Load control method and system of mobile energy storage equipment, medium and product

Abstract

The invention discloses a load control method and system for mobile energy storage equipment, a medium and a product, and the method comprises the steps: obtaining the motion state data and environment state data of target mobile energy storage equipment; performing second-order differential operation on the real-time speed to obtain a jerk value; generating a power demand impact sequence; based on the real-time polarization impedance and the real-time open-circuit voltage, calculating a maximum pulse discharge power threshold value under the condition that a preset under-voltage protection threshold value is not triggered; when the maximum power demand value in the power demand impact sequence is greater than the maximum pulse discharge power threshold, determining that the target mobile energy storage equipment has a power supply drop risk in a preset load prediction time window; calculating an energy gap prediction sequence based on waveform integration of the power demand impact sequence; and generating a feed-forward compensation instruction based on the energy gap prediction sequence, and controlling an auxiliary power conversion unit in the target mobile energy storage equipment to execute the feed-forward compensation instruction. And the accuracy of load control is improved.

Description

Technical Field

[0001] This application relates to the field of load control technology, specifically to a load control method, system, medium, and product for a mobile energy storage device. Background Technology

[0002] With the development of new energy technologies, mobile energy storage devices (such as new energy vehicles and mobile power systems) have been widely used in transportation, emergency power supply, and outdoor operations. As the core unit of mobile energy supply, these devices typically rely on energy storage arrays composed of multiple battery modules to achieve continuous power supply to different loads.

[0003] In existing technologies, in order to improve the power supply stability of mobile energy storage devices, the operating status of the devices is usually monitored in real time, and the power demand in the near future is predicted by load prediction algorithms. This allows for the adjustment of battery output strategies in advance to prevent the energy storage system from overloading or voltage drops.

[0004] However, when the operating status of equipment changes drastically or the resistance of the external environment suddenly increases, the load change exhibits strong suddenness and nonlinearity. Traditional load forecasting methods are unable to reflect the power impact caused by such sudden changes in a timely and accurate manner, resulting in forecast lag and reducing the accuracy of load control. Summary of the Invention

[0005] This application provides a load control method, system, medium, and product for mobile energy storage devices, which solves the technical problem of difficulty in timely and accurately reflecting power surges caused by such sudden changes, resulting in prediction lag, and improves the accuracy of load control.

[0006] The first aspect of this application provides a load control method for a mobile energy storage device, the method comprising: Acquire motion state data and environmental state data of the target mobile energy storage device. The motion state data includes real-time speed and operation control parameters, and the environmental state data includes the drag coefficient and environmental potential gradient of the location of the target mobile energy storage device. The second-order differential operation is performed on the real-time velocity to obtain the jerk value of the target mobile energy storage device. The jerk value is used to characterize the degree of abrupt change in the acceleration of the target mobile energy storage device. A power demand impact sequence is generated based on the real-time speed, the environmental state data, and the jerk value. The power demand impact sequence is used to characterize the load dynamics of the target mobile energy storage device within a preset load prediction time window. The real-time polarization impedance and real-time open-circuit voltage of the main power supply module in the working connection state of the battery module array of the target mobile energy storage device are obtained, and the maximum pulse discharge power threshold under the condition of not triggering the preset undervoltage protection threshold is calculated based on the real-time polarization impedance and the real-time open-circuit voltage. When the maximum power demand value in the power demand surge sequence is greater than the maximum pulse discharge power threshold, it is determined that the target mobile energy storage device has a power supply drop risk within the preset load prediction time window. Based on the power demand shock sequence, an energy gap prediction sequence is determined; Based on the energy gap prediction sequence, a feedforward compensation command is generated, and the auxiliary power conversion unit in the target mobile energy storage device is controlled to execute the feedforward compensation command.

[0007] Optionally, a power demand impact sequence is generated based on the real-time velocity, the environmental state data, and the jerk value, specifically including: The dynamic operating resistance is calculated based on the real-time speed and the drag coefficient, and the gravitational component resistance is calculated based on the environmental potential energy gradient. The sum of the dynamic operating resistance and the gravitational component resistance is determined as the environmental impedance description value, which is used to characterize the comprehensive physical gradient of the current location environment that hinders the movement of the equipment. When the absolute value of the acceleration value is greater than the preset oscillation threshold, and the rate of change of the torque command in the operation control parameters is greater than the preset stiffness threshold, dynamic stiffness coupling calculation is performed on the acceleration value and the rate of change of the torque command to obtain the inertial hysteresis parameter and stiffness excitation parameter. A dynamic load impact model is constructed based on the environmental impedance description value, the inertial hysteresis parameter, and the stiffness excitation parameter. Within the preset load prediction time window, the dynamic load impact model is discretized and sampled according to a preset sampling frequency to generate the power demand impact sequence.

[0008] Optionally, dynamic stiffness coupling calculations are performed on the jerk value and the rate of change of the torque command to obtain inertial hysteresis parameters and stiffness excitation parameters, specifically including: Phase compensation is performed on the time integral result of the acceleration value based on the torque command change rate to obtain the inertial hysteresis parameter; The stiffness excitation parameter is obtained by modulating the amplitude of the rate of change of the torque command based on the jerk value.

[0009] Optionally, a dynamic load impact model is constructed based on the environmental impedance description value, the inertial hysteresis parameter, and the stiffness excitation parameter, specifically including: Calculate the partial derivative of the environmental impedance description value with respect to the real-time speed to obtain the environmental damping change rate. If the environmental damping change rate is positive and greater than the preset abrupt change limit, then it is determined that the target mobile energy storage device is in a forced transition state from the first damping region to the second damping region, and the environmental damping coefficient of the first damping region is less than that of the second damping region. Under the forced transition state, the coupling relationship between the stiffness excitation parameter and the environmental damping change rate is analyzed, and a dynamic load impact factor is generated based on the coupling relationship. The dynamic load impact factor is used to characterize the transient energy density that the power system of the target mobile energy storage device needs to release additionally to overcome environmental changes. The impact initiation time is determined based on the inertial hysteresis parameter. The dynamic load impact factor is determined as a function value of the impact initiation time to construct a dynamic load impact model. The dynamic load impact factor decays at a rate proportional to the environmental impedance description value after the impact initiation time.

[0010] Optionally, an energy gap prediction sequence is determined based on the power demand impact sequence, specifically including: The power demand values ​​with amplitudes greater than the maximum pulse discharge power threshold in the power demand impulse sequence are combined into an overload power subsequence; Calculate the difference between the overload power subsequence and the maximum pulse discharge power threshold at the corresponding sampling time of the overload power subsequence according to the time step of the preset sampling frequency; Perform discrete integration on the difference to obtain the energy loss at each sampling time; The energy deficit amounts are combined in chronological order to form the energy gap prediction sequence. The energy deficit amount is used to characterize the cumulative instantaneous energy required to maintain the load at the sampling time.

[0011] Optionally, a feedforward compensation instruction is generated based on the energy gap prediction sequence, specifically including: Obtain the real-time energy conversion efficiency parameters and real-time bus voltage of the auxiliary power conversion unit in the target mobile energy storage device; Based on the real-time energy conversion efficiency parameter, the energy gap prediction sequence is corrected by loss compensation to generate the source-end energy demand sequence. The source-end energy demand sequence is subjected to time differentiation to obtain the compensation power waveform, and the compensation power waveform is converted into a compensation current sequence based on the real-time bus voltage. The compensation current sequence is shifted forward by a preset step size on the time axis to generate the feedforward compensation command.

[0012] Optionally, the compensation current sequence is phase-shifted ahead by a preset step size on the time axis to generate the feedforward compensation command, specifically including: Obtain the step response delay data of the auxiliary power conversion unit under different preset load ratios, and construct a load response delay mapping table; Calculate the rate of change of current at each sampling moment in the compensation current sequence, and match the adaptive lead time step corresponding to each sampling moment based on the rate of change of current and the load response delay mapping table; Based on the adaptive lead time step and the preset non-uniform time axis, the compensation current sequence is reconstructed using a non-uniform time axis to generate the feedforward compensation command.

[0013] In a second aspect, embodiments of this application provide a load control system for a mobile energy storage device. The load control system for the mobile energy storage device includes: one or more processors and a memory; the memory is coupled to the one or more processors and is used to store computer program code, the computer program code including computer instructions, and the one or more processors call the computer instructions to cause the load control system of the mobile energy storage device to perform the method described in the first aspect and any possible implementation thereof.

[0014] Thirdly, embodiments of this application provide a computer-readable storage medium including instructions that, when executed on a load control system of a mobile energy storage device, cause the load control system of the mobile energy storage device to perform the method described in the first aspect and any possible implementation thereof.

[0015] Fourthly, embodiments of this application provide a computer program product containing instructions that, when the computer program product is run on the load control system of a mobile energy storage device, cause the load control system of the mobile energy storage device to execute the method described in the first aspect and any possible implementation thereof.

[0016] In summary, one or more technical solutions provided in this application have at least the following technical effects or advantages: 1. By acquiring the real-time speed, operating control parameters, drag coefficient, and environmental potential energy gradient of the target mobile energy storage device, and combining this with the second-order derivative of the real-time speed to extract the jerk value, the system quantifies the degree of abrupt changes in motion state, enabling load prediction to perceive the dynamic trend of operating state changes. Furthermore, based on the above information, a power demand surge sequence is constructed to accurately reflect the load change characteristics of the device within the prediction time window. Simultaneously, the real-time polarization impedance and open-circuit voltage of the main power supply module are collected to calculate the maximum pulse discharge power threshold that will not trigger undervoltage protection under current conditions. By comparing the maximum power demand in the surge sequence, the system promptly identifies the risk of power supply drop. Based on this, the system predicts the energy gap through waveform integration and generates feedforward compensation commands to drive the auxiliary power conversion unit to participate in power supply regulation. Therefore, in scenarios of sudden load changes or drastic changes in the external environment, the system can achieve rapid prediction and proactive compensation of power surges, improving the accuracy of load control.

