A new energy emergency power generation vehicle control method and emergency power generation vehicle

Abstract

The application discloses a new energy emergency power generation vehicle control method and an emergency power generation vehicle, and relates to the technical field of new energy energy-saving, which comprises the following steps: receiving real-time load power and storing the same in a time sequence buffer area; calculating a dynamic load fluctuation potential index based on historical load power data in the time sequence buffer area; obtaining the current state of charge of an energy storage device in the new energy emergency power generation vehicle and real-time load power; determining a current operation mode in a preset three-dimensional decision space according to the current state of charge, the real-time load power and the dynamic load fluctuation potential index, and calculating a dynamic target state of charge; and in the case that the current operation mode is an active pre-charging mode, controlling a range extender of the new energy emergency power generation vehicle to improve the state of charge of the energy storage device to the dynamic target state of charge. The application can effectively reduce the energy consumption of the new energy emergency power generation vehicle when facing a sudden large load, and effectively reduce the risk of power supply interruption.

Description

Technical Field

[0001] This application relates to the field of new energy and energy-saving technology, and in particular to a control method for a new energy emergency power generation vehicle and an emergency power generation vehicle. Background Technology

[0002] With the increasing frequency of extreme weather events, natural disasters, and various public emergencies worldwide, emergency power supply has become an indispensable and crucial component of modern social security systems. New energy emergency power generation vehicles, as mobile emergency power supply equipment, play a vital role in disaster relief efforts such as earthquakes, floods, and typhoons, as well as in providing support for major events, temporary engineering projects, and power supply to remote areas. Compared to traditional fuel-powered generators, new energy emergency power generation vehicles offer significant advantages such as low noise, zero emissions, and rapid response, and have gradually become the mainstream choice in the field of emergency power supply.

[0003] New energy emergency power generation vehicles typically employ a hybrid power supply architecture of "energy storage device + range extender." The energy storage device (such as a lithium battery pack) serves as the primary power source, responsible for meeting daily load demands; the range extender (such as a diesel generator or gas turbine) acts as a backup power source, activating when the energy storage device's power is insufficient to provide continuous power replenishment to the system. The core of this architecture design lies in achieving coordinated operation of the two power sources through intelligent control strategies, ensuring stable and reliable power output under various operating conditions.

[0004] Traditional new energy emergency power generation vehicles generally adopt a reactive control strategy based on state-of-charge (SOC) thresholds. The basic principle of this strategy is to set an upper SOC threshold (e.g., 90%) and a lower SOC threshold (e.g., 20%) for the energy storage device. When the SOC drops to the lower threshold, the control system automatically activates the range extender to charge the device; when the SOC rises to the upper threshold, the range extender is shut down, and the system re-enters pure energy storage power supply mode. This control strategy is characterized by its simple logic and ease of implementation, and it can maintain basic power supply functions in application scenarios with relatively stable loads.

[0005] However, in real-world emergency power supply scenarios, load characteristics often exhibit intermittent, sudden, and highly volatile features. For example, at disaster relief sites, control systems may maintain low-power monitoring equipment operation for extended periods, but when high-power drainage pumps, demolition equipment, or lighting systems need to be activated, the load power can increase dramatically in a short period, sometimes reaching several times the rated power. In medical emergency response, the start-up and shutdown of life support equipment and the intermittent operation of imaging diagnostic equipment also cause significant load fluctuations. These complex and variable load patterns pose a severe challenge to the control strategies of emergency power generation vehicles.

[0006] Traditional reactive control strategies exhibit serious technical flaws when faced with sudden large loads. When the system is under low load or no-load conditions for an extended period, the control system is often in a relatively stable dormant mode, the energy storage device's state of charge (SOC) may remain at a moderate level, and the range extender is off. If a high-power load is suddenly applied at this time, the energy storage device must withstand a sudden large current discharge, causing a sharp drop in terminal voltage. In severe cases, the voltage drop may trigger the system's undervoltage protection mechanism, causing a power outage and directly threatening the normal operation of critical loads.

[0007] Even if the system can withstand the initial voltage surge, the range extender requires a considerable amount of time to start up and ramp up its power output. From receiving the start command to reaching rated power output, the range extender typically goes through multiple stages, including cold start, preheating, synchronization, grid connection, and power ramp-up, a process that can last tens of seconds or even minutes. During this period, the significant power shortfall is entirely borne by the energy storage device, resulting not only in high-rate battery discharge losses but also potentially permanent damage to battery performance due to over-discharge. Simultaneously, the fuel consumption and emissions levels of the range extender during cold start and low-load operation are far higher than during steady-state operation, leading to significant energy waste.

[0008] In summary, traditional reactive control strategies based on SOC thresholds have technical drawbacks when faced with sudden large loads, including not only huge energy consumption but also the potential for power outages. Summary of the Invention

[0009] This application provides a control method and an emergency power generation vehicle for new energy vehicles, which can effectively reduce the energy consumption of new energy emergency power generation vehicles when facing sudden large loads, and effectively reduce the risk of power outages.

[0010] To achieve the above objectives, the embodiments of this application adopt the following technical solutions:

[0011] Firstly, a control method for a new energy emergency power generation vehicle is provided, the method comprising:

[0012] In response to receiving the real-time load power output from the new energy emergency power generation vehicle, the real-time load power is stored in the time series buffer.

[0013] Based on historical load power data in the time series buffer, a dynamic load fluctuation potential index is calculated to characterize the probability of future load abrupt changes.

[0014] Obtain the current state of charge and real-time load power of the energy storage device inside the new energy emergency power generation vehicle;

[0015] Based on the current state of charge, real-time load power, and dynamic load fluctuation potential index, the current operating mode is determined in the preset three-dimensional decision space, and the dynamic target state of charge is calculated.

[0016] Under the current operating mode of active pre-charging mode, the range extender of the new energy emergency power generation vehicle is controlled to raise the state of charge of the energy storage device to the dynamic target state of charge.

[0017] In one possible implementation of the first aspect, a dynamic load fluctuation potential index, characterizing the probability of future load abrupt changes, is calculated based on historical load power data within a time-series buffer, including:

[0018] Obtain the load power data of the N most recent sampling points in the time series buffer, where N is a positive integer;

[0019] Based on N load power data, calculate the power variance component, the first-order rate of change component, and the second-order rate of change component respectively.

[0020] The power variance component, the first-order rate of change component, and the second-order rate of change component are weighted and summed according to the preset weighting coefficients to obtain the dynamic load fluctuation potential index.

[0021] In another possible implementation of the first aspect, based on N load power data, the power variance component, the first-order rate of change component, and the second-order rate of change component are calculated respectively, including:

[0022] Calculate the average value of N load power data points;

[0023] The difference between each load power data point and the average value is squared, and the average of the N squared values ​​is calculated to obtain the power variance component.

[0024] Calculate the absolute value of the difference between adjacent load power data, and average the absolute values ​​to obtain the first-order rate of change component;

[0025] Calculate the absolute value of the difference between adjacent first-order differences, and average the absolute values ​​to obtain the second-order rate of change component.

[0026] In another possible implementation of the first aspect, the current operating mode is determined in a preset three-dimensional decision space based on the current state of charge, real-time load power, and dynamic load fluctuation potential index, including:

[0027] The current state of charge is divided into three ranges: low state of charge, medium state of charge, and high state of charge.

[0028] The real-time load power is divided into low-power range, medium-power range and high-power range;

[0029] The dynamic load fluctuation potential index is divided into low fluctuation potential range, medium fluctuation potential range and high fluctuation potential range.

[0030] Based on the coordinate positions of the current state of charge, real-time load power, and dynamic load fluctuation potential index in the three-dimensional decision space, the corresponding operating mode is obtained by querying the preset decision matrix. When the coordinate position is in the medium state of charge range, low power range, or high fluctuation potential range, the operating mode is determined to be the active pre-charging mode.

[0031] In another possible implementation of the first aspect, the dynamic target state of charge is calculated, including:

[0032] When the current operating mode is active pre-charging mode, the preset basic safe state of charge value is obtained as the reference value;

[0033] The dynamic load fluctuation potential index is compared with the preset fluctuation potential upper limit value, and the smaller value between the two is taken as the adjustment value.

[0034] Multiply the adjustment value by the preset gain coefficient to obtain the state of charge increment;

[0035] The dynamic target state of charge is obtained by adding the baseline value to the state of charge increment.

[0036] In another possible implementation of the first aspect, when the current operating mode is active pre-charging mode, the range extender of the new energy emergency power generation vehicle is controlled to raise the state of charge of the energy storage device to a dynamic target state of charge, including:

[0037] Under the current operating mode of active pre-charging mode, obtain the output power corresponding to the optimal efficiency point of the range extender;

[0038] Send a start command to the range extender to control the range extender to start according to the preset soft start power curve and operate stably at the optimal efficiency point to output power;

[0039] The target charging power is calculated by subtracting the real-time load power from the output power at the optimal efficiency point.

[0040] Send charging commands to the energy storage device management system to control the energy storage device to charge at the target charging power;

[0041] The actual state of charge of the energy storage device is continuously monitored, and the range extender is shut down when the actual state of charge reaches the dynamic target state of charge.

[0042] In another possible implementation of the first aspect, the method also includes:

[0043] During the continuous monitoring of the actual state of charge of the energy storage device, the dynamic load fluctuation potential index is updated in real time.

[0044] If the dynamic load fluctuation potential index changes by a preset amplitude, the step of determining the current operating mode in the preset three-dimensional decision space is re-executed.

[0045] If the operating mode changes, the current charging process is interrupted, and the control strategy corresponding to the new operating mode is executed.

[0046] In another possible implementation of the first aspect, the method also includes:

[0047] Monitor whether the real-time load power output of the new energy emergency power generation vehicle exceeds the output power of the range extender at its optimal efficiency point;

[0048] When the real-time load power exceeds the output power of the range extender at its optimal efficiency point, the operating mode is determined to be hybrid power assist mode.

[0049] In hybrid power assist mode, the range extender is controlled to operate at the optimal efficiency point to output power, while the energy storage device is controlled to discharge to make up for the power difference.

[0050] Subtracting the range extender's optimal efficiency point output power from the real-time load power yields the required discharge power for the energy storage device.

[0051] Send a discharge command to the energy storage device management system to control the energy storage device to discharge according to the required discharge power.

[0052] Secondly, this application provides a new energy emergency power generation vehicle, comprising:

[0053] Energy storage devices;

[0054] Range extender;

[0055] The power acquisition unit is used to acquire the real-time load power output by the new energy emergency power generation vehicle;

[0056] Energy storage device management system, used to monitor the state of charge of energy storage devices and control the charging and discharging of energy storage devices; and

[0057] The controller is connected to the energy storage device, the range extender, the energy storage device management system, and the power acquisition unit.

[0058] The controller is configured to execute the above-described new energy emergency power generation vehicle control method.

[0059] Thirdly, this application provides a controller, including:

[0060] The memory is configured to store instructions; and

[0061] The processor is configured to retrieve the instructions from the memory and, when executing the instructions, to implement the aforementioned new energy emergency power generation vehicle control method.

