Self-energy-taking multi-source maximum energy collection and management system and method based on cooperative control

Abstract

The invention discloses a self-energy-taking multi-source maximum energy collection and management system and method based on cooperative control, and belongs to the technical field of power systems, and the system comprises an adjustable self-energy-taking module which is used for adjusting the working state of the adjustable self-energy-taking module according to a control signal so as to change the output power; the active gating module is used for switching an energy output path of the self-energy-taking unit according to the control signal to realize dynamic path management; the energy storage and management module is used for storing the electric energy collected by the self-energy-taking unit and managing the energy flow direction among the battery, the load and the input energy source according to the control instruction; and the control unit is used for collecting the running state of the system and generating a scheduling control instruction for optimizing the energy collection efficiency. According to the invention, by optimizing each energy working point, the system can extract the maximum energy from a plurality of fluctuating energies.

Description

Technical Field

[0001] This invention relates to the field of power system technology, specifically to a self-harvesting multi-source maximum energy harvesting and management system and method based on coordinated control. Background Technology

[0002] Existing self-harvesting systems are mostly used to power low-power electronic devices, such as wireless sensor nodes and remote monitoring terminals. These systems typically integrate several energy harvesting units, such as current transformer-based inductive energy harvesting modules, photovoltaic panels, piezoelectric vibration energy harvesters, or thermoelectric converters, to obtain electrical energy from the environment to maintain system operation. For single-type energy sources, existing research has widely adopted algorithms such as maximum power point tracking (MPPT) to improve the energy extraction efficiency of a single energy source. However, traditional methods have significant shortcomings when faced with the simultaneous existence of multiple heterogeneous energy sources.

[0003] On the one hand, most current multi-source energy harvesting systems adopt a parallel architecture, with each energy source configured with its own rectification, voltage regulation, and energy management units. Their outputs are automatically connected to the system bus via diodes or power management chips. This passive approach switches energy paths solely based on the input voltage levels, lacking unified coordination and strategy optimization. This often results in some energy sources being idle or their operating points being far from their optimal values, leading to a decrease in the overall energy harvesting efficiency of the system.

[0004] On the other hand, in complex and dynamic environments, the available power of each energy source fluctuates significantly, while parameters such as system load demand and battery state of charge are also constantly changing. If each energy unit continues to operate independently without a coordination mechanism, the system will struggle to adapt to changes in operating conditions, making it difficult to guarantee overall power supply stability and energy utilization. Furthermore, while some systems currently possess path management capabilities, they are mostly limited to fixed priority strategies and cannot dynamically adjust path control and operating point parameters based on real-time data, thus restricting the flexibility of energy scheduling.

[0005] Therefore, there is an urgent need for a multi-source energy harvesting management system capable of simultaneously sensing multiple energy states, system load demands, and energy storage status, and possessing dynamic optimization and control capabilities. This system should be able to collaboratively set the energy extraction operating points of multiple energy units and, combined with path control mechanisms, achieve optimal system-level energy harvesting efficiency, thereby solving problems such as rigid path management, low energy utilization efficiency, and weak system power supply capacity in existing technologies. Summary of the Invention

[0006] In view of the above-mentioned problems, the present invention is proposed.

[0007] Therefore, the technical problem solved by this invention is: in a self-energy harvesting system that simultaneously integrates current transformer energy harvesting units and photovoltaic energy harvesting units, how to design a control mechanism that can sense the system status in real time and dynamically coordinate the operating points and power supply paths of each unit, taking into account the differences in output characteristics, power path conflicts, and time-varying environmental inputs among the energy harvesting units, so as to achieve the optimization of the overall energy harvesting efficiency of the system under the premise of satisfying the stable power supply of the load and the safe management of energy storage.

[0008] To solve the above-mentioned technical problems, the present invention provides the following technical solution: a self-harvesting multi-source maximum energy harvesting and management system based on cooperative control, comprising,

[0009] The adjustable self-harvesting module includes at least two different types of self-harvesting units, each with an adjustable energy extraction parameter interface for adjusting its operating state to change output power according to control signals; an active gating module, connected to each energy harvesting unit of the adjustable self-harvesting module, is used to switch the energy output path of the self-harvesting unit according to control signals to achieve dynamic path management; an energy storage and management module is used to store the electrical energy collected by the self-harvesting units and manage the energy flow between the battery, load, and input energy source according to control commands; and a control unit, communicatively connected to the adjustable self-harvesting module, the active gating module, and the energy storage and management module, is used to collect system operating status and generate scheduling control commands to optimize energy harvesting efficiency.

[0010] As a preferred embodiment of the self-harvesting multi-source maximum energy harvesting and management system based on collaborative control described in this invention, the self-harvesting unit includes a current transformer energy harvesting unit and a photovoltaic energy harvesting unit; the current transformer energy harvesting unit is configured to adjust the duty cycle parameter of its rectifier bridge drive circuit based on the control signal issued by the control unit, so as to control its output power; the photovoltaic energy harvesting unit is configured to include a maximum power point tracking controller, which can receive the target operating voltage or target operating current set by the control unit and adjust the operating state of the photovoltaic cell module based on the target value.