[0017] 2. Based on real-time velocity and drag coefficient, dynamic operating resistance is calculated, and combined with the environmental potential energy gradient, the gravitational component resistance is calculated to construct an environmental impedance description value. This comprehensively characterizes the composite environmental resistance generated by the current position on the motion, providing an environmental constraint basis for dynamic modeling. When the jerk value and torque command change rate exceed the preset oscillation threshold and stiffness threshold, respectively, the system extracts the inertial lag parameter and stiffness excitation parameter through dynamic stiffness coupling calculation, thereby reflecting the system's response inertia and excitation intensity under control abrupt changes. The above parameters are further integrated to construct a dynamic load impact model, and discretized within the prediction time window to generate a power demand impact sequence with dynamic characteristics, achieving forward-looking prediction of load abrupt change trends. Simultaneously, by calculating the partial derivative of the environmental impedance description value with respect to real-time velocity, environmental damping abrupt changes are identified. When the system is in a forced transition state, a dynamic load impact factor is generated based on the coupling relationship between the stiffness excitation parameter and the environmental damping change rate, and the impact initiation time is determined by combining the inertial lag parameter, constructing an impact model with temporal and energy density characteristics. This enables dynamic modeling and predictable response to power surges under the combined effects of control excitation, inertial response, and environmental disturbances, improving the system's sensitivity to sudden load changes and control foresight, thereby further enhancing the power supply stability and control robustness of mobile energy storage devices in complex external environments.

[0018] 3. By acquiring the real-time energy conversion efficiency parameters and real-time bus voltage of the auxiliary power conversion unit in the target mobile energy storage device, the system can adjust the energy demand based on the actual losses in the current energy conversion path. Then, based on the energy conversion efficiency parameters, loss compensation is performed on the predicted energy gap sequence to obtain a source-end energy demand sequence that more closely reflects the actual load capacity of the source. Subsequently, by performing time differentiation on this sequence, a compensation power waveform reflecting short-term power change trends is constructed. Combined with the real-time bus voltage, the power-to-current dimension conversion is completed, generating a compensation current sequence. This allows the compensation control to have directly executable electrical parameter outputs. Furthermore, the compensation current sequence is shifted forward by a preset step size on the time axis to construct a control command with feedforward characteristics, enabling the auxiliary power conversion unit to proactively intervene in the energy regulation process before the predicted power gap occurs. This improves the timeliness and execution accuracy of the compensation response and also achieves advance compensation control for future load fluctuations, effectively enhancing the power support capability and bus voltage stability of the energy storage system under dynamic load impact scenarios. Attached Figure Description

[0019] Figure 1 This is a schematic flowchart of a load control method for a mobile energy storage device according to an embodiment of this application; Figure 2 This is a schematic diagram of the process for generating power demand impulse sequences in an embodiment of this application; Figure 3 This is a schematic diagram of the load control system of a mobile energy storage device provided in an embodiment of this application.

[0020] Explanation of reference numerals in the attached drawings: 301, Central Processing Unit; 302, Read-Only Memory; 303, Random Access Memory; 304, Bus; 305, Input / Output Interface; 306, Input Section; 307, Output Section; 308, Storage Section; 309, Communication Section; 310, Driver; 311, Removable Media. Detailed Implementation

[0021] To enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments.

[0022] In the description of the embodiments of this application, the words "for example" or "for instance" are used to indicate examples, illustrations, or explanations. Any embodiment or design that is described as "for example" or "for instance" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design options. Rather, the use of the words "for example" or "for instance" is intended to present the relevant concepts in a specific manner.

[0023] In the description of the embodiments of this application, the term "multiple" means two or more. For example, multiple systems means two or more systems, and multiple screen terminals means two or more screen terminals. Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the indicated technical features. Thus, a feature defined with "first" or "second" may explicitly or implicitly include one or more of that feature. The terms "comprising," "including," "having," and variations thereof all mean "including but not limited to," unless otherwise specifically emphasized.

[0024] Figure 1 This is a schematic flowchart of a load control method for a mobile energy storage device according to an embodiment of this application.

[0025] Please see Figure 1 This application provides a load control method for a mobile energy storage device, the method comprising: S101. Obtain motion state data and environmental state data of the target mobile energy storage device. The motion state data includes real-time speed and operation control parameters. The environmental state data includes the drag coefficient and environmental potential gradient of the location of the target mobile energy storage device. Motion status data includes real-time speed and operating control parameters. Real-time speed reflects the current rate of movement of the equipment in physical space and is a direct quantity describing the trend of its kinetic energy change. Operating control parameters include control command inputs from the drive system, such as wheel torque commands, vehicle speed setpoints, and accelerator pedal opening in electric drive systems, which reflect the real-time control intentions of the user or control system.

[0026] In practice, real-time speed can be acquired by speed sensors (such as Hall effect sensors or encoders) installed on the wheels or drive shaft and input to the control system at high frequency. Operating control parameters are generated in real-time by the control logic within the Vehicle Control Unit (VCU) and sent to the load control module via a control bus (such as a CAN bus). Unlike static parameters, this type of data has strong time-series characteristics, typically updated at millisecond intervals, ensuring the control system can respond to dynamic changes in the vehicle in real time.

[0027] Environmental status data includes the drag coefficient and environmental potential energy gradient corresponding to the geographical location of the target mobile energy storage device, reflecting the impact of the external physical environment on the device's operating status. The drag coefficient mainly includes air drag coefficient, rolling drag coefficient, and slope drag factor, which can be obtained by matching the device's geographical location information with environmental parameter models in high-definition map data or geographic information systems (GIS). The environmental potential energy gradient represents the rate of change of slope along the path of the device, and its physical meaning is the change of potential energy per unit distance. It can be obtained by measuring the elevation change of the device's current location using an onboard inertial measurement unit (IMU) or a high-precision positioning module (such as GNSS+RTK), and calculating the slope component by combining it with real-time speed.

[0028] The purpose of collecting the above data is to comprehensively consider the current dynamic state of the equipment and external resistance conditions in the load prediction model, thereby improving the modeling accuracy and responsiveness of power demand trends. By fusing motion state data and environmental state data, the system can predict the probability and intensity of power surges before they occur, providing complete input information support for subsequent calculation of acceleration values, generation of power surge sequences, and formulation of energy compensation strategies.

[0029] For example, when a mobile energy storage device is on an uphill slope and the accelerator pedal opening increases rapidly, the real-time speed changes slowly but the torque command increases suddenly. At this time, combined with the environmental potential energy gradient, it can be identified that the device is affected by gravitational resistance in the high potential energy region. The data obtained in this step can provide a key basis for judging the risk of sudden load increase.

[0030] S102. Perform a second-order differential operation on the real-time speed to obtain the jerk value of the target mobile energy storage device. The jerk value is used to characterize the degree of abrupt change in the acceleration of the target mobile energy storage device. To accurately identify the dynamic load fluctuation characteristics caused by rapid changes in control strategy during the operation of a target mobile energy storage device, step S102 is executed, which involves performing a second-order differential operation on the real-time velocity to obtain the jerk value, which characterizes the degree of abrupt change in control intent. Jerk is the second derivative of velocity, and physically represents the rate of change of acceleration over time. It is often used to characterize the degree of abrupt change in system response or the rapid adjustment trend caused by the controller input. Because the power system response of a mobile energy storage device typically exhibits a certain degree of inertial lag during operation, especially when there are sudden changes in driver operation or dynamic adjustments by the automatic control system, this response characteristic may not be obvious in the acceleration dimension, but it can be significantly amplified in the jerk dimension. Therefore, this parameter can effectively reveal whether the control intent has changed drastically.

[0031] In practical implementation, the real-time speed sequence is first acquired by the speed acquisition module in the vehicle control system at a fixed sampling period (e.g., 10 milliseconds). A differential algorithm is then used for numerical second-order differentiation to calculate the jerk value at each time point. To ensure the stability of the differential calculation, a sliding window filtering algorithm is used to smooth the raw speed data, reducing high-frequency noise interference caused by sensor jitter or sampling errors. The second-order differential formula can be expressed as: ,in This represents the acceleration value at time point t. This indicates the real-time speed at that point in time. This represents the sampling time interval.

[0032] The calculated jerk value serves as a key indicator for analyzing sudden changes in system control intent. It can reveal whether the target mobile energy storage device is experiencing sudden acceleration, deceleration, or a rapid adjustment command issued by the controller. In subsequent power surge sequence modeling, introducing the jerk value as a modeling factor helps enhance the predictive sensitivity to short-term power demand surges and improves the predictive model's adaptability to nonlinear dynamic changes.

[0033] For example, when a target mobile energy storage device is driving on a city road and encounters a red light, the driver quickly presses the accelerator pedal, causing the motor torque command to rise rapidly. At this time, although the speed change is still in the initial stage, the acceleration value shows an instantaneous peak, which can indicate in advance that the system will face the risk of power surge, providing a priori judgment basis for the timely intervention of subsequent compensation strategies.

