).

[0126] Step 3: Calculate the effectiveness of the interaction Interaction effectiveness =Σ(w i ×Performance improvement,i =0.3×0.89+0.25×0.06+0.2×0.15+0.15×0.025+0.1×0.08=0.267+0.015+0.03+0.00375+0.008=0.32375.

[0127] 6.3 Information Fusion Delay Computation Step 1: Calculation of each delay component T communication =5×10 -6 s (communication delay); T processing =15×10 -6 s (processing delay); T synchronization =10×10 -6 s (synchronization delay).

[0128] Step 2: Total Delay Calculation T fusion,delay =T communication +T processing +T synchronization =5×10 -6 +15×10 -6 +10×10 -6 =30×10 -6 s=30μs.

[0129] VII. Optimization and Control Effect Verification Based on the above calculation results, parameter interactive collaborative optimization control was implemented, and a comparative analysis of the system state before and after control was conducted: 7.1 Variation in voltage control accuracy 7.2 Improved Parameter Interaction Performance 7.3 System efficiency collaborative optimization effect 7.4 Improved Coordination Performance During Mode Switching 7.5 Synergistic Improvement Effect of Safety Performance 7.6 Parameter Interaction and Fusion Effect After 24 hours of continuous testing and verification, the parameter interaction and fusion mechanism described in this invention has the following technical effects: voltage control accuracy improved from ±0.5V to ±0.04V, an improvement of 92%; system efficiency improved from 94.7% to 96.1%, an improvement of 1.4 percentage points; mode switching time shortened from 3.5ms to 2.6ms, an improvement of 25.7% in response speed; parameter interaction effectiveness reached 32.38%, indicating a significant effect of collaborative optimization; information fusion latency was controlled within 30μs, meeting real-time requirements; overall system performance improved by 35% compared to when each algorithm worked independently; calculated annual energy savings increased by approximately 21,000 yuan; equipment reliability improved by 8%, and maintenance costs were further reduced by 15%.

[0130] VIII. Conclusion Based on the above calculations and results verification, the intelligent energy dispatching method for the DC-coupled energy storage and charging system of the present invention, under the action of the parameter interaction and fusion mechanism, demonstrates the following technical benefits in its industrial application prospects: The parameter interaction and collaboration effect is outstanding: through the multi-directional transmission of voltage stability information, duty cycle change rate, control stability parameters and safety status information, the various algorithm modules have achieved effective collaboration, the parameter interaction effectiveness has reached 32.38%, and the overall system performance has improved by 35% compared with the independent operation of each algorithm, which fully verifies the technical advancement of the parameter interaction and fusion mechanism.

[0131] Significantly improved control precision: Through the parameter interaction and synergy of the bidirectional buck-boost intelligent switching control algorithm and the duty cycle optimization algorithm, the voltage control precision has been further improved from ±0.5V to ±0.04V, an improvement of 92%, reaching an ultra-high precision control level, which meets the requirements of the new generation of high-end electric vehicles for extremely high precision charging.

[0132] The energy efficiency synergy optimization effect is obvious: the parameter interaction mechanism significantly improves the system efficiency from 94.7% to 96.1%, an efficiency improvement of 1.4 percentage points. In high-power charging applications, this efficiency improvement means significant energy saving effect and economic benefits.

[0133] Safety and reliability are comprehensively enhanced: Through real-time transmission of duty cycle change rate information and adaptive protection threshold adjustment, the response speed and protection accuracy of the safety interlock protection algorithm are significantly improved. The fault detection time is shortened from 6μs to 4.5μs, and the adaptive protection accuracy is improved by 18%, providing a more reliable safety guarantee for large-scale industrial applications.

[0134] Dynamic response performance optimization: The parameter interaction mechanism further reduces the mode switching time from 3.5ms to 2.6ms and the dynamic response time from 1.85ms to 1.45ms, improving the system's dynamic performance by 25.7% and enabling it to adapt more quickly to various operating conditions during electric vehicle charging.

[0135] Balancing real-time performance and accuracy: The information fusion delay is controlled within 30μs, which is acceptable compared to the system control cycle of 20μs. It achieves both accuracy and effectiveness of parameter interaction while ensuring real-time performance, and provides a feasible engineering implementation plan.

[0136] Deepening the value of technological innovation: The parameter interaction and fusion mechanism, as the core inventive point of this invention, not only establishes a complete multi-algorithm collaborative optimization system in theory, but also demonstrates excellent performance in practice, with broad prospects for industrial application and market promotion value.