[0011] The beneficial effects of this preferred technical solution are as follows: By introducing a current transformer energy harvesting unit with an adjustable rectifier bridge drive duty cycle and a photovoltaic energy harvesting unit with a maximum power point tracking controller having an external control interface into the adjustable self-harvesting module, the system can dynamically set the operating parameters of each type of energy unit according to the actual operating state, realize independent and efficient control of different types of fluctuating energy, effectively improve the response capability and energy harvesting efficiency of various energy harvesting paths under different operating conditions, especially in complex environments with uneven or rapidly changing resource distribution, significantly enhance the system's adaptability and energy efficiency ceiling.

[0012] As a preferred embodiment of the self-harvesting multi-source maximum energy harvesting and management system based on collaborative control described in this invention, the active selection module includes multiple MOSFET switching devices, which are respectively connected in parallel to the output terminals of each self-harvesting unit; the conduction state of the MOSFET switching devices is controlled by the control unit according to the current system optimization results to realize dynamic switching of energy input paths.

[0013] As a preferred embodiment of the self-harvesting multi-source maximum energy harvesting and management system based on collaborative control described in this invention, the energy storage and management module includes: an energy storage unit for storing the harvested energy; a power path switch group connected to the energy storage unit and the self-harvesting module for configuring the system's power supply path; and a voltage regulator circuit connected to the switch group for outputting a stable load power supply voltage. The control state of the path switch group is configured by the control unit to achieve dynamic switching of the power supply path.

[0014] As a preferred embodiment of the self-harvesting multi-source maximum energy harvesting and management system based on collaborative control described in this invention, the system operating status is used as an input parameter for the control unit's optimization decision-making; the system operating status includes the output voltage and current of the self-harvesting unit; the terminal voltage, current, and state of charge of the energy storage unit; the voltage and current values ​​in the load circuit; and environmental sensing parameters related to the self-harvesting unit, including the primary side current signal of the current transformer and the irradiance signal received by the photovoltaic module.

[0015] As a preferred embodiment of the self-harvesting multi-source maximum energy harvesting and management system based on collaborative control described in this invention, the control unit includes: an information acquisition unit for acquiring the output parameters of the self-harvesting unit, the state of charge of the energy storage unit, load voltage and current information, and environmentally related sensing parameters; a collaborative optimization decision-making unit for establishing an energy output characteristic model based on the acquired information, setting an objective function, and generating energy extraction parameters and path control commands through an optimization algorithm under system constraints; and a command execution unit for sending the energy extraction parameters and path control commands to their respective modules to control the working state, path selection, and energy flow mode of the self-harvesting interface.

[0016] The beneficial effects of this preferred technical solution are as follows: By setting up a control unit module that integrates information acquisition, optimization calculation, and control command distribution, the system can construct a parameterized energy characteristic model based on real-time operating status. This allows for the setting of an optimization function aimed at maximizing total energy acquisition, and the automatic search for the optimal combination of operating points and path strategies. Compared to traditional fixed-strategy control or passive gating methods, this solution can proactively adapt to power fluctuations, load changes, and battery status, achieving global optimization of energy scheduling. It improves the overall energy conversion efficiency of the system while reducing redundant switching losses and extending the lifespan of key components, demonstrating strong engineering practicality and broad application prospects.

[0017] As a preferred embodiment of the self-harvesting multi-source maximum energy harvesting and management system based on cooperative control described in this invention, the control unit is used to construct an optimization function with the goal of maximizing the total energy harvesting power of the system based on the system operating state, and, under the premise of satisfying the constraints, call the optimization algorithm to generate the optimal energy extraction parameters and path switching strategy for multiple self-harvesting units.

[0018] As a preferred embodiment of the self-harvesting multi-source maximum energy harvesting and management system based on collaborative control described in this invention, the objective function is used to maximize the total harvesting power of multiple self-harvesting units within a target time period, provided that the system load demand, battery status, and characteristic models of each energy harvesting unit meet the operating constraints; the optimization algorithm constructs the objective function based on the system harvesting status and energy characteristics, and performs the optimization process within the allowable range of energy extraction parameters to obtain the optimal combination of energy extraction parameters; the energy extraction parameters include the rectifier bridge drive duty cycle of the current transformer energy harvesting unit, and the target operating voltage or target operating current of the photovoltaic energy harvesting unit.

[0019] As a preferred embodiment of the self-harvesting multi-source maximum energy harvesting and management system based on collaborative control described in this invention, the control unit sends the energy extraction parameter combination and path control instructions determined by the optimization algorithm to the corresponding modules respectively; sends duty cycle control instructions to the current transformer energy harvesting unit to adjust the conduction characteristics of the semi-active rectifier bridge; sends target operating voltage or operating current parameters to the maximum power point tracking controller in the photovoltaic energy harvesting unit to control its output characteristics; sends MOSFET device turn-on control signals to the active selection module to select the optimal energy transmission path; and sends charging limit parameters and path switch control signals to the energy storage and management module to set the power supply mode for energy storage, discharging, and output.

[0020] This invention provides a method for maximizing energy harvesting and management from multiple self-harvesting sources based on collaborative control.

