Electric energy comprehensive management control method used for high and low load working conditions

Abstract

The invention relates to an electric energy comprehensive management control method for high and low load working conditions, and belongs to the field of energy supply system control. The method comprises the following steps: acquiring current operation information of an energy supply system, including a working condition mode, a target power demand and a battery state of charge (SOC) value, and further judging a working condition type and calculating a total power demand value of the system; on the basis, a power distribution scheme of a power generation unit and a battery is judged according to an SOC value and a power demand: under a normal working condition, the balance of power generation and battery charging and discharging is realized by setting an SOC threshold interval, and under an emergency working condition, the maximum rate discharging of the battery is dynamically controlled or the charging current is limited to cope with load fluctuation. According to the system, the bus voltage and the SOC are maintained in a safe range through a closed-loop regulation mode, so that the stability and the response capability of the energy supply system are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of electric energy management and control, and in particular to an integrated electric energy management and control method for use in high and low load working conditions. Background Art

[0002] Existing power management methods for multi-source power systems have been widely applied in scenarios including electric vehicles, renewable energy systems, and industrial equipment. These methods typically utilize fixed rules or model-based prediction strategies based on power demand, battery status, and load variation information to allocate energy between power generation units and energy storage batteries, ensuring stable and efficient system power supply. Some systems also incorporate mechanisms such as bus voltage monitoring and battery state-of-charge (SOC) limiting to ensure operational safety.

[0003] However, existing power management methods are mostly based on static operating conditions or linear power response designs, making them difficult to adapt to the frequent switching between high and low load operating states. In particular, they lack refined control mechanisms for emergency load fluctuations. When load demand changes dramatically or the energy supply path is reconfigured, the system often experiences response lags, voltage overruns, and battery overcurrent issues. This makes it difficult to simultaneously balance system stability, battery life, and load continuity, limiting its applicability in complex dynamic conditions.

[0004] In view of this, it is necessary to provide a comprehensive power management and control method for use in high and low load conditions to adapt to the energy supply coordination and safety control needs under variable operating environments. Summary of the Invention

[0005] The present application provides a comprehensive electric energy management and control method for use in high and low load conditions to improve the stability and responsiveness of the energy supply system.

[0006] The present application provides a comprehensive electric energy management and control method for use in high and low load conditions, which is characterized by comprising: Obtain the current operating information of the energy supply system, including operating mode information, target power demand, and current battery state of charge (SOC) value; Determining the current operating condition category based on the current operating information; Calculate the corresponding total system power demand value according to the operating condition category and the target power demand; The output power of the power generation unit and the battery charge and discharge power are determined based on the battery state of charge (SOC) value and the total power demand of the system. In normal operating conditions, the output power of the power generation unit is determined to meet the basic load demand based on a preset SOC threshold range, and the remaining power is balanced by the battery charge and discharge within the SOC range. In emergency operating conditions, when the load suddenly increases, the battery is controlled to discharge at the maximum discharge rate. When the load suddenly decreases, the output power of the power generation unit is controlled to decrease rapidly, and the battery charging current is adjusted to avoid exceeding the maximum charging current that the battery can withstand. Controlling the actual energy supply behavior of the power generation unit and the battery according to the output power of the power generation unit and the charge and discharge power of the battery; Based on the actual energy supply behavior, the system bus voltage and the battery state of charge (SOC) value are detected. If the bus voltage or the battery SOC value deviates from the set range, the power generation unit output power and the battery charge and discharge power are adjusted in a closed loop according to the deviation until the bus voltage and the battery SOC value return to the allowable range.

[0007] The beneficial effects of the technical solution provided by this application include: (1) By dynamically identifying the operating condition category and combining the target power demand with the battery SOC state, an organic coordination between the power generation unit and the battery is achieved, which is suitable for complex operating environments with frequent switching between high and low loads, and significantly enhances the system's adaptability to different operating conditions. (2) By real-time detection of the bus voltage and SOC state and establishing a closed-loop control mechanism based on deviation feedback, the voltage overshoot or undervoltage caused by load fluctuations is effectively suppressed, ensuring a stable and reliable energy supply process. (3) Under emergency conditions, by dynamically limiting the battery discharge rate and charging current, large current shocks and overcharge and discharge problems are avoided, thereby reducing the risk of battery loss and improving the overall safety of the system and battery life. (4) By using a phased power regulation strategy, combined with the SOC threshold and power matching algorithm, the main power supply capacity of the power generation unit and the battery regulation capacity are made to operate in a complementary manner, improving the system's rapid response and regulation accuracy under sudden changes in power demand. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] Figure 1 This is a comprehensive power management and control method for use in high and low load conditions, provided in the first embodiment of the present application. DETAILED DESCRIPTION

[0009] The following description sets forth many specific details to facilitate a thorough understanding of the present application. However, the present application can be implemented in many other ways than those described herein, and those skilled in the art can make similar generalizations without violating the scope of the present application. Therefore, the present application is not limited to the specific implementations disclosed below.

[0010] The first embodiment of the present application provides a comprehensive power management and control method for high and low load conditions. Figure 1 , which is a schematic diagram of the first embodiment of this application. Figure 1 A first embodiment of the present application provides a comprehensive electric energy management and control method for use in high and low load conditions.

[0011] Step S101: obtaining current operating information of the energy supply system, including operating mode information, target power demand, and current battery state of charge (SOC) value.

[0012] In the process of implementing the comprehensive electric energy management and control method for high and low load conditions of the present invention, step S101, as the starting link of the entire control strategy, plays a key role in collecting system operating status data and establishing the basis for operating condition identification and power allocation.

[0013] When executing step S101, various operating data contained in the energy supply system should first be obtained. "Operating mode information" should include, but is not limited to, the current system operating stage, such as static standby, light-load operation, high-load operation, charging phase, or power surge phase. This information can be provided by the system scheduling module or upper-level controller via status words, mode codes, or preset operating condition tags, using a standardized data format for easy parsing and access.

[0014] Next, the "target power demand" data should be obtained. This data represents the system's power usage demand at the current moment or within a short-term forecast window, and is typically generated in real time by the load modeling module, scheduling module, or upper-level controller. The target power demand can be expressed as a real number (e.g., kW) per unit time, or as a discrete state level to identify the current load level, and transmitted to the power management control module via the data bus. To ensure timely response, it is recommended that the power demand signal have a fixed sampling period, preferably updated every 50 to 200 milliseconds to accommodate high-frequency load fluctuations.

[0015] Subsequently, the "current battery state of charge (SOC) value" should be obtained. SOC (State of Charge) is a state parameter that measures the ratio of the remaining battery charge to the full charge, and is usually expressed as a real value between 0% and 100%. When obtaining the SOC value, it should be combined with the real-time measurement data provided by the battery management system (BMS). The BMS usually calculates the SOC state value through sampling data of voltage, current, and temperature signals, combined with algorithms such as extended Kalman filtering and Coulomb measurement. When the system calls the SOC value, it must ensure that the value is updated within the latest cycle and is filtered to reduce misjudgments caused by instantaneous sampling noise. To further improve the reliability of the SOC value, an SOC change rate constraint judgment mechanism can be introduced into the design to prevent abnormal jumps.

[0016] After collecting the three aforementioned information parameters, a structured data packet should be constructed in the management system for subsequent access. This packet should contain at least the operating mode code, target power value, current SOC value, and their corresponding timestamp or data validity indicator. It is recommended that the data be packaged and stored in the controller cache, and a shared memory channel established with the judgment module to ensure real-time data access and integrity.

[0017] Therefore, the implementation of step S101 should include multiple sub-processes, including multi-source data acquisition, numerical analysis, logical judgment, data packaging, and shared cache establishment. Each step should ensure accurate sampling, timely updates, and a clear structure, laying the foundation for subsequent operating condition identification, power calculation, and control execution. Only by ensuring the accuracy and completeness of this step can the response speed and control accuracy of the entire integrated power management system be achieved.

[0018] Furthermore, the obtaining of the current operation information of the energy supply system includes: Based on the task instructions or operation control instructions received by the energy supply system, the system analyzes the implicit operation phase, driving status, and environmental adaptation strategy, identifies the corresponding operating condition labels, and extracts and generates the current operating mode information; After obtaining the working condition mode information, the reference power data corresponding to the working condition tag in the historical load model and environmental database is retrieved, and combined with the real-time measured load current and voltage change trends, the target power demand required at the current moment is deduced and output; After the target power demand is determined, the battery voltage, current and temperature data fed back by the battery management system are read in real time. The remaining battery capacity is estimated using the Coulomb integration method corrected by temperature compensation, and the current battery state of charge (SOC) value is calculated and output. Based on the current target power demand and SOC value, determine whether the battery can meet the energy supply demand without exceeding the safe operating boundary, and compare the judgment result with the aforementioned operating mode information to confirm whether there is a risk of power supply and demand mismatch.

[0019] First, the process of acquiring the energy supply system's current operating information begins with the receipt of mission instructions or operational control commands. These instructions, typically issued by a higher-level scheduling system, task management platform, or human-machine interface, contain information such as the type of task the energy supply system is currently performing, its estimated duration, task priority, target route or path, required load level, and coordination requirements with the ground or environment. These instructions are transmitted in a predefined data format. The system uses a built-in protocol parser to read and extract the required fields, further matching these fields with pre-set operating condition definition rules. For example, when a mission instruction contains keywords such as "fixed-point hover," "vertical takeoff and landing," or "long-range cruise," the system determines that the current operating condition is in the corresponding label, such as silent flight, takeoff and climb, or stable cruise. In some scenarios, environmental adaptation strategies are also embedded in the mission instruction, such as "high wind speed adjustment," "extreme temperature warning," or "complex terrain obstacle avoidance," to activate specific operational strategies to improve system robustness and responsiveness.

[0020] After completing the identification of the operating mode information, the system will enter the stage of generating the target power demand. This stage relies on a joint reference knowledge base consisting of a historical load model and an environmental database. The historical load model is constructed through statistics and modeling of long-term operating data. During the system trial operation phase or after a large number of field missions, the parameters such as current, voltage, load response time and power consumption under multiple typical operating conditions are normalized and sample clustered. The final mathematical description set is formed. The model can be constructed using methods such as multidimensional linear regression, K-means clustering or BP neural network, and sub-models can be divided according to different environmental factors such as temperature, humidity, air pressure, altitude, wind speed, etc. to improve the accuracy of power prediction. The environmental database records environmental perception information under different time and space coordinates. The data sources include airborne environmental sensors, ground remote sensing systems and historical records. After identifying the current operating condition tag, the system automatically retrieves the corresponding historical load model and its power offset correction factor under the current environmental conditions. Combined with the current measured real-time load current and voltage trends, the system uses algorithms such as differential prediction or sliding weighted average within a short window to deduce the target power demand value at the current moment. This value not only drives subsequent energy allocation strategies but also enables feedforward power preparation in multi-module collaborative energy supply scenarios.