[0137] Enhanced market competitiveness: The system performance improvement achieved through parameter interaction and collaborative optimization gives this invention significant advantages over traditional solutions in terms of technical indicators, economic benefits, and reliability, providing technical support for the large-scale promotion and industrial application of V2G technology.

Claims

1. A smart energy dispatching method for a DC-coupled energy storage and charging system, characterized in that, include: The bidirectional buck-boost intelligent switching control module is used to intelligently select buck mode or boost mode based on the real-time comparison results of the energy storage battery voltage and the electric vehicle battery voltage, so as to realize bidirectional energy transfer from the energy storage battery to electric vehicles with different voltage platforms, and output voltage stability indicators and mode switching status information to the duty cycle optimization algorithm module and the safety interlock protection module. The duty cycle optimization algorithm module is used to calculate the optimal duty cycle in buck and boost modes to ensure that the output voltage accurately matches the target voltage. It includes a buck mode duty cycle calculation unit and a boost mode duty cycle calculation unit, and transmits the duty cycle change rate information and control stability parameters to the safety interlock protection module. At the same time, it receives voltage stability indicators from the bidirectional buck-boost intelligent switching control module. The intelligent control circuit switching module is used to control the on / off state of four switching devices to achieve flexible switching between buck and boost modes. It realizes intelligent reconstruction of circuit topology through combination control of switching devices and dynamically adjusts the switching sequence according to the safety status information provided by the safety interlock protection module. The mode switching judgment algorithm module is used to dynamically decide the system's operating mode based on voltage comparison and hysteresis logic, including intelligent selection of buck mode, boost mode and standby mode, and receives control stability parameters from the duty cycle optimization algorithm module to dynamically adjust the hysteresis threshold and realize adaptive mode switching control. The safety interlock protection module is used to ensure the safe mutual exclusion control of the four switching devices, prevent the bridge arm short circuit fault caused by the simultaneous conduction of the upper and lower switches of the same bridge arm, and receive the duty cycle change rate information from the duty cycle optimization algorithm module. Based on the change rate, the protection threshold and response time are dynamically adjusted to achieve adaptive safety protection. At the same time, the module feeds back the safety status information to the intelligent control circuit switching module. The parameter interaction and fusion mechanism is used to realize the collaborative optimization control between various algorithm modules. This includes the transmission of voltage stability monitoring parameters from the bidirectional buck-boost intelligent switching control module to the duty cycle optimization algorithm module, the transmission of duty cycle change rate and control stability parameters from the duty cycle optimization algorithm module to the safety interlock protection module and the mode switching judgment algorithm module, and the transmission of safety status information from the safety interlock protection module to the intelligent control circuit switching module. The bidirectional buck-boost intelligent switching control module realizes flexible charging of electric vehicles with different voltage platforms by the energy storage battery through mathematical modeling; the duty cycle optimization algorithm module includes a duty cycle change rate monitoring function to provide real-time feedback for the system's dynamic response; the intelligent control circuit switching module adopts a four-switch topology to realize the bidirectional buck-boost function of a single DC-DC module.

2. The intelligent energy dispatching method for a DC-coupled energy storage and charging system according to claim 1, characterized in that: The buck mode of the bidirectional buck-boost intelligent switching control module includes the output voltage control equation: V out =V batt ×D, Current balance equation: I batt =I EV / D, Power conservation equation: P batt =P EV =V batt ×I batt =V EV ×I EV Formula for calculating voltage stability monitoring parameters: V stability =1-|V out -V EV | / V EV ;where V out This is the actual output voltage of the DC-DC module, in volts (V). batt I represents the output voltage of the energy storage battery, measured in volts. D is the duty cycle, dimensionless, ranging from 0 to 1. batt I represents the output current of the energy storage battery, measured in amperes. EV The charging current of an electric vehicle battery, measured in amperes (P). batt P represents the output power of an energy storage battery, measured in watts. EV The input power of an electric vehicle battery, measured in watts (V). EV This refers to the battery voltage of an electric vehicle, measured in volts (V). stability It is a voltage stability index, a dimensionless parameter with a value range of 0 to 1, which is passed to the duty cycle optimization algorithm module as interactive information.

3. The intelligent energy dispatching method for a DC-coupled energy storage and charging system according to claim 1, characterized in that: The boost mode of the bidirectional buck-boost intelligent switching control module includes the output voltage control equation: V out =V batt / (1-D), Current balance equation: I batt =I EV ×(1-D), Power conservation equation: P batt =P EV =V batt ×I batt =V EV ×I EV The formula for calculating mode switching status information is: Mode state =α×V ratio +β×P ratio +γ×T switch ;where V out V represents the actual output voltage of the DC-DC module. batt Where is the output voltage of the energy storage battery, D is the duty cycle, ranging from 0 to 0.8, and the denominator (1-D) performs voltage boosting. batt I is the output current of the energy storage battery. EV For the charging current of the electric vehicle battery, in boost mode the input current is greater than the output current, and the current reduction factor is (1-D). state V is a dimensionless parameter representing the mode switching status indicator. ratio P is the voltage ratio. ratio For power ratio, T switch The switching time is denoted by α, β, and γ, which are weighting coefficients with values ​​of 0.5, 0.3, and 0.2, respectively.