[0021] To address the aforementioned technical problems, this invention provides the following technical solution: a self-harvesting multi-source maximum energy harvesting and management method based on collaborative control, comprising: acquiring the system operating status, including the output parameters, energy storage status, load status, and environmental sensing parameters of the self-harvesting unit; constructing an optimization objective function that satisfies the system operating constraints based on the acquired operating status; executing an optimization algorithm to generate a combination of energy extraction parameters and path control instructions for improving the system's energy harvesting efficiency; and distributing the parameters and control instructions to the corresponding modules respectively, and driving each module to complete duty cycle adjustment, target operating point setting, path switching, and energy management operations.

[0022] The beneficial effects of this invention are as follows: By coordinating and controlling the working states of multiple self-powered modules, this invention enables each energy unit to rationally allocate its output capacity under different input conditions, thereby achieving more efficient energy harvesting at the overall level and enhancing the system's adaptability to energy fluctuations.

[0023] By incorporating operating parameters such as remaining battery power and actual load power into the control logic, the energy acquisition, storage, and utilization processes work in tandem to achieve overall optimal energy utilization from a system perspective, thereby improving the continuity of power supply and operational stability.

[0024] The structural design proposed in this invention is modular and compatible with various self-powered energy harvesting methods. By simply defining the interface and control model, it can be expanded to adapt to different types of energy units, thus exhibiting strong engineering applicability. Attached Figure Description

[0025] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0026] Figure 1 This is an overall architecture diagram of a self-harvesting multi-source maximum energy harvesting and management system based on cooperative control, provided as an embodiment of the present invention.

[0027] Figure 2 This is a structural diagram of a current transformer energy harvesting unit in a self-powered multi-source maximum energy harvesting and management system based on collaborative control, provided as an embodiment of the present invention.

[0028] Figure 3 The diagram shows the structure of a photovoltaic energy harvesting unit in a self-harvesting multi-source maximum energy harvesting and management system based on collaborative control, according to an embodiment of the present invention.

[0029] Figure 4This is a structural diagram of the energy storage and management module of a self-harvesting multi-source maximum energy harvesting and management system based on collaborative control, provided in one embodiment of the present invention.

[0030] Figure 5 This is a flowchart illustrating the overall process of a self-harvesting multi-source energy maximization and management method based on collaborative control, provided in one embodiment of the present invention. Detailed Implementation

[0031] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0032] Example 1, referring to Figure 1 This is one embodiment of the present invention, which provides a self-harvesting multi-source maximum energy harvesting and management system based on cooperative control, including:

[0033] The adjustable self-harvesting module includes at least two different types of self-harvesting units, each with an adjustable energy extraction parameter interface for adjusting its operating state to change output power according to control signals; an active gating module, connected to each energy harvesting unit of the adjustable self-harvesting module, is used to switch the energy output path of the self-harvesting unit according to control signals to achieve dynamic path management; an energy storage and management module is used to store the electrical energy collected by the self-harvesting units and manage the energy flow between the battery, load, and input energy source according to control commands; and a control unit, communicatively connected to the adjustable self-harvesting module, the active gating module, and the energy storage and management module, is used to collect system operating status and generate scheduling control commands to optimize energy harvesting efficiency.

[0034] It should be noted that deploying self-harvesting equipment in distribution networks, especially schemes based on inductive coupling or environmental energy harvesting, often faces technical bottlenecks such as unstable energy output, significant differences in energy sources, and a single control strategy. Photovoltaics and current transformers, two types of energy sources, exhibit strong fluctuations in energy output under external conditions such as drastic changes in solar irradiance and current load. Most existing systems employ fixed energy paths and static parameter control methods, which cannot respond in real-time to dynamic changes in the source-side state. This limits the system's adaptability to input-side energy and fails to ensure optimal energy balance among multiple sources. Furthermore, when energy storage modules are present, a lack of global assessment capabilities regarding energy harvesting, distribution, and load demand can easily lead to energy redundancy and deep charging / discharging of batteries, further impacting system lifespan and efficiency.

[0035] Furthermore, the overall system involved in this embodiment achieves a dynamic optimization process from energy extraction at the source to energy supply at the load end by introducing a modular architecture design and having it managed by a control unit. Specifically, the self-energy extraction module supports fine-grained control of output characteristics through an adjustable parameter interface, adjusting between different operating points according to external commands to adapt to changes in system load and energy storage status; the active selection module acts as a bridge between its respective energy extraction channel and the main power path, enabling rapid switching to the current optimal path among multiple source paths; the energy storage and management module not only acts as an energy buffer but also dynamically allocates charging and discharging paths and voltage stabilization tasks under the command of the control unit. The core control unit acts as a scheduling hub, calculating the optimal operating point and path configuration based on multi-dimensional state acquisition and energy characteristic modeling, forming a closed-loop optimization structure across the entire link. Even without introducing other additional subsystems, this basic architecture alone can address typical problems in common power distribution scenarios such as severe energy fluctuations and uncertain load demands.