[0021] After the target power demand is determined, ensuring safe and efficient energy supply requires accurate measurement of the current battery state of charge (SOC). This process relies on a battery management system (BMS), which reads the battery's voltage, current, temperature, and charge / discharge status in real time. Given that lithium-ion battery SOC is significantly affected by temperature, the system requires a temperature compensation mechanism. This involves applying a temperature sensitivity coefficient to the BMS raw data, correcting the voltage based on the current ambient temperature or cell surface temperature, before recalculating the SOC. A modified Coulomb integration method can be used to estimate the SOC. This method records the total charge change from the initial charge / discharge state of the battery. The calculation considers current sensor drift error, sampling interval offset, and the integration window update strategy. Finally, the open-circuit voltage and empirical curve correction results are combined to output the current battery SOC estimate. To further improve estimation accuracy, the system can employ fusion methods, such as fusing model predictions with sensor measurements via a Kalman filter, to achieve dynamic correction.

[0022] Finally, to form an early warning and assessment capability for system supply and demand risks, the system compares and analyzes the target power demand with the current SOC value. The key here is to determine whether the battery, at its current state of charge, has the ability to continuously supply power to the next task node within a safe range. By comparing the current SOC with the minimum SOC threshold required for the task, and examining the load power gradient changes within the next scheduling cycle, the system predicts whether the battery's depth of discharge may exceed the recommended range. If there is a clear risk, further classification and judgment are made based on the operating mode information. For example, if the current operating condition is emergency obstacle avoidance and the SOC is about to fall below 10%, the system will report the risk in advance and recommend entering energy-saving mode or forced load reduction mode.

[0023] Step S102: Based on the current operating information, determine the current operating condition category.

[0024] When implementing the comprehensive electric energy management and control method for high and low load conditions described in the present invention, the role of step S102 is to determine the current operating condition category of the energy supply system based on the current operating information after obtaining the current operating information, and to provide a clear basis for subsequent power demand calculation and energy allocation strategy selection.

[0025] Specifically, the classification of operating conditions should be based on a comprehensive assessment of the system's current operating mode, load status, and the dynamic characteristics of power demand. Operating conditions can generally be divided into two broad categories: normal operating conditions and emergency operating conditions. Normal operating conditions include the energy supply system operating with stable power demand and no unexpected conditions, such as regular driving, steady-state power supply, and regular charging. Emergency operating conditions, on the other hand, encompass abnormal conditions such as sudden changes in system load, near-limit battery status, and power redundancy failure. These conditions are often characterized by rapid power increases or decreases and severe voltage fluctuations.

[0026] To automatically determine the operating condition, the controller performs a joint analysis of the three types of operating information obtained in step S101. First, based on the operating tag or status code indicated in the operating mode information, it determines whether the system is currently in a mode that could trigger a load change, such as transitioning from standby to operation or from normal operation to power compensation. This determination can be implemented using a pre-set state transition table, a finite state machine (FSM), or threshold judgment logic.

[0027] Secondly, based on the changing trend of the target power demand, we determine whether the current power request exhibits a sudden change. For example, if the power request increment ΔP per unit time exceeds a preset power surge threshold, or if the power request decreases below a preset power decrease threshold, we can preliminarily determine that the system has entered an emergency load state. This type of judgment can be achieved by performing real-time differential calculations on the power request curve using a sliding window approach, combined with delay filtering to avoid misjudgments.

[0028] Finally, the battery's state of charge (SOC) value plays a crucial role in determining operating conditions. When the SOC value approaches the warning threshold, for example, below 30% or above 90%, and coincides with a sudden power surge, it indicates that the system's energy storage buffering capacity is insufficient. In this case, the current state can be classified as an emergency operating condition, triggering a more conservative charge and discharge control strategy. A hysteresis mechanism can also be implemented to prevent control oscillations caused by frequent switching between operating conditions.

[0029] Ultimately, by logically synthesizing the above three types of judgment results, a stable and accurate operating condition category output can be generated. Preferably, the controller should establish a complete operating condition mapping table or decision tree, mapping different information combinations to clear operating condition identifiers, such as "normal-steady state," "normal-charging," "emergency-sudden increase," and "emergency-sudden decrease." This will drive the strategy branch selection of the subsequent power demand calculation module.

[0030] Therefore, step S102 is not just a static judgment operation, but a dynamic classification process that integrates pattern recognition, trend prediction, and state reasoning. Its accuracy directly determines the response rationality and operational stability of the entire electric energy integrated management and control system. In engineering implementation, it is best to avoid relying solely on a single variable for classification judgment. Instead, the operating condition identification module should be constructed based on a comprehensive analysis of multi-dimensional operating information to ensure that the system has good operating condition adaptability and abnormality identification robustness.

[0031] Furthermore, the operating condition categories include normal operating conditions and emergency operating conditions, wherein the normal operating conditions include parking charging, silent driving, land charging, vertical take-off and landing, and cruise flight, and the emergency operating conditions include emergency braking, high-power maneuvering, fan damage, and emergency obstacle crossing.

[0032] When implementing the comprehensive power management and control method proposed in this invention for high and low load conditions, the classification of operating conditions requires precise identification and responsive control based on the system's actual operating state. To this end, the operating conditions must be clearly categorized into two basic dimensions: normal and emergency conditions, taking into account the operating modes of aircraft or ground vehicles. Each category can then be further subdivided into several typical scenarios to meet the responsiveness requirements of power management under different load conditions.

[0033] Under normal operating conditions, the system is typically in steady state or routine operation, free from sudden disturbances or drastic load fluctuations. Specifically, parking charging refers to the process of charging the battery via an external charger while the system is stationary. During this period, the load is low and constant, and the system primarily focuses on precise control of charging power and managing a safe upper limit on the SOC. Silent driving refers to the slow movement of a vehicle or aircraft at very low power, often used for silent operations such as transporting people or materials. While requiring less transient energy response, it places high demands on smooth and continuous energy supply. Land charging, a simultaneous charging operation, is particularly common in hybrid or range-extended systems. In this scenario, the system must coordinate power distribution between engine output and battery charging to ensure that power supply and vehicle operation do not interfere with each other. Vertical takeoff and landing (VTOL) is a typical high-load startup process unique to aircraft. During this process, the propellers or propulsion units must provide a large amount of thrust in a short period of time, placing stringent demands on the energy supply system's ability to deliver high instantaneous power. Cruising flight is the stable stage during the flight process. The load is relatively stable. The system needs to maintain a constant power output and replenish the battery or recover energy in a timely manner to optimize energy efficiency.

[0034] In emergency situations, the system must respond to unexpected events or temporary high-risk operations, requiring the ability to rapidly adjust its power supply strategy to ensure safe operation. Emergency braking often occurs during land navigation, requiring the system to rapidly reduce output power and recharge the battery as needed. This requires extremely precise and timely power regulation. High-power maneuvers often involve rapid acceleration, sharp turns, or unusual flight attitude adjustments, requiring the power supply system to deliver exceptional power within a short period of time. The battery discharge rate scheduling strategy becomes the core control strategy in these situations. A damaged fan is a typical hardware failure emergency condition. Upon detecting signals such as abnormal speed, sudden current surges, or elevated temperatures, the system must rapidly reduce the overall load to prevent the fault from spreading, while also activating redundant power supply paths or forced load reduction mechanisms. Emergency obstacle avoidance is a high-risk condition triggered by temporary avoidance maneuvers in areas with restricted paths or complex terrain. This requires the system to quickly mobilize all available power resources to deliver a high-power burst, coupled with a rapid power reduction process to prevent system overheating or battery over-discharge caused by continuous high power output.

[0035] In summary, the setting of operating condition categories is the basis for this method to achieve intelligent energy control. In practical applications, it is necessary to combine operating data, the current mode of the vehicle / aircraft, and the battery status for real-time judgment and classification to ensure the refined control and scheduling of the energy supply behavior between the power generation unit and the battery under different operating conditions, ultimately improving the stability, safety, and energy efficiency of the overall operation of the system.

[0036] Furthermore, determining the current operating condition category based on the current operating information includes: Based on the operation target semantic fields, path planning labels and dynamic environment intervention items extracted from the operation mode information, a task context semantic vector is constructed and mapped to a multidimensional operation condition label embedding space; In the multi-dimensional operating condition label embedding space, a dynamic clustering algorithm is used to identify the closest historical operating condition cluster center, and combined with the current target power demand change trend, a preliminary operating condition classification result and a corresponding credibility score are output; Based on the preliminary operating condition classification results, the dynamic energy consumption curve family and the charge safety limit parameter group associated therewith are called to construct a safety mapping space of the battery state of charge (SOC) value, and the current SOC value position is combined with the determination of whether it is in the controllable load window; If it is determined to be at the edge of the load window, the real-time environmental disturbance index is integrated and matched with the historical working condition anti-disturbance model, the original working condition classification result is corrected and the working condition category priority is reordered, wherein the real-time environmental disturbance index includes sudden change in wind speed, sudden drop in temperature, and electromagnetic interference; The operating condition category corresponding to the highest confidence level in the comprehensive matching results is determined as the current operating condition category.

[0037] First, after obtaining the working condition mode information, the system needs to further extract the semantic fields contained therein. Specifically, it includes the work target field, path planning label and environmental intervention description item. For example, the work target field may correspond to instructions such as "completing a fixed-point route" and "continuous high-power vertical hovering", and the path label covers motion elements such as "straight line", "turning", and "climbing". Environmental intervention items refer to external events such as "crosswind influence" and "strong electric field interference". In order to characterize the above heterogeneous information, the system introduces a task semantics-oriented embedding method, which encodes the semantic field into a multi-dimensional embedding vector and combines it into a single task context semantic vector through an attention weighting strategy. This vector is projected into the working condition label embedding space as input. This space is a predefined multi-dimensional vector space. Each historical working condition category is encoded as a fixed label vector to form the reference cluster center.

[0038] Subsequently, a cluster matching algorithm based on dynamic cosine similarity is used to perform preliminary operating condition classification. This algorithm calculates the distance between the currently constructed task semantic vector and the cluster centers of historical operating condition labels based on similarity. It also incorporates the rate of change of the target power demand as a dynamic weighting factor in the clustering process, thereby increasing the classification results' sensitivity to real-time power fluctuations. The output is the preliminary determined operating condition category and its corresponding confidence score. The scoring mechanism adjusts the decision boundary based on the standard deviation of the similarity distribution to ensure numerically interpretable confidence scores.

[0039] Based on the preliminary operating condition classification results, the system further queries the dynamic energy consumption curve family and charge state limit parameters associated with the operating condition. The dynamic energy consumption curve family is obtained by training based on the measured data under historical tasks, describing the nonlinear response characteristics of the power load to the decrease in SOC value per unit time, while the charge limit parameters include information such as the SOC threshold range and the maximum charge and discharge current tolerance. After substituting the current SOC value into the above model, the system constructs a two-dimensional safety mapping space with SOC as the horizontal axis and load stability as the vertical axis to clarify whether the current SOC value is in the load controllable window, that is, whether the system still has sufficient buffer to deal with load disturbances.