[0062] By introducing a dynamic load fluctuation potential index and an active pre-charging mode, the range extender can be pre-started and the SOC level of the energy storage device can be increased before sudden load changes occur. When a sudden large load is connected, the energy storage device is in a high state of charge, providing sufficient instantaneous power output, effectively avoiding the risk of voltage drop and power outage caused by insufficient energy storage device power in traditional solutions. At the same time, predictive control avoids frequent cold starts of the range extender, significantly reducing high fuel consumption during the start-up phase. The range extender can operate under relatively stable conditions during the pre-charging phase, avoiding inefficient operation under sudden loads. The pre-charging strategy also reduces the frequency of high-rate discharge of the energy storage device, extending battery life and significantly reducing energy consumption and operating costs. The calculation of the dynamic target state of charge considers the current load, historical load patterns, and future load fluctuation trends, ensuring that the system always maintains the most suitable energy reserve level, ensuring stable power output under various complex operating conditions, which is particularly suitable for medical rescue and communication support. In emergency scenarios with extremely high requirements for power supply continuity, such as power outages, this approach improves power supply reliability and stability. The introduction of a three-dimensional decision space (state of charge, real-time load power, and dynamic load fluctuation potential) allows for adaptive adjustment of control strategies based on the actual operating environment. Compared to traditional fixed threshold control, this scheme can more accurately match the power supply demand characteristics of different emergency scenarios, achieving intelligent adaptive control. By reducing deep discharge of energy storage devices and frequent start-stop of range extenders, the operating conditions of key components are significantly improved, extending the overall service life of equipment and reducing maintenance costs and failure rates. In time-sensitive emergency scenarios such as disaster relief, this control method ensures the immediate availability of high-power rescue equipment, avoiding delays in rescue opportunities due to power supply system response lags, and improving emergency response efficiency, thus possessing significant social value. In summary, through predictive control and dynamic energy management, this approach fundamentally solves the technical defects of traditional reactive control strategies when facing sudden large loads, achieving a comprehensive improvement in energy efficiency, power supply reliability, and equipment lifespan.

[0063] Other features and advantages of the embodiments of this application will be described in detail in the following detailed description section. Attached Figure Description

[0064] Figure 1 A flowchart illustrating a control method for a new energy emergency power generation vehicle provided in this application embodiment;

[0065] Figure 2 A schematic diagram of a time-series buffer data structure provided in an embodiment of this application;

[0066] Figure 3 This is a structural schematic diagram of a new energy emergency power generation vehicle provided in an embodiment of this application. Detailed Implementation

[0067] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are only for illustration and explanation of the embodiments of this application and are not intended to limit the embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0068] It should be noted that if the embodiments of this application involve directional indicators (such as up, down, left, right, front, back, etc.), the directional indicators are only used to explain the relative positional relationship and movement of each component in a certain specific posture (as shown in the figure). If the specific posture changes, the directional indicators will also change accordingly.

[0069] Furthermore, if the embodiments of this application involve descriptions such as "first" or "second," these descriptions are for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Therefore, features defined with "first" or "second" may explicitly or implicitly include at least one of those features. Additionally, the technical solutions of various embodiments can be combined with each other, but this must be based on the ability of those skilled in the art to implement them. If the combination of technical solutions is contradictory or impossible to implement, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection claimed in this application.

[0070] Figure 1 The illustration shows a schematic flowchart of a new energy emergency power generation vehicle control method according to an embodiment of this application. Figure 1 As shown in the figure, this application provides a control method for a new energy emergency power generation vehicle, which may include the following steps.

[0071] S110. In response to receiving the real-time load power output from the new energy emergency power generation vehicle, store the real-time load power in the time series buffer.

[0072] S120. Based on historical load power data in the time series buffer, a dynamic load fluctuation potential index is calculated to characterize the possibility of future load abrupt changes.

[0073] S130. Obtain the current state of charge and real-time load power of the energy storage device inside the new energy emergency power generation vehicle;

[0074] S140. Based on the current state of charge, real-time load power, and dynamic load fluctuation potential index, determine the current operating mode in the preset three-dimensional decision space, and calculate the dynamic target state of charge.

[0075] S150. When the current operating mode is active pre-charging mode, control the range extender of the new energy emergency power generation vehicle to raise the state of charge of the energy storage device to the dynamic target state of charge.

[0076] Upon receiving the real-time load power output from the new energy emergency power generation vehicle, the controller monitors the power supplied by the vehicle to the external load in real time through the power acquisition unit. In specific implementation, the power acquisition unit includes a high-precision current sensor and a voltage sensor. The current sensor uses the Hall effect or electromagnetic induction principle to measure the current value in the output circuit in real time. The voltage sensor uses a differential amplifier circuit design to monitor the output voltage in real time, and the instantaneous power value is calculated by a microprocessor. To filter out high-frequency noise interference, a moving average filtering algorithm is used in the power calculation stage, averaging the power values ​​of the most recent 10 sampling points. The time series buffer adopts a circular buffer data structure, such as... Figure 2 As shown, the preset storage capacity can be set to multiple data points, for example, 1000 data points. Each data point contains a timestamp and a corresponding power value. When the buffer is full, the oldest data is overwritten using a first-in, first-out (FIFO) principle. For example, when a medical rescue device suddenly starts from standby (200W) to full power operation (5000W), the power acquisition unit can detect the power jump within 1 millisecond and store the data point containing the timestamp and power value "5000W" in the buffer index position. By establishing a complete historical power database, a data foundation is laid for subsequent load pattern analysis and predictive control, ensuring the accuracy and timeliness of control decisions.

[0077] Based on historical load power data in the time-series buffer, a dynamic load fluctuation potential index is calculated. This index quantifies the probability of a sudden load change in the future by comprehensively evaluating three dimensions: the magnitude, rate, and acceleration of power changes.

[0078] The specific calculation process first extracts the load power data from the most recent N sampling points from the buffer. The value of N is set according to the application scenario, generally ranging from 50 to 200 sampling points. The formula for calculating the power variance component is: ,in For the i-th power sample value, Let N be the average power across N sampling points. The formula for calculating the first-order rate of change component is: This reflects the average rate of change in power. The formula for calculating the second-order rate of change component is: This reflects the acceleration of power change trends. The final dynamic load fluctuation potential index is obtained through weighted summation: The weighting coefficient , , This data is derived from statistical optimization based on a large amount of measured data. For example, at disaster relief sites, just before drainage equipment is activated, although the current load power remains at a low level, preparatory operations by operators (such as equipment preheating and system checks) can cause small power fluctuations. At this time, the dynamic load fluctuation potential index will rise significantly, providing timely warnings of potential large load surges. Through this multi-dimensional quantitative assessment of load fluctuation, signs of sudden load changes can be identified in advance, providing a scientific basis for predictive control strategies.

[0079] The system acquires the current state of charge (SOC) and real-time load power of the energy storage device within the new energy emergency power generation vehicle, providing crucial status information for operational mode decisions. The SOC of the energy storage device is obtained through the battery management system (BMS), which employs a hybrid algorithm combining coulomb integration and open-circuit voltage correction to calculate the remaining percentage of battery capacity in real time.

[0080] In practice, the BMS continuously monitors the charging and discharging current of the battery pack, acquiring real-time current values ​​through a high-precision current sensor (accuracy ±0.05%) and calculating the cumulative charge change by integrating the current over time. Simultaneously, the BMS measures the open-circuit voltage when the battery is at rest, correcting the state of charge using a pre-calibrated SOC-OCV curve to eliminate accumulated errors from coulomb integration. To improve SOC estimation accuracy, the BMS also integrates a temperature compensation algorithm, correcting the SOC estimation results based on battery temperature. The temperature sensor uses a high-precision thermistor with a measurement accuracy of ±0.1℃. The real-time load power acquisition reuses the measurement results from the power acquisition unit in step S110 to ensure data consistency and synchronization. In a specific embodiment, when the energy storage device is a 500kWh lithium iron phosphate battery pack, the BMS updates the SOC value every second; when the external load is a 3kW communication device, the power acquisition unit outputs the status information "SOC: 65%, Load Power: 3000W" in real time. This status information provides a reliable data foundation for subsequent three-dimensional decision space analysis, ensuring that the control strategy can accurately respond to the current operating conditions and avoid malfunctions or control delays caused by inaccurate status information.

[0081] Based on three key parameters—current state of charge (SBC), real-time load power, and dynamic load fluctuation potential index—the current operating mode is determined within a pre-defined three-dimensional decision space, and the dynamic target SBC that best matches the current operating conditions is calculated. The construction of the three-dimensional decision space first involves dividing the three input parameters into intervals: SBC is divided into low charge interval (0%–30%), medium charge interval (30%–70%), and high charge interval (70%–100%); real-time load power is standardized according to the range extender's rated power and divided into low power interval (0–0.3 times rated power), medium power interval (0.3–0.7 times rated power), and high power interval (0.7–1 times rated power); and the dynamic load fluctuation potential index is divided based on historical statistical data into low fluctuation potential interval (0–0.3), medium fluctuation potential interval (0.3–0.7), and high fluctuation potential interval (0.7–1.0). This interval division discretizes the continuous parameter space into 27 decision units, each corresponding to a specific operating mode. The decision matrix is ​​optimized based on extensive simulation analysis and measured data. For example, when the coordinate position is (medium state of charge range, low power range, high potential fluctuation range), it indicates that the energy storage device has a moderate power capacity and a light current load, but a sudden load change may occur in the future. In this case, the decision matrix outputs "active pre-charging mode". The calculation of the dynamic target state of charge adopts an adaptive algorithm: First, the basic safe state of charge value (usually set to 80%) is obtained. Then, the current dynamic load potential fluctuation index is compared with the preset upper limit value (usually 1.0), and the smaller value is taken as the adjustment coefficient. The adjustment coefficient is multiplied by the preset gain coefficient (usually 0.15) to obtain the state of charge increment. Finally, the basic value and the increment are added together to obtain the dynamic target state of charge. For example, when the dynamic load potential fluctuation index is 0.8, the dynamic target state of charge = 80% + 0.8 × 0.15 = 92%. This adaptive calculation method ensures that the target state of charge can dynamically respond to the risk of load fluctuation, avoiding energy waste caused by overcharging while ensuring power supply safety.

[0082] Under the current active pre-charge mode, predictive energy management is achieved by precisely controlling the start-up, operation, and shutdown of the range extender to smoothly raise the state of charge of the energy storage device to the dynamic target state of charge. The active pre-charge control strategy first obtains the optimal efficiency point parameter of the range extender. For a typical 75kW diesel generator range extender, the optimal efficiency point is usually located in the range of 75% to 85% of the rated power, corresponding to an output power of approximately 60kW. At this point, the fuel consumption rate is the lowest and the emission level is optimal.

[0083] The range extender adopts a soft-start strategy during startup to avoid instantaneous high current surges and mechanical stress. After the startup command is issued, the range extender gradually increases its output power according to the preset power ramp-up curve: the power increases from 0 to 20% of the rated power within the first 5 seconds, then to 50% of the rated power within the next 10 seconds, and finally ramps up smoothly to the optimal efficiency point power of 60kW within the last 15 seconds.