[0036] Furthermore, in the specific implementation, the current transformer energy harvesting unit adopts a rectifier module with controllable drive. Its semi-active bridge rectifier circuit incorporates MOSFET switches with programmable duty cycles. The control unit can adjust its load matching state according to the primary side AC signal to precisely control its power output characteristics. The photovoltaic energy harvesting unit integrates an MPPT controller, which not only executes the traditional maximum power point tracking algorithm but also has the ability to accept externally set target voltages or currents, thereby switching the operating point under different operating strategies. The gating module uses parallel-configured synchronous MOSFET channels with low impedance characteristics. The conduction state is actively controlled by commands issued by the control unit. In the initial stage of system startup, it can automatically select the highest voltage path based on the output voltage levels of each source. After the system stabilizes, it performs gating switching based on the optimal path selection result. The energy storage module adopts a programmable BMS architecture. The control unit issues current limiting and path switching commands in real time, dynamically switching between discharging, charging, and bypass states to ensure the continuity of load power supply and the protection strategy of the energy storage unit. The overall system has good adaptability and configuration flexibility, making it suitable for deployment at end-of-network sensing nodes, microgrid edge terminals, and other power-sensitive scenarios.

[0037] It should be noted that a self-harvesting unit refers to a module that can capture energy from the surrounding environment and convert it into electrical energy, such as a current transformer (CT) energy harvesting unit, a photovoltaic (PV) cell energy harvesting unit, a thermoelectric power generation unit, and a piezoelectric vibration power generation unit.

[0038] The energy extraction interface refers to the part of the self-powered unit, or adjacent to its output terminal, whose electrical characteristics (such as equivalent impedance, operating voltage / current point) can be adjusted by external control signals, thereby affecting the energy extraction efficiency. For example, the duty cycle (D) of the lower diode drive in the semi-active rectifier bridge of the CT energy extraction unit, or the target operating voltage / current point set by the MPPT controller in the photovoltaic unit.

[0039] The optimal operating point refers to a set of operating parameters for each energy extraction interface that, under specific environmental conditions, maximizes the total energy collected by the entire system from the self-powered unit (or satisfies specific system-level optimization objectives, such as prioritizing load stability). This differs from maximum power point tracking (MPPT) of a single energy source, which only focuses on maximizing the output of a single energy source, while this invention pursues overall energy optimization at the system level.

[0040] Dynamic coordinated adjustment refers to the control unit calculating and simultaneously setting the operating point parameters of the energy extraction interfaces of multiple different types of self-powered units based on the real-time status information of each module (such as energy output, battery power, load demand) and preset system-level optimization goals through optimization algorithms (for example, setting the duty cycle D* of the CT energy extraction unit and setting the target voltage Vmppt* of the photovoltaic unit MPPT).

[0041] Active path management refers to the control unit explicitly controlling the on and off states of power path selection switches (such as OringFETs or switching transistors in power path management chips) based on optimization decision results, in order to select the current optimal energy transfer path and efficiently guide the energy of one or more self-harvesting units to subsequent circuits.

[0042] Power Path BMS refers to a battery management system with power path management capabilities. In addition to basic battery charge and discharge protection and status monitoring, it can also actively coordinate the energy flow path between external input power (from the self-powered unit), the battery, and the load according to the instructions of the control unit or internal logic.

[0043] System-level energy optimization refers to maximizing the energy harvested from selected self-harvesting units, or achieving other preset system-level performance indicators (such as maximum power supply stability), by coordinating and controlling the operating points of each energy source and managing the energy flow path, while meeting the basic constraints of system operation (such as ensuring the minimum power supply requirements of the load and complying with the safe charging and discharging specifications of the battery).

[0044] It should be noted that, based on the real-time status of each unit, battery status, and load requirements, the control unit runs an optimization algorithm to calculate the optimal operating point parameter combination for the energy extraction interfaces of all controllable self-harvesting units. The optimal operating point of the self-harvesting unit with the highest power is selected as the maximum total energy harvesting power operating point of the system under the constraints.

[0045] The control unit sends the calculated optimal operating point parameters to the corresponding self-powered unit interface control circuit in real time (such as adjusting the duty cycle D* of the rectifier bridge for CT power extraction, and setting the target voltage / current Vmppt* / Imppt* of the photovoltaic MPPT).

[0046] Based on the optimization results and the actual output capacity of each energy source after adjustment, the control unit actively controls the oringFETs and the switches in the PowerPathBMS to select the optimal energy flow path and ensure efficient energy transmission.

[0047] Through this closed-loop control method of collaborative optimization, dynamic adjustment and proactive management, the present invention can significantly improve the overall energy harvesting efficiency and management level of multi-source energy harvesting systems.

[0048] Example 2, refer to Figures 1-4 As one embodiment of the present invention, based on the previous embodiment, a self-harvesting multi-source maximum energy harvesting and management system based on cooperative control is provided, including:

[0049] like Figure 1 As shown, the adjustable self-harvesting module contains at least two different types of self-harvesting units, each of which has an energy extraction interface that can be controlled externally.

[0050] like Figure 2 As shown, the CT power harvesting unit includes a current transformer (CT), an input filter / buffer circuit (such as a smoothing reactor), a semi-active rectifier bridge (with the lower rectifier whose drive signal is precisely controlled externally), a discharge circuit, and an output filter. The control unit can effectively change the equivalent load impedance on the secondary side of the CT by setting the lower rectifier drive duty cycle D*, thereby controlling its operating point and output power.

[0051] like Figure 3 As shown, the photovoltaic (PV) energy harvesting unit includes a photovoltaic panel and an MPPT controller. The MPPT controller is designed not only to execute the standard MPPT algorithm but also to receive external commands from the control unit to force the operating point of the photovoltaic panel to be set at a specified target voltage Vmppt or target current Imppt. The MPPT controller is responsible for internal closed-loop control to track this externally set target.