4. The intelligent energy dispatching method for a DC-coupled energy storage and charging system according to claim 1, characterized in that: The formula for calculating the duty cycle in the buck mode of the duty cycle optimization algorithm module is: D buck =V EV / V batt ×(1+K correction ×(1-V stability The formula for calculating the duty cycle in boost mode is: D boost =(1-V batt / V EV )×(1+K correction ×(1-V stability The formula for monitoring the duty cycle change rate is: dD / dt = (D current -D previous The formula for calculating the control stability parameter is: Stability / Δt. control =1-|dD / dt| / dD max ; where D buck D represents the optimal duty cycle in buck mode, is dimensionless, and ranges from 0 to 1. boost The optimal duty cycle in boost mode is dimensionless, K. correction This is a correction factor, typically with a value of 0.05, V stability The voltage stability index received from the bidirectional buck-boost intelligent switching control module, V EV V is the battery voltage of an electric vehicle. batt D is the output voltage of the energy storage battery. current D represents the duty cycle at the current moment. previous The duty cycle value of the previous control cycle is given, and Δt is the control cycle time in seconds, typically 10 microseconds. Stability control To control the stability index, a dimensionless parameter, dD, is used, with a value ranging from 0 to 1. max The maximum allowable duty cycle change rate is typically 1000s. -1 .

5. The intelligent energy dispatching method for a DC-coupled energy storage and charging system according to claim 1, characterized in that: The mode switching judgment algorithm module includes an adaptive hysteresis threshold, and the formula for calculating the adaptive hysteresis threshold is: V hys,adaptive =V hys,base ×(1+K adapt ×(1-Stability control The mode selection logic is as follows: when V batt >V EV +V hys,adaptive When selecting buck mode, when V batt <V EV -V hys,adaptive The system selects boost mode during normal operation and standby mode during other operations. The intelligent control circuit switching module includes an adaptive dead-time calculation formula, which is: T... dead,adaptive =T dead,base ×(1+K safety ×(1-Safety status The switching state control is as follows: in buck mode, S1=1, S2=1, S3=0, S4=0; in boost mode, S1=0, S2=0, S3=1, S4=1; where V hys,adaptive The adaptive hysteresis threshold is expressed in volts (V). hys,base The basic hysteresis threshold, typically 15V, K adapt This is the adaptive adjustment coefficient, typically 0.3, for stability. control V is the control stability index received from the duty cycle optimization algorithm module. batt V is the output voltage of the energy storage battery. EV For electric vehicle battery voltage, T dead,adaptive For adaptive dead time, the unit is seconds, T dead,base The base dead time, typically 2 microseconds, K safety This is a safety adjustment factor, typically 0.

5. status S1, S2, S3, and S4 are the control signals for four switching devices, which are comprehensive safety status indicators received from the safety interlock protection module.

6. The intelligent energy dispatching method for a DC-coupled energy storage and charging system according to claim 1, characterized in that: The safety interlock protection module also includes basic safety interlock conditions, which are: Safety Check =NOT (S1 AND S3) AND NOT(S2 AND S4), the adaptive protection threshold calculation formula is: Threshold adaptive =Threshold base ×(1+K threshold ×|dD / dt| / dD max The safety status assessment formula is: Safety status =w1×Safety Check +w2×(1-|dD / dt| / dD max )+w3×Temperature factor The formula for calculating fault response time is: T response =T responsebase / (1+K response ×|dD / dt| / dD max );in Safety Check Based on the basic safety check results, this is a Boolean variable. A true value indicates a safe switch state, while a false value indicates a potential safety hazard. S1, S2, S3, and S4 are control signals for four switching devices, taking values ​​of 0 or 1. NOT represents a logical NOT operation, AND represents a logical AND operation, and Threshold... adaptive Threshold is an adaptive protection threshold. base Based on the protection threshold, K threshold dD / dt is the threshold adjustment coefficient, typically 0.2, and dD / dt is the duty cycle change rate received from the duty cycle optimization algorithm module. max For the maximum duty cycle change rate, Safety status The comprehensive safety status index is a dimensionless parameter with a value range of 0 to 1. w1, w2, and w3 are weighting coefficients with values ​​of 0.5, 0.3, and 0.2 respectively. Temperature factor For temperature safety factor, T response T represents the fault response time, expressed in seconds. responsebase The base response time is typically 10 microseconds, K response The response adjustment coefficient is typically 2.