[0084] The target charging power is calculated by balancing the range extender's output power and load power demand in real time: Target charging power = Range extender's optimal efficiency point output power - Current real-time load power. For example, when the range extender is operating at its optimal efficiency point of 60kW, and the current load power is 15kW, the target charging power is 45kW. At this time, the energy storage device management system receives the charging command and controls the DC-DC charging converter to charge the battery pack at a constant power of 45kW. During the charging process, the controller continuously monitors the actual state of charge (SOC) of the energy storage device, using high-frequency monitoring at 1-second intervals to ensure charging accuracy. When the actual SOC reaches the pre-calculated dynamic target SOC, the controller immediately sends a shutdown command, and the range extender shuts down smoothly according to the preset power reduction curve. The entire shutdown process lasts about 20 seconds, avoiding the impact damage to the range extender caused by sudden power outages. Through pre-charging control, the energy storage device has sufficient energy reserves before load surges occur, significantly improving the emergency generator's ability to cope with sudden large loads. At the same time, through optimal efficiency point operation and soft start-stop strategies, fuel consumption and equipment wear are minimized.

[0085] In this embodiment, multi-dimensional load pattern analysis enables early warning of load surges, effectively advancing the warning time and providing ample time for system pre-charging. The construction of a three-dimensional decision space allows the control strategy to comprehensively consider the energy storage device's status, current load level, and future load risks, significantly improving decision accuracy compared to traditional single SOC threshold control. The proactive pre-charging mode, through predictive energy management, ensures the energy storage device reaches its optimal energy reserve state before a sudden large load load, effectively avoiding voltage drops and power outages, and significantly improving power supply reliability. The range extender's steady-state operation at its optimal efficiency point replaces frequent cold starts in traditional solutions, effectively reducing fuel consumption and extending equipment lifespan. Adaptive calculation of the dynamic target state of charge avoids overcharging and undercharging issues, effectively improving the cycle life of the energy storage device and significantly reducing operating costs. Overall, this approach achieves comprehensive optimization of energy efficiency, equipment lifespan, and operating costs, making it particularly suitable for critical application scenarios with extremely high requirements for power supply continuity, such as medical rescue, disaster emergency response, and communication support.

[0086] In one embodiment of this invention, a dynamic load fluctuation potential index, which characterizes the probability of future load abrupt changes, is calculated based on historical load power data in a time-series buffer, including the following steps:

[0087] S210. Obtain the load power data of the N most recent sampling points in the time series buffer, where N is a positive integer;

[0088] S220. Based on N load power data, calculate the power variance component, the first-order rate of change component, and the second-order rate of change component respectively.

[0089] S230. The power variance component, the first-order rate of change component, and the second-order rate of change component are weighted and summed according to the preset weighting coefficients to obtain the dynamic load fluctuation potential index.

[0090] In acquiring load power data from the most recent N sampling points in the time-series buffer, the selection of the value of N directly affects the calculation accuracy and response speed of the dynamic load fluctuation potential index, and can be configured according to specific application scenarios. In practice, the determination of the value of N follows the principle of matching the time window with the load characteristics: for medical emergency scenarios, since the start-up and shutdown of life support equipment has a fixed time pattern, N can be set to 100 sampling points, corresponding to a 10-second historical data window; for industrial emergency power supply, since the start-up process of large equipment is relatively long, the value of N can be set to 200 sampling points, corresponding to a 20-second analysis window.

[0091] The data acquisition process employs a sliding window mechanism. Whenever a new power sampling point is added to the buffer, the algorithm automatically extracts historical data from N time steps backward from the current moment, forming a power sequence of length N. ,in This is the latest power sampling value. This represents the earliest value among N sampling points. To ensure data validity, the algorithm performs an integrity check on the extracted data. If missing or abnormal values ​​are found (such as negative power values ​​or values ​​exceeding the equipment's rated power), linear interpolation or previous value preservation methods are used to repair the data. For example, at a disaster relief site, if communication interruption causes the loss of data at a certain sampling point, the algorithm will automatically fill the gap using the linear interpolation results of two adjacent valid sampling points, ensuring the continuity of the historical data sequence. Through this dynamic data window management mechanism, it is ensured that the data basis for calculating the load fluctuation potential index is always the latest and relevant historical information, providing data support for load change prediction.

[0092] Based on N load power data points, the power variance component, first-order rate of change component, and second-order rate of change component are calculated respectively. Multi-dimensional statistical analysis is used to comprehensively quantify the fluctuation characteristics of load power. The calculation of the power variance component first involves obtaining the arithmetic mean of the N power data points. Then, the squared deviation of each power value from the average value is calculated, and finally the variance is obtained. This component reflects the dispersion of the power data. The variance is larger when the load power changes drastically and smaller when the load is stable.

[0093] The first-order rate of change component quantifies the rate of power change by calculating the absolute value of the power difference between adjacent sampling points. The calculation formula is as follows: This component can identify rapid trends in power change. Even if the overall power level is not high, frequent small fluctuations will increase the first-order rate of change. The second-order rate of change component identifies the inflection point of the load change mode by analyzing the acceleration characteristics of power change. The calculation formula is as follows: This component is suitable for detecting precursor signals of an impending change in load.

[0094] In a specific embodiment, when the drainage pump is about to start, due to the operator's preparatory actions, the load power may gradually increase from 2kW to 2.2kW and then to 2.5kW. The coordinated changes in these values ​​provide an early warning signal for the upcoming large load start-up. Through comprehensive analysis of these three dimensions, the amplitude, speed, and acceleration characteristics of load power changes can be captured, providing a mathematical basis for predicting load abrupt changes.

[0095] The power variance component, first-order rate of change component, and second-order rate of change component are weighted and summed according to preset weighting coefficients to obtain a dynamic load fluctuation potential index that comprehensively reflects the load fluctuation risk. The weighting coefficients are set based on statistical analysis of measured data. By comparing load change patterns under different emergency scenarios, the weight ratios are determined: the power variance component weight w_1 can be set to 0.4, mainly considering its basic assessment role in load stability; the first-order rate of change component weight w_2 can be set to 0.4, focusing on its sensitivity to the rate of load change; and the second-order rate of change component weight w_3 can be set to 0.2, as a supplementary indicator of the acceleration of the change trend.

[0096] The calculation formula for the dynamic load fluctuation potential index is based on S120. To ensure comparability and standardization, each component needs to be normalized before weighting. The power variance component is divided by the square of the equipment's rated power, and the first and second rate of change components are divided by the equipment's rated power, so that the final fluctuation potential index value is controlled between 0 and 1. In actual calculations, when the medical equipment is in a stable operating state, the fluctuation potential index is low, indicating a very low risk of load mutation. However, when the equipment is about to start a high-power mode, the corresponding fluctuation potential index is significantly higher than that in the stable state, triggering the pre-charging mode activation condition. Through this multi-dimensional weighted fusion calculation method, the dynamic load fluctuation potential index can quantify the probability of load mutation at different times, providing a decision-making basis for predictive control strategies.

[0097] In this embodiment, the calculation method of the dynamic load fluctuation potential index realizes the quantitative analysis of the load change pattern of new energy emergency power generation vehicles. The sliding window mechanism with N sampling points ensures the timeliness and relevance of the analysis data, avoids interference from outdated information on prediction, and ensures the validity of the analysis basis through data integrity checks and anomaly handling. The separate calculation of the three-dimensional components comprehensively describes the load power change characteristics from different perspectives: the variance component reflects the fluctuation amplitude, the first-order rate of change reflects the rate of change, and the second-order rate of change reflects the acceleration of change. Compared with the traditional single-index evaluation, this multi-dimensional analysis method effectively improves the predictive ability. The weighted summation algorithm organically integrates the information of the three dimensions into a unified fluctuation potential index through the configured weight ratio. This index has a good correlation with actual load change events, providing a quantitative basis for predictive control. In practical applications, this calculation method can identify load change signs in advance, effectively advance the warning time, and significantly improve the emergency power generation vehicle's response capability to sudden large loads. By quantifying the risk of load fluctuations, the timing of the proactive pre-charging strategy can be made more accurate, avoiding energy waste caused by premature startup and power supply risks caused by delayed startup, thus effectively improving the overall control effect and energy utilization efficiency.

[0098] In one embodiment of this invention, based on N load power data, the power variance component, the first-order rate of change component, and the second-order rate of change component are calculated respectively, including the following steps:

[0099] S310. Calculate the average value of N load power data;

[0100] S320. Squaring the difference between each load power data and the average value, and averaging the N squared values ​​to obtain the power variance component;

[0101] S330. Calculate the absolute value of the difference between adjacent load power data, and average the absolute values ​​to obtain the first-order rate of change component.

[0102] S340. Calculate the absolute value of the difference between adjacent first-order differences, and average the absolute values ​​to obtain the second-order rate of change component.

[0103] In calculating the average value of N load power data points, high-precision floating-point arithmetic and a numerical stability optimization algorithm are employed. In practice, the algorithm first converts the data types of the N input power data points, transforming integer power values ​​into 64-bit double-precision floating-point numbers to avoid precision loss due to data type limitations. The average value is calculated using a cumulative summation method. Then divide by the number of sampling points to obtain the arithmetic mean.

[0104] To prevent overflow caused by the accumulation of large numbers, especially in industrial-grade emergency power generation vehicles that operate continuously for long periods, the algorithm introduces a segmented summation mechanism: when N exceeds a certain number, such as 1000 sampling points, the data is divided into several subgroups, each of which can contain a certain number of data points, such as 100 data points. First, the partial sum of each subgroup is calculated, and then the partial sums are accumulated to finally obtain the global average.

[0105] The process of squaring the difference between each load power data point and the average value, and then averaging the N squared values ​​to obtain the power variance component, employs an online incremental calculation algorithm and memory optimization strategy to achieve efficient variance calculation. The difference calculation uses vectorization, performing parallel subtraction operations on the vector formed by the N power data points and the average value. ,in Let be the deviation value of the i-th data point. The squaring operation uses a dedicated square function, avoiding the use of general exponentiation functions.

[0106] To handle large datasets without consuming excessive memory, the algorithm employs a streaming computation model: instead of storing all square values, it immediately accumulates each square value into a sum variable after calculation, and then releases the memory space for that value. In the specific implementation, a cumulative variable is maintained. The cumulative value is updated after processing each data point, and the final variance component is... .

[0107] In terms of numerical accuracy control, since squaring amplifies numerical errors, the algorithm employs a compensated sum-of-squares algorithm, similar to the Kahane summation concept, to correct accumulated calculation errors by maintaining a compensation term. For example, when processing load data for medical equipment with power fluctuations ranging from 1kW to 10kW, if the average power is 5.5kW and a certain power value is 8.2kW, the deviation is 2.7kW, and the square value is 7.29kW². This square calculation provides assurance for the variance component. Through optimized variance calculation, the dispersion of load power can be quantified, providing a key indicator for load fluctuation risk assessment.

