Bidirectional DC-DC energy feedback system based on multi-source integration and control method thereof

Abstract

The invention provides a bidirectional DC-DC energy feedback system based on multi-source integration and a control method thereof, and relates to the technical field of energy recovery. The system comprises an input source module used for obtaining multiple input sources; the bidirectional DC-DC power conversion module is adaptively connected to the input source module and the energy storage power grid interface module so as to realize bidirectional controllable transmission of energy; the multi-source energy management module is used for being adaptively connected with the control unit, inputting real-time data acquired by the sensor network into the AI optimization unit for dynamic parameter adjustment and then transmitting the real-time data to the control unit; the control unit is integrated with a digital signal processor DSP and an FPGA; the DSP is used for executing a dynamic control strategy; the FPGA is used for generating a high-frequency PWM driving signal; and the energy storage power grid interface module is used for realizing standardized power interaction between load ends. The energy utilization efficiency can be improved, harmonic pollution can be reduced, the dynamic response capability can be enhanced, and the method is suitable for new energy automobile braking energy recovery, renewable energy source storage and industrial test equipment scenes.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent control and energy recovery, and in particular to a bidirectional DC-DC energy feedback system based on multi-source integration and a control method thereof. Background Art

[0002] Bidirectional DC-DC energy feedback systems are widely used in new energy vehicle braking energy recovery, renewable energy storage systems, and regenerative power supply scenarios for industrial test equipment. However, traditional DC-DC converters usually adopt a unidirectional energy transmission design and cannot effectively recover the excess energy generated by the load, resulting in energy waste and low system efficiency. For example, during the braking of electric vehicles or the discharge of industrial test equipment, a large amount of energy is dissipated in the form of heat energy, which not only causes an efficiency loss of about 30%-40%, but also significantly increases heat dissipation costs and carbon emissions, which has an adverse impact on both the environment and the economy.

[0003] Although some bidirectional DC-DC converters in the existing technology can achieve energy feedback, there are still many technical bottlenecks that need to be solved. First, due to high-frequency switching losses and unreasonable topology design, the overall efficiency of existing systems is generally less than 90%, which is difficult to meet the needs of efficient energy management. Secondly, the dynamic response performance is poor, and it is easy to cause voltage fluctuations when switching modes, affecting system stability, especially in complex application scenarios. In addition, the energy fed back to the power grid often has harmonic interference problems, and the quality of grid-connected power does not meet international standards, which limits its wide application in actual engineering. More importantly, the existing system has a low level of intelligence, lacks adaptive energy management strategies, and usually relies on fixed control strategies, which are difficult to adapt to the actual needs of dynamic changes in multi-source inputs.

[0004] Therefore, there is an urgent need for an energy feedback system with high efficiency, fast response, low harmonic pollution and intelligent decision-making capabilities. In view of this, the applicant has studied the existing technology and proposed this application to overcome the shortcomings of the existing technology and promote the further development and application of energy feedback technology. Summary of the invention

[0005] The present invention aims to provide a bidirectional DC-DC energy feedback system based on multi-source integration and a control method thereof, so as to solve the problems of low bidirectional energy transmission efficiency, unstable mode switching and grid-connected power quality in traditional technologies, and realize efficient energy recovery and intelligent management.

[0006] In order to solve the above technical problems, the present invention is implemented through the following technical solutions: A bidirectional DC-DC energy feedback system based on multi-source integration, comprising: Input source module, used to obtain various input sources; A bidirectional DC-DC power conversion module, comprising a bidirectional DC-DC converter and a power switch, adapted to be connected to the input source module and the energy storage grid interface module, for bidirectional controllable transmission of energy; A multi-source energy management module, including a sensor network and an AI optimization unit, is adapted to be connected to the control unit and is used to input the real-time data collected by the sensor network into the AI ​​optimization unit for dynamic parameter adjustment and then transmit it to the control unit; A control unit, integrating a digital signal processor DSP and an FPGA; the DSP is configured to communicate with the sensor network to execute a dynamic control strategy; the FPGA is configured to communicate with the bidirectional DC-DC power conversion module to generate a high-frequency PWM drive signal; The energy storage grid interface module is adapted to be connected to the bidirectional DC-DC power conversion module and the load end, and is used to realize standardized power interaction between the load ends.

[0007] Preferably, the bidirectional DC-DC converter adopts a dual active bridge topology structure; the power switch is made of wide bandgap semiconductor materials, including silicon carbide MOSFET or gallium nitride HEMT.

[0008] Preferably, the input source includes photovoltaics, batteries and supercapacitors; the load end includes a power grid and an energy storage device; the input source and the load end are collected and monitored in real time through the sensor network.