[0040] When it is determined that the SOC value is at the edge of the controllable window, that is, it is about to exceed or just enter the safe zone, the system further retrieves the real-time environmental disturbance index and integrates it with the historical working condition anti-disturbance model. Real-time disturbance indicators may include wind speed change amplitude, temperature drop, electromagnetic interference frequency, etc., while the historical anti-disturbance model is based on prior experience or measured data to establish a mapping relationship between environmental disturbance intensity and power compensation failure probability. The fusion of the two is carried out through feature interaction through the residual convolutional network to fine-tune the credibility of the working condition classification, correct the original sorting results and rearrange the working condition priority, so that the final output is more dynamically adaptable.

[0041] Finally, after integrating all matching and correction results, the system selects the operating condition category with the highest confidence as the current operating condition type and enters the subsequent power allocation and energy supply control phase. It should be noted that this method does not adopt the traditional static model based on fixed logic judgment during the operating condition identification process. Instead, it achieves high adaptability and scalability by constructing a semantic vector space and environmental disturbance fusion channel. It is particularly suitable for real-time energy supply decision-making scenarios under different load changes for new flight platforms, electric vehicles, or equipment operating in complex terrain.

[0042] This embodiment proposes and introduces a historical operating condition immunity model. The core purpose of this model is to provide a quantifiable, comparable, and decision-making auxiliary judgment mechanism for different external disturbance scenarios faced by the system, such as sudden changes in wind speed, sudden temperature drops, or electromagnetic interference. Based on the initial identification of the current operating condition category, the operating condition classification results can be further adjusted according to changes in disturbance conditions, thereby improving the adaptability and robustness of the control strategy.

[0043] The model construction process is as follows: First, during the initial deployment or operation of the system, the system is designed to collect and record historical mission data under multiple typical operating conditions. Specifically, for operating conditions such as "cruise flight", "vertical take-off and landing", and "parking charging", external disturbance indicators are continuously collected during their operation cycle, such as the instantaneous change amplitude of wind speed, the rapid drop rate of ambient temperature, and the attenuation amplitude of the signal-to-noise ratio of wireless communication under electromagnetic interference. At the same time, it is also necessary to record the dynamic response behavior of the system after these disturbances occur, including but not limited to the offset of the battery voltage, the fluctuation amplitude of the power output, and the time required for the system bus voltage to return to a stable state.

[0044] Next, for each type of operating condition, its response behavior under various disturbance conditions is archived and organized to form an operating condition disturbance response profile. For example, when the wind speed rapidly increases to 20 meters per second, the battery voltage under one operating condition may drop by 0.5 volts within 5 seconds and return to the normal fluctuation range after 8 seconds; under another operating condition, it may take 15 seconds or even longer to recover. This information will be organized into a set of data records to describe the "anti-disturbance performance" of the operating condition under this type of disturbance.

[0045] By statistically analyzing the same operating conditions under different disturbances across a large number of historical missions, we can summarize the disturbance immunity characteristics for each operating condition across various disturbance dimensions. These characteristics can be expressed using qualitative descriptors, such as "high adaptability" to sudden wind speed changes, "medium adaptability" to temperature fluctuations, and "low adaptability" to electromagnetic interference. We can also use normalized scoring criteria to convert recovery time, fluctuation amplitude, and system offset into a unified adaptability level (e.g., a score between 0 and 1), thereby constructing a profile of the operating condition's disturbance immunity characteristics.

[0046] When the system enters the operational phase, if it detects a significant change in the current disturbance environment, such as a sudden increase in wind speed, a sharp drop in ambient temperature, or unstable wireless communication, it immediately extracts the actual observed value of the current disturbance indicator and matches it with the operating condition immunity profile. This matching method uses "similarity comparison," which determines which operating conditions in history most closely resemble the current environmental disturbance. At the same time, referring to the system's performance under similar conditions, it infers whether the currently determined operating condition category has sufficient immunity.

[0047] For example, the system initially identifies the operating condition as "silent driving," but in real time detects a rapid increase in wind speed from 5 meters per second to 20 meters per second. Historical records show that "silent driving" frequently experiences excessive voltage fluctuations under these wind speed conditions, while "cruise flight" maintains stable battery voltage under similar disturbances. Therefore, the system will combine current environmental disturbance data, the changing trend of the target power demand, and whether the battery state of charge is approaching its limit to determine whether the current operating condition identification result needs to be revised, switching to a more resistant operating condition category and deploying a more appropriate energy allocation strategy in advance.

[0048] The entire disturbance rejection model is adaptively updated as mission data accumulates. For example, after each mission, the system reassesses the actual disturbance conditions and system performance, adding this new data to the operating profile for more accurate disturbance rejection matching analysis in future missions.

[0049] The following provides a detailed example: During a vertical takeoff and landing (VTOL) unmanned flight mission, the energy supply system received the following mission instruction: "Vertical takeoff and landing mission, target area radius 15 meters, expected hovering time 2 minutes, ambient wind speed warning level 2, path set." In this example, the energy supply system first extracts the key elements of the mission instruction based on the original mission data issued by the mission dispatch center using a preset semantic parsing model, including: Operation target semantic fields: vertical take-off and landing, hovering Path planning tags: target area radius, fixed path Dynamic environmental intervention item: Wind speed warning level 2 The above fields are constructed into a contextual semantic vector. The construction method is: each semantic keyword is encoded into a sparse feature vector, embedded through Word2Vec model pre-training, and then the final task semantic vector is obtained by weighted average. Assumptions: ("Vertical Take-off and Landing", "Hanging", "Target Area 15 meters", "Wind Speed ​​Warning Level 2") Output a dimension of The vector of .

[0050] Then, the system maintains a multi-dimensional working condition label embedding space , which predefines the embedding vectors and cluster centers of all historical working condition labels. For example: : Parking charging center vector : Silent driving center vector : vertical take-off and landing center vector Through the dynamic Kmeans clustering algorithm, the system can identify To which cluster center it is closest, the cosine similarity is used for calculation: in, Represents the current task context semantic vector. This vector is a multidimensional numerical vector obtained by feature extraction and embedding coding of input operation instructions, path planning, environmental intervention and other information, usually located in the real vector space Its dimensions It depends on the output design of the semantic feature encoder, for example, it can be set to 128 dimensions or 256 dimensions. Indicates the The semantic center vector corresponding to the historical working condition category label. The “representative” semantic vector representing a certain type of working condition (such as vertical take-off and landing, cruise flight, etc.) in history is formed by clustering or averaging in multiple missions and is used to construct the cluster center in the embedding space. Its dimension is the same as Stay consistent.

[0051] Assume that at this time (Vertical take-off and landing) has the highest similarity, and the output preliminary working condition classification result is vertical take-off and landing, and its credibility score is set to 0.94.

[0052] To further improve judgment accuracy, the system also performs cross-validation based on the current target power demand trend. The system monitors the flight control system's load current and power fluctuation rate in real time. Assuming a power growth rate of 2.1 kW per second over the past 10 seconds, it identifies a non-resident operation and supports the initial determination of vertical takeoff and landing.

[0053] Next, the system reads the SOC value provided by the battery management system , current temperature , the real-time voltage is 51.2 V and the current is 4.5 A. Through the temperature compensation coefficient (correspond Conditions), the current is calculated as follows: in, Is the current state of charge; indicates the battery at the time point The ratio of the actual remaining capacity of a battery to its rated capacity, usually expressed as a percentage (%). For example, = 62% indicates that the current remaining capacity is 62% of the battery's rated capacity. This value is a key factor in the battery control system's power supply strategy decisions. Indicates the battery at time point The SOC value. Usually obtained from the previous round of measurements or system records, it is the starting point for the current SOC calculation. Is a battery temperature The coefficient determined reflects the effect of temperature on battery charge and discharge efficiency. When the temperature deviates from room temperature (e.g., 25°C), the battery's discharge efficiency decreases, necessitating an adjustment to the current estimate. For example, at 25°C, the empirical value κ(25°C) = 0.96 indicates that the actual current efficiency at this temperature is 96% of the theoretical value. This coefficient can be obtained by fitting the efficiency-temperature curve provided by the battery manufacturer or by constructing a lookup table using experimental data.

[0054] Battery in time The current value at the moment, in amperes (A). If the discharge current is constant, this value is a constant (for example, 4.5 A in the example).

[0055] The rated capacity of a battery, measured in ampere-hours (Ah), is the maximum total charge the battery can provide at a normal discharge rate. For example, 10Ah means the battery can continuously provide a current of 1A for 10 hours.

[0056] The estimated available battery capacity is approximately 6.2 Ah, which satisfies the current total power demand of approximately 0.126 kWh for 10 seconds. The system then loads the dynamic energy consumption curve family and SOC limit parameter set associated with the "vertical take-off and landing" tag, for example, stipulating that the minimum SOC for the "vertical take-off and landing" operation should be , the maximum is .current The system is in the safe window, but it further detects the environmental disturbance item "wind speed surge" with a value of 3.7. , exceeding the second-level warning threshold in the local model .

[0057] To this end, the system invoked an environmental immunity fusion model, matching the disturbance index with the historical operating condition immunity vector. Using a similarity rescoring mechanism, the system calculated the current operating condition stability value to be 0.79, slightly below the default safety threshold of 0.85, prompting the system to adjust the operating condition priority. In this scenario, "silent hover" was elevated to the suboptimal operating condition category.

[0058] Ultimately, after evaluating the credibility, the system still identifies "vertical take-off and landing" as the current operating condition category, but with a disturbance monitoring mark, indicating that redundant power should be dynamically reserved in subsequent control strategies.

[0059] Step S103: Calculate the corresponding total system power demand value according to the operating condition category and the target power demand.

[0060] During step S103, the system calculates the total power demand that the energy supply system must meet at the current moment, based on the determined operating condition and data obtained from the target power demand. The core of this process is to comprehensively consider the current load operating status, the power system's responsiveness, and the energy storage device's participation capabilities, thereby forming a reasonable and dynamically adjustable power demand benchmark to guide subsequent power allocation.

[0061] First, the system sets the basic framework for the entire calculation process using the operating condition category obtained in step S102. If the current system is in normal operating conditions, it means that the load demand is relatively stable and predictable. At this time, the target power demand can be directly used as the main reference value. For example, if the target power demand reflects the average power consumption of the current load equipment during normal operation, the system can use this value as the main body of the total power demand. On this basis, the system also needs to consider a certain operating margin to cope with minor load fluctuations that may occur in a short period of time. This margin can be set as a fixed ratio based on experience, or automatically estimated by analyzing historical operating data.