[0108] In calculating the absolute value of the difference between adjacent load power data and averaging these absolute values ​​to obtain the first-order rate of change component, a difference algorithm and moving average technique are used to accurately capture the characteristics of the load power change rate. The calculation of adjacent differences employs the forward difference method. Where i ranges from 1 to N-1, generating N-1 difference sequences. To improve computational efficiency, the algorithm adopts an in-situ computation strategy, without allocating additional storage space to store all differences, but instead processing the absolute values ​​and cumulative sums as the computation progresses.

[0109] The absolute value operation employs a bitwise optimized algorithm, determining the sign of the value by checking the sign bit, thus avoiding the use of the standard library's absolute value function. When processing high-frequency sampled data, to reduce the impact of noise on the first-order rate of change, the algorithm introduces an adaptive filtering mechanism: when the difference between two adjacent sampling points exceeds a preset noise threshold (e.g., 5% of the average power), it is considered a valid power change; otherwise, it may be measurement noise, and appropriate smoothing is performed.

[0110] For example, in an emergency power supply scenario for a communication base station, when the power changes from 2kW in the standby state to 2.8kW in the data transmission state, the difference is 0.8kW, with an absolute value of 0.8kW; while when it changes from 2.8kW to 0.5kW in the fault protection state, the difference is -2.3kW, with an absolute value of 2.3kW. The cumulative summation process employs an enhanced numerical stability algorithm to prevent accuracy loss when handling a large number of small changes. The final first-order rate of change component is... This indicator can sensitively reflect the activity level of load power changes and provides an important basis for identifying load change trends.

[0111] In the process of calculating the absolute value of the difference between adjacent first-order differences and averaging these absolute values ​​to obtain the second-order rate of change component, a second-order difference algorithm and acceleration detection technology are used to identify inflection points and precursors of abrupt changes in load change patterns. The calculation of the second-order difference is based on a sequence of first-order differences: firstly, the second-order difference between adjacent differences is calculated from N-1 first-order differences. , where i ranges from 1 to N-2, generating N-2 second-order differences.

[0112] This calculation method can effectively identify inflection points in power change trends: a positive second-order difference indicates an accelerating upward trend in power change; a negative second-order difference indicates a decelerating or accelerating downward trend; and a value close to zero indicates a stable trend. In practical implementation, the algorithm uses a three-point difference scheme to optimize calculation accuracy and reduces the accumulation of numerical errors through the central difference method.

[0113] The absolute value processing employs a vectorized batch processing approach, organizing all second-order differences into vectors for parallel absolute value operations, fully utilizing the SIMD (Single Instruction Multiple Data) instruction set of modern processors. To enhance the sensitivity of the second-order rate of change to load change warnings, the algorithm introduces a weight decay mechanism: second-order differences closer to the current time are assigned higher weights, while those farther away are assigned lower weights, thus better reflecting the latest load change acceleration trend.

[0114] For example, in an industrial emergency scenario, when a large motor is about to start, the load power may change from a steady state (3kW) to a pre-start state (3.2kW) and then to a rapid ramp-up state (4.5kW). The corresponding second-order difference increases from near zero to a significantly positive value, promptly reflecting the sign that the load is about to increase substantially. The final second-order rate of change component is... This indicator serves as an early warning signal for load changes, providing early warning information before significant power fluctuations occur.

[0115] In this embodiment, the calculation of the power variance component, the first-order rate of change component, and the second-order rate of change component enables a comprehensive mathematical modeling of the load fluctuation characteristics of the new energy emergency power generation vehicle. High-precision average value calculation provides a benchmark reference for subsequent analysis, and the numerical stability-optimized algorithm ensures computational stability under various data conditions. The optimized variance component calculation, through streaming processing and memory optimization, can efficiently process large-scale real-time data and quantify the dispersion of load power. The calculation of the first-order rate of change component, through a difference algorithm and noise filtering, captures the rate of change of load power, effectively shortening the response time to load changes. The second-order rate of change component, through acceleration detection technology, can identify the turning point of the load change trend in advance, effectively advancing the warning time. The synergistic effect of the three components forms a complete load fluctuation characteristic description system, providing a mathematical basis for the calculation of dynamic load fluctuation potential indicators. In practical applications, this calculation method significantly improves the practicality of predictive control of new energy emergency power generation vehicles, providing technical support for the safe and stable operation of emergency power supply.

[0116] In one embodiment of this invention, the current operating mode is determined in a preset three-dimensional decision space based on the current state of charge, real-time load power, and dynamic load fluctuation potential index, including the following steps:

[0117] S410. Divide the current state of charge into a low state of charge interval, a medium state of charge interval, and a high state of charge interval.

[0118] S420: Divide the real-time load power into low-power range, medium-power range and high-power range;

[0119] S430. Divide the dynamic load fluctuation potential index into low fluctuation potential range, medium fluctuation potential range and high fluctuation potential range.

[0120] S440. Based on the coordinate positions of the current state of charge, real-time load power, and dynamic load fluctuation potential index in the three-dimensional decision space, the corresponding operating mode is obtained by querying the preset decision matrix. When the coordinate position is in the medium state of charge range, low power range, or high fluctuation potential range, the operating mode is determined to be the active pre-charging mode.

[0121] In the process of dividing the current state of charge into low state of charge, medium state of charge and high state of charge intervals, an adaptive threshold setting and dynamic boundary adjustment mechanism are adopted to match the interval division with the actual performance characteristics of the energy storage device.

[0122] In practical implementation, the division of the state of charge (SOC) range is set based on the battery discharge characteristic curve and emergency power supply safety requirements. The low SOC range can be set to 0%–30%, within which the battery terminal voltage begins to drop, discharge capacity is limited, and the system prioritizes power supply to the basic load to avoid over-discharge damage to the battery. The medium SOC range can be set to 30%–70%, which is the stable operating range for the battery, with gradual voltage changes and good discharge characteristics, suitable for flexible switching of various control strategies. The high SOC range can be set to 70%–100%, at which point the battery possesses strong instantaneous power output capability, capable of handling sudden large load impacts.

[0123] The boundary values ​​of the intervals were determined using statistical analysis methods. A correlation model between the state of charge (SOC) and output capacity was established by testing the performance of different types of energy storage devices (lithium iron phosphate, ternary lithium batteries, etc.) under various temperature and aging conditions. For example, for lithium iron phosphate battery packs, at ambient temperature, the usable discharge power corresponding to 30% SOC is approximately 60% of the rated power, and the usable discharge power corresponding to 70% SOC is approximately 95% of the rated power.

[0124] To adapt to the effects of battery aging and changes in ambient temperature, the algorithm also integrates a dynamic boundary adjustment function: when an increase in battery internal resistance or an ambient temperature deviates from standard conditions is detected, the interval boundary value is automatically adjusted. For example, in a low-temperature environment, the lower boundary of the medium-charge interval may be increased from 30% to 35%, so that the interval division reflects the true performance state of the battery.

[0125] In the process of dividing real-time load power into low-power, medium-power, and high-power ranges, relative power standardization and load characteristic analysis techniques are used to achieve power range settings that match the equipment capacity.

[0126] The power normalization process first obtains the rated output power of the range extender as a baseline value, and then converts the real-time load power into a relative power ratio: ,in For real-time load power, This refers to the rated power of the range extender. The range is divided into ranges based on relative power: the low-power range is 0-0.3 times the rated power, corresponding to light-load operation, where the range extender efficiency is low and energy storage devices are used for power supply; the medium-power range is 0.3-0.7 times the rated power, corresponding to medium-load operation of the range extender, with good fuel economy; the high-power range is 0.7-1.0 times the rated power, corresponding to heavy-load operation, close to the optimal efficiency point of the range extender.

[0127] In practical applications, the algorithm also considers the dynamic characteristics and duration of the load: short-term pulse loads (duration less than 10 seconds) may be classified as medium-power loads even if the power is high, because the energy storage device has good short-term high-power output capability; while continuous heavy loads (more than 5 minutes) require the range extender to participate in power supply even if the power is relatively low.

[0128] For example, for a 75kW diesel generator range extender, when the real-time load is 20kW, the relative power is 0.27, classifying it as a low-power range; when the load is 45kW, the relative power is 0.6, classifying it as a medium-power range; and when the load is 60kW, the relative power is 0.8, classifying it as a high-power range. To adapt to the specific needs of different emergency scenarios, the algorithm also supports user-defined range boundary adjustments.

[0129] In the process of dividing the dynamic load fluctuation potential index into low fluctuation potential range, medium fluctuation potential range and high fluctuation potential range, statistical distribution analysis and threshold self-learning algorithm are used to realize the quantitative classification of load change risk.

[0130] The range division of the volatility index is based on statistical analysis of a large amount of historical data. By collecting load fluctuation patterns under different emergency scenarios, a probability distribution model of the volatility index is established. The low volatility range can be set to 0-0.3, corresponding to a relatively stable load state. In this state, the probability of a sudden large load is low, and a conventional energy storage-first power supply strategy can be adopted. The medium volatility range can be set to 0.3-0.7, corresponding to a load with some fluctuations but not yet reaching the level of a sudden change warning. It is necessary to appropriately increase the state-of-charge readiness of the energy storage device. The high volatility range can be set to 0.7-1.0, corresponding to a state where the load is about to change, initiating an active pre-charging mode.

[0131] The determination of interval thresholds employs machine learning optimization methods. By analyzing the correlation between volatility indicators in historical load data and subsequent load changes, a classification model is trained using support vector machines or random forest algorithms to automatically optimize interval boundaries and improve prediction accuracy. The algorithm also integrates an adaptive learning mechanism: when a deviation between the predicted result and the actual load change is detected, the interval threshold is automatically adjusted to further enhance prediction accuracy.

[0132] For example, in hospital emergency power supply scenarios, by analyzing the usage patterns of operating room equipment, it was found that when the fluctuation potential index exceeds 0.6, high-power equipment will be started within 3 minutes in most cases. Therefore, the lower boundary of the high fluctuation potential range in this scenario can be adjusted to 0.6. The algorithm also considers the impact of time factors: at different stages of emergency response (such as the initial stage of disaster, the peak of rescue, and the recovery period), the load patterns have different characteristics, and the fluctuation potential threshold needs to be adjusted accordingly.

[0133] The decision matrix is ​​stored using a three-dimensional array structure. The three dimensions of the array correspond to the state of charge range, power range, and fluctuation potential range, respectively. Each array element stores the operating mode under the corresponding operating condition.

[0134] The matrix lookup process first converts the continuous values ​​of the three input parameters into discrete interval indices: state of charge interval index. Power range index Volatility range index Then through three-dimensional coordinates The algorithm operates by directly accessing the corresponding position in the decision matrix. To improve query efficiency, it employs a hash table-optimized query mechanism, converting three-dimensional coordinates into one-dimensional hash keys, achieving constant-time queries.