[0009] Preferably, the objective function of the dynamic control strategy is The formula is: ; in, is the tracking error at the kth time step; , is the weight matrix; k represents the time step index, i.e., the discrete time step in the prediction time domain; N represents the prediction time domain length, i.e., the time range for the controller to optimize the future state and input; represents the change in the control input at the kth time step; T represents transpose.

[0010] Preferably, the sensor network includes a Hall voltage sensor, a precision shunt resistor and an NTC thermistor; Wherein, the Hall voltage sensor is used to monitor and collect input voltage; The precision shunt resistor is used to measure and collect the output current; The NTC thermistor is used to monitor and collect the temperature of key components.

[0011] Preferably, the AI ​​optimization unit is used to use the data collected by the sensor network to predict power demand through a pre-trained LSTM network, and use a reward function for reinforcement learning to dynamically adjust the energy feedback ratio, and transmit it to the control unit; The formula of the reward function is: ; in, is the reward function; The revenue from the power grid price; Battery health status; , are the corresponding weight factors respectively.

[0012] Preferably, the energy storage grid interface includes a full-bridge PWM inverter circuit, a digital phase-locked loop and an island protection unit; Wherein, the output stage of the full-bridge PWM inverter circuit integrates an LCL filter; the LCL filter comprises an inductor and a capacitor, and is used to suppress switching frequency harmonics; The digital phase-locked loop is used to achieve phase synchronization with the power grid, and the frequency tracking range of phase synchronization is 45Hz~65Hz; The island protection unit is used to cut off energy feedback when the power grid is abnormal.

[0013] The present invention also provides a bidirectional DC-DC energy feedback control method, comprising: Obtain the input source status and load demand collected in real time by the sensor network, and calculate the SOC of the battery in the input source; wherein the load demand includes the electricity price of the power grid and the load power demand; According to the input source status and load requirements, the working mode is judged and switched in combination with the preset logic. The preset logic is: When the data of the input source meets the set power threshold or the electricity price of the power grid is less than the set valley threshold, the working mode is switched to the charging mode and the charging strategy is executed; When the battery SOC is greater than the set threshold or the load power demand is lower than the set rated power, the working mode is switched to the regenerative mode and the energy regenerative strategy is executed; The charging strategy is: according to the input source state collected in real time, the bidirectional DC-DC converter is dynamically controlled by DSP and FPGA to adjust the charging current and voltage of the battery; The energy feedback strategy is: The sensor network samples the grid feedback current and voltage in real time and performs FFT analysis on harmonics to identify harmonic amplitudes; According to the identified harmonic amplitude, the PWM compensation harmonic is adjusted through FPGA until the total harmonic distortion error is less than the set threshold; After PWM adjusts the harmonics, it uses a phase-locked loop to synchronize the grid frequency in combination with the frequency fluctuation dynamics of the grid to reduce the feedback current harmonic pollution and feed energy back to the grid; The real-time data collected by the sensor network is input into the pre-trained LSTM network to output the power prediction value for a period of time in the future; According to the power prediction value, combined with the reward function, deep reinforcement learning is used to calculate and select the optimal reward ratio in real time; According to the optimal feedback ratio, the output parameters of the bidirectional DC-DC power conversion module are adjusted to achieve bidirectional DC-DC energy feedback control.

[0014] The present invention also provides a computer-readable storage medium, including computer-readable instructions stored on the computer-readable storage medium, and when the computer-readable instructions are executed by a processor of a device where the computer-readable storage medium is located, a bidirectional DC-DC energy feedback control method as described above is implemented.

[0015] In summary, compared with the prior art, the present invention has the following beneficial effects: The present invention realizes efficient conversion and utilization of energy through the efficient power conversion technology of the bidirectional DC-DC power conversion module and the intelligent energy management strategy of the control unit and the multi-source energy management module, significantly improves the overall energy utilization efficiency of the system, and reduces energy waste.

[0016] The present invention has the ability to comprehensively manage and coordinate multiple energy sources (such as batteries, supercapacitors, renewable energy, etc.), and can intelligently allocate energy according to the parameter characteristics, status and corresponding needs of different energy sources, realize the complementary advantages of multiple energy sources, and improve the comprehensive utilization efficiency of energy.

[0017] The present invention uses the objective function dynamic control algorithm and FPGA to generate PWM signals to optimize dynamic performance, and combines LSTM network AI technology to predict load demand and optimize feedback strategy. When energy supply is abnormal or fails, it can respond quickly and automatically adjust the energy distribution strategy to ensure the stable operation of the system and improve the reliability and fault tolerance of the system. The present invention can significantly improve energy utilization efficiency, reduce harmonic pollution, and enhance dynamic response capabilities. It meets the requirements of green energy development and is suitable for new energy vehicle braking energy recovery, renewable energy storage, and industrial testing equipment scenarios.