[0034] S103. Generate a power demand impact sequence based on the real-time speed, the environmental state data and the jerk value. The power demand impact sequence is used to characterize the load dynamic characteristics of the target mobile energy storage device within a preset load prediction time window. The preset load prediction time window refers to the future time range set by the system when performing load dynamic modeling and power surge prediction. It is used to assess the load change trend and power demand fluctuation that mobile energy storage devices may face in the coming period. The length of this time window is usually determined by a combination of the device control cycle, response delay, and the time scale of load changes under typical operating conditions. For example, it can be set to 0.5 seconds or 1 second in the future so that the system can generate compensation commands in advance to provide feedforward response to upcoming load surges, thereby improving the stability and security of dynamic power supply. In this embodiment, in order to further improve the ability to predict the load change trend of the target mobile energy storage device in the short term and enable the system to identify potential power surges in advance and respond accordingly, step S103 is executed, which generates a power demand surge sequence based on real-time speed, environmental state data, and jerk values. By coupling the current motion state of the device with the physical disturbance factors of the environment and introducing jerk values ​​to reflect the degree of abrupt change in the motion state, the constructed power demand surge sequence can not only reflect the power demand under steady-state conditions but also has the ability to provide a forward-looking description of sudden load changes. To ensure accurate generation of this sequence, a series of modeling processes were further refined, including the calculation of dynamic running resistance and gravitational component resistance, the construction of environmental impedance description values, dynamic stiffness coupling calculation, and the construction of a dynamic load impact model based on the above multi-parameters, followed by discretization sampling. Figure 2 This is a schematic diagram of the process for generating power demand impulse sequences in an embodiment of this application. The following is a summary of the process. Figure 2 Step 103 will be explained in detail.

[0035] S201. Calculate the dynamic running resistance based on the real-time speed and the resistance coefficient, calculate the gravitational component resistance based on the environmental potential energy gradient, and determine the sum of the dynamic running resistance and the gravitational component resistance as the environmental impedance description value. The environmental impedance description value is used to characterize the comprehensive physical gradient of the current location environment that hinders the movement of the equipment. To improve the physical accuracy and dynamic response capability of the power demand impact sequence, in step S201, the dynamic operating resistance is calculated based on the real-time velocity resistance coefficient, and the gravitational component resistance is calculated based on the environmental potential energy gradient. The sum of the two resistance components is determined as the environmental impedance description value, which is used to uniformly characterize the comprehensive physical resistance caused by the current location environment of the target mobile energy storage device to its movement. The purpose of this process is to introduce external environmental factors into the load prediction model, so that the generated power demand impact sequence not only reflects the internal control behavior, but also responds to the load change trend caused by complex environmental disturbances.

[0036] Specifically, dynamic running resistance is the relative resistance experienced by equipment when moving on flat or uneven surfaces, mainly composed of air resistance and rolling resistance. Rolling resistance typically has a weakly linear relationship with speed, while air resistance is proportional to the square of the speed. The resistance coefficient is an empirical set of parameters used to model these two types of resistance; it can be obtained through vehicle parameter calibration or adjusted in real time according to different road conditions. After collecting real-time speed within the control cycle, the system calculates the dynamic running resistance using the following formula: ,in, ρ is the rolling resistance coefficient, m is the equipment mass, g is the acceleration due to gravity, and ρ is the air density. denoted as the air resistance coefficient, A as the frontal area, and v as the real-time speed.

[0037] On the other hand, gravitational drag is the additional resistance generated by the component of gravity in the direction of travel when equipment moves on slopes or in areas of changing terrain; its magnitude is proportional to the sine of the slope angle. The environmental potential gradient is a physical quantity used to describe the rate of change of terrain at the equipment's current location, typically calculated in real-time using a high-precision positioning system (such as RTK-GNSS) combined with a digital elevation model. The formula for calculating gravitational drag is as follows: Where θ is the slope angle corresponding to the environmental potential energy gradient. The two types of resistance are algebraically summed to form the environmental impedance description value. This environmental impedance description value serves as an important input variable for subsequent dynamic load impact models, comprehensively reflecting the intensity of external resistance experienced by the equipment at the current speed. Introducing this variable into load prediction helps to accurately capture the additional power demand impact caused by terrain changes or sudden increases in operating resistance, thereby improving the model's adaptability to real operating conditions and providing a more physically interpretable and predictive input basis for the control system.

[0038] For example, when a target mobile energy storage device enters a continuous uphill section, even if the speed does not change much, the gravity resistance corresponding to the calculated environmental potential energy gradient increases significantly due to the continuous increase in slope, which in turn leads to an increase in the environmental impedance description value. Based on this, the system can identify the trend that the future power demand will rise rapidly and intervene in the compensation control process in advance to avoid bus voltage drop or power outage.

[0039] S202. When the absolute value of the acceleration value is greater than the preset oscillation threshold and the torque command change rate in the operation control parameters is greater than the preset stiffness threshold, dynamic stiffness coupling calculation is performed on the acceleration value and the torque command change rate to obtain the inertial hysteresis parameter and stiffness excitation parameter. To further enhance the dynamic modeling capability of power demand shocks and make the generated shock sequence sensitive to system response delays and excitation intensity caused by sudden changes in control commands, step S202 is executed. This involves performing dynamic stiffness coupling calculations on the jerk value and torque command change rate when the absolute value of the jerk exceeds a preset oscillation threshold and the torque command change rate exceeds a preset stiffness threshold. This extracts inertial hysteresis parameters and stiffness excitation parameters that characterize the system response characteristics and excitation level. The preset oscillation threshold is a fixed reference value used to determine whether the jerk change of the mobile energy storage device is abnormally drastic. If the absolute value of the jerk exceeds this threshold, the system considers there to be a severe motion control disturbance or sudden operating condition, thus triggering subsequent dynamic stiffness coupling calculations to identify potential load shock risks. The preset stiffness threshold is a benchmark value used to measure whether the torque command change reaches a significant control excitation level. When the torque command change rate exceeds this threshold, the system determines that there is a strong demand for drive stiffness change, thus triggering dynamic stiffness coupling calculations together with the jerk to identify and model possible load shock behaviors. The inertial hysteresis parameter physically corresponds to the response delay time constant of the mechanical transmission system; the stiffness excitation parameter physically corresponds to the frequency intensity of the driver's torque request.

[0040] This process aims to establish a dynamic load impact model that better reflects actual control scenarios by analyzing the nonlinear coupling relationship between the input signal of the control system and the mechanical response. Specific coupling calculation methods include phase compensation based on the time integral of the jerk value using the torque command change rate to obtain the inertial hysteresis parameter, and amplitude modulation based on the torque command change rate using the jerk value to obtain the stiffness excitation parameter.

[0041] The absolute value of jerk and the rate of change of torque command are introduced as joint judgment conditions to accurately identify whether the target mobile energy storage device is in a critical state of load excitation due to the superposition of abrupt changes in control commands and response inertia during operation. The absolute value of jerk quantifies the instantaneous change intensity of the device's mechanical response, reflecting the degree of response fluctuation at the control execution end, while the rate of change of torque command reflects the excitation intensity at the control input end. When both exceed their respective preset thresholds, it indicates that the system experiences both high control input disturbance and significant response lag and dynamic oscillation within a short period, constituting a key factor in the formation of power surges. Therefore, the dynamic stiffness coupling calculation process is only triggered when both conditions are simultaneously met, thus avoiding unnecessary modeling complexity in steady-state or slightly disturbed scenarios.

[0042] The aforementioned preset oscillation threshold and preset stiffness threshold were obtained through extensive experimental data statistics and system identification methods. During the actual testing phase, operating samples of the equipment were collected under different working conditions. The distribution of changes in jerk values ​​and torque command change rates was analyzed, and combined with the actual fluctuation response of power output, typical critical values ​​for both were determined when significant power surge events occurred. Subsequently, through cluster analysis and boundary sensitivity calibration, a minimum critical value capable of distinguishing between steady-state disturbances and sudden excitations was set, thereby ensuring that the judgment conditions are both trigger-sensitive and effectively suppress false judgments. Specifically, this may include the following steps: Phase compensation is performed on the time integral result of the acceleration value based on the torque command change rate to obtain the inertial hysteresis parameter; The stiffness excitation parameter is obtained by modulating the amplitude of the rate of change of the torque command based on the jerk value.

[0043] In the dynamic stiffness coupling calculation of jerk value and torque command change rate, phase compensation is first performed on the time integral result of the jerk value based on the torque command change rate to obtain the inertial hysteresis parameter. The jerk value, as the second derivative of velocity, reflects the trend of acceleration change and reveals the abrupt response characteristics of the equipment under control input disturbances. The torque command change rate reflects the degree of dynamic excitation actively applied by the control system and has strong feedforward characteristics. Since the mechanical system's response usually lags behind the control command under inertial influence, the raw integral result of the jerk value alone cannot accurately characterize the actual inertial response trajectory of the system. Therefore, a phase compensation mechanism is introduced. After performing time integration on the jerk value to restore its corresponding acceleration trend, the integral result is corrected based on the phase information of the torque command change rate, ensuring that the compensated result is synchronized with the timing of the control input, thereby improving the accuracy of inertial response modeling. This phase compensation can be achieved by introducing a hysteresis filter or a phase alignment operator. Specifically, an adaptive sliding window-based delay alignment algorithm is used to ensure high consistency of the compensation effect under different excitation frequencies. Inertial hysteresis parameters, as an important input to dynamic load impact models, help to quantify the time delay between the control excitation input and the mechanical response of the system, thereby improving the power demand forecasting model's ability to perceive and adapt to response hysteresis phenomena.