[0135] The decision matrix is ​​designed based on energy management optimization theory and practical operating experience, and contains a total of 27 operating condition combinations, each with a corresponding operating strategy. For example, coordinates (1,0,2) correspond to a medium-load state, low power, and high fluctuation potential. Although the current load is relatively light, the high fluctuation potential indicates that a sudden load change is about to occur. Therefore, the decision matrix outputs "active pre-charging mode", which starts the range extender in advance to charge the energy storage device, ensuring sufficient energy reserves when a sudden large load occurs.

[0136] The algorithm also integrates a decision conflict handling mechanism: when the input parameters are near the interval boundary, a weighted voting method is used to comprehensively consider the decision results of adjacent intervals, avoiding frequent mode switching due to minor parameter changes. The switching of operating modes adopts a state machine design, ensuring the smoothness and safety of mode transitions and avoiding disturbances caused by sudden switching.

[0137] In this embodiment, the state-of-charge (SOC) interval division fully considers the performance characteristics and safety requirements of the energy storage device. The control strategies under different charge levels are reasonable, and the dynamic adjustment capability of the interval boundaries allows the decision-making to adapt to battery aging and environmental changes, effectively improving decision accuracy. Standardized power interval division and adaptive adjustment mechanisms enable power grading to reflect the actual operating state of the equipment. The concept of relative power eliminates differences between devices of different capacities, improving the algorithm's versatility and adaptability. Fluctuation potential interval division, optimized through statistical analysis and machine learning, achieves quantitative grading of load mutation risks, effectively improving prediction accuracy. Decision matrix query and multi-mode switching logic enable control to respond to changes in operating conditions in a short time, effectively improving response speed while avoiding the impact of frequent mode switching on the equipment. This three-dimensional decision framework allows the emergency power generation vehicle to automatically select operating strategies based on real-time operating conditions, optimizing energy efficiency and extending equipment lifespan while ensuring power supply reliability. It is particularly suitable for emergency scenarios with high requirements for power supply continuity and stability, such as medical rescue and communication support.

[0138] In one embodiment of this invention, the dynamic target state of charge is calculated, including the following steps:

[0139] S510. When the current operating mode is active pre-charging mode, obtain the preset basic safe state of charge value as the reference value.

[0140] S520. Compare the dynamic load fluctuation potential index with the preset fluctuation potential upper limit value, and take the smaller value of the two as the adjustment value.

[0141] S530. Multiply the adjustment value by the preset gain coefficient to obtain the state of charge increment;

[0142] S540. Add the reference value to the state of charge increment to obtain the dynamic target state of charge.

[0143] When the current operating mode is active pre-charging mode, in the process of obtaining the preset basic safe state of charge value as the reference value, a multi-level safety strategy and scenario adaptive configuration mechanism are adopted so that the reference value can not only meet the safety requirements of emergency power supply, but also adapt to the special requirements of different application scenarios.

[0144] The setting of the basic safe state of charge (SBC) value is based on a comprehensive consideration of multiple factors, including the technical characteristics of the energy storage device, the power requirements of the emergency load, and the start-up response time of the range extender. Under standard configuration, the basic safe SBC value can be set to 80%. This value has been verified through extensive simulation analysis and actual testing, showing that when the energy storage device maintains an 80% SBC, it can independently withstand sudden load shocks within the cold start time window of the range extender, while ensuring that the battery terminal voltage does not drop below the protection threshold.

[0145] The algorithm integrates a scenario-adaptive adjustment function, automatically optimizing the baseline value according to the specific application environment: In medical emergency scenarios, considering the power supply reliability requirements of life support equipment, the basic safe state of charge value can be increased to 85%; in communication emergency support, since communication equipment has good voltage fluctuation tolerance, the baseline value can be set to 75%; in industrial emergency power supply, the baseline value can be dynamically adjusted within the range of 70% to 85% according to the starting characteristics of the load equipment.

[0146] The baseline value was also determined by considering the aging state of the energy storage device and the influence of ambient temperature: the battery management system monitors changes in internal resistance and capacity decay in real time, and automatically increases the baseline safe state of charge value to compensate for performance loss when a decline in battery performance is detected. For example, when the battery capacity decays to 85% of its initial capacity, the baseline safe value is automatically adjusted from 80% to 85%.

[0147] The temperature compensation mechanism adjusts based on the impact of ambient temperature on battery discharge characteristics: in low-temperature environments, the baseline safety value is increased to 88%; in high-temperature environments, the baseline safety value is reduced to 78%. Through this multi-dimensional benchmark optimization mechanism, the starting basis for dynamic target state of charge calculation is both safe and economically reasonable.

[0148] In the process of comparing the dynamic load fluctuation potential index with the preset fluctuation potential upper limit and taking the smaller value as the adjustment value, a saturation function processing and boundary protection strategy are adopted to prevent overcharging and energy waste caused by abnormally high fluctuation potential index.

[0149] The upper limit of the fluctuation potential is set based on a balance between the maximum safe state of charge of the energy storage device and the economics of energy management. It can be set to 1.0, corresponding to the theoretical maximum value of the fluctuation potential index. The comparison operation uses a numerically stable minimum value selection algorithm. ,in This is the current dynamic load fluctuation potential index, and this is the preset upper limit value of the fluctuation potential.

[0150] The mechanism is designed to prevent excessive charging demand under extreme load fluctuations. In practical applications, electromagnetic interference or sensor malfunctions can lead to abnormally high values ​​in the fluctuation potential index calculation. Directly using these abnormal values ​​for target state of charge calculation could cause the energy storage device to be charged to a dangerously overcharged state. Through the upper limit comparison mechanism, even if the fluctuation potential index becomes abnormal, the adjustment value is limited to a safe range.

[0151] The algorithm also integrates anomaly detection and self-recovery functions: when the fluctuation potential index reaches its upper limit for multiple consecutive sampling periods, an anomaly detection procedure is triggered, verifying the validity of the index by re-initializing the sensor and recalculating historical data. For example, in an outdoor emergency scenario with lightning interference, the power acquisition sensor may be affected by electromagnetic interference, resulting in an abnormal reading and a calculated fluctuation potential index of 1.8, far exceeding the normal range. In this case, the comparison mechanism limits the adjustment value to 1.0, effectively avoiding abnormal reactions.

[0152] The upper limit setting also takes into account the risk tolerance of different emergency scenarios: in critical medical equipment protection scenarios, the upper limit can be set to 0.8, adopting a more conservative strategy; in general industrial emergencies, the upper limit can be set to 1.2, allowing for a more aggressive pre-charging strategy.

[0153] In the process of multiplying the adjustment value by a preset gain coefficient to obtain the state of charge increment, nonlinear gain control and dynamic coefficient adjustment techniques are used to control and optimize the charging increment.

[0154] The gain coefficient is set based on the charging characteristics of the energy storage device, the response time requirements for sudden load changes, and the optimization goal of energy efficiency, and is determined through a large amount of experimental data and numerical simulation. The standard gain coefficient can be set to 0.15. The selection of this value takes into account the following factors: when the fluctuation potential index is at its maximum value of 1.0, the state of charge increment is 15%. Combined with a basic safety value of 80%, the final target state of charge is 95%, which can provide sufficient energy reserves to cope with sudden loads without causing the battery to be in an excessively high state of charge for a long time, thus affecting its lifespan.

[0155] The calculation of the gain coefficient also integrates a nonlinear adjustment mechanism, using the sigmoid function to correct the linear gain. This nonlinear function makes the gain coefficient slightly smaller than the base value when the fluctuation potential is low, and moderately increase the gain coefficient when the fluctuation potential is high, thus achieving more precise control.

[0156] The algorithm also considers the impact of the energy storage device's current state of charge (SOC) on the gain coefficient: when the current SOC is low, the gain coefficient is appropriately increased to ensure sufficient energy storage; when the current SOC is already high, the gain coefficient is decreased to avoid overcharging risk. Specifically, the formula for adjusting the gain coefficient with SOC is: , The initial gain coefficient is used in this formula to represent the aggressive charging strategy in the low-power state and the conservative strategy in the high-power state.

[0157] For example, when the current SOC is 40% and the adjustment value is 0.8, the final gain coefficient is approximately 0.18, and the state of charge increment is 14.4%; while when the current SOC is 70% and the adjustment value is 0.8, the final gain coefficient is approximately 0.12, and the state of charge increment is 9.6%. The gain coefficient also integrates temperature compensation and aging compensation functions: it appropriately increases the gain coefficient in low-temperature environments to compensate for the decline in battery performance, and it also increases the gain coefficient accordingly when the battery ages.

[0158] In the process of adding the baseline value to the state of charge increment to obtain the dynamic target state of charge, numerical precision control and boundary constraint checking techniques are used to ensure the accuracy and safety of the final result.

[0159] The addition operation uses a high-precision floating-point algorithm, ensuring calculation accuracy across various numerical ranges. ,in Based on the basic safe state of charge value, This represents the increment of the state of charge.

[0160] To prevent the calculation results from exceeding the safe operating range of the energy storage device, the algorithm integrates multiple boundary check mechanisms: First, it checks whether the target state of charge exceeds the maximum safe state of charge of the energy storage device (which can be 95%). If it does, it automatically limits it to the maximum safe value. Second, it checks whether the difference between the target state of charge and the current state of charge is within a reasonable range to avoid excessive charging demand from impacting the system.

[0161] The calculation results still need to be verified for feasibility: based on the current charging power capacity of the energy storage device and the estimated charging time, it needs to be determined whether the target state of charge can be reached within a reasonable time. For example, when the basic safety value is 80% and the state of charge increment is 12%, the calculated dynamic target state of charge is 92%. If the current actual SOC is 60%, then 32% charging is required (from 60% to 92%). The charging time can be estimated based on the battery capacity and charging power. If the estimated time is too long, the target value should be appropriately reduced for practicality.

[0162] The algorithm also integrates historical data verification, identifying possible abnormal calculation results by comparing the current calculation results with the target state of charge under similar historical operating conditions. The final dynamic target state of charge also needs to consider the charging curve characteristics of the energy storage device: the charging rate decreases when approaching full charge, so when the target value exceeds 90%, the algorithm will assess the actual time cost required to reach the target and make appropriate adjustments if necessary.

[0163] In this embodiment, the baseline safe state of charge (SBC) provides the starting point for target calculation. Multi-scenario adaptation and environmental compensation mechanisms ensure that the baseline value reflects the actual needs under different application scenarios and operating conditions, effectively improving the accuracy of the baseline value setting. Fluctuation potential index limiting and anomaly detection mechanisms prevent overcharging risks caused by sensor failures or environmental interference. Nonlinear gain control and multi-factor coupling adjustment enable the calculation of SBC increments, achieving a high degree of matching between charging increments and actual needs. This ensures the ability to handle sudden loads while avoiding unnecessary energy waste. Numerical calculations and multiple boundary verifications confirm the accuracy and feasibility of the final target SBC. This dynamic target calculation method enables the pre-charging strategy to adaptively adjust according to real-time load risks and states. Compared to traditional fixed-target methods, it effectively improves energy utilization efficiency and extends the cycle life of the energy storage device, making it particularly suitable for emergency power supply scenarios with complex and variable load characteristics.