[0062] If the current operating condition is determined to be an emergency state, the calculation logic will be more conservative. The system needs to determine whether there is a trend of rapid load increase at this time, such as the sudden start-up of certain critical loads, load transfer, or transient pressure caused by the failure of the previous power supply. At this time, the system needs to manually increase a reasonable safety margin while referring to the target power demand to reserve dynamic adjustment space for the energy supply system. This margin can be obtained through a comprehensive assessment of the operating condition level and the load response curve. Its size depends on the growth rate and duration of the sudden load and the response capability of the battery and power generation unit.

[0063] In addition to the above considerations for the load itself, the current state of charge of the energy storage device must also be considered when calculating the total power demand value of the system. If the battery's state of charge is within a reasonable range, that is, not close to the low or high limit, the system assumes that the battery has normal discharge or charging capabilities at the current moment and can be added to the total energy supply framework as a power balancer. However, if the battery SOC is close to the edge of the allowable range, for example, below the minimum safe value, the system should reduce its reliance on the battery's energy supply capacity and use the power generation unit as the main energy supply object, thereby maintaining the continuity of the system's power supply while ensuring battery safety.

[0064] Furthermore, power demand calculations must consider the characteristics of each load in the system. For example, if there are high-impact loads or intermittent loads, the system should identify these loads based on past operating data and pre-set characteristic templates, and account for dynamic power demands during startup or changes in the power calculation. This identification can be achieved by the controller monitoring the power change slope or by using feedforward predictions based on the load access plan provided by the upper-level task scheduling system.

[0065] After the calculation is complete, the system generates a comprehensive output containing information such as actual power demand, safety margins, and energy storage availability assessments, which is then fed into the subsequent power allocation logic. This output should be in a structured data format that supports real-time sharing and rapid access across different submodules, ensuring consistency, stability, and traceability of the overall control process under all operating conditions.

[0066] Therefore, the implementation of step S103 is not only a process of generating power figures, but also a multi-dimensional comprehensive judgment of load status, power supply capacity, system safety and dynamic response. The key lies in providing a power demand benchmark with strong adaptability and high control accuracy through working condition drive, information fusion and dynamic adjustment mechanism, thereby supporting subsequent energy management decisions.

[0067] Furthermore, the calculating of the corresponding total system power demand value according to the operating condition category and the target power demand includes: Combined with the current operating condition category, a power demand record sequence matching the operating condition is extracted from historical data. Based on the power value changes in each period in the record, the average power level, maximum power surge amplitude, and power fluctuation duration at different stages are statistically analyzed to form a basic load characteristic description. Based on the task type, execution phase, and running time under the current working conditions, the target power demand is subdivided into multiple continuous time periods, and a corresponding load level is assigned to each time period. Then, the load level is matched with the corresponding power parameters in the basic load characteristic description to determine the preliminary target power demand sequence; Real-time collection of influencing factors related to the current operating environment, including wind speed, temperature, humidity, terrain undulation, and obstacle density. Based on the impact of different environmental factors on power consumption, the power value of each period in the preliminary target power demand sequence is adjusted step by step to obtain a target power sequence that includes environmental correction factors. Compare the current battery state of charge with the power buffer to assess whether the battery has the ability to handle a sudden load surge or release part of the base load. If the conditions are met, a corresponding load transfer correction value is introduced into the target power sequence to match the target power with the battery's response capability. The adjusted target power values ​​for each time period are combined with the basic power demand to form a set of final system total power demand values ​​in chronological order, which are used for the subsequent execution of power allocation and energy control strategies, so that the calculation results can fully reflect the comprehensive effects of working condition history, mission characteristics, environmental impact and battery capacity.

[0068] In actual integrated power management scenarios, systems often face complex factors such as alternating high and low loads, sudden load shocks, and uncertain external environmental interference. Traditional approaches that rely on static power mapping or simple task power tables are difficult to adapt to dynamically changing operating conditions. To this end, this embodiment proposes a target power calculation strategy that integrates multiple factors and refines them in multiple stages. This strategy aims to achieve highly timely and adaptable total system power demand forecasts, and provide an accurate reference for subsequent coordinated power supply control strategies between power generation units and batteries.

[0069] First, the power demand record sequences archived in the historical database are filtered based on the currently identified operating condition category. Specifically, each historical record sample contains multiple record fields corresponding to the operating state, such as task type, phase description, timestamp, real-time load power, and battery participation ratio. Based on a matching mechanism for operating condition categories, the system extracts a collection of historical samples with similar tags and statistically processes the power values ​​from different time periods within this collection to generate a typical load profile for that operating condition category. This profile includes not only the average power level for each operating phase, but also the maximum power surge magnitude, load fluctuation duration, and average response delay, revealing the stability and dynamic trends of the operating condition in actual operation. For example, for a typical "hill start + hill cruise" operating condition, historical power records typically exhibit a sudden instantaneous power surge of 3-5 seconds during the start phase, while exhibiting a relatively stable output curve during the cruise phase. Therefore, the generated load profile reflects the dynamic power characteristics required by the system during different mission phases.

[0070] Secondly, the system divides the task objectives corresponding to the current working condition into time periods. This division is not only based on the running time, but also introduces the logical segmentation of the execution phase, such as "start-acceleration-constant speed-climbing-downhill", and sets the load level for each time period. The load level is quantified using a grading mechanism, for example, it is set to five levels from L1 to L5, corresponding to very low, low, medium, high, and very high load levels respectively. The system matches the load level of each time period with the load characteristic description extracted above, thereby giving each time period a preliminary power value estimate. Assuming that the first 3 seconds of the current task phase is the starting phase, its load level is calibrated as L5. The system will find the average power and maximum power values ​​of the corresponding phase L5 in the historical sample of the working condition, and assign a preliminary target power value to the current segment.

[0071] Next, in order to make the target power estimation closer to the actual operating status, the system also needs to introduce real-time environmental factors to correct the target power step by step. These environmental factors include but are not limited to the currently detected wind speed, temperature, humidity, terrain undulation, and obstacle density. By labeling the historical sample data with environmental factors and modeling the impact weights during the training phase, the system can establish a correction mapping relationship for the "impact of environmental changes on unit load power." For example, when the wind speed is greater than 8m / s and the vehicle is traveling against the wind, the system will increase the original power value by a correction of 10%-15%. Each environmental factor will dynamically adjust the target power value for the corresponding time period in units of time periods, so that the output power is adaptable.

[0072] Then, in order to further improve the controllability of the system's supply and demand matching, the system will jointly compare the target power demand sequence with the current battery SOC state. As an important medium for buffering and regulation, the battery's state of charge directly affects its ability to respond to sudden load changes. Therefore, the system first divides the battery state into three sub-states: "dischargeable", "rechargeable", and "maintain" according to the preset SOC range, and combines the power buffer bandwidth to determine whether it has the ability to access instantaneous surge loads or replace the generator output. If the conditions are met, the system will introduce a load transfer correction factor into the target power sequence, that is, introduce additional power in a certain period of time, which means that the battery will take on the load instead of the generator, or reversely absorb surplus power and charge during the load trough period, thereby achieving two-way coordination.

[0073] Finally, all adjusted target power value sequences are combined with the base loads of each phase to form a chronological set of total system power demand values. This result not only preserves the load characteristics of the current task, but also incorporates environmental influences and battery responsiveness. Furthermore, the model's adaptability to abnormal load fluctuations is enhanced by introducing historical interference immunity parameters. The resulting total system power demand value can be used for subsequent power scheduling of power generation units, battery charge and discharge management, and bus power allocation, achieving dynamic optimal control of the energy system under complex operating conditions.

[0074] This comprehensive power prediction method, which combines operating condition identification, historical sample statistics, real-time environmental perception, battery response modeling and phased adjustment factors, is a significant technological advancement. Different from existing power distribution schemes based on real-time load monitoring or simple empirical table scheduling, it has higher real-time performance, adaptability and power accuracy, and can effectively improve the overall energy supply efficiency and operational safety of the integrated power management system under alternating high and low load conditions.

[0075] Step S104: Determine the output power of the power generation unit and the battery charge and discharge power based on the battery state of charge (SOC) value and the total power demand value of the system. Under normal operating conditions, the output power of the power generation unit is determined to meet the basic load demand based on a preset SOC threshold range, and the remaining power is balanced by the battery for charge and discharge within the SOC range. Under emergency operating conditions, when the load suddenly increases, the battery is controlled to discharge at the maximum discharge rate. When the load suddenly decreases, the output power of the power generation unit is controlled to decrease rapidly, and the battery charging current is adjusted to avoid exceeding the maximum charging current that the battery can withstand.

[0076] In the comprehensive electric energy management and control method described in the present invention, the implementation of step S104 is crucial. Its core lies in scientifically and rationally determining the power distribution relationship between the power generation unit and the battery based on the total system power demand value calculated in the previous step and the current state of charge (SOC) value of the battery, thereby providing clear and executable output instructions for subsequent energy supply execution and system stability control.

[0077] In practice, this step first receives two key inputs: the current battery SOC value and the total system power demand value obtained in step S103. These two parameters represent the available capacity of the energy storage unit and the current real-time energy supply demand of the system. After receiving these inputs, the controller determines and allocates different strategy paths based on the current operating condition.

[0078] When the system is operating normally, indicating no sudden changes in load demand and the battery state is within normal limits, the control strategy prioritizes stable energy supply and efficiency. The power generation unit will serve as the primary energy source, and its output power should primarily cover the system's base load. To avoid wasted resources or excessive battery involvement, the controller prioritizes a preset SOC threshold range. This range is typically determined based on battery type, safety standards, and historical system operating experience; a range of 30% to 90% state of charge is generally recommended. If the SOC falls within this range, the system allows the battery to assist in providing power. Specifically, if the power generation unit's output slightly exceeds the current load, the battery enters a charging state to absorb excess energy. If the power generation unit cannot independently meet the load demand, the battery enters a discharging state to fill the shortfall. This entire process requires dynamic balance to ensure that while the battery participates in regulation, its SOC fluctuations remain within the permitted range, preventing overcharging and discharging.

[0079] When the system is judged to be in an emergency condition, the load behavior often changes dramatically, such as a surge in power demand in a short period of time, or the sudden disconnection of certain high-power loads, resulting in a reversal of supply and demand. The control strategy in such situations must have a fast response and protection mechanism. In the case of a sudden increase in load, the response speed of the power generation unit may not be enough to immediately follow the load increase, so the battery must be immediately controlled to discharge at the maximum allowable rate to quickly release electrical energy to support the supply and demand balance of the system. During implementation, the maximum rate should be determined by the safe discharge parameters specified by the battery manufacturer. The controller needs to monitor the battery temperature, current and internal voltage status in real time to ensure that the discharge process does not exceed the safety limit.

[0080] Conversely, in the event of a sudden load drop, the system faces the challenge of oversupply, potentially causing a transient increase in bus voltage. Prioritizing this, the power generation unit's output power should be rapidly reduced to its base operating power level while simultaneously switching the battery to charging mode to absorb the excess energy. To prevent thermal runaway or battery life degradation from high-current charging, it is necessary to further determine whether the current charging current is close to the maximum charge current the battery can withstand. The controller can dynamically limit the charging rate by referencing the SOC rise rate, the current sensor sampling value, and the internal protection threshold. If necessary, it can suspend power supply from the power generation unit until power returns to a stable state.