[0044] Based on the obtained inertial hysteresis parameter, to further reflect the influence of the control input excitation on the system output response amplitude, an amplitude modulation operation based on the jerk value and the torque command change rate is performed to obtain the stiffness excitation parameter. The amplitude modulation in this step is a signal coupling processing method. Its core idea is to dynamically adjust the torque change rate using the jerk value, so that the output stiffness excitation parameter can not only reflect the intensity of the control input but also the mechanical response characteristics of the controlled object. In the implementation process, the torque command change rate is first normalized, and the absolute value of the jerk value is used as the modulation factor. The modulation result is output through multiplicative coupling, thus forming the stiffness excitation parameter. Physically, this parameter represents the system's response stiffness under the current control input excitation, that is, the acceleration change capability exhibited under a unit control excitation. By introducing this parameter, the model's ability to model power jumps caused by sudden control excitations can be enhanced, especially when the equipment faces complex operating conditions (such as frequent starts, rapid acceleration, or ramp switching), helping to identify potential power surge amplitude trends in advance. For example, when the equipment accelerates rapidly in a high-slope area, the rate of change of torque command increases rapidly. At the same time, due to the heavy load on the equipment, the acceleration value also fluctuates significantly. The stiffness excitation parameter obtained after amplitude modulation shows a significant leap. The system can use this to determine that there is a combination of high excitation input and high response intensity, thereby providing a precise scheduling basis for subsequent power compensation strategies.

[0045] S203. Construct a dynamic load impact model based on the environmental impedance description value, the inertial hysteresis parameter, and the stiffness excitation parameter; In this embodiment, to further improve the modeling accuracy of the power demand impact sequence for the transient load evolution process under complex operating conditions, step S203 is executed, namely, constructing a dynamic load impact model based on environmental impedance description values, inertial hysteresis parameters, and stiffness excitation parameters. This model aims to comprehensively describe the transient power impact behavior of mobile energy storage devices under the combined effects of control input fluctuations, mechanical response hysteresis, and sudden changes in the operating environment. By introducing the acquired environmental impedance description values, the distribution of physical resistance faced by the device at its current location can be reflected; combined with inertial hysteresis parameters and stiffness excitation parameters, the system's dynamic characteristics and controlled response characteristics can be further reflected. However, to achieve an adaptive expression of this model under nonlinear operating conditions (such as transitioning from low-damping to high-damping regions), it is necessary to further analyze the derivative relationship of environmental resistance with respect to device state variables (such as real-time speed), identify resistance abrupt change regions, and on this basis, construct a dynamic load impact factor characterizing the degree of transient energy impact and an impact evolution process function. Specifically, this can include the following steps: Calculate the partial derivative of the environmental impedance description value with respect to the real-time speed to obtain the environmental damping change rate. If the environmental damping change rate is positive and greater than the preset abrupt change limit, then it is determined that the target mobile energy storage device is in a forced transition state from the first damping region to the second damping region, and the environmental damping coefficient of the first damping region is less than that of the second damping region. Under the forced transition state, the coupling relationship between the stiffness excitation parameter and the environmental damping change rate is analyzed, and a dynamic load impact factor is generated based on the coupling relationship. The dynamic load impact factor is used to characterize the transient energy density that the power system of the target mobile energy storage device needs to release additionally to overcome environmental changes. The impact initiation time is determined based on the inertial hysteresis parameter. The dynamic load impact factor is determined as a function value of the impact initiation time to construct a dynamic load impact model. The dynamic load impact factor decays at a rate proportional to the environmental impedance description value after the impact initiation time.

[0046] In constructing a dynamic load impact model, to accurately identify the power surge behavior of a target mobile energy storage device caused by sudden changes in resistance in a complex operating environment, the partial derivative of the environmental impedance description value with respect to real-time velocity is first calculated to obtain the environmental damping change rate. The environmental impedance description value is a comprehensive resistance index calculated in the preceding steps using the sum of dynamic operating resistance and gravitational component resistance, reflecting the intensity of physical resistance exerted on the device's movement by the current environment. Since this impedance value essentially changes with velocity, its partial derivative with respect to real-time velocity can be used to measure the rate of response of the environment to velocity changes, i.e., the damping change, defined as the environmental damping change rate. When this value is positive and exceeds a preset abrupt change threshold, it indicates that the device is undergoing a forced transition from low damping (first damping region) to high damping (second damping region). The abrupt change threshold is a typical environmental change critical value obtained through experimental calibration, used to distinguish between normal impedance fluctuations and significant resistance transitions. At this time, the device's power system needs to quickly adapt to the drastic increase in external resistance within a short period of time, which can easily trigger transient power surges. Therefore, identifying this transition state becomes a key prerequisite for dynamic modeling.

[0047] After identifying the equipment as being in a forced transition state, analyzing the coupling relationship between stiffness excitation parameters and the rate of change of environmental damping is to extract a comprehensive index reflecting the intensity of power impact from both the system control layer and the physical environment layer. The stiffness excitation parameter essentially reflects the acceleration response amplitude of the equipment under a sudden change in unit control torque command; it is a quantity reflecting the relationship between the excitation intensity of the control system and the dynamic stiffness of the controlled object. The rate of change of environmental damping, calculated using the first partial derivative of the environmental impedance description value with respect to real-time velocity, represents the rate of change of external resistance experienced by the equipment during the velocity change at its current position. A larger value indicates a stronger dynamic response from the environment and a stronger constraint on the equipment's motion.

[0048] To analyze the coupling relationship between the two, a normalization and correlation mapping method was adopted in the implementation. First, the stiffness excitation parameter and the rate of change of environmental damping were standardized to unify their distribution to the [0, 1] interval, thereby eliminating the influence of dimensional differences. Next, a coupling mapping function was constructed, for example, using a nonlinear product function or an exponential enhancement function: Let the coupling function be in the form of: C(t) = α·K'(t)·exp(β·ΔR'(t)), where: C(t) represents the coupling strength at the current moment; K'(t) is the normalized stiffness excitation parameter; ΔR'(t) is the normalized rate of change of environmental damping; α and β are empirical weighting coefficients used to adjust the sensitivity of the coupling response to control input and environmental factors.

[0049] In a physical sense, this function represents the following: when the control excitation is strong (K' is large) and the environmental resistance changes drastically (ΔR' is large), the coupling strength increases exponentially, indicating that the system may face severe transient power shocks; conversely, if either factor is small, the shock response is not significant. This coupling strength is the fundamental logic for the dynamic load shock factor.

[0050] In addition, to improve the model's adaptability to actual operating conditions, a time window convolution mechanism can be introduced, which involves weighted averaging of the coupling function values ​​within a short sliding window (e.g., 0.5 to 1 second) to filter out false triggers caused by high-frequency noise or occasional disturbances, thereby ensuring the stability and robustness of the coupling analysis.

[0051] For example, when equipment moves from a flat road to a steep slope, the terrain causes a sudden increase in the gravity component and a sharp increase in resistance, resulting in a rapid rise in the rate of change of environmental damping. If the control system rapidly increases the torque output to maintain speed, the stiffness excitation parameter will be increased simultaneously. The value calculated by the coupling function will be significantly higher than that under normal operating conditions. The system can then identify this area as a high-impact-risk section and generate a high-intensity dynamic load impact factor to guide subsequent energy compensation scheduling strategies.

[0052] To achieve continuous modeling of dynamic load impacts over time, the initial impact time needs to be determined based on the changing trend of the inertial hysteresis parameter. The dynamic load impact factor obtained from the coupling analysis is then used as the initial impact amplitude at that moment, constructing a dynamic load impact model with time decay characteristics. The inertial hysteresis parameter essentially reflects the time delay characteristic of the actual motion response of the equipment relative to the control input command, manifested as a jump in its value under sudden control excitation. By monitoring the time derivative of this parameter and identifying the moment when its slope first exceeds a set abrupt change threshold, the initial time point of the load impact can be determined, denoted as t0.

[0053] At time t0, the dynamic load impact factor D(t0) obtained in the previous step is used as the initial impact amplitude of the model to describe the peak energy released by the device to overcome the sudden external resistance at that instant. Subsequently, an impact decay function is constructed so that the impact value gradually decays to a steady-state level over time. To ensure the model's adaptability to the physical environment, the decay rate is set to be inversely proportional to the environmental impedance description value Z_env; that is, the greater the impedance, the more complex or steeper the terrain, the longer the impact duration, and the slower the decay.

[0054] In a non-limiting implementation, the following exponential decay function can be used as an example: F(t)=D(t0)·exp[-k·(t-t0) / (Z_env+ε)], where t≥t0, and where: F(t) is the dynamic load impact amplitude at time t; D(t0) is the impact factor value at the start of the impact; k is an adjustment coefficient used to control the decay rate; Z_env is the current environmental impedance description value; ε is a small constant to prevent division by zero error; exp[-x] represents the standard exponential decay form.

[0055] This function form offers good adjustability and environmental adaptability. If the device is in a high-impedance region (such as a steep slope or muddy terrain), Z_env is larger, and F(t) decays more slowly, indicating that the system needs more time to buffer the impact load. However, under stable operating conditions with lower impedance, the impact decays rapidly, which helps improve power dispatch efficiency.