[0018] The present invention can not only feed back the energy in the energy storage system to the power grid to support the stable operation of the power grid, but also obtain energy from the power grid to charge the energy storage system, thus realizing flexible scheduling and optimal configuration of energy. In the process of energy interaction, advanced power electronics technology and reward function control methods are adopted to effectively reduce harmonic pollution, improve power quality, and ensure the stable operation of the power grid and the normal operation of the equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0020] Figure 1 It is an overall architecture diagram of a bidirectional DC-DC energy feedback system based on multi-source integration provided by the first embodiment of the present invention.

[0021] Figure 2 This is a schematic diagram of energy flow provided by Example 1 of the present invention.

[0022] Figure 3 This is a braking energy recovery flow chart provided in Example 1 of the present invention.

[0023] Figure 4 This is a configuration diagram of an energy feedback system of an industrial testing device provided in Embodiment 1 of the present invention.

[0024] Figure 5 It is a flow chart of a bidirectional DC-DC energy feedback control method provided in the second embodiment of the present invention.

[0025] Figure 6 It is a flow chart of the energy feedback strategy provided in the second embodiment of the present invention.

[0026] The present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. DETAILED DESCRIPTION

[0027] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the invention claimed for protection, but merely represents the selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0028] Embodiment 1 like Figure 1 As shown, a bidirectional DC-DC energy feedback system based on multi-source integration includes: an input source module, a control unit, a bidirectional DC-DC converter module, an energy storage grid interface module and a multi-source energy management module.

[0029] An input source module, used to obtain multiple input sources; the input sources include photovoltaics, batteries and super capacitors; A bidirectional DC-DC power conversion module, including a bidirectional DC-DC converter and a power switch, adapted to be connected to the input source module and the energy storage grid interface module (e.g., the input end is electrically connected to the input source module through a pre-charging circuit, and the output end is electrically connected to the energy storage grid interface module) to achieve bidirectional controllable transmission of energy; A control unit, integrating a digital signal processor DSP and an FPGA; the DSP is configured to communicate with the sensor network (such as through an SPI / CAN interface) to execute a dynamic control strategy; the FPGA is configured to communicate with the bidirectional DC-DC power conversion module (such as through an optical fiber isolation driver and a power switch) to generate a high-frequency PWM drive signal; The energy storage grid interface module is adapted to be connected to the bidirectional DC-DC power conversion module and the load end, and is used to realize standardized power interaction between the load ends; the load end includes a grid and an energy storage device.

[0030] The multi-source energy management module includes a sensor network and an AI optimization unit, which are used to adapt and connect with the control unit, and input the real-time data of the input source and load collected by the sensor network into the AI ​​optimization unit for dynamic parameter adjustment and then transmit it to the control unit.

[0031] Furthermore, the bidirectional DC-DC converter adopts a dual active bridge topology structure; the power switch is made of wide bandgap semiconductor materials, including silicon carbide MOSFET or gallium nitride HEMT. The switching frequency of the silicon carbide MOSFET is adapted to the dual active bridge topology.

[0032] In a preferred embodiment, if Figure 2 As shown in the figure, the input source module uses an 800V high-voltage power battery pack as the input energy source. Its model is NCM 811 battery cell with a capacity of 120kWh. The battery pack realizes bidirectional energy transmission through a dual active bridge (DAB) converter, and supports dynamic switching between forward charging mode and reverse energy feedback mode. The design of the DAB converter (i.e., bidirectional DC-DC converter) adopts a high-frequency switching topology. The power device (i.e., power switch) uses silicon carbide (SiC) MOSFET, such as the specific model C3M0075120K. The switching frequency can be set to 100kHz. Such a design reduces the conduction loss by 40%-60% compared with traditional silicon-based devices, and the overall efficiency is measured to reach 96.2%.

[0033] In addition, the transformer turns ratio in the DAB topology can be designed to be 1:1, and ferrite core materials can be used to reduce hysteresis loss and eddy current loss to further improve system energy efficiency.

[0034] The control unit consists of a digital signal processor (DSP) and a field programmable gate array (FPGA), where the DSP executes a model predictive control (MPC) algorithm to optimize the switching timing and duty cycle in real time.

[0035] MPC balances control performance and constraints in the prediction time domain by optimizing the objective function to minimize the state tracking error (such as output voltage fluctuation) and control cost (such as switching loss).

[0036] Objective Function The formula is: ; in, is the tracking error at the kth time step; , is the weight matrix, , used to adjust the weight of the tracking error. The larger Q is, the more the controller tends to eliminate the error quickly. , used to adjust the weight of the control input change. The larger R is, the more the controller tends to take smooth control actions. k represents the time step index, i.e., the discrete time step in the prediction time domain; N represents the length of the prediction time domain, i.e., the time range over which the controller optimizes future states and inputs; represents the change in the control input at the kth time step; T represents transpose, which is used to convert a column vector into a row vector for matrix multiplication.