[0164] In one embodiment of this invention, when the current operating mode is active pre-charging mode, controlling the range extender of the new energy emergency power generation vehicle to raise the state of charge of the energy storage device to the dynamic target state of charge includes the following steps:

[0165] S610. When the current operating mode is active pre-charging mode, obtain the output power corresponding to the optimal efficiency point of the range extender.

[0166] S620: Send a start command to the range extender to control the range extender to start according to the preset soft start power curve and operate stably at the optimal efficiency point to output power.

[0167] S630: Subtract the real-time load power from the output power at the optimal efficiency point to calculate the target charging power;

[0168] S640: Send a charging command to the energy storage device management system to control the energy storage device to charge at the target charging power;

[0169] S650 continuously monitors the actual state of charge of the energy storage device and controls the range extender to shut down when the actual state of charge reaches the dynamic target state of charge.

[0170] When the current operating mode is active pre-charging mode, in the process of obtaining the output power corresponding to the optimal efficiency point of the range extender, multi-parameter optimization analysis and real-time performance monitoring technology are adopted to ensure that the range extender always operates at the balance point between fuel economy and power conversion efficiency.

[0171] The optimal efficiency point of a range extender is determined based on a coupled analysis of the engine's universal characteristic curve and the generator's efficiency characteristic curve, taking into account multiple factors such as fuel consumption rate, emission levels, mechanical wear, and power quality. For a typical 75kW diesel generator range extender, the optimal efficiency point can be located in the range of 75% to 85% of the rated power. The specific value needs to be determined through bench testing: under standard environmental conditions, by gradually adjusting the engine speed and load power, the fuel consumption rate, power generation efficiency, and overall efficiency under different operating conditions are measured, a three-dimensional efficiency curve is plotted, and the output power corresponding to the highest overall efficiency point is determined.

[0172] In practical applications, the impact of environmental factors on the optimal efficiency point also needs to be considered: for every 1000 meters increase in altitude, air density decreases, requiring a reduction in the optimal efficiency power; for every 10°C increase in ambient temperature, the optimal efficiency power is reduced; and when relative humidity exceeds 85%, the optimal efficiency power is also reduced. The algorithm integrates real-time environmental compensation, acquiring environmental parameters through temperature, barometric pressure, and humidity sensors to automatically correct the optimal efficiency power value. For example, during emergency power supply in high-altitude areas, the standard optimal efficiency power is automatically adjusted from 60kW to 45kW.

[0173] The range extender is also equipped with an operating status monitoring system, which monitors parameters such as engine speed, cylinder temperature, oil pressure, and coolant temperature to assess the engine's health status in real time. When a performance degradation is detected, it automatically adjusts the power at the optimal efficiency point to protect the equipment.

[0174] The process of sending the start command to the range extender employs a multi-stage power ramp-up and protection strategy to achieve a smooth start-up process.

[0175] The start command is sent using the CAN bus communication protocol. The command format includes information such as target power, start mode, and timing parameters. The data frame uses a 29-bit extended identifier format, ensuring reliable and real-time communication. Before sending the start command, the controller first executes a pre-start check procedure: checking if the fuel level is sufficient (not less than 25%), checking if the engine oil pressure is normal (not less than 0.2 MPa), checking if the coolant temperature is within the allowable range, and checking if the exhaust system is unobstructed. Only after all checks are passed will the formal start command be sent.

[0176] The soft-start power curve is designed using an S-shaped function: ,in Let k be the target power and k be the climb rate parameter. The parameter represents the midpoint of the time interval. This curve design allows the power output to rise slowly in the initial stage of startup, climb rapidly in the middle stage, and stabilize in the later stage, reducing startup shock.

[0177] The startup process is divided into five stages: the preheating stage, where the engine idles to preheat the cylinder block and lubrication system; the initial loading stage, where power increases from 0 to 20% of the optimal efficiency point; the mid-term ramp-up stage, where power increases from 20% to 70% of the optimal efficiency point; the final stabilization stage, where power increases from 70% to 100% of the optimal efficiency point; and the stable operation stage, where power stabilizes at the optimal efficiency point. During startup, the controller continuously monitors key parameters, and immediately interrupts the startup procedure and implements protective measures if any abnormality is detected.

[0178] In the process of calculating the target charging power by subtracting the real-time load power from the output power at the optimal efficiency point, a dynamic power balance algorithm and a power limiting protection mechanism are adopted to effectively ensure the accuracy of power allocation and the safety of system operation.

[0179] Calculating the target charging power requires considering multiple factors, including power conversion efficiency, system losses, and dynamic response characteristics. The basic calculation formula is: ,in For the efficiency of DC-DC converters, The optimal power for the range extender. For real-time load power, This refers to the system power loss (including inverter losses, line losses, etc.).

[0180] The algorithm also integrates a dynamic power tracking mechanism, automatically recalculating the target charging power and updating the charging command when the real-time load power changes. To protect the energy storage device and the charging system, the algorithm sets multiple power limits: the maximum charging power must not exceed the rated charging power of the energy storage device, and the minimum charging power must not be lower than 5kW (to avoid efficiency loss caused by charging with too low a power). When the calculated target charging power exceeds the limit range, it is automatically adjusted to the limit boundary value.

[0181] For example, when the range extender operates at its optimal efficiency point of 60kW and the real-time load is 10kW, considering a conversion efficiency of 97% and a system loss of 1.5kW, the target charging power is: 0.97×(60-10)-1.5=46.5kW. The algorithm also considers the dynamic power response characteristics during charging: when the load power suddenly increases, it prioritizes meeting the load demand and automatically reduces the charging power; when the load power suddenly decreases, it redirects excess power to the charging system, but the rate of increase is limited by the charging power ramp-up limit of the battery's BMS.

[0182] During the process of sending charging commands to the energy storage device management system to control the energy storage device to charge at the target charging power, communication protocols and power control technologies are used to achieve safe charging of the energy storage device.

[0183] The charging command uses a CAN-FD-based communication protocol, increasing the data transmission rate to 2Mbps and enabling millisecond-level command response. The charging command data packet includes information such as the target charging power, charging mode, voltage and current limits, and temperature protection parameters, and uses CRC-32 to verify the integrity of the data transmission.

[0184] After receiving the instruction, the energy storage device management system (BMS) first performs a safety check: verifying whether the target charging power is within the allowable range, checking whether the battery temperature is suitable for charging, checking whether the battery voltage is normal, and checking whether the insulation resistance of the charging circuit is qualified. After passing the safety check, the BMS starts the constant power charging mode, adjusting the charging current to maintain the target power output by controlling the switching frequency and duty cycle of the DC-DC charging converter.

[0185] The charging control employs a dual closed-loop control strategy: the outer loop is a power control loop, which adjusts the reference current value through a PI controller to maintain the target charging power; the inner loop is a current control loop, which tracks the reference current through a PWM controller. To protect battery safety, the BMS integrates multiple protection mechanisms: overvoltage protection (automatically stops charging when the cell voltage exceeds 4.2V), overcurrent protection (current limiting protection when the charging current exceeds 1.2 times the rated value), overtemperature protection (reduces charging power when the battery temperature exceeds 50℃), and insulation monitoring protection (stops charging when the insulation impedance is below 100kΩ).

[0186] During charging, the BMS feeds back charging status information to the controller every 100 milliseconds, including parameters such as actual charging power, charging voltage, charging current, battery temperature, and SOC. The controller dynamically adjusts the charging strategy based on the feedback information.

[0187] The process of continuously monitoring the actual state of charge of the energy storage device and controlling the shutdown of the range extender adopts high-frequency monitoring and predictive control technology to achieve charging termination control.

[0188] State of Charge (SOC) monitoring employs a multi-algorithm fusion estimation method, combining the coulomb integration method, open-circuit voltage method, and Kalman filtering algorithm to achieve SOC estimation. The coulomb integration method monitors the charging current in real time using a high-precision Hall current sensor and accumulates the charging capacity. The open-circuit voltage method measures the battery terminal voltage during charging intervals or low-current charging and corrects the SOC using a pre-calibrated SOC-OCV curve. The Kalman filtering algorithm integrates current integration and voltage measurement information, estimating the SOC through a state-space model.

[0189] To improve the accuracy of charging termination control, the algorithm employs a predictive control strategy: when the State of Charge (SOC) approaches the target value, it begins calculating the estimated time to reach the target SOC and adjusts the charging power in advance to achieve a smooth charging termination. The range extender shutdown control uses a gradual power reduction strategy to avoid thermal and mechanical shocks to the engine caused by sudden power outages: first, the output power is linearly reduced to 30% within 10 seconds, then to 10% within 5 seconds, and finally to zero within 5 seconds, shutting off the ignition system.

[0190] During the shutdown process, the system continuously monitors parameters such as engine coolant temperature and oil temperature to ensure safety. When the actual SOC reaches the target value, the system automatically records charging completion information, including charging time, charging capacity, and average charging power, providing a basis for subsequent charging strategy optimization.

[0191] In this embodiment, the optimal efficiency point acquisition and environmental adaptation mechanisms enable the range extender to operate under various conditions, effectively improving fuel economy. The start-up control and fault protection mechanisms ensure the range extender starts successfully with a high success rate and controlled start-up time. Power calculation and dynamic allocation algorithms guarantee controlled charging power, controlling power allocation errors and improving energy conversion efficiency. Communication protocols and multiple safety protection mechanisms ensure the safety of the charging process and high charging efficiency. SOC monitoring and predictive shutdown control terminate the charging process with high accuracy in achieving the target SOC and a smooth range extender shutdown process. The coordinated operation of the entire pre-charging control system allows the new energy emergency power generation vehicle to build up sufficient energy reserves before load surges, effectively reducing the risk of power outages. Simultaneously, through efficiency point operation and smooth start-stop control, equipment lifespan is effectively extended, and operating costs are reduced, making it particularly suitable for critical emergency scenarios with high requirements for power continuity, such as medical rescue and communication support.

[0192] In one embodiment of this invention, the method further includes the following steps:

[0193] S710. During the continuous monitoring of the actual state of charge of the energy storage device, the dynamic load fluctuation potential index is updated in real time.

[0194] S720. When the dynamic load fluctuation potential index changes by a preset amplitude, the step of determining the current operating mode in the preset three-dimensional decision space is re-executed.

[0195] S730: When the operating mode changes, the current charging process is interrupted and the control strategy corresponding to the new operating mode is executed.

[0196] During the continuous monitoring of the actual state of charge of the energy storage device, the dynamic load fluctuation potential index is updated in real time using a sliding window real-time calculation and incremental update algorithm. The fluctuation potential index can reflect the latest load change trend in a timely manner.

[0197] Real-time updates employ an incremental calculation strategy to avoid recalculating the entire historical data window with each update: when a new load power sampling point arrives, the algorithm first adds it to the time series buffer, while removing the oldest data point, maintaining a fixed window size of N sampling points.