7. The intelligent energy dispatching method for a DC-coupled energy storage and charging system according to claim 1, characterized in that: The parameter interaction and fusion mechanism includes a voltage stability information transmission equation, which is: V stability,transfer =α1×V stability,raw +β1×V stability,filtered The equation for the information transmission of duty cycle change rate is: dD dt,transfer =α2×dD dt,current +β2×dD dt,average The control stability information transmission equation is: Stability control,transfer =α3×Stability control,instant +β3×Stability control,trend The equation for transmitting safety status information is: Safety status,transfer =α4×Safety status,hardware +β4×Safety status,software ;where V stability,transfer To provide voltage stability information to the duty cycle optimization algorithm module, V stability,raw V is the original voltage stability index. stability,filtered dD represents the voltage stability index after filtering, α1 and β1 are the fusion weighting coefficients, with values ​​of 0.7 and 0.3 respectively. dt,transfer To transmit the duty cycle change rate information to the safety interlock protection module, dD dt,current dD represents the current duty cycle change rate. dt,average The moving average duty cycle change rate is represented by α2 and β2, which are fusion weighting coefficients with values ​​of 0.8 and 0.2, respectively. Stability control,transfer Stability is the control stability information passed to the mode switching decision algorithm module. control,instant For instantaneous control stability, Stability control,trend To represent the stability trend, α3 and β3 are fusion weight coefficients, with values ​​of 0.6 and 0.4 respectively. status,transfer To transmit safety status information to the intelligent control circuit switching module, Safety status,hardware For hardware-level security status, Safety status,software The security status is represented at the software level, with α4 and β4 being fusion weight coefficients, taking values ​​of 0.7 and 0.3 respectively.

8. The intelligent energy dispatching method for a DC-coupled energy storage and charging system according to claim 1, characterized in that: The control stability parameters include the system transfer function considering parameter interactions: G(s) = V out (s) / D(s)=V batt ×K interaction / (1+s×L / R+s 2 ×L×C), considering the phase margin constraint of parameter interaction: PM interactive =180°-∠G interactive (jω c ≥45°, considering the gain margin constraint for parameter interaction: GM interactive =-20log|G interactive (jω 180° )|≥8dB; where G(s) is the transfer function of the system in the complex frequency domain, K interaction V is the parameter interaction influence factor, typically with a value of 1.05, reflecting the impact of parameter interaction mechanisms on system gain. out (s) represents the Laplace transform of the output voltage, D(s) represents the Laplace transform of the duty cycle, and V batt The voltage rating is: L = (energy storage battery voltage) / (filter inductance ... interactive To account for the phase margin after parameter interaction, the unit is degrees, G interactive For the transfer function that includes interactive effects, ω c For traversal frequency, GM interactive To account for the gain margin after parameter interaction, the unit is decibel, ω 180° is the 180° phase frequency; s is the complex frequency variable in the Laplace transform; j is the imaginary unit.

9. The intelligent energy dispatching method for a DC-coupled energy storage and charging system according to claim 1, characterized in that: The system also includes efficiency optimization parameters, which include a system efficiency calculation formula that takes into account parameter interactions: η interactive =(P out ×η interaction ) / P in =(V EV ×I EV ×η interaction ) / (V batt ×I batt ), Parameter interaction loss compensation model: P loss,compensated =P switching +P conduction +P core -P interaction,benefit Efficiency optimization objective function: η target =max(η interactive )subject to V stability ≥0.95,Safety status ≥0.98, Stability control ≥0.90; where η interactive To account for system efficiency after parameter interaction, dimensionless, η interaction P is the parameter interaction efficiency gain factor, typically with a value of 1.08, reflecting the efficiency improvement effect of collaborative optimization. out Output power, measured in watts (P). in Input power, measured in watts (V). EV For electric vehicle battery voltage, I EV V is the charging current for an electric vehicle battery. batt I is the output voltage of the energy storage battery. batt P is the output current of the energy storage battery. loss,compensated To compensate for the total power loss, P switching For switching losses, P conduction For conduction loss, P core For core loss, P interaction,benefit The reduction in losses due to parameter interaction is typically 5-8% of the total loss, η target To optimize efficiency, the system efficiency is maximized while meeting constraints on voltage stability, safety conditions, and control stability. All power parameters are measured in watts.

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