[0037] Specifically, a discrete-time state space model of a bidirectional DC-DC converter is first established. Based on the current state, the predicted state sequence is recursively calculated. The predicted state is substituted into the objective function and converted into a quadratic programming (QP) problem in combination with constraints (such as duty cycle range constraints, voltage / current limits, switching frequency limits, dead time, etc.). The first control quantity of the optimization result is taken, and the prediction is updated in a rolling manner. The DSP transmits the optimization results (such as duty cycle) to the FPGA through the AXI bus; the FPGA feeds back the real-time state to the DSP to form a closed-loop control.

[0038] In charging mode, MPC optimizes the charging current and quickly tracks the SOC target (such as 90%); FPGA generates constant current PWM to limit the battery temperature rise.

[0039] In the feedback mode, MPC predicts grid harmonics, adjusts PWM to compensate THD, and combines LSTM to predict electricity prices to dynamically optimize the feedback power.

[0040] like Figure 3 As shown in the figure, when the vehicle is in a deceleration state, the braking energy generated by the drive motor is fed back to the battery pack through the DAB converter. The control unit calculates the optimal control strategy based on the current system status data (including input voltage, output current, temperature, etc.) collected by the sensor network to ensure maximum energy transmission efficiency.

[0041] The FPGA generates a PWM drive signal with a resolution of 0.1ns and supports dynamic adjustment of the dead time from 10ns to 100ns, thereby effectively suppressing the voltage spike phenomenon during the switching process. In terms of dynamic performance, the measured value of the mode switching time is 78μs, the voltage fluctuation is only 1.8%, and the overshoot is less than 5%, meeting the high dynamic response requirements.

[0042] The multi-source energy management module includes a sensor network and an AI optimization unit. The sensor network monitors the system status in real time. The sensor network includes a Hall voltage sensor, a precision shunt resistor, and an NTC thermistor. The range of the Hall voltage sensor can be set to 0-1000V and the accuracy to 0.2% according to the needs, which is used to monitor the input voltage; the temperature drift of the precision shunt resistor can be set to less than 50ppm / ℃, which is used to measure the output current; the resolution of the NTC thermistor can be set to 0.1℃ according to the needs, which is used to monitor the temperature of key components.

[0043] The AI ​​optimization unit is used to predict power demand based on the data collected by the Hall voltage sensor, precision shunt resistor, and NTC thermistor through a pre-trained LSTM network, and uses a reward function for reinforcement learning to dynamically adjust the energy feedback ratio.

[0044] The data from these sensors is transmitted to the AI ​​optimization unit, which integrates the long short-term memory network (LSTM) and deep reinforcement learning (DRL) algorithms.

[0045] Specifically, the collected data such as voltage, current and temperature (for example, data with a time window of 1 hour) are filtered to eliminate noise and interference; the filtered data are then normalized to facilitate the training and prediction of the LSTM network; the normalized data are constructed into a time series in chronological order as the input of the LSTM network.

[0046] In this embodiment, the LSTM network includes an input layer, an LSTM layer, a fully connected layer, and an output layer. The input layer receives time series data, the LSTM layer is used to capture long-term dependencies in the time series, the fully connected layer is used to map the output of the LSTM layer to the prediction results, and the output layer gives the predicted value of the power demand. The LSTM network is trained using historical data, and loss functions such as mean square error (MSE) are used to evaluate the prediction performance of the model. The generalization ability of the model is verified by methods such as cross-validation to ensure that the model can give accurate prediction results on unseen data.

[0047] The real-time collected and pre-processed data is input into the trained LSTM network to obtain the future power demand forecast (such as the next 5 minutes). The prediction error can be controlled within the range of less than 5% and guide the system pre-adjustment strategy.

[0048] In this embodiment, the deep reinforcement learning (DRL) algorithm defines the state space of the system, including the current power demand forecast value, energy feedback ratio, battery status (such as power, temperature, etc.), etc. The action space of the system is defined, that is, the adjustment range of the energy feedback ratio. For example, the energy feedback ratio can be adjusted to any value in the interval [0,1], or defined as several discrete levels. And a reward function is designed to evaluate the effect of different actions taken by the system in different states.

[0049] In this embodiment, the DRL algorithm uses the grid electricity price revenue and battery health status as the reward function R to dynamically adjust the energy feedback ratio. The reward function can comprehensively consider multiple factors such as energy utilization efficiency, system stability, and battery life. When the energy feedback ratio is properly adjusted so that the system can efficiently utilize energy and maintain stable operation, a positive reward is given; otherwise, a negative reward is given.

[0050] For example, energy is fed back preferentially during peak hours of grid electricity prices, which improves the economic efficiency of the system by 10%-15%. For example, when the grid electricity price is 0.8 yuan / kWh, the average daily income increases by 12.7%.