[0198] The incremental update algorithm uses a recursive formula to update each component: for the power variance component, the recursive formula for calculating the variance is used. ,in To add sampling points, The removal of sampling points reduces computational cost. Incremental updates for the first-order rate of change component are achieved by maintaining the cumulative sum of adjacent differences; each update only requires calculating two new adjacent differences and removing two old differences. The second-order rate of change component similarly employs a three-point difference incremental update strategy.

[0199] To ensure the accuracy of real-time updates, the algorithm employs an error compensation mechanism, performing a full recalculation every 100 incremental updates to eliminate accumulated numerical errors. The update frequency is synchronized with the load sampling frequency, which can be 10Hz, allowing the fluctuation potential index to reflect load changes promptly.

[0200] The algorithm also integrates a trend prediction function. By analyzing the rate of change of the recent volatility index, it predicts the index value at the next moment and triggers decision assessment in advance. For example, in a medical emergency scenario, when the operating room equipment is about to be activated, the volatility index gradually rises from 0.2 to 0.4 and 0.6. The algorithm predicts through trend analysis that the index will continue to rise to above 0.8, allowing for adjustments to the control strategy in advance.

[0201] When the dynamic load fluctuation potential index changes by a preset amplitude, the step of determining the current operating mode in the preset three-dimensional decision space is re-executed. A threshold detection and triggering mechanism is used to avoid frequent mode switching caused by minor changes, while responding to changes in a timely manner.

[0202] The preset threshold for amplitude variation is set based on a balance between the stability requirements and response sensitivity of the control system. It can be set to trigger a re-decision when the fluctuation potential index changes by more than 0.1. The threshold detection adopts the principle of hysteresis comparator, setting different rising and falling thresholds: a re-decision is triggered when the fluctuation potential index rises above the current value +0.1, and a re-decision is also triggered when it falls below the current value -0.15. This asymmetric threshold design prevents frequent triggering when the index fluctuates around the threshold.

[0203] The algorithm also integrates a rate of change detection function: in addition to amplitude changes, when the rate of change of the volatility index exceeds 0.05 / second, a re-decision process is also triggered, thus enabling timely capture of rapidly changing load patterns. The re-execution process uses the same decision logic, but adds consideration for the continuity of the current state: when making a new operating mode decision, the algorithm evaluates the necessity and cost of mode switching, and only executes mode conversion when the benefits of the new mode outweigh the switching cost.

[0204] The decision-making process also considers the safety of mode switching: direct switching between certain modes is not allowed; an intermediate buffer state must be used. For example, switching directly from high-power mode to shutdown mode may damage the equipment; it is necessary to switch to medium-power mode first and then gradually shut down. A time window limit is also set for triggering re-decision: the interval between two consecutive re-decisions must not be less than 30 seconds to prevent the system from switching modes too frequently and affecting operational stability.

[0205] When the operating mode changes, the current charging process is interrupted, and the control strategy corresponding to the new operating mode is executed. The safety interruption and smooth switching technology are adopted to ensure the safety and continuity of the mode conversion process.

[0206] The current charging process is interrupted using a gradual power decay strategy: first, the charging power is linearly reduced to 50% within 2 seconds, then to 20% within 3 seconds, and finally to zero within 2 seconds, disconnecting the charging circuit. The entire interruption process lasts 7 seconds, avoiding the impact of sudden power outages on the energy storage device and charging system. During the interruption, the controller continuously monitors the voltage and current changes of the energy storage device, ensuring that the parameters remain within safe ranges. Simultaneously, a charging interruption notification is sent to the energy storage device management system, and the BMS adjusts its internal protection parameters and equalization strategies accordingly.

[0207] The execution of the new operating mode adopts state machine switching logic. Each operating mode corresponds to an independent control state, which includes the specific control parameters and execution strategy under that mode. For example, when switching from active pre-charging mode to pure energy storage power supply mode, the controller first completes the safety interruption of the current charging, then shuts down the range extender (using a gradual shutdown strategy), and finally activates the pure energy storage power supply control logic to adjust the inverter parameters to optimize the discharge characteristics of the energy storage device.

[0208] During mode switching, the controller maintains a switching status log table, recording information such as switching time, switching reason, and system status before and after the switching, for subsequent performance analysis and fault diagnosis. To ensure power supply continuity, mode switching adopts the principle of "build first, then disconnect": after the control loop of the new mode is established and running stably, the control loop of the old mode is disconnected, and the load power supply is not interrupted throughout the entire switching process.

[0209] After the switch is completed, the system automatically performs a status check and parameter verification. Once it confirms that the new mode is running normally, it sends a mode switch completion notification to the superior monitoring system.

[0210] In this embodiment, the real-time updated dynamic load fluctuation potential index, through incremental calculation and error compensation technology, effectively improves calculation efficiency by promptly identifying load change trends while maintaining calculation accuracy. The triggering mechanism, through threshold detection and rate of change analysis, achieves sensitive response to load changes and suppression of minor fluctuations, resulting in high trigger accuracy and controlled false triggering rate. Safety interruption and smooth switching technologies ensure the safety and continuity of operating mode transitions, controlling voltage and current fluctuations during switching and achieving zero power interruption time. This dynamic adjustment mechanism enables the control system to automatically optimize control strategies based on real-time load changes, effectively improving energy utilization efficiency and equipment response speed compared to fixed strategy methods. In complex and ever-changing emergency power supply scenarios, this mechanism can automatically identify load mode transitions (such as from standby to operating state, from light load to heavy load, etc.) and adjust control parameters in a timely manner to match new operating requirements, improving the system's adaptability and robustness. Through continuous mode optimization and dynamic adjustment, the overall performance of the new energy emergency power generation vehicle is improved, making it particularly suitable for emergency power supply scenarios with complex and frequently changing load characteristics, providing technical support for ensuring continuous and stable power supply to critical equipment.

[0211] In one embodiment of this invention, the method further includes the following steps:

[0212] S810, monitor whether the real-time load power output of the new energy emergency power generation vehicle exceeds the output power of the range extender at its optimal efficiency point;

[0213] S820: When the real-time load power exceeds the output power of the range extender at its optimal efficiency point, the operating mode is determined to be hybrid power assist mode.

[0214] S830, in hybrid power assist mode, controls the range extender to operate at the optimal efficiency point to output power, while controlling the energy storage device to discharge to make up for the power difference.

[0215] S840: Subtract the output power of the range extender at its optimal efficiency point from the real-time load power to obtain the discharge power required by the energy storage device.

[0216] S850 sends a discharge command to the energy storage device management system to control the energy storage device to discharge according to the required discharge power.

[0217] In the process of monitoring whether the real-time load power output of the new energy emergency generator exceeds the output power of the range extender at its optimal efficiency point, a high-frequency sampling and comparison algorithm is used to realize real-time monitoring and judgment of load power changes.

[0218] The monitoring process employs a dual-threshold comparison strategy to avoid frequent mode switching caused by power fluctuations around the thresholds: the upper threshold is set at 105% of the range extender's optimal efficiency power, and the lower threshold is set at 95%. When the real-time load power rises above the upper threshold, the hybrid power assist mode is triggered; when it falls below the lower threshold, the mode is exited. Power comparison uses a moving average, taking the average of the most recent 5 sampling points and comparing it with the threshold to eliminate the influence of instantaneous power spikes on the judgment.

[0219] The monitoring algorithm obtains the optimal efficiency point power after environmental compensation in real time as a comparison benchmark. To improve the reliability of monitoring, the algorithm integrates multiple verification mechanisms: when the load power is detected to exceed the threshold, the continuity of the load is automatically verified, and the trigger condition is confirmed only if the state exceeding the threshold lasts for more than 3 seconds; at the same time, the operating status of the range extender is checked, and the range extender has been started and is running stably, avoiding the accidental triggering of hybrid mode when the range extender is not running.

[0220] The monitoring process also considers the trend of load power changes: when the load power is close to the threshold and shows an upward trend, mode switching is prepared in advance to shorten the response time; when it shows a downward trend, the judgment time is extended to avoid unnecessary mode switching. For example, when the optimal efficiency point power of the 75kW diesel generator range extender is 60kW, the upper threshold is set to 63kW and the lower threshold is set to 57kW; when the load power is detected to rise from 58kW to 65kW and remain there for more than 3 seconds, the hybrid power assist mode is triggered.

[0221] When the real-time load power exceeds the output power of the range extender at its optimal efficiency point, the operating mode is determined to be hybrid power assist mode using rapid decision-making and status verification technologies, ensuring the accuracy and timeliness of mode switching.

[0222] The determination of the hybrid power assist mode is based not only on power comparison results but also on a comprehensive consideration of multiple factors, including the state of charge (SOC) of the energy storage device, system operating conditions, and load characteristics. The mode determination algorithm first verifies whether the energy storage device has discharge capability: it checks whether the current SOC is higher than the minimum discharge threshold (which can be 20%), whether the battery temperature is within the allowable discharge range, and whether there are any fault alarms in the battery system. Only when all conditions are met is the hybrid power assist mode confirmed.

[0223] The algorithm also evaluates the extent of the load power exceedance: when the exceedance is small, a standard hybrid mode is used, with the energy storage device providing supplementary power; when the exceedance is large, an enhanced hybrid mode is used, which simultaneously increases the range extender's output power to its maximum and enables the energy storage device to discharge at high power.

[0224] During the mode determination process, the controller automatically calculates the duration of hybrid power supply: based on the current SOC, expected discharge power, and battery capacity, it estimates the duration of pure energy storage supplementary power supply. If the estimated time is too short, the system will issue a warning signal, reminding the operator to prepare backup power or adjust the load configuration. For example, when the load power is 70kW, the range extender's optimal efficiency point is 60kW, the current SOC is 65%, and the battery capacity is 500kWh, the energy storage device needs to supplement 10kW of power, and the estimated sustainable power supply time is approximately 32 hours, meeting long-term operation requirements, thus confirming entry into hybrid power assist mode.

[0225] Once the mode is determined, the running status indicator is automatically updated, a mode change notification is sent to the monitoring interface, and the time and reason for the mode switch are recorded to provide data support for subsequent operation analysis.

[0226] In hybrid power assist mode, the range extender is controlled to operate at its optimal efficiency point to output power, while the energy storage device is controlled to discharge to compensate for the power difference. Dual-source coordinated control and power optimization allocation technology are used to achieve cooperation between the range extender and the energy storage device.

[0227] The range extender control employs a constant power output strategy, maintaining the output power at the optimal efficiency point by adjusting the engine throttle opening and the generator excitation current. The energy storage device discharge control adopts a power following strategy, dynamically adjusting the discharge power according to real-time changes in load power, ensuring that the total output power matches the load demand.

[0228] The core of dual-source coordinated control is the power allocation algorithm: the range extender always prioritizes providing base power (power at the optimal efficiency point), while the energy storage device provides supplementary power as needed. This allocation method ensures the operation of the range extender while fully utilizing the rapid response characteristics of the energy storage device. The control algorithm adopts a master-slave control architecture: the range extender is the master control source, maintaining a constant power output; the energy storage device is the slave control source, tracking the power difference in real time.