[0052] It should be noted that the adjustable self-harvesting module can also be expanded to include other adjustable energy units. The system can be expanded to include other energy sources with controllable energy extraction interfaces, such as piezoelectric ceramic power generation units with adjustable loads.

[0053] In a preferred embodiment of the present invention, the adjustable self-harvesting module includes two typical energy interfaces: a current transformer energy harvesting unit and a photovoltaic energy harvesting unit. The current transformer energy harvesting unit is configured to adjust the rectifier bridge drive duty cycle according to instructions from the control unit to dynamically control its output power. The photovoltaic energy harvesting unit is configured to include a maximum power point tracking controller, which can receive target voltage or current signals and adjust the photovoltaic panel operating point in real time to adapt to changes in ambient light and maintain optimal power output. Both have standardized control interfaces, enabling fine-grained operating point control and participation in system optimization scheduling under different operating conditions.

[0054] The beneficial effects of this preferred technical solution are as follows: the adjustable self-harvesting module realizes dynamic management and coordinated scheduling of multi-source energy through collaborative control, which significantly improves the stability and energy harvesting capability of the system in weak energy scenarios, while avoiding energy waste or path conflicts caused by differences in energy characteristics, and effectively enhances the system's adaptability and deployment flexibility.

[0055] In an optional embodiment of the present invention, the adjustable self-powered module further includes a piezoelectric vibration energy acquisition unit. The unit is installed on a bridge, the ground, or the surface of a structure and uses changes in mechanical stress to drive the piezoelectric element to generate electrical energy. It is equipped with a rectification and boost module and has a control unit interface, which can dynamically adjust the acquisition frequency and the timing of output switching, so as to realize the effective utilization of intermittent low power sources.

[0056] In another optional embodiment of the present invention, the adjustable self-harvesting module includes a thermoelectric conversion energy harvesting unit, which uses thermoelectric material components attached to the surface of a heat source to obtain electrical energy output driven by temperature difference, and combines a step-down voltage regulator module to realize the regulation of output voltage, and communicates with the control unit. It can adjust the output strategy in real time according to the heat flux density, and work with other self-harvesting units to optimize the overall energy harvesting efficiency.

[0057] The active gating module consists of a set of low on-resistance MOSFETs (configured as ideal diodes, i.e., OringFETs) connected in parallel to the outputs of each self-powered unit. Unlike traditional passive gating, the enable signals of these MOSFETs are actively controlled by the control unit (Soring*). During system startup, a voltage comparison mode can be used to automatically select the highest voltage source to start the system. After the system is running normally, based on the optimization decision results, it is determined which source's FET should be turned on, ensuring that the source with the optimal power is connected to subsequent circuits.

[0058] like Figure 4The energy storage and management module shown adopts the PowerPathBMS architecture. It includes energy storage components (lithium battery), a battery management chip (responsible for protecting and monitoring the SoC, temperature, etc.), and a series of power path switches (such as Q3, Q4, and Q5) controlled by the control unit or internal logic of the BMS. Q3 and Q4, together with the inductor, form a synchronous Buck circuit to stabilize the output voltage at the preset battery voltage, i.e., the load operating voltage. Q4 connects the battery to the system bus. This module receives status commands from the control unit (such as the maximum charging current limit Ichg_limit*, and control signals g3* / g4* / g5* for switches Q3 / Q4 / Q5), and executes corresponding path management strategies based on the current energy flow state (input power Pin, output power Po, battery state).

[0059] In a preferred embodiment of the present invention, the energy storage and management module adopts a multi-path power management architecture, specifically including an energy storage unit, a power path control circuit, and a voltage regulation and conversion circuit. The energy storage unit is a rechargeable lithium battery or a supercapacitor, used to store the electrical energy collected by the self-harvesting module; the power path control circuit achieves flexible switching of the system power supply path through multiple controlled switching devices, including direct battery supply, direct supply from the energy harvesting side, or a combination of both; the voltage regulation and conversion circuit is responsible for converting the unstable voltage of the energy storage unit or the self-harvesting unit into a stable output voltage required by the system load. All the above functional circuits are centrally managed by the control unit, which can dynamically adjust the power supply path and charging strategy according to the current load demand, battery state of charge, and harvesting power status.

[0060] The beneficial effects of this preferred technical solution are as follows: through dynamic path management and unified coordination control mechanism, the energy storage and management module can achieve optimal matching of multi-path energy dispatch and load power supply, ensure stable system operation under different energy and load conditions, improve energy utilization, and support robust power supply in the event of insufficient energy or sudden power fluctuations.

[0061] In an optional embodiment of the present invention, the energy storage and management module is further configured with a dual-battery pack structure, used for the main power supply and auxiliary power supply paths respectively. The control unit dynamically switches the operating states of the main and auxiliary batteries according to the load conditions, and can independently charge the auxiliary battery during the discharge of the main battery, or connect it in parallel to enhance the system output capacity when the energy harvesting power is surplus, thereby extending the continuous working time of the system and supporting high-reliability output during critical task periods.