[0051] The formula for the reward function R is: ; in, The revenue from the power grid price; Battery health status; , are the corresponding weight factors respectively.

[0052] Next, you can choose a suitable reinforcement learning algorithm for training so that it can learn the strategy of taking the optimal action in different states, such as Q-learning, deep Q network (DQN), etc.

[0053] The energy storage grid interaction interface module includes a full-bridge PWM inverter circuit, a digital phase-locked loop (PLL), and an island protection mechanism. The output stage of the full-bridge PWM inverter circuit integrates an LCL filter, where the inductance value of the LCL filter can be 2mH and the capacitance value can be 10μF, which is used to suppress switching frequency harmonics and ensure that the total harmonic distortion (THD) of the grid-connected current is less than the set threshold, such as 3%. The digital phase-locked loop (PLL) is used to achieve phase synchronization with the grid, the synchronization error is set to less than 0.5°, and the frequency tracking range of phase synchronization is set to 45Hz to 65Hz to meet the requirements of the IEEE 1547-2018 standard. The island protection mechanism can cut off the feedback path within 10ms when a grid anomaly is detected to comply with the UL1741 standard and ensure safe operation of the system.

[0054] In addition, this embodiment also includes: in terms of liquid cooling and heat dissipation design, a liquid cooling controller is directly bonded to the ceramic substrate to ensure that the operating temperature of the power device is maintained within a reasonable range. The actual measured temperature rise after 12 hours of full load operation is 14.2°C, which significantly improves system reliability.

[0055] In another preferred embodiment, the present invention is demonstrated for use in industrial testing equipment.

[0056] The hardware configuration of the industrial test equipment is as follows: Input source: 48V lithium-ion battery; Bidirectional DC-DC topology: synchronous Buck-Boost non-isolated design, GaN HEMT switching frequency 1MHz; Communication interface: Wi-Fi module connects to the host computer monitoring platform.

[0057] Test energy feedback: When the electronic load is discharged, 80% of the energy is fed back to the grid, and the Python monitoring platform displays energy efficiency data in real time; Heat dissipation management: When the temperature is >85℃, air cooling is enabled and the temperature rise is suppressed to <12℃.

[0058] Fault protection: When overcurrent is triggered, the FPGA cuts off the PWM signal within 2μs and sends an alarm message via RS485.

[0059] like Figure 4 As shown in the figure, the input source module uses a 48V lithium-ion battery pack, the bidirectional DC-DC converter module adopts a synchronous Buck-Boost topology design, the power device uses gallium nitride (GaN) HEMT, the specific model is GS61008P, the switching frequency is increased to 1MHz, and the overall efficiency is measured to be 95.8%. During the discharge process of the electronic load, 80% of the energy is fed back to the grid through the synchronous Buck-Boost converter, and the remaining energy is stored in the battery pack.

[0060] The control unit generates PWM signals through the DSP+FPGA hardware platform and realizes precise control in combination with the PID algorithm to ensure a smooth and reliable energy transmission process. The communication interface uses a Wi-Fi module to connect to the host computer monitoring platform to display energy efficiency data in real time, facilitating remote monitoring and management by users.

[0061] In terms of heat dissipation management, the air-cooled radiator automatically starts according to the feedback data from the temperature sensor. When the temperature exceeds 85°C, the air-cooling system starts to work, suppressing the temperature rise to within 12°C to ensure long-term stable operation of the system. The fault protection mechanism is implemented through the RS485 communication interface. When an overcurrent fault is detected, the FPGA cuts off the PWM signal within 2μs and sends an alarm message to the monitoring platform via RS485 to remind users to handle it in time.

[0062] In terms of efficient energy management, the overall efficiency of the system reaches more than 95%, and the measured values ​​are 96.2% (800V input) and 95.8% (48V input), which is 15%-20% higher than the traditional silicon-based solution. Secondly, in terms of dynamic performance, the mode switching time is less than 100μs, the measured value is 78μs, the voltage fluctuation is 1.8%, and the overshoot is less than 5%. Thirdly, in terms of green compatibility, the grid-connected current THD is less than 3%, the measured value is 2.6%, the grid synchronization error is less than 0.5°, and the liquid cooling temperature rise is less than 15°C. Finally, in terms of intelligent decision-making, the LSTM load prediction error is less than 5%, and the DRL dynamic feedback strategy improves the economy by 10%-15%. In addition, the system has high reliability, the dual MCU redundant monitoring MTBF is greater than 100,000 hours, and the island protection response time is less than 10ms.

