A dynamic power regulation method and system for a two-wheeled electric vehicle super charging pile

Abstract

The present application relates to the field of two-wheel electric vehicle charging, and particularly relates to a dynamic power regulation method and system for two-wheel electric vehicle super-charging piles. The method comprises: obtaining the maximum available power of a power distribution network and collecting vehicle battery and service state data, calculating the charging urgency weight and distributing high-frequency pulse charging parameters, phase-shifting and staggering the pulse on and off time to limit the actual total power with the maximum available power as a constraint, and calculating the battery polarization degree in combination with the open circuit voltage during the zero current period of pulse off, dynamically correcting the pulse parameters based on the polarization degree and cyclically executing the scheduling. The present application improves the traditional logic of single power distribution, fuses service and battery data to ensure that emergency distribution has priority to obtain charging resources, avoids concurrent super-charging tripping risk by staggered scheduling, and effectively suppresses concentration polarization and protects the safety of underlying batteries through polarization closed-loop monitoring.

Description

Technical Field

[0001] This invention relates to the field of charging two-wheeled electric vehicles, and more particularly to a dynamic power adjustment method and system for a supercharging station for two-wheeled electric vehicles. Background Technology

[0002] With the booming development of the on-demand delivery industry, two-wheeled electric vehicles have become an indispensable core mode of transportation for food delivery and courier riders. In pursuit of higher delivery efficiency and economic benefits, riders have placed extremely high demands on the speed of vehicle recharging. As a result, supercharging stations supporting concurrent multi-vehicle access have emerged and been widely deployed. During peak business hours, there are often scenarios where a large number of riders return to the station for rapid recharging. At this time, supercharging stations need to simultaneously provide high-power electrical output to multiple two-wheeled electric vehicles to ensure the continuous and stable operation of the vast delivery network.

[0003] However, existing power allocation strategies typically rely solely on the remaining battery charge or the order of access. This allocation logic is completely detached from real-world on-demand delivery scenarios, making it difficult to identify the urgency of orders for different riders. This can easily lead to riders with orders nearing their due date facing penalties for slow charging, while riders with ample time occupy core supercharging resources, impacting overall business scheduling efficiency. Summary of the Invention

[0004] To overcome the above shortcomings, this invention provides a dynamic power adjustment method and system for supercharging piles for two-wheeled electric vehicles, aiming to improve the problem that existing allocation strategies are out of touch with actual delivery scenarios and have difficulty in identifying the urgency of orders, resulting in low scheduling efficiency.

[0005] In a first aspect, the present invention provides the following technical solution: a dynamic power adjustment method for a supercharging pile for two-wheeled electric vehicles, comprising:

[0006] S1. When multiple two-wheeled electric vehicles are connected to the supercharging pile, the maximum available power of the power distribution network is obtained, the battery status data and business status data of each two-wheeled electric vehicle are collected in real time, and the charging urgency weight of each two-wheeled electric vehicle is calculated based on the battery status data and business status data.

[0007] S2. Based on the charging urgency weight of each of the two-wheeled electric vehicles, allocate corresponding high-frequency pulse charging parameters, the high-frequency pulse charging parameters including pulse peak power and duty cycle composed of pulse on time and pulse off time;

[0008] S3. Using the maximum available power of the distribution network as a constraint, and in combination with the pulse peak power, the pulse on-time and pulse off-time are phase-shifted and staggered on the time axis to limit the total actual output power superimposed at the same moment to the maximum available power of the distribution network.

[0009] S4. When performing the phase shift interleaving scheduling, during the zero current period corresponding to the pulse disconnection time, the open circuit voltage of the two-wheeled electric vehicle is collected, and the battery polarization degree of the two-wheeled electric vehicle is calculated in combination with the charging terminal voltage of the supercharging pile.

[0010] S5. Based on the battery polarization degree, dynamically correct the pulse on-time and pulse off-time, and use the corrected high-frequency pulse charging parameters to cyclically execute the phase shift interleaving scheduling.

[0011] Preferably, in step S1, the step of real-time collection of battery status data and business status data of each of the two-wheeled electric vehicles includes:

[0012] The remaining battery power, battery health, and temperature data of each of the two-wheeled electric vehicles are obtained through a communication protocol to construct the battery status data.