[0056] Of course, the above function forms are merely examples. In actual modeling, linear decay functions, piecewise functions, or custom functions based on empirical data fitting can be selected according to the equipment type, control strategy, and operating environment characteristics, without limitation. The final dynamic load impact model not only possesses the characteristics of timed triggering, amplitude adaptation, and time decay, but also provides high-precision timing input for the generation of subsequent power demand impact sequences, improving the prediction accuracy of power supply drop risks and the response sensitivity to auxiliary power scheduling.

[0057] S204. Within the preset load prediction time window, the dynamic load impact model is discretized and sampled according to the preset sampling frequency to generate the power demand impact sequence.

[0058] After the dynamic load impact model is constructed, to achieve quantitative prediction of the load change trend of mobile energy storage devices in the future, step S204 needs to be executed. This involves discretizing the dynamic load impact model within a preset load prediction time window according to a preset sampling frequency, thereby generating a power demand impact sequence for subsequent power supply capacity assessment and energy dispatch control. The preset sampling frequency refers to the time reference for the system to periodically collect and process various dynamic parameters (such as speed, jerk, power demand, etc.) during load prediction and control. It is usually set in Hertz (Hz) to ensure the real-time nature of the model input and the timeliness of the prediction results. For example, setting it to 100Hz means sampling once every 0.01 seconds. This sequence not only carries the power response information generated by the combined effects of control input disturbances and sudden changes in external physical resistance under complex operating environments, but also provides a time-series basis for judging whether there is a risk of power supply drop. Therefore, high-precision discretization modeling can significantly improve the system's prediction resolution and response timeliness for power impacts.

[0059] In the specific implementation process, the dynamic load impact model is first expanded as a continuous-time function. This model uses the impact initiation moment as a reference point and combines the initial impact amplitude with the time-varying attenuation characteristics. The core parameters included in the model are the dynamic load impact factor, the environmental impedance description value, and the impact attenuation function form, which have been constructed and calibrated in the previous steps. To make the model applicable to digital processing systems, an equal-interval time discretization method is adopted, and the model output is sampled at a fixed sampling frequency within a preset load prediction time window. The sampling frequency is set according to the system control cycle or equipment response bandwidth. For example, if the control cycle is 10 milliseconds, the sampling frequency can be set to 100Hz to ensure the maximum reproduction of the detailed changes in the dynamic impact.

[0060] At each sampling moment, the current time point is substituted into the dynamic load impact model to calculate the instantaneous power impact value at that moment. This value represents the additional power that the equipment needs to release at that moment to overcome inertial lag and external resistance. The power impact values ​​of all sampling points are arranged in chronological order to form a power demand impact sequence, which describes the dynamic load demand changes of the equipment within a future load forecast cycle. This sequence not only contains impact amplitude information but also reflects the impact duration and decay rate, and can completely express the entire process from impact occurrence to dissipation.

[0061] This sequence allows the system to compare the maximum pulse discharge power threshold that the current battery module can provide to determine whether there is a risk point that exceeds the power supply capacity at a certain moment. In addition, this sequence can also be used as a direct input for energy gap integral calculation, providing data support for subsequent feedforward compensation strategies.

[0062] For example, in an uphill start-up scenario, the equipment control system issues a rapid acceleration command, resulting in a sudden change in torque command. This, combined with the sudden increase in gravitational resistance caused by the steep incline, leads the dynamic load impact model to output a high-amplitude impact power at the initial moment. The system samples at 50ms intervals within the prediction time window, continuously obtaining a sequence of impact power values ​​such as [5.2, 4.8, 4.1, 3.3, 2.7, 1.9…] kW, forming a complete power demand impact sequence that reflects the entire process of the impact event's occurrence, decay, and dissipation. This sequence will serve as the basic data input for subsequent power supply dip risk analysis and energy compensation control, ensuring the system can intervene in advance and guarantee power supply stability.

[0063] In summary, by performing high-frequency discretization sampling on the dynamic load impact model, not only was a digital representation of complex impact behavior achieved, but a time-series mapping mechanism between equipment load response and environmental conditions was also established, effectively improving the accuracy of power prediction and the foresight of load management.

[0064] S104. Obtain the real-time polarization impedance and real-time open-circuit voltage of the main power supply module in the working connection state of the battery module array of the target mobile energy storage device, and calculate the maximum pulse discharge power threshold without triggering the preset undervoltage protection threshold based on the real-time polarization impedance and the real-time open-circuit voltage. The Battery Management System (BMS) collects the real-time open-circuit voltage of the main power supply module under its current operating state. Open-circuit voltage refers to the battery's terminal voltage under no-load conditions, and is typically affected by the State of Charge (SOC), temperature, and electrochemical aging. It is an important indicator of battery health and remaining energy. Simultaneously, the real-time polarization impedance of the main power supply module is acquired. This parameter reflects the total impedance of the battery under energized conditions due to electrochemical reactions and internal structure, including ohmic internal resistance, electrochemical polarization impedance, and concentration impedance. A higher polarization impedance value indicates a greater voltage drop during discharge, making it more prone to triggering undervoltage protection mechanisms due to excessively low output voltage.

[0065] The preset undervoltage protection threshold is the minimum allowable output voltage set by the system to prevent performance degradation or safety risks caused by over-discharge of the battery module. When the real-time output voltage of the main power supply module falls below this threshold, a protection mechanism will be triggered to limit further discharge or switch to a protection state, ensuring the operational safety of the energy storage system and battery life. To ensure that the system's preset undervoltage protection threshold is not triggered during high-intensity power output, the maximum allowable pulse power output by the battery needs to be calculated based on the current open-circuit voltage, polarization impedance, and the preset minimum allowable operating voltage. This calculation follows the basic circuit power model, specifically: P_max = [(U_ocv - U_min)]. 2 ] / R_p, where: P_max represents the maximum pulse discharge power threshold; U_ocv is the open circuit voltage at the current moment; U_min is the preset undervoltage protection threshold; R_p is the polarization impedance measured at the current moment.

[0066] The physical meaning of the above formula is: under the condition that the battery terminal voltage does not drop below the minimum operating voltage, the current at which the battery can discharge for a short time is determined, thereby inversely estimating the corresponding maximum power that it can handle. By calculating this power threshold in real time, the battery's rapid discharge limit capability can be dynamically grasped, providing boundary constraints for subsequent load power demand impact prediction.

[0067] For example, at a typical operating moment, the open-circuit voltage of the main power supply module is 3.7V, the polarization impedance is 0.05Ω, and the undervoltage protection threshold is set to 3.2V. Then, according to the above formula, we can get: P_max=[(3.7-3.2)^2] / 0.05=(0.5)^2 / 0.05=0.25 / 0.05=5.0W, that is, the maximum instantaneous discharge power of the current module without triggering the protection is 5.0W.

[0068] The above calculation method enables precise quantification of the battery module's instantaneous power supply capacity. In subsequent steps, if any predicted power point in the power demand surge sequence exceeds this threshold, the risk of insufficient power supply to the device at a certain moment can be accurately identified, and the energy compensation scheduling mechanism can be triggered in advance. Therefore, the implementation of S104 not only provides accurate battery discharge boundaries for power surge assessment but also lays the foundation for real-time decision-making in power supply stability control.

[0069] S105. When the maximum power demand value in the power demand impact sequence is greater than the maximum pulse discharge power threshold, it is determined that the target mobile energy storage device has a power supply drop risk within the preset load prediction time window. Step S105 identifies potential risks of insufficient power supply capacity for the target mobile energy storage device during its future operating cycle based on the comparison between the power demand forecast results and the current power supply capacity. This identification process plays a crucial role in achieving proactive energy management and improving the operational stability of the device. Specifically, by comparing the maximum power demand value in the power demand surge sequence with the calculated maximum pulse discharge power threshold, if the former exceeds the latter, it is determined that the device faces a power supply drop risk, and a subsequent compensation control mechanism is triggered accordingly.

[0070] In the preceding steps, a power demand surge sequence was constructed based on a dynamic load surge model. This sequence reflects the changes in power demand of the target mobile energy storage device within a preset load prediction time window due to the coupling between control excitation and environmental impedance. This sequence has a clear temporal order and amplitude distribution, and can capture the short-term high power requests brought about by sudden load surges, thereby realistically reproducing the operating load pressure faced by the device.

[0071] Meanwhile, by calculating the real-time polarization impedance and open-circuit voltage of the main power supply module, the maximum pulse discharge power threshold under the condition of not triggering the undervoltage protection threshold has been obtained. This threshold is a boundary value reflecting the transient discharge limit capability of the current battery module. It is affected by factors such as battery health, temperature, and charge state, and is a core indicator for evaluating power supply safety.

[0072] In the specific implementation of this step, the maximum value in the power demand surge sequence is first extracted and denoted as P_demand_max, which represents the most extreme power request within the prediction time window. Simultaneously, the maximum pulse discharge power threshold calculated in S104 is called and denoted as P_discharge_max. Then, a logical judgment is executed: if P_demand_max > P_discharge_max, it is determined that there is a risk of power supply dip. Here, "risk of power supply dip" refers to the situation where, at a future moment, the power required by the equipment load exceeds the transient discharge capacity that the current power supply module can withstand. Without external energy intervention, this may cause a voltage dip or trigger undervoltage protection, leading to power outage, equipment shutdown, or performance degradation.