[0063] In summary, compared with the prior art, the present invention has the following beneficial effects: The present invention includes a bidirectional DC-DC power conversion module, a control unit, a multi-source energy management module and an energy storage grid interaction interface module. The system achieves efficient energy conversion through high-frequency switching topology and wide bandgap semiconductor devices, and uses the objective function dynamic control algorithm and FPGA to generate PWM signals to optimize dynamic performance, while combining LSTM network AI technology to predict load demand and optimize feedback strategy. The present invention can significantly improve energy utilization efficiency, reduce harmonic pollution, and enhance dynamic response capabilities, and is suitable for new energy vehicle braking energy recovery, renewable energy storage and industrial testing equipment scenarios.

[0064] The present invention has the ability to comprehensively manage and coordinate multiple energy sources (such as batteries, supercapacitors, renewable energy, etc.), and can intelligently allocate energy according to the parameter characteristics, status and needs of different energy sources, realize the complementary advantages of multiple energy sources, and improve the comprehensive utilization efficiency of energy. At the same time, when there is an abnormality or failure in the energy supply, it can respond quickly and automatically adjust the energy allocation strategy to ensure the stable operation of the system and improve the reliability and fault tolerance of the system.

[0065] The present invention can not only feed back the energy in the energy storage system to the power grid to support the stable operation of the power grid, but also obtain energy from the power grid to charge the energy storage system, thus realizing flexible scheduling and optimal configuration of energy. In the process of energy interaction, advanced power electronics technology and reward function control algorithm are adopted to effectively reduce harmonic pollution, improve power quality, and ensure the stable operation of the power grid and the normal operation of the equipment.

[0066] The present invention solves the key technical problems in traditional technologies by combining hardware design, control algorithms and intelligent management strategies, and provides an efficient solution for the regenerative power supply scenarios of new energy vehicle braking energy recovery, renewable energy storage systems and industrial testing equipment. The present invention fully embodies the advantages of efficient energy management, excellent dynamic performance, green compatibility and intelligent decision-making.

[0067] Embodiment 2 like Figure 5 , Figure 6 As shown, the second embodiment of the present invention further provides a bidirectional DC-DC energy feedback control method, comprising: Obtain the input source status (such as battery voltage, current, temperature) and load demand (such as grid voltage, current, frequency, electricity price) collected in real time by the sensor network, and calculate the SOC of the battery in the input source; wherein the load demand includes the electricity price of the grid and the load power demand; According to the input source status and load requirements, the working mode is judged and switched in combination with the preset logic, namely: When the data of the input source meets the set power threshold or the electricity price of the power grid is less than the set valley threshold, the working mode is switched to the charging mode and the charging strategy is executed; When the battery SOC is greater than the set threshold or the load power demand is lower than the set rated power, the working mode is switched to the regenerative mode and the energy regenerative strategy is executed; The charging strategy is: according to the input source state collected in real time, the bidirectional DC-DC converter is dynamically controlled by DSP and FPGA to adjust the charging current and voltage of the battery; The energy feedback strategy is: The sensor network samples the feedback current and voltage of the power grid in real time and performs FFT (Fast Fourier Transform) analysis on harmonics to identify the harmonic amplitude; According to the identified harmonic amplitude, the PWM compensation harmonic is adjusted through FPGA until the total harmonic distortion error is less than the set threshold (such as 3%); After PWM adjustment, combined with the frequency fluctuation dynamics of the power grid, a phase-locked loop is used to synchronize the grid frequency to reduce the feedback current harmonic pollution and feed energy back to the grid; The real-time data (such as voltage, current, frequency, electricity price, SOC, etc.) collected by the sensor network is input into the pre-trained LSTM network to output the power prediction value for a period of time in the future (such as 5 minutes); According to the power prediction value, combined with the reward function, deep reinforcement learning is used to calculate and select the optimal reward ratio in real time; According to the optimal feedback ratio, the output parameters of the bidirectional DC-DC power conversion module are adjusted to achieve bidirectional DC-DC energy feedback control.

[0068] In this embodiment, the battery SOC may be calculated based on the ampere-hour integration method or the extended Kalman filter (EKF) algorithm in combination with the battery temperature and the charge and discharge current.

[0069] The DSP calculates the optimal charging current and voltage based on the real-time collected input source status (voltage, current, temperature). The FPGA generates a PWM signal to control the bidirectional DC-DC converter to achieve constant current-constant voltage (CC-CV) charging.

[0070] Specifically, it can be divided into mode switching control, harmonic suppression and grid synchronization, and intelligent optimization strategy.

[0071] In mode switching control, the system dynamically switches the working mode according to the input source status and load demand. When the photovoltaic input is sufficient or the grid electricity price is at a low point, the system enters the charging mode, gives priority to charging the battery, and adopts a constant current / constant voltage control strategy; when the battery SOC is greater than 90% or the load demand drops sharply, the system switches to the feedback mode, and realizes energy feedback through the maximum power point tracking (MPPT) technology, with an efficiency value of 99%; in the seamless switching mode, the FPGA hard real-time control ensures that there is no voltage interruption during the switching process, and the transition time is less than 50μs.