[0013] By calling the business server interface, order delivery time, delivery distance, and rider tags are extracted to construct the business status data;

[0014] The battery state data and the business state data are processed into dimensionless form using a normalization algorithm to generate standard state feature vectors for each of the two-wheeled electric vehicles.

[0015] Preferably, in step S1, the step of calculating the charging urgency weight of each of the two-wheeled electric vehicles includes:

[0016] Based on a preset priority scheduling logic, initial weight coefficients are assigned to the battery status data and the business status data, wherein the shorter the order delivery time and the lower the remaining battery power, the higher the initial weight coefficient.

[0017] The battery status data, the business status data, and the initial weighting coefficients are input into a weighted evaluation function for combined calculation, and a baseline urgency value is output.

[0018] The baseline urgency value is mapped and transformed by an activation function to generate the final charging urgency weight.

[0019] Preferably, in step S2, the step of allocating the corresponding high-frequency pulse charging parameters includes:

[0020] Combining the health and temperature data from the battery status data, the maximum safe current for output is calculated and set as the peak power of the pulse;

[0021] Using a preset mapping model, the charging urgency weight is positively mapped to the target duty cycle value, and the charging urgency weight and the target duty cycle value are positively correlated.

[0022] According to a preset reference period, the target duty cycle value is divided into on-time and off-time, and the pulse on-time and pulse off-time in the high-frequency pulse charging parameters are generated respectively.

[0023] Preferably, in step S3, the step of performing phase-shift interleaving scheduling on the time axis includes:

[0024] Extract the charging urgency weights of each of the two-wheeled electric vehicles, and generate a global dynamic scheduling sequence using a descending order sorting algorithm;

[0025] A phase shift angle is assigned to each of the two-wheeled electric vehicles in the global dynamic scheduling sequence, and the pulse conduction time is mapped to different time slots of the synchronization cycle;

[0026] Using a time axis alignment algorithm, the pulse turn-on time corresponding to the two-wheeled electric vehicle with a lower charging urgency weight is inserted into the pulse disconnect time corresponding to the two-wheeled electric vehicle with a higher charging urgency weight, forming a complementary staggered pulse waveform sequence.

[0027] Preferably, in step S3, the step of limiting the total actual output power superimposed at the same time within the maximum available power of the distribution network includes:

[0028] Discrete-time sampling calculations are performed on the pulse on-time and pulse off-time after the phase-shift interleaving scheduling to obtain the predicted superimposed total power at each discrete sampling point;

[0029] The predicted total power is compared with the maximum available power of the distribution network to locate the potential overload time window that exceeds the maximum available power of the distribution network;

[0030] Within the potential overload time window, the pulse conduction time corresponding to the two-wheeled electric vehicle with a lower charging urgency weight is compressed in reverse order according to the charging urgency weight, so that the adjusted actual total output power is limited to the maximum available power of the power distribution network.

[0031] Preferably, in step S4, the step of calculating the battery polarization degree of the two-wheeled electric vehicle includes:

[0032] During the zero-current period, the voltage drop slope of the battery terminals is continuously monitored, and the actual open-circuit voltage value is extracted when the voltage drop slope is lower than a preset slope threshold.

[0033] Obtain the peak output voltage record before the pulse disconnection time is triggered, and define it as the charging terminal voltage;

[0034] Calculate the voltage difference between the charging terminal voltage and the actual open-circuit voltage value, and input it into the equivalent circuit model to separate the polarization voltage drop, and quantify the degree of battery polarization.

[0035] Preferably, in step S5, the step of dynamically correcting the pulse on-time and the pulse off-time includes:

[0036] The polarization degree of the battery is compared with a preset safety benchmark threshold to calculate the polarization deviation rate;

[0037] When the degree of battery polarization is greater than the safety benchmark threshold, the pulse disconnection time is extended proportionally based on the polarization deviation rate, and the pulse conduction time is reduced simultaneously.

[0038] The corrected pulse off-time and pulse on-time are recombined into the duty cycle, which serves as the basic input parameter for executing the phase shift interleaving schedule in the next cycle.