[0081] All of the above determinations and adjustments should be executed within millisecond cycles to ensure that the system's energy balance is not disrupted. It is recommended to introduce a state-triggered priority queue into the control strategy to prioritize responses to emergencies and avoid uncertain behavior caused by conflicting control commands. To ensure safety, a protection priority strategy can also be set. When the discharge rate or charging current is about to exceed the limit, the controller immediately suspends all other optimization controls and forcibly switches into protection logic to ensure the safe operation of the battery and power generation unit.

[0082] Therefore, the technical core of step S104 is not only the simple distribution of power, but also the multiple judgments and coordinated control based on the dynamic changes in supply and demand, energy storage response capabilities and safe operating boundaries, so as to achieve a smooth, accurate and controllable energy supply strategy under both normal and emergency conditions, ensuring that the entire power system can maintain an efficient, stable and safe operating state under various complex load scenarios.

[0083] Furthermore, the determining of the output power of the power generation unit and the battery charge and discharge power according to the battery state of charge (SOC) value and the total power demand value of the system includes: According to the historical basic load model and current operating mode information corresponding to the operating condition label, the target power demand value of the system at the current moment is extracted, and the basic power part of the demand value is preliminarily set as the power output target of the power generation unit; Read the current SOC value of the battery and compare it with the preset SOC threshold range to determine whether it is in the charging zone, discharging zone or balance maintenance zone, and accordingly form an acceptable battery charge and discharge power adjustment range; If the current operating condition is normal, then on the premise of meeting the basic load demand, the remaining power is allocated to the battery for corresponding charging or discharging operations based on the interval position of the SOC value and the load change trend, and the combined allocation result of the power generation unit output power and the battery charging and discharging power is generated; If the current operating condition is an emergency condition and the power demand shows a sudden increase, the battery discharge power is set to the current maximum allowable value, subject to the constraint that it does not exceed the maximum rate discharge capability of the battery. The power of the power generation unit is temporarily maintained unchanged or slightly increased to stabilize the bus voltage. If a sudden drop in load is detected, the output power of the power generation unit will be quickly reduced, and the battery charging current limit model will be called up to set the battery charging power value without exceeding the maximum safe charging current of the battery. Finally, based on various judgment conditions, the target output power value of the power generation unit and the battery charging and discharging power value at the current moment will be output.

[0084] After receiving the current operating mode information and target power demand value, the system first extracts the standard base power value for that operating condition based on the historical base load model corresponding to the operating condition label. The historical base load model is constructed by long-term recording of the vehicle or system's power consumption curves under different operating conditions. In particular, it extracts load fluctuation data under typical operating conditions such as cruising, climbing, vertical take-off and landing, and silent driving, and statistically clusters these data to form a standardized load reference set. In this process, to avoid error accumulation, each operating condition label must be matched to the time window corresponding to its typical operating segment to ensure that the obtained load baseline is representative and stable.

[0085] The system then dynamically adjusts this base power based on the current operating path and real-time meteorological interference factors (such as wind speed and electromagnetic disturbances) to form the system's target power demand. This target value combines the static demand (base load) with a dynamic correction factor. The dynamic correction factor is derived from environmental factors that affect the system's power consumption, such as propulsion power fluctuations caused by wind resistance or the impact of external interference on the system's cooling load.

[0086] Subsequently, the system reads the current state of charge (SOC) value of the battery, and dynamically corrects the SOC value through the real-time information such as voltage, current, temperature, etc. provided by the battery management system, combined with the embedded temperature compensation model. The temperature compensation model takes into account the nonlinear changes in the discharge efficiency of the battery at different temperatures. For example, the temperature coefficient κ(T) = 0.96 is introduced at 25°C, and the effective current value used in the current integral formula is adjusted under low or high temperature conditions to obtain a more accurate SOC estimate. The SOC range is preset into three ranges: charging zone, discharge zone and balance maintenance zone. Generally speaking, SOC below 20% is considered to be the discharge zone, above 80% is the charging zone, and the middle is the balance maintenance zone. The specific threshold value can be set according to the battery characteristics during the product design stage and solidified in the control strategy.

[0087] If the system identifies the current operating condition as normal (such as cruising or charging on land), the control strategy is centered on the base load, and based on this load, it determines whether the remaining power can be used to charge the battery or whether the battery needs to be discharged to make up for short-term fluctuations. Specifically, if the current SOC value is at the upper limit of the charging range, it means that the battery is close to being fully charged. At this time, the system avoids forced charging and chooses to let the power generation unit bear the entire load independently; if it is at the lower limit of the discharge range, it means that the battery is insufficient. At this time, the power generation unit will bear most of the load as much as possible to avoid further discharge of the battery. Within the balance maintenance range, the system sets the output power of the power generation unit to stably cover the base load based on the short-term load trend forecast results, and the battery provides the rapidly changing part of the power to balance the load fluctuations and improve the system response sensitivity.

[0088] If the current operating condition is identified as an emergency, the control strategy will quickly enter the dynamic adjustment phase. In the event of a sudden load surge, the system first retrieves the battery's maximum discharge rate capability. This rate is fixed to a multiple of the rated capacity (such as 2C or 3C) based on the battery's design parameters. Once this limit is confirmed, the control system sets the battery discharge power to the current maximum allowable value to quickly respond to the load surge and ensure system voltage stability. At the same time, the current output power of the power generation unit is maintained unchanged or slightly increased, and the bus voltage is supported by two parallel power supply channels.

[0089] If a sudden load drop occurs, that is, the system load demand drops significantly, the system needs to avoid the risk of bus overvoltage caused by continuous high power output of the power generation unit. Therefore, the target output power of the power generation unit is quickly lowered first, and the built-in battery charging current limit model is called to obtain the maximum safe charging current value under the current temperature and SOC conditions. The model determines the current maximum charging power by looking up the table or interpolation based on the battery charging characteristic diagram. For example, under the conditions of SOC of 62% and temperature of 25°C, the maximum safe charging current is 6A and the voltage is 51.2V, then the maximum charging power is approximately 307W. The system uses this power as the upper limit to control the battery to enter the current limiting charging mode to ensure its safety and life are not affected.

[0090] Ultimately, based on the judgment results of each path, the system summarizes the target output power of the power generation unit and the target charge and discharge power of the battery to form a combined power output configuration plan for the energy supply unit. This plan is used to drive the actual energy distribution behavior in the next control cycle, ensuring efficient and stable operation of the entire system under complex operating conditions and load disturbances.

[0091] For example, in a hybrid energy supply system, the base load under normal cruising conditions is set to a constant 5.0 kW. The energy supply system consists of a diesel generator unit with a rated power of 8.0 kW and a lithium battery pack with a rated capacity of 10 Ah and a nominal voltage of 51.2 V. The system is currently executing a path planning operation with the operating mode information set to "low-speed climbing" and a target power demand of 6.2 kW. At this time, the battery SOC obtained by the system status monitoring module is 62%, the ambient temperature is 25°C, the real-time current is 4.5 A, and the bus voltage is 51.2 V.

[0092] First, the system identifies the current operating condition as "slow-speed climbing" and uses the operating condition tag to establish a matching relationship with the historical operating condition database. This database is constructed using historical operating data and has a structure of <operating condition tag, time period, base load mean, standard deviation>. Under the "slow-speed climbing" tag, the base load mean is 5.0 kW, with a load standard deviation of 0.4 kW. The system combines the slope information (2.8%) and wind speed information (light wind, wind resistance correction factor of 1.05) along the current path and, based on environmental intervention correction rules, corrects the base load to 5.0 × 1.05 ≈ 5.25 kW.

[0093] The system then calls the battery SOC assessment module and makes corrections based on the current reading. The initial SOC value is 62% at a temperature of 25°C. Based on an empirically constructed temperature compensation curve, the current temperature compensation coefficient, κ(T), is 0.96. Because the system calculates SOC using current integration—specifically, subtracting the ratio of the discharge per unit time to the battery's rated capacity from the previous moment's SOC—due to high current volatility, the system uses a small time window integration method for real-time correction. In this example, the current is stable at 4.5 A per unit time. Based on an estimate, the charge change in one minute is approximately 4.5 A × 1 min = 0.075 Ah, representing 0.75% of the total capacity of 10 Ah. With temperature compensation, the actual discharge impact is 0.75% × 0.96 = 0.72%. Therefore, the system updates the SOC to approximately 61.28%.

[0094] Next, the charge and discharge strategy is determined based on the preset SOC threshold range: SOC < 30% is the low range, requiring priority charging; SOC > 80% is the high range, requiring limited charging; and 30% to 80% is the range where charge and discharge can be balanced. The current SOC is 61.28%, which is within the adjustable range. The system then enters the load distribution strategy determination process.

[0095] Because the current total power demand is 6.2 kW and the base load is 5.25 kW, the remaining 0.95 kW is regulated by the battery. At this point, the system determines this 0.95 kW as compensation power. Since the SOC is in the charge-discharge balance zone, the system further integrates the short-term load prediction model to determine the direction. The prediction model is constructed using a simple sliding average method, calculating the power change trend every minute for the previous 5 minutes. During the first 5 minutes, the power was 5.4, 5.6, 5.8, 6.0, and 6.2 kW, respectively, showing a linear growth trend. Therefore, it is predicted that the power will continue to increase in the next cycle. The system therefore interprets this trend as a need for increased output and determines that the battery will perform an auxiliary discharge operation.

[0096] In the emergency judgment branch, assume a sudden load increase occurs during the current cycle. The system records a power change exceeding 1.2 kW per unit time, deeming it a sudden increase and initiating emergency operation. Based on the battery specifications, the maximum discharge rate is set at 2C, meaning the maximum discharge current is 20 A (2 × 10 Ah) and the maximum discharge power is 20 A × 51.2 V = 1.024 kW. The current load increase is 1.2 kW, exceeding the maximum discharge capacity. Therefore, the system sets the battery discharge power to 1.024 kW, with the power generation unit increasing its output by 0.176 kW to support the transient load.

[0097] In a sudden load drop scenario, assuming a sudden drop from 6.2 kW to 4.2 kW, the system determines a 2 kW drop. The emergency strategy quickly reduces the power generation unit to 4.2 kW and introduces a battery charging current limiter. This model predefines a maximum safe charging current of 8 A (corresponding to the battery's internal thermal response model), resulting in a maximum charging power of 8 A × 51.2 V = 409.6 W. The system controls the battery to charge at only 409.6 W, preventing the generation unit from outputting excess power to prevent energy recirculation or overvoltage risks.

[0098] Finally, based on the results of real-time dynamic judgment, the system outputs the energy supply configuration plan for the current cycle: the output power of the power generation unit is 4.2 kW, and the battery charging power is 409.6 W, ensuring the stable operation of the bus while taking into account battery safety and efficient energy utilization.