[0072] In the harmonic suppression and grid-connected synchronization link, the system samples the feedback current at a rate 10 times the switching frequency through FFT spectrum analysis, identifies the amplitudes of the second and third harmonics, and adjusts the PWM duty cycle through active damping control to compensate for the harmonic components, so that the total harmonic distortion (THD) is less than 3%; then, according to the dynamics of the grid frequency fluctuations, the adaptive phase-locked technology is used to adjust the PLL parameters to ensure that the synchronization error is less than 0.5°.

[0073] The intelligent optimization strategy predicts the power demand in the next 5 minutes through the LSTM network, calculates the optimal feedback ratio in real time in combination with the DRL algorithm, and adjusts the output parameters (such as voltage, current, and frequency) of the bidirectional DC-DC converter according to the optimal feedback ratio. The present invention can give priority to feeding back energy during the peak period of power grid electricity prices, significantly improving the economy and energy efficiency of the system.

[0074] Embodiment 3 The third embodiment of the present invention further provides a computer-readable storage medium, which includes computer-readable instructions stored on the computer-readable storage medium. When the computer-readable instructions are executed by a processor of a device where the computer-readable storage medium is located, a bidirectional DC-DC energy feedback control method as described above is implemented.

[0075] In several embodiments provided in the embodiments of the present invention, it should be understood that the disclosed apparatus and method can also be implemented in other ways. The apparatus and method embodiments described above are merely schematic. For example, the flowcharts in the accompanying drawings show the possible architecture, functions and operations of the apparatus, method and computer program product according to multiple embodiments of the present invention. In this regard, each box in the flowchart or block diagram can represent a module, a program segment or a part of a code, and the module, program segment or a part of the code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flow chart, and the combination of boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or action, or can be implemented with a combination of dedicated hardware and computer instructions.

[0076] In addition, the functional modules in the various embodiments of the present invention may be integrated together to form an independent part, or each module may exist independently, or two or more modules may be integrated to form an independent part.

[0077] If the function is implemented in the form of a software function module and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art or the part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions to enable a computer device (which can be a personal computer, an electronic device, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program code. It should be noted that in this article, the term "include", "include" or any other variant thereof is intended to cover non-exclusive inclusion, so that the process, method, article or device including a series of elements includes not only those elements, but also includes other elements that are not explicitly listed, or also includes elements inherent to such process, method, article or device. Without more constraints, an element defined by the phrase "comprising a..." does not exclude the existence of other identical elements in the process, method, article or apparatus comprising the element.

[0078] The terms used in the embodiments of the present invention are only for the purpose of describing specific embodiments, and are not intended to limit the present invention. The singular forms "a", "said" and "the" used in the embodiments of the present invention and the appended claims are also intended to include plural forms, unless the context clearly indicates other meanings.

[0079] It should be understood that the term "and / or" used in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship.

[0080] The word "if" as used herein may be interpreted as "at the time of" or "when" or "in response to determining" or "in response to detecting", depending on the context. Similarly, the phrases "if it is determined" or "if (stated condition or event) is detected" may be interpreted as "when it is determined" or "in response to determining" or "when detecting (stated condition or event)" or "in response to detecting (stated condition or event)", depending on the context.

[0081] The "first\second" mentioned in the embodiments is only to distinguish similar objects, and does not represent a specific order for the objects. It is understandable that the "first\second" can be interchanged with the specific order or sequence where permitted. It should be understood that the objects distinguished by "first\second" can be interchanged where appropriate, so that the embodiments described herein can be implemented in an order other than those illustrated or described herein.

[0082] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A bidirectional DC-DC energy feedback system based on multi-source integration, characterized in that: include: Input source module, used to obtain various input sources; A bidirectional DC-DC power conversion module, comprising a bidirectional DC-DC converter and a power switch, adapted to be connected to the input source module and the energy storage grid interface module, for bidirectional controllable transmission of energy; A multi-source energy management module, including a sensor network and an AI optimization unit, is adapted to be connected to the control unit and is used to input the real-time data collected by the sensor network into the AI ​​optimization unit for dynamic parameter adjustment and then transmit it to the control unit; A control unit, integrating a digital signal processor DSP and an FPGA; the DSP is configured to communicate with the sensor network to execute a dynamic control strategy; the FPGA is configured to communicate with the bidirectional DC-DC power conversion module to generate a high-frequency PWM drive signal; The energy storage grid interface module is adapted to be connected to the bidirectional DC-DC power conversion module and the load end, and is used to realize standardized power interaction between the load ends.

2. A bidirectional DC-DC energy feedback system based on multi-source integration according to claim 1, characterized in that: The bidirectional DC-DC converter adopts a dual active bridge topology structure; the power switch is made of wide bandgap semiconductor materials, including silicon carbide MOSFET or gallium nitride HEMT.