[0073] The risk assessment results will serve as a key basis for subsequent control strategies. Once the risk is confirmed, the energy gap prediction and feedforward compensation process will be initiated immediately, and the auxiliary power conversion unit will be called in advance to inject energy, thereby effectively mitigating power surges and ensuring that the main power supply module operates within a safe range.

[0074] For example, under a certain operating condition, the maximum value of the power demand impulse sequence is 6.3kW, while the maximum pulse discharge power threshold is 5.5kW. Since 6.3kW>5.5kW, the system immediately determines that there is a risk of power supply drop in the equipment during the prediction period and triggers the subsequent compensation mechanism accordingly.

[0075] Implementing step S105 in the above manner not only enables accurate identification of future power supply risks but also ensures the system possesses forward-looking judgment and proactive defense capabilities during actual operation, thereby effectively avoiding system stability risks caused by high-frequency triggering protection mechanisms. Essentially, this method transforms the power supply and demand matching problem into a quantifiable comparative judgment, establishing a risk warning mechanism oriented towards actual operating scenarios, ensuring that mobile energy storage devices have higher operational reliability and energy dispatch efficiency in complex environments.

[0076] S106. Determine the energy gap prediction sequence based on the power demand impact sequence; Step S106 involves quantitatively extracting the portion of the power demand surge sequence that exceeds the maximum pulse discharge power threshold of the main power supply module, and converting it into a short-term energy gap trend prediction through time-series integration. This provides a direct data basis for subsequent feedforward compensation control. Since power is the derivative of energy with respect to time, integrating the power excess over time yields the total energy deficit caused by supply-demand imbalance within the prediction window and its evolution. This process is not a simple summation of the entire sequence, but rather involves extracting sub-segments from the power demand surge sequence that exceed the power supply capacity threshold to form an overload power sub-sequence, and further performing discrete integration on the difference between this sub-sequence and the threshold to ultimately form a time-ordered energy gap prediction sequence. This sequence not only reveals the severity of the power supply drop risk but can also be used to determine the response magnitude and duration of the compensation strategy. Specifically, it may include the following steps: The power demand values ​​with amplitudes greater than the maximum pulse discharge power threshold in the power demand impulse sequence are combined into an overload power subsequence; Calculate the difference between the overload power subsequence and the maximum pulse discharge power threshold at the corresponding sampling time of the overload power subsequence according to the time step of the preset sampling frequency; Perform discrete integration on the difference to obtain the energy loss at each sampling time; The energy deficit amounts are combined in chronological order to form the energy gap prediction sequence. The energy deficit amount is used to characterize the cumulative instantaneous energy required to maintain the load at the sampling time.

[0077] In the process of calculating the energy gap prediction sequence based on the waveform integration of the power demand impulse sequence, the first step is to filter and reorganize all power data points in the power demand impulse sequence whose amplitude exceeds the maximum pulse discharge power threshold, forming a new sequence called the overload power subsequence. The power demand impulse sequence is a future power request waveform derived from factors such as the current and predictive control behavior of the equipment and environmental conditions, and thus possesses time-series attributes. The maximum pulse discharge power threshold is the upper limit of the instantaneous discharge capacity calculated from the real-time polarization impedance and open-circuit voltage of the battery module. Comparing these two thresholds helps identify at which moments within the prediction time window the power request has exceeded the safe carrying capacity of the battery module. The purpose of constructing the overload power subsequence is to clarify the time interval and power amplitude of the energy supply-demand imbalance, providing input data for subsequent energy gap assessment. To illustrate with a set of sample data, if a certain power demand impulse sequence is [4.5, 5.2, 6.1, 4.8, 5.9] kW, and the maximum pulse discharge power threshold is 5.0 kW, then the overload power subsequence is [5.2, 6.1, 5.9] kW, corresponding to time points t2, t3, and t5, respectively.

[0078] Based on the aforementioned overload power subsequence, it is necessary to further calculate the power over-limit value at each sampling moment, i.e., the difference between the power demand and the maximum pulse discharge power threshold. This difference represents the load's "excess" energy request at that moment and is the basis for subsequent energy integration. During the calculation, it is necessary to ensure point-by-point comparison using a preset sampling frequency time step to maintain time alignment and integration accuracy. The sampling frequency is usually set according to the system response speed, with the unit being Hz, and the corresponding sampling time step is its reciprocal; for example, 10Hz corresponds to a 0.1-second sampling interval. Continuing with the above example, if the maximum threshold is 5.0kW, then the over-limit values ​​at each moment are 0.2kW, 1.1kW, and 0.9kW, respectively.

[0079] After calculating the difference, a discrete integral operation needs to be performed on the excess power difference sequence to obtain the energy deficit at each sampling time. Since the relationship between power and energy is that energy equals the integral of power over time, numerical methods such as the rectangular method and the trapezoidal method can be used to approximate the estimate under discrete conditions. In actual deployment, the rectangular method is usually used, which involves multiplying the power difference at each sampling point by the sampling time step to obtain the energy deficit value at the corresponding time, in joules or watt-hours. Continuing with the example above, if the sampling time step is 0.1 seconds, the energy deficit corresponding to the difference of [0.2, 1.1, 0.9] kW over time is [0.02, 0.11, 0.09] kWh, or converted to [72, 396, 324] kJ.

[0080] Finally, the calculated energy deficits are combined in chronological order to form a complete energy gap prediction sequence. This sequence can be understood as the cumulative instantaneous energy value that the equipment needs to supplement with auxiliary power sources or energy dispatch mechanisms to maintain reliable power supply over a continuous period of time, exhibiting a clear temporal trend and amplitude distribution characteristics. This sequence can not only be used to determine whether the feedforward compensation mechanism needs to be activated, but also provide a basis for the target value of the compensation power and the required compensation duration. For example, in the above example, the energy gap prediction sequence is [72, 396, 324] kJ, indicating that at times t2 to t5, the system needs to release the corresponding additional energy to avoid power supply drops, thus providing a quantitative input for the generation of the feedforward control command in S107.

[0081] Through the implementation of the above series of steps, not only was a quantitative analysis of power supply risks achieved, but the short-term power over-limit problem was also transformed into an energy dispatch problem, providing data basis and control objectives for subsequent control strategies, enabling the system to have a forward-looking response capability to sudden load shocks.

[0082] S107. Generate a feedforward compensation command based on the energy gap prediction sequence, and control the auxiliary power conversion unit in the target mobile energy storage device to execute the feedforward compensation command.

[0083] In step S107, based on the energy gap prediction sequence calculated in the previous steps, the system further generates a forward-looking feedforward compensation command and sends this command in real time to the auxiliary power conversion unit in the target mobile energy storage device. This is used to intervene in advance and compensate for the insufficient power supply capacity that may be caused by the instantaneous overload of the main power supply module. Since the energy gap prediction sequence essentially only reflects the trend of energy demand loss on the load side in the future time domain, and does not consider the efficiency characteristics of the auxiliary power conversion unit itself and the real-time bus status, in the specific process of generating the compensation command, the energy conversion efficiency parameters of the auxiliary power module and the current bus voltage level must first be introduced to correct and convert the energy gap sequence to obtain the actual executable source-end energy demand. Then, the energy demand sequence is converted into a power waveform using the time differentiation method, and the current compensation target value is further calculated in combination with the bus voltage. In order to ensure that the compensation behavior has sufficient forward response capability, the phase of the finally generated current sequence should also be shifted forward to form a true feedforward control command. The specific steps for generating the feedforward compensation command based on the energy gap prediction sequence are as follows: Obtain the real-time energy conversion efficiency parameters and real-time bus voltage of the auxiliary power conversion unit in the target mobile energy storage device; Based on the real-time energy conversion efficiency parameter, the energy gap prediction sequence is corrected by loss compensation to generate the source-end energy demand sequence. The source-end energy demand sequence is subjected to time differentiation to obtain the compensation power waveform, and the compensation power waveform is converted into a compensation current sequence based on the real-time bus voltage. The compensation current sequence is shifted forward by a preset step size on the time axis to generate the feedforward compensation command.

[0084] To ensure the auxiliary power conversion unit (APC) can respond promptly and effectively compensate for the energy deficit of the main power supply module before load surges occur, it is first necessary to obtain the real-time energy conversion efficiency parameters of the APC and the real-time bus voltage of the current system. The energy conversion efficiency parameter refers to the efficiency with which the APC converts input energy (such as from a supercapacitor or backup battery) into usable electrical energy for the load side under the current operating state. This efficiency is typically affected by fluctuations in temperature, current load rate, and internal control strategies. This parameter can be dynamically extracted using embedded sensors or a system state estimation model. The real-time bus voltage is the DC bus voltage between the APC output and the load side, directly determining the current capacity provided per unit time. This parameter is measured in real-time by the bus voltage acquisition module. Obtaining these two key parameters provides an accurate system state basis for subsequent compensation command generation, ensuring the executability of energy matching and power conversion.

[0085] Based on this, and combining the energy gap prediction sequence calculated in the previous steps, the system needs to correct the gap sequence for loss compensation according to the real-time energy conversion efficiency of the auxiliary power conversion unit, thereby generating the source-side energy demand sequence. Since the predicted energy gap only reflects the net energy required by the load, and in the actual compensation process, the auxiliary unit inevitably experiences energy loss during energy conversion, the target compensation amount must be adjusted upwards. Specifically, each item in the energy gap prediction sequence is divided by the current energy conversion efficiency parameter to obtain the actual energy that needs to be released from the source at the current efficiency. For example, if the predicted gap is 100kJ at a certain moment, and the current efficiency is 80%, then the source needs to release 125kJ to compensate for the gap. Through this correction, the generated source-side energy demand sequence more accurately reflects the actual energy supply task at the system's compensation end, laying the foundation for subsequent power and current control.