[0039] Secondly, the present invention provides the following technical solution: a dynamic power adjustment system for a supercharging pile for two-wheeled electric vehicles, comprising:

[0040] The data sensing and computing module includes a smart meter, a controller local area network communication bus, and a microprocessor. When multiple two-wheeled electric vehicles are connected to the supercharging pile, it is used to obtain the maximum available power of the power distribution network, collect the battery status data and business status data of each two-wheeled electric vehicle in real time, and calculate the charging urgency weight of each two-wheeled electric vehicle based on the battery status data and business status data.

[0041] The parameter allocation module, including a main control chip and a memory, is used to allocate corresponding high-frequency pulse charging parameters according to the charging urgency weight of each of the two-wheeled electric vehicles. The high-frequency pulse charging parameters include the pulse peak power and the duty cycle composed of the pulse on time and the pulse off time.

[0042] The power dispatch control module, including a DC bus controller and a power electronic power converter, is used to use the maximum available power of the distribution network as a constraint condition, combined with the pulse peak power, to perform phase-shifted interleaving scheduling of the pulse on-time and pulse off-time on the time axis, so as to limit the actual total output power superimposed at the same time to within the maximum available power of the distribution network;

[0043] The polarization state monitoring module includes a high-frequency voltage sampling circuit and an analog-to-digital converter, which is used to collect the open-circuit voltage of the two-wheeled electric vehicle during the zero-current period corresponding to the pulse disconnection time when the phase shift interleaving scheduling is performed, and calculate the battery polarization degree of the two-wheeled electric vehicle in combination with the charging terminal voltage of the supercharging pile.

[0044] The closed-loop correction drive module includes a pulse width modulation generator, which is used to dynamically correct the pulse on-time and pulse off-time based on the battery polarization degree, and use the corrected high-frequency pulse charging parameters to drive the power electronic power converter to cyclically execute the phase shift interleaving scheduling.

[0045] The present invention has the following beneficial effects:

[0046] 1. In this invention, the single logic of traditional charging piles that allocate power based solely on remaining power is improved. The business status data such as order delivery time and rider tags unique to two-wheeled delivery vehicles are normalized and integrated with the underlying physical data of the battery. The comprehensive charging urgency weight is calculated and directly mapped to the target high-frequency pulse duty cycle, ensuring that the most urgent delivery tasks can obtain core charging resources first, thereby improving the overall business scheduling efficiency of the battery swapping station.

[0047] 2. In this invention, the charging urgency weight is introduced into the phase shift staggered scheduling strategy. The predicted total power is compared with the distribution network constraints at discrete sampling points. The pulse conduction time of low-priority vehicles is compressed in reverse order within the potential overload window. Without increasing the physical expansion cost of transformers, the staggered scheduling of the time axis is used to effectively avoid the risk of distribution network over-limit tripping caused by multiple high-power two-wheeled vehicles overcharging simultaneously.

[0048] 3. In this invention, the natural disconnection gap of high-frequency pulse power supply is used as a non-destructive monitoring window. The actual open-circuit voltage is extracted during the zero-current period to quantify the degree of battery polarization. Based on the polarization deviation rate, the pulse disconnection time is adaptively extended and the conduction time is reduced. A closed-loop feedback link from bottom-level polarization monitoring to duty cycle dynamic correction is constructed, which effectively suppresses the internal concentration polarization caused by continuous high-current overcharging and greatly ensures the safety of the battery. Attached Figure Description

[0049] Figure 1 This is a flowchart of a dynamic power adjustment method for a supercharging pile for two-wheeled electric vehicles proposed in this invention;

[0050] Figure 2 This is a flowchart of the multidimensional data heterogeneous normalization and charging urgency weight evaluation process proposed in this invention;

[0051] Figure 3This is a flowchart illustrating the forward mapping and segmentation process from business weights to underlying high-frequency pulse parameters proposed in this invention.

[0052] Figure 4 This is a flowchart of the dynamic scheduling process for phase shift interleaving and reverse peak shaving under power grid constraints proposed in this invention.