3. The bidirectional DC-DC energy feedback system based on multi-source integration according to claim 1 is characterized in that: The input source includes photovoltaics, batteries and supercapacitors; the load end includes a power grid and an energy storage device; the input source and the load end are collected and monitored in real time through the sensor network.

4. The bidirectional DC-DC energy feedback system based on multi-source integration according to claim 1 is characterized in that: The objective function of the dynamic control strategy The formula is: ； in, is the tracking error at the kth time step; , is the weight matrix; k represents the time step index, i.e., the discrete time step in the prediction time domain; N represents the prediction time domain length, i.e., the time range for the controller to optimize the future state and input; represents the change in the control input at the kth time step; T represents transpose.

5. The bidirectional DC-DC energy feedback system based on multi-source integration according to claim 1 is characterized in that: The sensor network includes a Hall voltage sensor, a precision shunt resistor and an NTC thermistor; Wherein, the Hall voltage sensor is used to monitor and collect input voltage; The precision shunt resistor is used to monitor and collect the output current; The NTC thermistor is used to monitor and collect the temperature of key components.

6. A bidirectional DC-DC energy feedback system based on multi-source integration according to claim 5, characterized in that: The AI ​​optimization unit is used to use the data collected by the sensor network to predict power demand through a pre-trained LSTM network, and use a reward function to perform reinforcement learning to dynamically adjust the energy feedback ratio, and transmit it to the control unit; The formula of the reward function is: ； in, is the reward function; The revenue from the power grid price; Battery health status; , are the corresponding weight factors respectively.

7. The bidirectional DC-DC energy feedback system based on multi-source integration according to claim 1, characterized in that: The energy storage grid interface includes a full-bridge PWM inverter circuit, a digital phase-locked loop and an island protection unit; Wherein, the output stage of the full-bridge PWM inverter circuit integrates an LCL filter; the LCL filter comprises an inductor and a capacitor, and is used to suppress switching frequency harmonics; The digital phase-locked loop is used to achieve phase synchronization with the power grid, and the frequency tracking range of phase synchronization is 45Hz~65Hz; The island protection unit is used to cut off energy feedback when the power grid is abnormal.

8. A bidirectional DC-DC energy feedback control method, applied to a bidirectional DC-DC energy feedback system based on multi-source integration as claimed in any one of claims 1 to 7, characterized in that: include: Obtain the input source status and load demand collected in real time by the sensor network, and calculate the SOC of the battery in the input source; wherein the load demand includes the electricity price of the power grid and the load power demand; According to the input source status and load requirements, the working mode is judged and switched in combination with the preset logic. The preset logic is: When the data of the input source meets the set power threshold or the electricity price of the power grid is less than the set valley threshold, the working mode is switched to the charging mode and the charging strategy is executed; When the battery SOC is greater than the set threshold or the load power demand is lower than the set rated power, the working mode is switched to the regenerative mode and the energy regenerative strategy is executed; The charging strategy is: according to the input source state collected in real time, the bidirectional DC-DC converter is dynamically controlled by DSP and FPGA to adjust the charging current and voltage of the battery; The energy feedback strategy is: The sensor network samples the grid feedback current and voltage in real time and performs FFT analysis on harmonics to identify harmonic amplitudes; According to the identified harmonic amplitude, the PWM compensation harmonic is adjusted through FPGA until the total harmonic distortion error is less than the set threshold; After PWM adjusts the harmonics, it uses a phase-locked loop to synchronize the grid frequency in combination with the frequency fluctuation dynamics of the grid to reduce the feedback current harmonic pollution and feed energy back to the grid; The real-time data collected by the sensor network is input into the pre-trained LSTM network to output the power prediction value for a period of time in the future; According to the power prediction value, combined with the reward function, deep reinforcement learning is used to calculate and select the optimal reward ratio in real time; According to the optimal feedback ratio, the output parameters of the bidirectional DC-DC power conversion module are adjusted to achieve bidirectional DC-DC energy feedback control.

9. A bidirectional DC-DC energy feedback control method according to claim 8, characterized in that: When the charging current and voltage are adjusted by dynamically controlling the bidirectional DC-DC converter through DSP and FPGA, the objective function is used for control. The formula is: ； in, is the tracking error at the kth time step; , is the weight matrix; k represents the time step index, i.e., the discrete time step in the prediction time domain; N represents the prediction time domain length, i.e., the time range for the controller to optimize the future state and input; represents the change in the control input at the kth time step; T represents transpose.

10. A bidirectional DC-DC energy feedback control method according to claim 8, characterized in that: The formula of the reward function is: ； in, is the reward function; The revenue from the power grid price; Battery health status; , are the corresponding weight factors respectively.

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