[0099] Step S105: controlling the actual energy supply behavior of the power generation unit and the battery according to the output power of the power generation unit and the charge and discharge power of the battery.

[0100] In the comprehensive electric energy management and control method described in this invention, step S105 aims to effectively convert the power generation unit output power and battery charge / discharge power, determined in the previous step, into specific control instructions, driving the system's actual energy supply components to respond accurately, thereby completing the closed-loop transition from theoretical energy allocation to actual power supply behavior. The technical implementation of this step involves not only the generation and distribution of power control signals but also the real-time coordination and status feedback mechanism between the power generation unit and the battery management system. Its correct execution is crucial for stable system operation.

[0101] During actual execution, the controller first calls the power allocation data output from step S104, which contains the target output power value for the power generation unit and the charging or discharging power range that the battery should perform under the current operating conditions. Based on this value, the system determines the primary power supply path for the current operating mode: the power generation unit, the battery, or a combination of both. At this point, the controller generates matching control instructions based on the current operating status of each power supply channel and sends them to the power generation control module and battery management module, respectively.

[0102] Regarding the power generation unit, if the system structure consists of an internal combustion generator coupled with an electronically controlled inverter module, the controller must adjust the throttle opening or excitation control signal based on the power target value to synchronize the generator's output frequency, voltage, and current with the load demand. Furthermore, if the generator outputs power to the system via a DC bus, the output channel parameters of the inverter or DC-DC converter must be controlled to ensure voltage matching and controlled current limiting during energy transmission. Furthermore, when the generator power changes rapidly, current slope limiting logic should be implemented to prevent sudden output changes that could cause system oscillations.

[0103] In terms of battery energy supply, the controller transmits the target charging or discharging power to the battery management system (BMS). The BMS controls the conduction state of the battery according to the instructions, and combines its internal voltage, current, temperature, SOC and other parameters to determine whether it has the corresponding discharge or charging capabilities. If the conditions are met, the BMS controls the output interface on and off and the current level, so that the battery performs the corresponding energy exchange behavior according to the controller's requirements. If the current SOC is close to the upper and lower thresholds or there is a risk of overheating of the battery, the BMS has the right to feedback a "reject response" signal, requiring the controller to readjust the allocation plan and enter degraded operation or switch to the main supply mode of the power generation unit. This type of feedback mechanism should be set as a high-speed response channel to ensure that the system completes the path switching within milliseconds to avoid power interruption.

[0104] In a joint operation scenario where both the generator unit and the battery are involved in the energy supply, the controller should introduce power synchronization coordination logic. This logic dynamically adjusts the power ratio between the generator output and the battery output by synchronously comparing the current, voltage, and load coupling relationship between the two. It also fine-tunes the output on both sides based on the bus voltage fluctuation trend at the load end to avoid mutual interference or short-term reverse current. In addition, in this joint energy supply mode, it is recommended to use a master-slave control architecture, with the generator unit providing the reference power curve and the battery providing the dynamic compensation margin to achieve higher response speed and smaller bus voltage fluctuation range.

[0105] To ensure accurate control results, the controller should receive real-time feedback from the power generation and battery modules after the control command is issued, and continuously monitor key parameters such as output power, output voltage, and operating status. If correct feedback is not received within the specified time, or if the output power deviates from the set target and exceeds the warning range, the system should immediately enter the exception handling process, including power redistribution, standby generation unit switching, battery charge and discharge mode reversal, or temporary load reduction mechanisms, to ensure uninterrupted power supply and suppress bus voltage fluctuations.

[0106] In summary, the implementation of step S105 involves not only basic signal transmission and device response processes but also serves as a mechanism for ensuring the control system's execution of the overall power management strategy. This requires the controller to possess high-precision control algorithms, fast-response logic, interfacing with power supply hardware, and a comprehensive safety protection and exception handling system. Only with this foundation can the power generation unit and battery modules achieve multiple control objectives, including coordinated power supply, dynamic regulation, load matching, and system protection, under both normal and emergency conditions.

[0107] Furthermore, controlling the actual energy supply behavior of the power generation unit and the battery according to the output power of the power generation unit and the charge and discharge power of the battery includes: Receive the aforementioned determined power generation unit output power and battery charge and discharge power, and dynamically compare the sum of the two with the target bus power demand value to identify whether the current total energy supply meets the system's instantaneous power balance requirements, and generate an energy supply balance adjustment signal when insufficient or redundant power is detected; Based on the energy balance adjustment signal, the dynamic adjustment limit of the power generation unit under the current mechanical speed, fuel supply and ambient temperature conditions is preferentially calculated to form a set of power generation adjustment boundary intervals, which are then output for the next power adjustment judgment; Based on the battery's current SOC value, temperature, aging level, and current cycle rate, the preset maximum charge current and maximum discharge rate parameters are queried to generate a battery regulation capability description vector. This vector is then compared with the aforementioned power generation adjustment boundary interval to derive a feasible multi-strategy energy supply combination for this round of regulation. Based on the current load type, predicted power fluctuation trends, and energy utilization priority rules, the optimal strategy is selected from all feasible energy supply combinations. If short-term load fluctuations are predicted to be frequent, battery response is prioritized; if the load is stable over a long period, power generation units are prioritized to provide basic energy. This determines the target execution output values ​​of the power generation units and batteries for each control cycle. The target execution output values ​​of the power generation unit and the battery are converted into corresponding control instruction parameters, including speed setting, excitation regulation, PWM control, and current loop control, and are respectively sent to the power generation unit and battery management system through the execution control module to drive them to perform actual energy supply operations, thereby realizing dynamic energy collaborative supply under closed-loop control based on power demand.

[0108] In the integrated power management and control method described herein, the actual power supply behavior control process for the power generation units and batteries first requires receiving the target power output power of the power generation units and the target charge and discharge power of the batteries, determined by the system during the previous control cycle based on multiple factors, including target power demand, operating condition type, and battery SOC status. These two power values ​​constitute the system's initial total power supply plan for the current control cycle. Before actual power supply control is executed, this total power supply plan is compared and verified with the target bus power demand value on the load side. The system uses a high-speed sampling circuit and a real-time bus power monitoring module to obtain the instantaneous power demand of the current system bus. This value is then subtracted from the sum of the power of the power generation units and batteries. If the difference is within the acceptable range, the power supply plan can meet the current load balancing requirements and no further adjustments are required. If the difference deviates from the acceptable range, the system immediately generates an energy supply balance adjustment signal, indicating a risk of power shortage or redundancy, requiring adjustments to the power of the power generation units and batteries to maintain bus power stability.

[0109] After receiving the energy balance adjustment signal, the system determines whether the generator unit has room for adjustment. To do this, it reads the generator unit's current mechanical speed, fuel injection pulse width, air intake temperature, and ambient temperature in real time. These parameters are then fed into the generator unit's dynamic capability model to predict its limits. This model, built on a database of engine dynamic response curves and load dynamic characteristics, outputs the generator unit's maximum power increase and maximum power reduction capabilities under current conditions. For example, if the fuel injection pulse width remains unchanged while the external temperature rises, the generator unit's maximum power increase capability will be limited due to reduced cooling capacity. Therefore, the model appropriately reduces its upper adjustment limit to ensure the generator unit operates within a safe range. The output of this boundary model is expressed as upper and lower limits, forming the boundary range within which the generator unit can adjust its power during the current adjustment cycle.

[0110] In parallel with the power generation unit capacity boundary, the system also needs to evaluate the dynamic adjustment capability of the battery. To this end, the system will read the current SOC value of the battery, and in combination with real-time temperature information, battery aging assessment indicators (such as number of cycles, internal resistance change rate), and current rate discharge status, query the preset battery safety operating parameter table. This parameter table lists the maximum charging current, maximum discharge current and its duration corresponding to different SOC intervals at different temperatures and aging levels, and is corrected with a safe operation factor. Based on this, the system will construct a set of battery adjustable power range vectors, including key parameters such as the current maximum allowable discharge power, maximum charging power, and optimal working rate, and then jointly compare this vector with the aforementioned power boundary interval of the power generation unit. This comparison process is used to identify all possible energy supply combinations that can maintain supply and demand balance and do not exceed the adjustment capability of any energy supply component during this round of adjustment cycle.

[0111] After obtaining multiple feasible energy supply combinations, the system needs to optimize them based on the current load type and its power fluctuation trend. For example, when the prediction model determines that the load will show a trend of frequent and high-amplitude fluctuations in the next few control cycles (such as the start and stop of welding equipment, the activation of impact loads, etc.), a battery-based fast response strategy is preferred, so that the battery takes on the power transient regulation, while the power generation unit maintains stable operation to avoid mechanical loss caused by frequent load increases and decreases. If the load trend is predicted to be long-term stable, the power generation unit is preferentially dispatched to take on the basic power output, and the battery only participates in peak and valley regulation or energy recovery operations. The judgment of the load fluctuation trend is based on historical load data, real-time fluctuation amplitude indicators and prediction model output. The prediction model can be established based on technologies such as sliding window regression analysis and power spectrum density analysis, and has a high instantaneous judgment accuracy.

[0112] After selecting the optimal energy supply strategy, the system will assign target execution output values ​​for the power generation unit and battery within the current control cycle. For the power generation unit, this output value will be converted into specific control instructions such as the engine speed setpoint, fuel injection amount setpoint, cooling fan speed control value, and excitation current adjustment value. These control quantities will be sent to the power generation unit controller via the CAN bus or Modbus communication protocol in a standardized command format to achieve refined output control. For the battery, the target execution output value will be converted into control signals such as current command, voltage setting, current limit flag, and battery protection logic control switch status, and sent to the battery management system (BMS). The BMS will execute current loop control, voltage loop regulation, and overcurrent / overvoltage / undervoltage protection logic to ensure safe and efficient charging and discharging.

[0113] During the energy supply control process, the system also establishes a rapid feedback channel, using high-frequency sampling to feed back data such as the actual output power of the power generation unit, the actual battery charge and discharge current, bus voltage, and power error, to the energy control center for use in the next control cycle to adjust the prediction model and optimize strategy parameters, achieving closed-loop control. The entire process progresses in a rolling manner, with each round of execution ensuring a dynamic balance between power supply and demand while also taking into account component safety, energy efficiency, and response time. This results in a comprehensive power management strategy based on the identification of high and low load conditions and demand response.

[0114] Step S106: Based on the actual energy supply behavior, the system bus voltage and the battery state of charge (SOC) value are detected. If the bus voltage or the battery SOC value deviates from the set range, the power generation unit output power and the battery charge and discharge power are adjusted according to the deviation closed loop until the bus voltage and the battery SOC value return to the allowable range.