[0053] Figure 5 This is a flowchart of the closed-loop monitoring of non-destructive polarization state under zero-current disconnection gap proposed in this invention;

[0054] Figure 6 This is a flowchart of the parameter adaptive dynamic correction and closed-loop process based on polarization degree proposed in this invention.

[0055] Figure 7 This is an architecture diagram of a dynamic power adjustment system for a two-wheeled electric vehicle supercharging pile proposed in this invention. Detailed Implementation

[0056] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0057] Example 1

[0058] In a first embodiment of the present invention, the present invention provides a dynamic power adjustment method for a supercharging station for two-wheeled electric vehicles, such as... Figure 1 As shown, it includes:

[0059] S1. When multiple two-wheeled electric vehicles are connected to the supercharging pile, obtain the maximum available power of the power distribution network, collect the battery status data and business status data of each two-wheeled electric vehicle in real time, and calculate the charging urgency weight of each two-wheeled electric vehicle based on the battery status data and business status data.

[0060] Furthermore, step S1, which involves real-time collection of battery status data and business status data for each two-wheeled electric vehicle, includes:

[0061] The remaining battery power, battery health, and temperature data of each two-wheeled electric vehicle are obtained through communication protocols to construct battery status data.

[0062] By calling the business server interface, order delivery time, delivery distance, and rider tags are extracted to construct business status data;

[0063] A normalization algorithm is used to perform dimensionless processing on battery state data and business state data to generate standard state feature vectors for each two-wheeled electric vehicle.

[0064] Further, step S1, which involves calculating the charging urgency weight for each two-wheeled electric vehicle, includes:

[0065] Based on the preset priority scheduling logic, initial weight coefficients are assigned to battery status data and business status data. The shorter the order delivery time and the lower the remaining battery power, the higher the initial weight coefficient.

[0066] The battery status data, business status data and initial weighting coefficients are input into the weighted evaluation function for combined calculation, and the baseline urgency value is output.

[0067] The baseline urgency value is mapped and transformed by an activation function to generate the final charging urgency weight.

[0068] Specifically, when multiple two-wheeled electric vehicles connect to a supercharging station, the supercharging station's control board first reads the maximum available power of the current power distribution network through a smart meter or grid interaction interface. Simultaneously, the supercharging station's underlying controller area network (CLAN) communication bus establishes a handshake protocol with the battery management systems of each two-wheeled electric vehicle, reading and acquiring the battery's remaining charge, health status, and cell temperature in real time to construct initial battery status data. Next, the supercharging station uses its built-in wireless radio frequency communication module to call the service application interface of a food delivery or courier service to extract the currently bound order delivery time, delivery distance, and rider's level tag from the cloud, thus constructing initial business status data.

[0069] Since the acquired battery status data and business status data have different physical dimensions, the system uses the remaining power, health status, temperature, delivery time, delivery distance, and rider tags into the min-max normalization algorithm to perform dimensionless processing on the above multi-source heterogeneous data, thereby merging and generating standard state feature vectors corresponding to each two-wheeled electric vehicle.

[0070] After data standardization, the system assigns initial weight coefficients to each data item according to a preset priority scheduling logic. The core principle of allocation is that the shorter the order delivery time and the lower the remaining battery power, the higher the initial weight coefficient assigned by the system program in the global comparison. Subsequently, the system inputs the standardized battery status data, business status data, and the assigned initial weight coefficients into a multi-objective weighted evaluation function for combined calculation, outputting a baseline urgency value. The calculation formula for this multi-objective weighted evaluation function is as follows:

[0071] ;

[0072] In the formula, Indicates the first The baseline urgency value for a two-wheeled electric vehicle The total number of features representing battery state data. Indicates the first The initial weighting coefficients corresponding to the battery state features, Indicates the first The first vehicle Normalized values ​​of battery state characteristics This represents the total number of features in the business status data. Indicates the first The initial weight coefficients corresponding to the business status features. Indicates the first The first vehicle Normalized values ​​of service status characteristics. In this scheme, the initial weighting coefficient corresponding to the remaining battery power. The preferred value is 0.4, which is the initial weighting coefficient corresponding to the order delivery time. The preferred value is 0.6, which highlights the dominant role of business urgency.