[0086] Next, the energy demand sequence at the source needs to be converted into a compensation power waveform reflecting the instantaneous power supply rate. This step is achieved by performing a time differential operation on the energy sequence. Since energy is the integral of power over time, conversely, power can be estimated using the derivative of energy over time. In discrete systems, numerical differential methods such as forward differential or central differential are commonly used. For example, if the energy demand at the source of two adjacent sampling points is 125kJ and 130kJ respectively, with a time interval of 0.1 seconds, the corresponding instantaneous compensation power is approximately (130-125) / 0.1 = 50kW. After obtaining the compensation power waveform, it needs to be further converted into current form for the auxiliary power unit to execute. This process depends on the real-time bus voltage. Based on the electric power formula P=U×I, the compensation power can be divided by the bus voltage value to calculate the compensation current sequence required for output at each moment. Taking a compensation power of 50kW and a bus voltage of 500V as an example, the corresponding compensation current is 100A. This compensation current sequence constitutes the core execution quantity of the control command and directly determines the current output target of the auxiliary unit.

[0087] To ensure the compensation behavior exhibits feedforward characteristics—that is, to complete energy injection before a voltage drop occurs in the main power supply module—the aforementioned compensation current sequence needs to undergo phase advance shifting. Phase advance refers to applying the current control quantity that should be output at a future time to the current or even earlier time, thus reserving the delay time required for system response and ensuring that the compensation action is completed before the actual load surge occurs. Specifically, the system can set a preset step size, such as 0.2 seconds, based on historical response data and shift the entire compensation current sequence forward by 0.2 seconds on the time axis, so that the compensation current command originally at time t is issued at t-0.2 seconds. This operation can be achieved through time series index reconstruction or completed within the controller's internal time axis mapping function. The effect of phase advance processing is to transform the system from "response" control to "predictive" control, effectively suppressing voltage drops and system instability caused by power surges.

[0088] Preferably, generating the feedforward compensation command by performing a phase lead shift of the compensation current sequence on the time axis by a preset step size may further include the following steps: Obtain the step response delay data of the auxiliary power conversion unit under different preset load ratios, and construct a load response delay mapping table; Calculate the rate of change of current at each sampling moment in the compensation current sequence, and match the adaptive lead time step corresponding to each sampling moment based on the rate of change of current and the load response delay mapping table; Based on the adaptive lead time step and the preset non-uniform time axis, the compensation current sequence is reconstructed using a non-uniform time axis to generate the feedforward compensation command.

[0089] In this embodiment, to achieve proactive response control to instantaneous load impacts, it is necessary to further improve the time matching accuracy of the feedforward compensation command. Therefore, it is necessary to model the dynamic response characteristics of the auxiliary power conversion unit under different load conditions, and based on this, complete the non-uniform phase lead processing of the compensation current sequence. Specifically, it is first necessary to obtain the step response delay data of the auxiliary power conversion unit under different preset load ratios. The preset load ratio refers to the ratio between the actual load power and the rated output power of the auxiliary unit, which is often used to characterize the dynamic response of the auxiliary module under different operating pressures. The step response delay data refers to the time delay required for the auxiliary power conversion unit to reach a steady-state value from receiving the control command under a unit step load change. It is usually affected by various factors such as circuit bandwidth, controller parameters, and energy storage medium discharge characteristics. This data can be obtained through experimental calibration, that is, applying step loads of different ratios on an experimental bench, recording the time for the output current to reach a steady-state value each time, forming a set of correspondences between load ratios and response delays.

[0090] After completing the above tests, the obtained data can be organized into a load response delay mapping table. This table is a lookup table structure with load multiplier as input and response delay time as output, forming the basis of system time scheduling control. This mapping table can dynamically allocate reasonable advance response time for control commands of different intensities, solving the problems of insufficient compensation or resource waste that may be caused by traditional fixed advance step size. Based on this mapping table, the system further calculates the rate of change of current at each sampling moment in the compensation current sequence. The rate of change of current refers to the slope of the current change per unit time, reflecting the load impact intensity at the current sampling point; the larger the rate of change, the more drastic the load change, and the higher the requirement for response speed. The rate of change of current can be obtained by calculating the difference between adjacent sampling points. For example, if the currents at two adjacent points are 80A and 100A respectively, and the sampling interval is 0.05 seconds, then the rate of change is 400A / s.

[0091] Based on the matching relationship between the current change rate and load response delay mapping table, the system matches a corresponding adaptive lead time step for each sampling point. This step step represents the time that the control signal should be triggered in advance to ensure that the system completes compensation before the target time under the current change rate. For example, when the current change rate is 400A / s, the corresponding response delay is 0.15 seconds, so the current compensation point needs to issue the command 0.15 seconds in advance. The matching of this adaptive step step can be achieved by looking up a table or by dynamically calculating it through a linear interpolation function, ensuring that the time compensation granularity is fine and continuous and smooth.

[0092] After obtaining the adaptive lead time step corresponding to all sampling points, the system needs to perform non-uniform time axis reconstruction on the original compensation current sequence to generate the feedforward compensation command for final execution. Non-uniform time axis reconstruction means that the compensation current values ​​of different sampling points are no longer issued at equal intervals, but are remapped according to the corresponding lead step. That is, the control quantity that should have been issued at time t is issued in advance to time t-Δt(t), where Δt(t) is the adaptive lead time step corresponding to that time. This reconstruction process can be achieved through interpolation or interpolation point operations. At the same time, interpolation smoothing processing is required for timing conflict points to avoid command stacking or distortion caused by time axis compression.

[0093] Through the above steps, the feedforward compensation command ultimately generated by the system not only achieves early intervention at the current control level, but also dynamically adjusts the forward shift amplitude according to the real-time changes in load impact intensity, significantly improving the accuracy and efficiency of compensation. For example, in a typical acceleration ramp-up scenario, the current change rises first and then falls. The system automatically allocates a larger advance step size (e.g., 0.2 seconds) to the peak compensation point in the first stage, and a smaller advance step size (e.g., 0.05 seconds) to the transition stage in the second stage. This allows the auxiliary unit output behavior to achieve higher precision alignment and matching with the load power impact waveform, ultimately improving system robustness and reducing the risk of voltage drop.

[0094] In summary, the system generates a complete feedforward compensation command with both execution feasibility and timing foresight by sequentially extracting energy conversion efficiency and bus voltage, performing energy compensation correction, converting it into power and current sequences, and implementing phase lead processing. For example, if the energy gap prediction sequence shows a gradual upward trend in the next second, after loss correction and power conversion, a gradually increasing compensation current sequence is obtained. By shifting it forward by 0.2 seconds, the output behavior of the auxiliary unit can be triggered in advance, thereby achieving proactive suppression of future load impacts.

[0095] To ensure that feedforward compensation commands can be effectively translated into physical energy release behavior, they need to be accurately transmitted and sent to the auxiliary power conversion unit in the target mobile energy storage device. This unit then drives the auxiliary power conversion unit to execute current output actions according to the commands, thereby dynamically compensating for the power supply capacity of the main power supply module. The key to this process lies in the real-time performance, synchronization, and executability of the commands. Therefore, the system needs to construct a data interaction mechanism with high-frequency communication capabilities and low-latency response characteristics. Specifically, the system uses a real-time scheduling module built into the embedded central controller (such as a DSP or ARM main control chip) to extract the latest set of compensation current target values ​​from the feedforward compensation command generation module at fixed time intervals (e.g., every 10ms). This target value is then packaged into a control frame and sent to the power control interface module inside the auxiliary power conversion unit via an industrial bus interface such as CAN bus, RS485, or Ethernet.

[0096] Upon receiving the compensation control frame, the controller within the auxiliary power conversion unit first parses the data, extracting the target compensation current value for the current moment. Simultaneously, it combines this with its own collected operating status parameters, such as input voltage, internal temperature, and inductor current, to determine the executability of the compensation command. If the system is in a discharge-allowed state and the current power margin meets the compensation requirements, it drives the internal DC-DC converter to execute the current command, adjusting the PWM duty cycle to control the on-time of the output bridge arm, thereby precisely controlling the output current to gradually approach the issued compensation target value. To improve response speed, the controller typically operates in a high-frequency closed-loop feedback control mode, such as employing a current loop PI regulation + feedforward pre-compensation control strategy to ensure the output current can stably reach the target level in the shortest possible time.

[0097] In addition, to enhance the safety and robustness of the control process, the system incorporates multiple protection mechanisms during command execution. For example, before issuing a command, it compares whether the current bus voltage is close to the undervoltage threshold. If there is a downward trend, the compensation command is executed first. During execution, if the current ramp-up rate is detected to exceed the safety limit or the output voltage fluctuates abnormally, the fast current limiting protection logic is immediately triggered to prevent the auxiliary power conversion unit from being damaged due to overcurrent or overheating.