[0115] In implementing the comprehensive power energy management and control method of the present invention, the core purpose of step S106 is to establish a dynamic closed-loop regulation mechanism based on feedback from energy supply behavior, ensuring that the system bus voltage and battery state of charge (SOC) always operate stably within a preset safety range. Due to the combined interference of various factors during the operation of the energy supply system, such as load fluctuations, changes in battery status, and ambient temperature, the bus voltage and battery SOC may deviate from expectations within a short period of time. If not adjusted in a timely manner, this may lead to disrupted energy management, increased battery loss, and even system power supply interruption. Therefore, this step provides a strategy that coordinates real-time detection and feedback control to ensure that the system maintains the stability and safety of electrical parameters during operation.

[0116] Specifically, after the energy supply control command is issued and executed, the system collects the bus voltage in real time through voltage sensors. Simultaneously, the battery management system monitors the current SOC value and feeds these two key operating states back to the central control module. The bus voltage reflects whether the total power between the current power generation unit and the battery output meets the load demand. A rapid voltage rise may indicate excessive battery charging current or underload. A sharp voltage drop may be due to a sudden load increase or delayed power response. The SOC value is the core indicator for measuring the battery's energy storage status, and its changing trend directly determines the upper and lower limits of subsequent charge and discharge capacity allocation.

[0117] After acquiring the latest bus voltage and SOC values, the control module first compares them with the system's preset normal operating range. This range is typically determined by engineering experience and system characteristics. For example, the bus voltage can be set to allow fluctuations of ±5%, while the battery SOC range is generally between 20% and 90% to prevent overcharging or over-discharging that could reduce battery life or pose safety risks.

[0118] If the monitored value is within the set range, the system maintains its original control strategy and only continues monitoring as a warning. However, if any indicator is detected to be outside this range, the closed-loop regulation logic is triggered. This regulation process uses the deviation as input, determines whether the current deviation trend is upward or downward, and formulates a response strategy accordingly. If the bus voltage is too high and the battery SOC is close to the upper limit, the system prioritizes reducing the output power of the power generation unit and limiting the battery charging current. If the bus voltage is too low and the SOC is approaching the lower limit, the power generation output should be increased or the backup power supply should be activated. At the same time, the battery discharge rate should be controlled to reduce, and if necessary, even discharging should be suspended to prevent over-discharge.

[0119] In its closed-loop regulation strategy, the system employs a feedback mechanism with proportional gain, which determines the intensity of regulation based on the magnitude of the deviation. For example, if the bus voltage deviates slightly from the permitted range, the controller can simply fine-tune the generator output voltage or frequency to quickly stabilize it. If the deviation is severe, it initiates multi-parameter coordinated adjustments, including changing battery current limits, switching load priorities, and other multi-level measures, to achieve a faster and more stable return.

[0120] Furthermore, to improve system robustness and regulation accuracy, it is recommended that the control process incorporate hysteresis and jitter elimination mechanisms to prevent frequent triggering of regulation commands due to sampling fluctuations, which can lead to system oscillation. In practice, a minimum deviation threshold can be set, with regulation initiated only when the deviation exceeds this threshold for a sustained period of time. This ensures the system maintains fault tolerance and buffering capabilities even under minor disturbances.

[0121] After the bus voltage and battery SOC return to the allowable range, the controller should automatically mark the system operating status as "stable" and reinitialize the current round of monitoring and adjustment logic to enter the next cycle of closed-loop monitoring process of energy supply behavior. The entire step not only realizes the dynamic tracking and rapid response of the system power supply status, but also strengthens the safety guarantee mechanism in energy flow control. It is a key component of the present invention to achieve adaptive control of high and low load conditions. This technical path is applicable to many scenarios such as new energy vehicle power supplies, distributed microgrid power supply systems and mobile energy platforms, and has strong versatility and practicality.

[0122] Furthermore, based on the actual energy supply behavior, the system bus voltage and battery state of charge (SOC) value are detected. If the bus voltage or battery SOC value deviates from a set range, the power generation unit output power and battery charge and discharge power are adjusted in a closed loop according to the deviation until the bus voltage and battery SOC value return to an allowable range, including: During each control cycle, the system bus voltage and battery SOC values ​​are acquired in real time and compared with their respective dynamically set thresholds to determine whether there are any deviations beyond the range. The bus voltage threshold is dynamically set based on the current load type, grid structure, and target stability margin, while the battery SOC threshold is adjusted based on the current temperature, discharge rate, and cycle aging. If any indicator is found to have a deviation, the adjustment sequence is triggered. First, the preset deviation response mapping table is consulted to obtain the recommended priority adjustment resources and adjustment direction corresponding to the deviation amplitude. Then, combined with the power dynamic data in the current control cycle, a preliminary response strategy plan is constructed. Based on the preliminary response strategy plan, the actual adjustable power range of the power generation unit is calculated, and the current acceptable charge and discharge rate limit of the battery is simultaneously evaluated. These two sets of boundary parameters are used as input to generate multiple sets of combined adjustment configurations for selection. Among multiple configuration combinations, the system prioritizes energy utilization and prioritizes those that do not cause additional load disturbances, exceed thermal management standards, or increase battery lifespan loss. Ultimately, it determines a set of optimal regulation paths and issues adjustment command parameters to the power generation units and batteries accordingly. After the regulation path is executed, a high-speed feedback channel is established to continuously monitor the recovery progress of the bus voltage and battery SOC value, and dynamically adjust the regulation amplitude and control cycle rhythm until both are stabilized and return to the allowable range. The regulation process is terminated and enters the next regular control cycle.

[0123] In order to achieve fine-grained control of the operating status of the power system under high and low load conditions, the present invention proposes a dual closed-loop regulation mechanism of voltage and state of charge based on feedback from actual energy supply behavior. When the system bus voltage or battery state of charge (SOC) value deviates from the allowable operating range, it is used to dynamically adjust the output power of the power generation unit and the battery charge and discharge power, thereby achieving stable regression of the bus voltage and battery SOC value, and ensuring the overall operation safety and energy utilization efficiency of the system.

[0124] First, during each control cycle, the system uses a monitoring module to collect the current bus voltage and battery state-of-charge (SOC) values. The real-time bus voltage is typically collected via high-precision voltage sensors installed at both ends of the bus, while the SOC value is calculated by the battery management system (BMS) using a fusion method combining current integration, voltage estimation, and a historical calibration model. To ensure dynamic adaptability, the system dynamically generates reasonable threshold ranges for the bus voltage and battery SOC before performing a comparative judgment based on the current operating load type, grid structure stability requirements, battery temperature, cycle count, and other status information. For example, if the current load type is a motor-driven load, the allowable voltage fluctuation range should be appropriately expanded to buffer transient shocks. Under low-temperature conditions, the battery's charge and discharge capacity decreases, so the lower SOC limit should be appropriately increased to avoid the risk of over-discharge.

[0125] After completing the comparison of the voltage and SOC values ​​with the threshold range, if any indicator is found to have deviated, the activation of the adjustment sequence will be triggered immediately. After the adjustment sequence is started, the system will first call the preset deviation response mapping table, which records the recommended adjustment resource allocation scheme, response strategy priority and power adjustment direction corresponding to different deviation types and amplitudes. The construction of the deviation response mapping table is based on long-term operating data and expert experience rules, and can be updated according to the online operation status of the system. The system combines the power dynamic behavior recorded in the current control cycle, including the load power change trend, the current energy supply combination mode, energy utilization efficiency, etc., to generate a set of preliminary response strategy plans. The plan will clearly indicate whether to prioritize the adjustment of the power generation unit or battery output power, the initial value of the adjustment amplitude and the expected adjustment duration.

[0126] Next, based on the above preliminary strategic plan, the system calculates the adjustable capacity ranges of the power generation unit and the battery respectively. For the power generation unit, its maximum output power limit is affected by the current mechanical speed, fuel supply rate and cooling system capacity. The system reads these operating data in real time and calculates the upper and lower limits of the power generation power in combination with the equipment performance curve model. For example, when the ambient temperature is high, the maximum power output limit of the power generation unit will automatically be reduced to avoid overheating. At the same time, the battery's charge and discharge capacity will also be comprehensively evaluated based on multiple indicators such as the current SOC value, temperature status, current rate and historical cycle count. The system combines the parameter table provided by the battery manufacturer with its own charge and discharge capacity calculation model to determine the currently acceptable upper limit of the charging current and the discharge rate boundary, forming a description vector of the battery's adjustment capacity.

[0127] The system then compares the power generation unit's adjustment interval with the battery's regulation capability vector, deriving a set of multi-strategy regulation configuration combinations based on this. Each combination contains a set of possible power generation unit output power and battery charge and discharge power allocation schemes that meet their respective boundary constraints. To select the optimal strategy among these schemes, the system introduces an energy utilization priority sorting rule, which dynamically assigns weights based on system objectives (such as maximum efficiency, minimum loss, and minimum disturbance). For example, when power demand is predicted to fluctuate frequently in the short term, the system will give priority to batteries to take on the main regulation tasks to reduce the mechanical losses caused by the frequent speed changes of the power generation unit; when the power load is expected to operate stably for a long time, the system will give priority to using the power generation unit to provide basic power to extend the battery life and reduce its deep cycle number.

[0128] After the optimal regulation strategy is selected, the system converts the target power of the power generation unit and the target battery charge and discharge power defined in the strategy into specific control command parameters, which are then sent to the power generation control unit and battery management system via control interfaces. For the power generation unit, these control commands typically include parameters such as the excitation current setpoint, fuel injection rate, fan speed, and generator voltage setpoint; for the battery, these include information such as the target charge and discharge current, maximum allowable voltage, and current regulation slope. These parameters are then applied to the specific execution units through PWM control, voltage and current loop regulation, and analog-to-digital conversion interfaces.

[0129] After the adjustment instruction is issued, the system will not stop its monitoring behavior, but will continue to monitor the changes in bus voltage and SOC value through the high-speed feedback channel, and compare it with the set recovery target, and continuously evaluate whether the adjustment effect meets the expectations during the adjustment process. If it is found that the recovery trend is not obvious or the adjustment process causes new offsets in other parameters, the system will immediately trigger the next round of adjustment path calculation, update the adjustment amplitude or adjust the target value. For example, when the power generation unit has reached the output limit and the bus voltage has not returned, the system will try to further compress the battery discharge limit or temporarily reduce some non-critical loads. The entire closed-loop adjustment will be officially terminated after the bus voltage and SOC value return to the allowable range, and the system status returns to the normal scheduling cycle.

[0130] The regulation method of the present invention also possesses a certain degree of self-learning capability. In actual operation, the system records the response effect of each regulation sequence, analyzes the correlation between the regulation scheme and the regulation results, and gradually optimizes the deviation response mapping table and energy priority sorting rules to form an adaptive regulation knowledge base for different operating conditions. Through this mechanism, the system can gradually improve response speed, reduce the risk of over-regulation, and enhance its adaptability to complex operating conditions.

[0131] A second embodiment of the present application provides an electronic device, comprising: processor; The memory is used to store a program. When the program is read and executed by the processor, it executes an electric energy comprehensive management and control method for high and low load conditions provided in the first embodiment of the present application.