[0062] In another optional embodiment of the present invention, the energy storage and management module includes a modular battery array with configurable capacity. The control unit can dynamically activate or deactivate some battery sub-modules according to the system application scenario and task cycle to adapt to varying energy harvesting and power supply demands. Under low load or insufficient energy harvesting, the system's no-load loss is reduced by shutting down some energy storage modules; under high load or high energy harvesting, more energy storage units are activated for rapid energy regulation, enabling the system to adapt to highly dynamic demands.

[0063] The control unit, implemented by a microcontroller (MCU) or digital signal processor (DSP), is responsible for the decision-making and control of the entire system. Its main functional modules include:

[0064] The information acquisition unit monitors key system status parameters in real time through sensors and sampling circuits, including: CT primary current / voltage (for feedback), light intensity, photovoltaic panel temperature, actual output voltage / current of each energy unit (for verifying whether the operating point has reached the set value), battery voltage / current / temperature / SoC, and load current / voltage / power.

[0065] The collaborative optimization decision-making unit periodically performs the following tasks: Status assessment: Integrating real-time monitoring data from G1.

[0066] Model update: Update or calibrate the power-operating point characteristic model of each energy source based on real-time data.

[0067] Optimization Objective and Constraint Definition: Set the optimization objective, which is usually to maximize the total expected energy harvested in the near future.

[0068]

[0069] Pct_extracted(t) and Ppv_extracted(t) represent the expected output power of the CT energy harvesting unit at time t under the set duty cycle D*, and the expected output power of the photovoltaic energy harvesting unit at the set target voltage Vmppt* and target current Imppt*, respectively. Soring1 and Soring2 are binary gating variables (0 or 1), determined by the collaborative optimization decision unit. Soringi = 1 indicates that the i-th energy source is selected and connected to the system bus, and its energy participates in the contribution; Soringi = 0 indicates that the energy source is disconnected. ∫ represents the integration of the instantaneous total power of the selected energy combination over the future time window T.

[0070] It should be noted that the objective function aims to maximize the total energy harvesting of the system within a specific time window by dynamically selecting the best energy combination and the optimal operating point of each energy source through coordinated control.

[0071] Solve for the optimal parameter combination: Run an optimization algorithm (such as model-based numerical optimization) to search within the allowed parameter space for the parameter combination of the operating point {D*, Vmppt*, Imppt*,...} that can meet the constraints and optimize the objective function, as well as the corresponding optimal path gating state {Soring*} and BMS control parameters {Ichg_limit*, g3*, g4*, g5*}. The optimization process needs to consider the expected output power of each energy source after adjusting the operating point, path loss, and their interactions with the battery and load. For example, when the total energy is only enough to meet the load, the operating point may be adjusted to sacrifice some collection efficiency to prioritize ensuring stable output voltage; which source to select for adjustment depends on the characteristic curves of each energy source, the current load voltage, and power demand.

[0072] It should be noted that the duty cycle D* of the CT energy harvesting unit tube drive, the target voltage Vmppt* and target current Imppt* specified for the photovoltaic (PV) energy harvesting unit, the control signal Soring* for the active gating module to gate the FET, the maximum charging current limit Ichg_limit* of the BMS, and the control signals g3* / g4* / g5* of the switches Q3 / Q4 / Q5.

[0073] BMS state coordination: According to the calculated energy balance situation (PinvsPo), determine the operating state that the BMS should be in and generate the corresponding control signals (Ichg_limit*, g3*, g4*).

[0074] State 1 (Pin = 0): Q3 and Q4 are turned off, and Q5 is turned on. The battery supplies power alone. G indicates that the BMS and C enter the low-power mode, and Pin represents the input power.

[0075] State 2 (0 < Pin < Pload): Q3 and Q4 operate normally in synchronous buck, and Q5 is turned on. The battery supplies supplementary power. G indicates that C and the selected A module operate in the mode that maximizes Pin, and at the same time the BMS manages the battery discharge, and Pload represents the load power.

[0076] State 3 (Pin > Pload, low battery charge): Q3 and Q4 operate normally in synchronous buck converter buck, and Q5 operates in the saturation region, and the charging current Ichg is controllable (set by G through Ichg_limit*). G indicates that the A module operates in the mode that maximizes Pin to meet the load demand and the set charging current / voltage requirements.

[0077] State 4 (Pin > Pload, battery not full): Q3 and Q4 operate normally in synchronous buck. Priority is given to supplying the load, and the surplus is used to charge the battery. Q5 operates in the linear region for constant voltage charging. G indicates that C and the A module adjust the operating point, and at the same time the BMS manages the charging process.

[0078] State 5 (Pinmax > Pload, battery full): Q3 and Q4 synchronous buck converters are operating normally. Q5 is off. G instructs modules C and A to adjust their operating points, sacrificing some acquisition efficiency to prioritize output voltage stability.

[0079] The instruction execution sends the calculated optimal parameters and control commands to the corresponding modules for execution: The target duty cycle D* is sent to the CT energy harvesting unit (A1). The target voltage Vmppt* or target current Imppt* is sent to the photovoltaic MPPT controller. The switching status Soring* of each OringFET is sent to the active gating module. The charging limit Ichg_limit* and switching control signals g3*, g4*, and g5* are sent to the PowerPathBMS.

[0080] It should be noted that Q3, Q4, and Q5 are power electronic devices, such as MOSFETs and IGBTs; Pinmax > Pload indicates that the input power is greater than the load power.