[0229] To ensure the synchronization of dual-source output, the controller employs clock synchronization, with power regulation commands for the range extender and energy storage device taking effect simultaneously, thus avoiding system oscillations caused by power imbalance. The algorithm also integrates a dynamic load-sharing mechanism: when the rate of change of load power exceeds the response capability of the energy storage device, the output power of the range extender is temporarily increased to handle part of the rapidly changing load, and then gradually returns to the optimal efficiency point, with the energy storage device bearing the remaining power difference.

[0230] For example, when the load suddenly jumps from 60kW to 85kW, the range extender first temporarily increases to 75kW to cope with the rapid change, and the energy storage device simultaneously starts discharging to provide 10kW of supplementary power. Then, within 5 seconds, the range extender returns to the optimal efficiency point of 60kW, and the energy storage device correspondingly increases the discharge power to 25kW to achieve a smooth power transition.

[0231] In the process of subtracting the range extender's optimal efficiency point output power from the real-time load power to obtain the required discharge power of the energy storage device, real-time calculation and dynamic optimization algorithms are used to optimize the accuracy of power allocation and system efficiency.

[0232] The calculation of the required discharge power needs to consider multiple factors such as system losses, conversion efficiency, and dynamic response. The basic calculation formula is: ,in For real-time load power, The optimal power for the range extender. For inverter efficiency, Power compensation for dynamic response.

[0233] The calculation of dynamic response compensation power takes into account the rate and magnitude of load power changes: when the load power increases rapidly, the energy storage device needs to provide additional power to compensate for the range extender's response delay; when the load power decreases rapidly, the discharge power needs to be reduced accordingly to avoid over-supply. The formula for calculating compensation power is: Where k is the compensation coefficient, This represents the rate of change of load power.

[0234] The algorithm also integrates a power limiting protection mechanism: the maximum discharge power must not exceed the rated discharge power of the energy storage device, and the minimum discharge power must not be lower than 1kW (to avoid efficiency loss caused by low-power discharge). When the calculated required discharge power exceeds the limit range, the range extender output power is adjusted first or load priority management is activated.

[0235] For example, when the load power is 72kW, the range extender's optimal efficiency point is 60kW, and the inverter efficiency is 97%, the basic required discharge power is (72-60) / 0.97 = 12.37kW. If the load power is increasing at a rate of 5kW / s, the compensation power is 0.2×5 = 1kW, and the final required discharge power is 13.37kW. The calculation results still need to be verified for feasibility: check whether the current discharge capacity of the energy storage device meets the demand, assess the impact of continuous discharge on battery temperature and lifespan, and adjust power limiting or thermal management if necessary.

[0236] The process of sending discharge commands to the energy storage device management system and controlling the energy storage device to discharge according to the required discharge power adopts communication technology and power control strategy, but has been specially optimized for discharge characteristics.

[0237] The discharge command also uses the CAN-FD communication protocol, but the data packet format has been optimized for discharge control, including information such as target discharge power, discharge mode, voltage and current limits, temperature monitoring parameters, and power ramp-up rate. The discharge control adopts a constant power discharge mode, which adjusts the discharge current to maintain the target power output by controlling the modulation depth and switching frequency of the DC-AC inverter.

[0238] Unlike charging control, discharging control requires special attention to power response speed: the adjustment speed of discharging power must be completed within 100 milliseconds, faster than the adjustment requirement of charging power. Discharging control adopts a composite control strategy of feedforward and feedback: feedforward control quickly adjusts inverter parameters according to power commands, while feedback control makes corrections based on the actual output power.

[0239] The protection strategies of the BMS during discharge also differ: the overcurrent protection threshold is set to 1.5 times the rated discharge current, the undervoltage protection threshold is dynamically adjusted according to the discharge rate, and the overtemperature protection is more stringent during high-power discharge. During discharge, the BMS feeds back discharge status information to the controller every 50 milliseconds, doubling the response frequency compared to charging, resulting in rapid power regulation response.

[0240] The algorithm also integrates discharge efficiency optimization: based on the current SOC and discharge power, it automatically selects a discharge strategy to optimize discharge efficiency while ensuring power requirements are met. For example, when a discharge power of 25kW is required and the current SOC is 70%, the BMS selects a constant power discharge mode and sets the discharge current to 80A, achieving a stable power output of 25kW through current control.

[0241] In this embodiment, the power monitoring and comparison algorithm ensures timely activation of the hybrid power mode, controlling response time and avoiding power instability caused by response delays. The mode determination mechanism, through comprehensive evaluation of multiple factors, guarantees the accuracy and safety of mode switching, resulting in a high success rate. Dual-source coordinated control technology enables the range extender and energy storage device to work together, ensuring the range extender always operates at its optimal efficiency point, effectively reducing fuel consumption, while the energy storage device provides rapid supplementary power with high load tracking accuracy. Power calculation and dynamic optimization algorithms ensure accurate power allocation, controlling power allocation errors and achieving high overall system efficiency. The optimized discharge control strategy ensures stable discharge of the energy storage device, shortening discharge power adjustment time and achieving high discharge efficiency. The implementation of the hybrid power assist mode enables the new energy emergency power generation vehicle to handle high-power loads far exceeding the range extender's single-unit power supply capacity, effectively improving power supply capability while maintaining good fuel economy and system stability. This mode is particularly suitable for high-power demand scenarios such as industrial emergencies, large equipment startup, and medical equipment cluster power supply, expanding the application scope and practical value of new energy emergency power generation vehicles and providing technical support for various emergency power supply needs.

[0242] Reference Figure 3 This application also provides a new energy emergency power generation vehicle, including:

[0243] Energy storage devices;

[0244] Range extender;

[0245] The power acquisition unit is used to acquire the real-time load power output by the new energy emergency power generation vehicle;

[0246] Energy storage device management system, used to monitor the state of charge of energy storage devices and control the charging and discharging of energy storage devices; and

[0247] The controller is connected to the energy storage device, range extender, energy storage device management system, and power acquisition unit.

[0248] The controller is configured to execute the aforementioned control method for the new energy emergency power generation vehicle.

[0249] This application also provides a controller, including:

[0250] The memory is configured to store instructions; and

[0251] The processor is configured to retrieve instructions from memory and, when executing instructions, to implement the aforementioned control method for new energy emergency power generation vehicles.

[0252] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0253] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0254] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0255] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0256] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0257] Memory may include non-persistent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0258] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0259] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0260] The above are merely embodiments of this application and are not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A control method for a new energy emergency power generation vehicle, characterized in that, include: In response to receiving the real-time load power output from the new energy emergency power generation vehicle, the real-time load power is stored in the time series buffer. Obtain the load power data of the N most recent sampling points in the time series buffer, where N is a positive integer; Based on N load power data, calculate the power variance component, the first-order rate of change component, and the second-order rate of change component respectively. The power variance component, the first-order rate of change component, and the second-order rate of change component are weighted and summed according to the preset weighting coefficients to obtain the dynamic load fluctuation potential index. Obtain the current state of charge and real-time load power of the energy storage device inside the new energy emergency power generation vehicle; The current state of charge is divided into three ranges: low state of charge, medium state of charge, and high state of charge. The real-time load power is divided into low-power range, medium-power range and high-power range; The dynamic load fluctuation potential index is divided into low fluctuation potential range, medium fluctuation potential range and high fluctuation potential range. Based on the coordinate positions of the current state of charge, real-time load power, and dynamic load fluctuation potential index in the three-dimensional decision space, the corresponding operating mode is obtained by querying the preset decision matrix. When the coordinate position is in the medium state of charge range, low power range, and high fluctuation potential range, the operating mode is determined to be the active pre-charging mode. When the current operating mode is active pre-charging mode, the preset basic safe state of charge value is obtained as the reference value; The dynamic load fluctuation potential index is compared with the preset fluctuation potential upper limit value, and the smaller value between the two is taken as the adjustment value. Multiply the adjustment value by the preset gain coefficient to obtain the state of charge increment; The dynamic target state of charge is obtained by adding the baseline value to the state of charge increment. Under the current operating mode of active pre-charging mode, the range extender of the new energy emergency power generation vehicle is controlled to raise the state of charge of the energy storage device to the dynamic target state of charge.

2. The method according to claim 1, characterized in that, Based on N load power data, calculate the power variance component, first-order rate of change component, and second-order rate of change component, including: Calculate the average value of N load power data points; The difference between each load power data point and the average value is squared, and the average of the N squared values ​​is calculated to obtain the power variance component. Calculate the absolute value of the difference between adjacent load power data, and average the absolute values ​​to obtain the first-order rate of change component; Calculate the absolute value of the difference between adjacent first-order differences, and average the absolute values ​​to obtain the second-order rate of change component.

3. The method according to claim 1, characterized in that, Under the current operating mode of active pre-charging, the range extender of the new energy emergency power generation vehicle is controlled to raise the state of charge of the energy storage device to the dynamic target state of charge, including: Under the current operating mode of active pre-charging mode, obtain the output power corresponding to the optimal efficiency point of the range extender; Send a start command to the range extender to control the range extender to start according to the preset soft start power curve and operate stably at the optimal efficiency point to output power; The target charging power is calculated by subtracting the real-time load power from the output power at the optimal efficiency point. Send charging commands to the energy storage device management system to control the energy storage device to charge at the target charging power; The actual state of charge of the energy storage device is continuously monitored, and the range extender is shut down when the actual state of charge reaches the dynamic target state of charge.

4. The method according to claim 1, characterized in that, The method also includes: During the continuous monitoring of the actual state of charge of the energy storage device, the dynamic load fluctuation potential index is updated in real time. If the dynamic load fluctuation potential index changes by a preset amplitude, the step of determining the current operating mode in the preset three-dimensional decision space is re-executed. If the operating mode changes, the current charging process is interrupted, and the control strategy corresponding to the new operating mode is executed.

5. The method according to claim 1, characterized in that, The method also includes: Monitor whether the real-time load power output of the new energy emergency power generation vehicle exceeds the output power of the range extender at its optimal efficiency point; When the real-time load power exceeds the output power of the range extender at its optimal efficiency point, the operating mode is determined to be hybrid power assist mode. In hybrid power assist mode, the range extender is controlled to operate at the optimal efficiency point to output power, while the energy storage device is controlled to discharge to make up for the power difference. Subtracting the range extender's optimal efficiency point output power from the real-time load power yields the required discharge power for the energy storage device. Send a discharge command to the energy storage device management system to control the energy storage device to discharge according to the required discharge power.

6. A new energy emergency power generation vehicle, characterized in that, include: Energy storage devices; Range extender; The power acquisition unit is used to acquire the real-time load power output by the new energy emergency power generation vehicle; An energy storage device management system is used to monitor the state of charge of energy storage devices and control the charging and discharging of energy storage devices. as well as The controller is connected to the energy storage device, the range extender, the energy storage device management system, and the power acquisition unit. The controller is configured to perform the method as described in any one of claims 1 to 3.

7. A controller, characterized in that, include: The memory is configured to store instructions; as well as The processor is configured to retrieve the instructions from the memory and, when executing the instructions, to implement the new energy emergency power generation vehicle control method according to any one of claims 1 to 5.

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