[0132] The third embodiment of the present application provides a computer-readable storage medium having a computer program stored thereon. When the program is executed by a processor, the program executes an electric energy comprehensive management and control method for high and low load conditions provided in the first embodiment of the present application.

[0133] Although the present application is disclosed as above with the preferred embodiments, it is not intended to limit the present application. Any person skilled in the art may make possible changes and modifications without departing from the spirit and scope of the present application. Therefore, the scope of protection of the present application shall be based on the scope defined by the claims of the present application.

Claims

1. A comprehensive power management and control method for high and low load conditions, characterized in that: include: Obtain the current operating information of the energy supply system, including operating mode information, target power demand, and current battery state of charge (SOC) value; Determining the current operating condition category based on the current operating information; Calculate the corresponding total system power demand value according to the operating condition category and the target power demand; The output power of the power generation unit and the battery charge and discharge power are determined based on the battery state of charge (SOC) value and the total power demand of the system. In normal operating conditions, the output power of the power generation unit is determined to meet the basic load demand based on a preset SOC threshold range, and the remaining power is balanced by the battery charge and discharge within the SOC range. In emergency operating conditions, when the load suddenly increases, the battery is controlled to discharge at the maximum discharge rate. When the load suddenly decreases, the output power of the power generation unit is controlled to decrease rapidly, and the battery charging current is adjusted to avoid exceeding the maximum charging current that the battery can withstand. Controlling the actual energy supply behavior of the power generation unit and the battery according to the output power of the power generation unit and the charge and discharge power of the battery; Based on the actual energy supply behavior, the system bus voltage and the battery state of charge (SOC) value are detected. If the bus voltage or the battery SOC value deviates from the set range, the power generation unit output power and the battery charge and discharge power are adjusted in a closed loop according to the deviation until the bus voltage and the battery SOC value return to the allowable range.

2. The electric energy comprehensive management and control method for high and low load conditions according to claim 1 is characterized in that: The operating condition categories include normal operating conditions and emergency operating conditions, wherein the normal operating conditions include parking charging, silent driving, land charging, vertical take-off and landing, and cruise flight, and the emergency operating conditions include emergency braking, high-power maneuvering, fan damage, and emergency obstacle crossing.

3. The electric energy comprehensive management and control method for high and low load conditions according to claim 1 is characterized in that: The obtaining of the current operating information of the energy supply system includes: Based on the task instructions or operation control instructions received by the energy supply system, the system analyzes the implicit operation phase, driving status, and environmental adaptation strategy, identifies the corresponding operating condition labels, and extracts and generates the current operating mode information; After obtaining the working condition mode information, the reference power data corresponding to the working condition tag in the historical load model and environmental database is retrieved, and combined with the real-time measured load current and voltage change trends, the target power demand required at the current moment is deduced and output; After the target power demand is determined, the battery voltage, current and temperature data fed back by the battery management system are read in real time. The remaining battery capacity is estimated using the Coulomb integration method corrected by temperature compensation, and the current battery state of charge (SOC) value is calculated and output. Based on the current target power demand and SOC value, determine whether the battery can meet the energy supply demand without exceeding the safe operating boundary, and compare the judgment result with the aforementioned operating mode information to confirm whether there is a risk of power supply and demand mismatch.

4. The electric energy comprehensive management and control method for high and low load conditions according to claim 1 is characterized in that: The determining of the current operating condition category based on the current operating information includes: Based on the operation target semantic fields, path planning labels and dynamic environment intervention items extracted from the operation mode information, a task context semantic vector is constructed and mapped to a multidimensional operation condition label embedding space; In the multi-dimensional operating condition label embedding space, a dynamic clustering algorithm is used to identify the closest historical operating condition cluster center, and combined with the current target power demand change trend, a preliminary operating condition classification result and a corresponding credibility score are output; Based on the preliminary operating condition classification results, the dynamic energy consumption curve family and the charge safety limit parameter group associated therewith are called to construct a safety mapping space of the battery state of charge (SOC) value, and the current SOC value position is combined with the determination of whether it is in the controllable load window; If it is determined to be at the edge of the load window, the real-time environmental disturbance index is integrated and matched with the historical working condition anti-disturbance model, the original working condition classification result is corrected and the working condition category priority is reordered, wherein the real-time environmental disturbance index includes sudden change in wind speed, sudden drop in temperature, and electromagnetic interference; The operating condition category corresponding to the highest confidence level in the comprehensive matching results is determined as the current operating condition category.

5. The electric energy comprehensive management and control method for high and low load conditions according to claim 1 is characterized in that: The calculating of the corresponding total system power demand value according to the operating condition category and the target power demand includes: Combined with the current operating condition category, a power demand record sequence matching the operating condition is extracted from historical data. Based on the power value changes in each period in the record, the average power level, maximum power surge amplitude, and power fluctuation duration at different stages are statistically analyzed to form a basic load characteristic description. Based on the task type, execution phase, and running time under the current working conditions, the target power demand is subdivided into multiple continuous time periods, and a corresponding load level is assigned to each time period. Then, the load level is matched with the corresponding power parameters in the basic load characteristic description to determine the preliminary target power demand sequence; Real-time collection of influencing factors related to the current operating environment, including wind speed, temperature, humidity, terrain undulation, and obstacle density. Based on the impact of different environmental factors on power consumption, the power value of each period in the preliminary target power demand sequence is adjusted step by step to obtain a target power sequence that includes environmental correction factors. Compare the current battery state of charge with the power buffer to assess whether the battery has the ability to handle a sudden load surge or release part of the base load. If the conditions are met, a corresponding load transfer correction value is introduced into the target power sequence to match the target power with the battery's response capability. The adjusted target power values ​​for each time period are combined with the basic power demand to form a set of final system total power demand values ​​in chronological order, which are used for the subsequent execution of power allocation and energy control strategies, so that the calculation results can fully reflect the comprehensive effects of working condition history, mission characteristics, environmental impact and battery capacity.

6. The electric energy comprehensive management and control method for high and low load conditions according to claim 1 is characterized in that: The determining of the output power of the power generation unit and the charge and discharge power of the battery according to the battery state of charge (SOC) value and the total power demand value of the system includes: According to the historical basic load model and current operating mode information corresponding to the operating condition label, the target power demand value of the system at the current moment is extracted, and the basic power part of the demand value is preliminarily set as the power output target of the power generation unit; Read the current SOC value of the battery and compare it with the preset SOC threshold range to determine whether it is in the charging zone, discharging zone or balance maintenance zone, and accordingly form an acceptable battery charge and discharge power adjustment range; If the current operating condition is normal, then on the premise of meeting the basic load demand, the remaining power is allocated to the battery for corresponding charging or discharging operations based on the interval position of the SOC value and the load change trend, and the combined allocation result of the power generation unit output power and the battery charging and discharging power is generated; If the current operating condition is an emergency condition and the power demand shows a sudden increase, the battery discharge power is set to the current maximum allowable value, subject to the constraint that it does not exceed the maximum rate discharge capability of the battery. The power of the power generation unit is temporarily maintained unchanged or slightly increased to stabilize the bus voltage. If a sudden drop in load is detected, the output power of the power generation unit will be quickly reduced, and the battery charging current limit model will be called up to set the battery charging power value without exceeding the maximum safe charging current of the battery. Finally, based on various judgment conditions, the target output power value of the power generation unit and the battery charging and discharging power value at the current moment will be output.

7. The electric energy comprehensive management and control method for high and low load conditions according to claim 1 is characterized in that: The controlling of the actual energy supply behavior of the power generation unit and the battery according to the output power of the power generation unit and the charge and discharge power of the battery includes: Receive the aforementioned determined power generation unit output power and battery charge and discharge power, and dynamically compare the sum of the two with the target bus power demand value to identify whether the current total energy supply meets the system's instantaneous power balance requirements, and generate an energy supply balance adjustment signal when insufficient or redundant power is detected; Based on the energy balance adjustment signal, the dynamic adjustment limit of the power generation unit under the current mechanical speed, fuel supply and ambient temperature conditions is preferentially calculated to form a set of power generation adjustment boundary intervals, which are then output for the next power adjustment judgment; Based on the battery's current SOC value, temperature, aging level, and current cycle rate, the preset maximum charge current and maximum discharge rate parameters are queried to generate a battery regulation capability description vector. This vector is then compared with the aforementioned power generation adjustment boundary interval to derive a feasible multi-strategy energy supply combination for this round of regulation. Based on the current load type, predicted power fluctuation trends, and energy utilization priority rules, the optimal strategy is selected from all feasible energy supply combinations. If short-term load fluctuations are predicted to be frequent, battery response is prioritized; if the load is stable over a long period, power generation units are prioritized to provide basic energy. This determines the target execution output values ​​of the power generation units and batteries for each control cycle. The target execution output values ​​of the power generation unit and the battery are converted into corresponding control instruction parameters, including speed setting, excitation regulation, PWM control, and current loop control, and are respectively sent to the power generation unit and battery management system through the execution control module to drive them to perform actual energy supply operations, thereby realizing dynamic energy collaborative supply under closed-loop control based on power demand.

8. The electric energy comprehensive management and control method for high and low load conditions according to claim 1 is characterized in that: The system bus voltage and battery state of charge (SOC) value are detected based on the actual energy supply behavior. If the bus voltage or battery SOC value deviates from a set range, the power generation unit output power and battery charge and discharge power are adjusted in a closed loop according to the deviation until the bus voltage and battery SOC value return to an allowable range, including: During each control cycle, the system bus voltage and battery SOC values ​​are acquired in real time and compared with their respective dynamically set thresholds to determine whether there are any deviations beyond the range. The bus voltage threshold is dynamically set based on the current load type, grid structure, and target stability margin, while the battery SOC threshold is adjusted based on the current temperature, discharge rate, and cycle aging. If any indicator is found to have a deviation, the adjustment sequence is triggered. First, the preset deviation response mapping table is consulted to obtain the recommended priority adjustment resources and adjustment direction corresponding to the deviation amplitude. Then, combined with the power dynamic data in the current control cycle, a preliminary response strategy plan is constructed. Based on the preliminary response strategy plan, the actual adjustable power range of the power generation unit is calculated, and the current acceptable charge and discharge rate limit of the battery is simultaneously evaluated. These two sets of boundary parameters are used as input to generate multiple sets of combined adjustment configurations for selection. Among multiple configuration combinations, the system prioritizes energy utilization and prioritizes those that do not cause additional load disturbances, exceed thermal management standards, or increase battery lifespan loss. Ultimately, it determines a set of optimal regulation paths and issues adjustment command parameters to the power generation units and batteries accordingly. After the regulation path is executed, a high-speed feedback channel is established to continuously monitor the recovery progress of the bus voltage and battery SOC value, and dynamically adjust the regulation amplitude and control cycle rhythm until both are stabilized and return to the allowable range. The regulation process is terminated and enters the next regular control cycle.

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