[0081] In a typical deployment scenario, the wireless sensor nodes connected to the system have two energy harvesting paths: current transformers (CTs) and photovoltaic modules (PVs). These two paths share the system bus and can be switched via a gating mechanism. Under operating conditions with ample sunlight but weak line current, this system, through its control unit, provides real-time assessment of the status of each input source, significantly outperforming the passive gating methods of existing technologies.

[0082] Specifically, in the existing passive voltage-selective architecture, the outputs of the two energy harvesting units are directly connected to the bus in parallel via diodes. The connection priority is determined by the voltage level, which may result in energy harvesting paths with lower actual power occupying the bus and causing energy waste. For example, even if the voltage of the current transformer (CT) is slightly higher than that of the photovoltaic unit when the current is low, its output power is often far lower than the maximum power point of the photovoltaic module under the current strong light conditions, thus forcing the solar energy that could have been harvested to remain idle.

[0083] In this invention, the control unit first acquires key operational data such as current ambient illuminance, grid current, battery state of charge, and load power demand through its information acquisition module. Subsequently, the collaborative optimization decision module calculates the optimal duty cycle achievable by the CT unit under the current current conditions based on the system power-operating point model and current state parameters, and sets the target MPPT operating voltage or current of the photovoltaic module according to the light intensity. After calculating and comparing the achievable power of the two paths, the control unit selects the path with the maximum available power, such as selecting the photovoltaic channel and adjusting its operating point to the target value, while simultaneously issuing a non-zero duty cycle to the CT unit to fully utilize its residual energy.

[0084] By implementing the aforementioned optimization strategies, the system not only ensures that the main energy channel operates at its current optimal power point but also achieves efficient coordination and utilization of auxiliary energy sources, while avoiding path conflicts and energy redundancy caused by passive switching. Furthermore, the system can dynamically allocate charging and discharging paths based on the battery's current state of charge and load requirements by controlling the energy storage and management module, thus balancing power supply stability and battery lifespan.

[0085] Example 3, referring to Figure 5 This embodiment of the invention provides a method for maximizing energy harvesting and management from multiple self-harvesting sources based on collaborative control, comprising: collecting the system operating status, including the output parameters, energy storage status, load status, and environmental sensing parameters of the self-harvesting unit; constructing an optimization objective function that satisfies the system operating constraints based on the collected operating status; executing an optimization algorithm to generate a combination of energy extraction parameters and path control instructions for improving the system's energy harvesting efficiency; and distributing the parameters and control instructions to the corresponding modules respectively, and driving each module to complete duty cycle adjustment, target operating point setting, path switching, and energy management operations.

[0086] like Figure 5 As shown, the system's workflow is typically a periodic closed-loop control process: Initialization: The system powers on and starts up, with each module performing self-tests and initialization settings; Periodic task begins: Data acquisition: Real-time monitoring of all necessary environmental parameters, energy output status, battery status, and load status.

[0087] Status assessment and model update: By combining real-time data, the current system status is assessed and the energy model is updated.

[0088] Collaborative optimization decision-making: Run optimization algorithms to calculate the optimal combination of energy operating point parameters {D*,Vmppt*,...}, path selection {Soring*}, and BMS control parameters {Ichg_limit*,g3*,g4*,g5*}.

[0089] Command issuance: Send the calculated control commands to each execution module (A, B, C).

[0090] Instruction execution: Each module adjusts its working status (adjusting interface parameters, switch paths, etc.) according to the received instructions.

[0091] By continuously monitoring the actual effects of command execution (such as actual energy output power, voltage, etc.), this feedback information can be used for adjustment and correction during the next optimization cycle, forming a closed-loop control.

[0092] By repeating the steps cyclically, the system can continuously adapt to changes in the environment and the state of the system, always pursuing the optimal energy management strategy.

[0093] This embodiment also provides an electronic device applicable to the self-harvesting multi-source maximum energy harvesting and management method based on cooperative control, comprising: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to realize the self-harvesting multi-source maximum energy harvesting and management method based on cooperative control as proposed in the above embodiment.

[0094] This embodiment also provides a storage medium storing a computer program that, when executed by a processor, implements the self-harvesting multi-source maximum energy harvesting and management method based on cooperative control proposed in the above embodiments.

[0095] The storage medium proposed in this embodiment and the method for maximizing energy harvesting and management from multiple self-sources based on collaborative control proposed in the above embodiments belong to the same inventive concept. Technical details not described in detail in this embodiment can be found in the above embodiments, and this embodiment has the same beneficial effects as the above embodiments.

[0096] Based on the above description of the implementation methods, those skilled in the art can clearly understand that the present invention can be implemented using software and necessary general-purpose hardware, and of course, it can also be implemented using hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ROM), random access memory (RAM), flash memory, hard disk, or optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods of the various embodiments of the present invention.

[0097] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A self-harvesting multi-source maximum energy harvesting and management system based on cooperative control, characterized in that: include, An adjustable self-powered module includes at least two different types of self-powered units, each self-powered unit having an adjustable energy extraction parameter interface for adjusting its operating state to change the output power according to a control signal; The active selection module is connected to the respective energy harvesting units of the adjustable self-harvesting module and is used to switch the energy output path of the self-harvesting unit according to the control signal to realize dynamic path management. The energy storage and management module is used to store the electrical energy collected by the self-powered unit and manage the energy flow between the battery, load and input energy source according to control commands. The control unit is connected to the adjustable self-harvesting module, the active gating module, and the energy storage and management module, respectively, to collect the system operating status and generate scheduling control commands to optimize energy harvesting efficiency.