[0098] Through the above control process, the system realizes a complete closed-loop control chain from the generation of feedforward compensation commands to the output of physical current. For example, when the system predicts a load surge of approximately 80A in 0.3 seconds, the feedforward module generates and issues a set of gradually increasing compensation current commands (e.g., from 60A to 80A) 0.2 seconds in advance. Upon receiving the commands, the auxiliary power conversion unit quickly adjusts the output of its internal converter, enabling the bus current to pre-charge in advance. This effectively alleviates the discharge pressure on the main power supply module and avoids output voltage drops or undervoltage protection triggering due to instantaneous overload. This embodiment verifies the system's real-time execution capability under predictive control framework and enhances the power supply stability and safety of energy storage devices under dynamic operating conditions.

[0099] The following describes the load control system of a mobile energy storage device according to an embodiment of the present invention from the perspective of hardware processing. Please refer to [link / reference needed]. Figure 3 This is a schematic diagram of the load control system of a mobile energy storage device in an embodiment of this application.

[0100] It should be noted that, Figure 3 The structure of the load control system for a mobile energy storage device shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of the present invention.

[0101] like Figure 3As shown, a load control system for a mobile energy storage device includes a central processing unit 301, which can perform various appropriate actions and processes according to a program stored in a read-only memory 302 or a program loaded from a storage section 308 into a random access memory 303, such as executing the methods described in the above embodiments. The random access memory 303 also stores various programs and data required for system operation. The central processing unit 301, the read-only memory 302, and the random access memory 303 are interconnected via a bus 304. An input / output interface 305 is also connected to the bus 304.

[0102] The following components are connected to the input / output interface 305: an input section 306 including audio input devices, push-button switches, etc.; an output section 307 including an LCD display, audio output devices, indicator lights, etc.; a storage section 308 including a hard disk, etc.; and a communication section 309 including a network interface card such as a LAN (Local Area Network) card, modem, etc. The communication section 309 performs communication processing via a network such as the Internet. A drive 310 is also connected to the input / output interface 305 as needed. A removable medium 311, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed on the drive 310 as needed so that computer programs read from it can be installed into the storage section 308 as needed.

[0103] In particular, according to embodiments of the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing computer programs for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 309, and / or installed from removable medium 311. When the computer program is executed by central processing unit 301, it performs the various functions defined in the present invention.

[0104] It should be noted that specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory, read-only memory, erasable programmable read-only memory, flash memory, optical fiber, portable compact disk read-only memory, optical storage devices, magnetic storage devices, or any suitable combination thereof. In this invention, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0105] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. Each block in a flowchart or block diagram may represent a module, segment, or portion of code, which contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those shown in the drawings.

[0106] Specifically, the load control system of a mobile energy storage device in this embodiment includes a processor and a memory. The memory stores a computer program. When the computer program is executed by the processor, it implements the load control method of a mobile energy storage device provided in the above embodiment.

[0107] In another aspect, the present invention also provides a computer-readable storage medium, which may be included in the load control system of a mobile energy storage device described in the above embodiments; or it may exist independently and not assembled into the load control system of the mobile energy storage device. The storage medium carries one or more computer programs, which, when executed by a processor of the load control system of the mobile energy storage device, cause the load control system of the mobile energy storage device to implement the load control method of the mobile energy storage device provided in the above embodiments.

Claims

1. A load control method for a mobile energy storage device, characterized in that, The method includes: Acquire motion state data and environmental state data of the target mobile energy storage device. The motion state data includes real-time speed and operation control parameters, and the environmental state data includes the drag coefficient and environmental potential gradient of the location of the target mobile energy storage device. The second-order differential operation is performed on the real-time velocity to obtain the jerk value of the target mobile energy storage device. The jerk value is used to characterize the degree of abrupt change in the acceleration of the target mobile energy storage device. A power demand impact sequence is generated based on the real-time speed, the environmental state data, and the jerk value. The power demand impact sequence is used to characterize the load dynamics of the target mobile energy storage device within a preset load prediction time window. The real-time polarization impedance and real-time open-circuit voltage of the main power supply module in the working connection state of the battery module array of the target mobile energy storage device are obtained, and the maximum pulse discharge power threshold under the condition of not triggering the preset undervoltage protection threshold is calculated based on the real-time polarization impedance and the real-time open-circuit voltage. When the maximum power demand value in the power demand surge sequence is greater than the maximum pulse discharge power threshold, it is determined that the target mobile energy storage device has a power supply drop risk within the preset load prediction time window. Based on the power demand shock sequence, an energy gap prediction sequence is determined; Based on the energy gap prediction sequence, a feedforward compensation command is generated, and the auxiliary power conversion unit in the target mobile energy storage device is controlled to execute the feedforward compensation command.

2. The method according to claim 1, characterized in that, The generation of the power demand impact sequence based on the real-time velocity, the environmental state data, and the jerk value specifically includes: The dynamic operating resistance is calculated based on the real-time speed and the drag coefficient, and the gravitational component resistance is calculated based on the environmental potential energy gradient. The sum of the dynamic operating resistance and the gravitational component resistance is determined as the environmental impedance description value, which is used to characterize the comprehensive physical gradient of the current location environment that hinders the movement of the equipment. When the absolute value of the acceleration value is greater than the preset oscillation threshold, and the rate of change of the torque command in the operation control parameters is greater than the preset stiffness threshold, dynamic stiffness coupling calculation is performed on the acceleration value and the rate of change of the torque command to obtain the inertial hysteresis parameter and stiffness excitation parameter. A dynamic load impact model is constructed based on the environmental impedance description value, the inertial hysteresis parameter, and the stiffness excitation parameter. Within the preset load prediction time window, the dynamic load impact model is discretized and sampled according to a preset sampling frequency to generate the power demand impact sequence.

3. The method according to claim 2, characterized in that, The dynamic stiffness coupling calculation of the jerk value and the rate of change of the torque command to obtain the inertial hysteresis parameter and stiffness excitation parameter specifically includes: Phase compensation is performed on the time integral result of the acceleration value based on the torque command change rate to obtain the inertial hysteresis parameter; The stiffness excitation parameter is obtained by modulating the amplitude of the rate of change of the torque command based on the jerk value.

4. The method according to claim 2, characterized in that, The construction of the dynamic load impact model based on the environmental impedance description value, the inertial hysteresis parameter, and the stiffness excitation parameter specifically includes: Calculate the partial derivative of the environmental impedance description value with respect to the real-time speed to obtain the environmental damping change rate. If the environmental damping change rate is positive and greater than the preset abrupt change limit, then it is determined that the target mobile energy storage device is in a forced transition state from the first damping region to the second damping region, and the environmental damping coefficient of the first damping region is less than that of the second damping region. Under the forced transition state, the coupling relationship between the stiffness excitation parameter and the environmental damping change rate is analyzed, and a dynamic load impact factor is generated based on the coupling relationship. The dynamic load impact factor is used to characterize the transient energy density that the power system of the target mobile energy storage device needs to release additionally to overcome environmental changes. The impact initiation time is determined based on the inertial hysteresis parameter. The dynamic load impact factor is determined as a function value of the impact initiation time to construct a dynamic load impact model. The dynamic load impact factor decays at a rate proportional to the environmental impedance description value after the impact initiation time.

5. The method according to claim 2, characterized in that, The determination of the energy gap prediction sequence based on the power demand impact sequence specifically includes: The power demand values ​​with amplitudes greater than the maximum pulse discharge power threshold in the power demand impulse sequence are combined into an overload power subsequence; Calculate the difference between the overload power subsequence and the maximum pulse discharge power threshold at the corresponding sampling time of the overload power subsequence according to the time step of the preset sampling frequency; Perform discrete integration on the difference to obtain the energy loss at each sampling time; The energy deficit amounts are combined in chronological order to form the energy gap prediction sequence, whereby the energy deficit amounts are used to characterize the cumulative instantaneous energy required to maintain the load at the sampling time.

6. The method according to claim 1, characterized in that, The generation of feedforward compensation instructions based on the energy gap prediction sequence specifically includes: Obtain the real-time energy conversion efficiency parameters and real-time bus voltage of the auxiliary power conversion unit in the target mobile energy storage device; Based on the real-time energy conversion efficiency parameter, the energy gap prediction sequence is corrected by loss compensation to generate the source-end energy demand sequence. The source-end energy demand sequence is subjected to time differentiation to obtain a compensation power waveform, and the compensation power waveform is converted into a compensation current sequence based on the real-time bus voltage. The compensation current sequence is shifted forward by a preset step size on the time axis to generate the feedforward compensation command.

7. The method according to claim 6, characterized in that, The step of shifting the compensation current sequence by a preset step size on the time axis to generate the feedforward compensation command specifically includes: Obtain the step response delay data of the auxiliary power conversion unit under different preset load ratios, and construct a load response delay mapping table; Calculate the rate of change of current at each sampling moment in the compensation current sequence, and match the adaptive lead time step corresponding to each sampling moment based on the rate of change of current and the load response delay mapping table; Based on the adaptive lead time step and the preset non-uniform time axis, the compensation current sequence is reconstructed using a non-uniform time axis to generate the feedforward compensation command.

8. A load control system for a mobile energy storage device, characterized in that, The load control system of the mobile energy storage device includes: one or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code including computer instructions, and the one or more processors call the computer instructions to cause the load control system of the mobile energy storage device to perform the method as described in any one of claims 1-7.

9. A computer-readable storage medium comprising instructions, characterized in that, When the instruction is executed on the load control system of the mobile energy storage device, the load control system of the mobile energy storage device performs the method as described in any one of claims 1-7.

10. A computer program product, characterized in that, When the computer program product is run on the load control system of the mobile energy storage device, it causes the load control system of the mobile energy storage device to perform the method as described in any one of claims 1-7.

Images