2. The self-harvesting multi-source maximum energy harvesting and management system based on collaborative control as described in claim 1, characterized in that: The self-powered unit includes a current transformer power harvesting unit and a photovoltaic power harvesting unit; The current transformer energy harvesting unit is configured to adjust the duty cycle parameters of its rectifier bridge drive circuit based on the control signal issued by the control unit, so as to control its output power. The photovoltaic energy harvesting unit is configured to include a maximum power point tracking controller, which can receive a target operating voltage or target operating current set by the control unit and adjust the operating state of the photovoltaic cell module based on the target value.

3. The self-harvesting multi-source maximum energy harvesting and management system based on collaborative control as described in claim 2, characterized in that: The active selection module includes multiple MOSFET switching devices, which are connected in parallel to the output terminals of each of the self-powered units. The conduction state of the MOSFET switching device is controlled by the control unit based on the current system optimization results to achieve dynamic switching of the energy input path.

4. The self-harvesting multi-source maximum energy harvesting and management system based on collaborative control as described in claim 3, characterized in that: The energy storage and management module includes: an energy storage unit for storing the collected energy; The power path switch group, connected to the energy storage unit and the self-powered module, is used to configure the system's power supply path; A voltage regulator circuit, connected to a switching group, is used to output a stable load supply voltage; The control state of the path switch group is configured by the control unit to achieve dynamic switching of the power supply path.

5. The self-harvesting multi-source maximum energy harvesting and management system based on collaborative control as described in claim 4, characterized in that: The system operating status is used as input parameters for the control unit's optimization decision-making; the system operating status includes the output voltage and current of the self-harvesting unit; the terminal voltage, current, and state of charge of the energy storage unit; the voltage and current values ​​in the load circuit; and environmental sensing parameters related to the self-harvesting unit, including the primary current signal of the current transformer and the irradiance signal received by the photovoltaic module.

6. The self-harvesting multi-source maximum energy harvesting and management system based on collaborative control as described in claim 5, characterized in that: The control unit includes: an information acquisition unit, used to acquire the output parameters of the self-powered unit, the state of charge of the energy storage unit, the load voltage and current information, and environmental sensing parameters; The collaborative optimization decision-making unit is used to establish an energy output characteristic model based on the collected information, set the objective function, and generate energy extraction parameters and path control instructions through optimization algorithms under the condition of satisfying system constraints. The instruction execution unit is used to send energy extraction parameters and path control instructions to their respective modules to control the working status, path selection and energy flow mode of the self-powered interface.

7. The self-harvesting multi-source maximum energy harvesting and management system based on collaborative control as described in claim 6, characterized in that: The control unit is used to construct an optimization function based on the system operating state, with the goal of maximizing the total power of system energy harvesting, and, under the premise of satisfying the constraints, call the optimization algorithm to generate the optimal energy extraction parameters and path switching strategies for multiple self-harvesting units.

8. The self-harvesting multi-source maximum energy harvesting and management system based on collaborative control as described in claim 7, characterized in that: The objective function is used to maximize the total power collected by multiple self-powered units within a target time period, provided that the system load demand, battery status, and characteristic models of each power harvesting unit meet the operating constraints. The optimization algorithm constructs an objective function based on the system's acquisition status and energy characteristics, and performs an optimization process within the allowable range of energy extraction parameters to obtain the optimal combination of energy extraction parameters; The energy extraction parameters include the rectifier bridge drive duty cycle of the current transformer energy harvesting unit, and the target operating voltage or target operating current of the photovoltaic energy harvesting unit.

9. The self-harvesting multi-source maximum energy harvesting and management system based on collaborative control as described in claim 8, characterized in that: The control unit sends the energy extraction parameter combination and path control command determined by the optimization algorithm to the corresponding modules, and sends the duty cycle control command to the current transformer energy harvesting unit to adjust the conduction characteristics of the semi-active rectifier bridge. Send the target operating voltage or operating current parameters to the maximum power point tracking controller in the photovoltaic energy harvesting unit to control its output characteristics; Send the MOSFET device turn-on control signal to the active gating module to select the optimal energy transfer path; Send charging limit parameters and path switch control signals to the energy storage and management module to set the power supply mode for energy storage, discharging and output.

10. A self-harvesting multi-source maximum energy harvesting and management method based on cooperative control, employing the self-harvesting multi-source maximum energy harvesting and management system based on cooperative control as described in any one of claims 1 to 9, characterized in that, include: The system's operating status is collected, including the output parameters of the self-harvesting unit, energy storage status, load status, and environmental sensing parameters. Based on the collected operating status, an optimization objective function that satisfies the system operating constraints is constructed; Execute optimization algorithms to generate energy extraction parameter combinations and path control instructions to improve the system's energy harvesting efficiency; The parameters and control commands are sent to the corresponding modules, and each module is driven to complete duty cycle adjustment, target operating point setting, path switching and energy management operations.

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