[0073] To differentiate vehicles with varying levels of urgency and meet the stringent range requirements of subsequent system control, the control board employs a non-linear activation function to map and transform the calculated baseline urgency values, thereby generating the final charging urgency weights. The formula for this non-linear activation function is:

[0074] ;

[0075] In the formula, Indicates the final assignment of the first The weighting of the charging urgency of each two-wheeled electric vehicle This is the baseline urgency value output from the pre-calculated data. This indicates that the system's preset slope adjustment parameter is used to control the mapping sensitivity of the urgency curve. This represents the preset urgency threshold parameter. Through the mapping transformation of this nonlinear activation function, the system forcibly constrains the charging urgency weights of each generated two-wheeled electric vehicle to a continuous physical interval greater than zero and less than or equal to one. In this scheme, the slope adjustment parameter... The preferred value is 5, the urgency inflection point threshold parameter. The preferred value is 0.5.

[0076] This step integrates the underlying physical battery parameters with the upper-level business operation requirements, accurately quantifies the actual charging priority when multiple vehicles access the network concurrently, and provides reliable data support for subsequent high-frequency pulse parameter allocation and power staggered scheduling.

[0077] S2. Based on the charging urgency weight of each two-wheeled electric vehicle, allocate corresponding high-frequency pulse charging parameters. The high-frequency pulse charging parameters include the pulse peak power and the duty cycle consisting of the pulse on time and the pulse off time.

[0078] Further, step S2, the step of allocating the corresponding high-frequency pulse charging parameters, includes:

[0079] By combining the health and temperature data from the battery status data, the maximum safe current for the allowable output is calculated and set as the pulse peak power;

[0080] Using a pre-defined mapping model, the charging urgency weight is positively mapped to the target duty cycle value, and the charging urgency weight and the target duty cycle value are positively correlated.

[0081] According to the preset reference period, the target duty cycle value is divided into the on-time and off-time, and the pulse on-time and pulse off-time in the high-frequency pulse charging parameters are generated respectively.

[0082] Specifically, the battery state data parsed in the aforementioned steps is obtained, and the health and temperature data are extracted. This data, combined with a safe charging physical boundary model, is used to calculate the permissible maximum safe current, which is then set as the pulse peak power. To prevent ultra-high power pulses from causing thermal runaway or lithium plating damage to aging cells or cells operating in non-ideal temperature ranges, the system employs a nonlinear current constraint algorithm based on state derating for calculation. The formula for the maximum safe current algorithm is expressed as follows:

[0083] ;

[0084] in This indicates the set peak pulse power, i.e., the maximum safe current. This indicates the maximum allowable reference charging current for a two-wheeled electric vehicle battery under ideal conditions. This indicates the percentage of battery health reported by the battery management system. This indicates the real-time temperature of the battery cell currently being collected. This indicates the optimal charging reference temperature for this type of battery. This represents the preset temperature sensitivity attenuation coefficient. In this scheme, the maximum reference charging current... The optimal parameters are 20A and the best charging reference temperature. The optimal parameter is 25℃, and the temperature sensitivity attenuation coefficient is [missing information]. The optimal parameter is 0.05.

[0085] After determining the peak pulse power, the system calls a preset mapping model to convert the charging urgency weight of the two-wheeled electric vehicle into a specific pulse duty cycle. To ensure that vehicles with high urgency are allocated more concentrated energy pulses, the system establishes a positive linear interpolation mapping logic from weight to duty cycle, making the charging urgency weight and the target duty cycle value absolutely positively correlated. The mapping model algorithm formula is expressed as follows:

[0086] ;

[0087] in This represents the target duty cycle value generated by the forward mapping. This indicates the charging urgency weight calculated from the previous steps. This indicates the maximum safe duty cycle threshold that the system is allowed to set. This represents the minimum duty cycle threshold reserved by the system to maintain underlying communication and voltage sampling. In this scheme, the maximum safe duty cycle upper limit threshold... The optimal parameter is 0.95, which is the minimum duty cycle lower limit threshold. The optimal parameter is 0.10.

[0088] Based on the high-frequency operating reference cycle preset by the underlying hardware of the supercharging pile, the calculated target duty cycle value is divided into specific high-frequency pulse charging parameters according to the time ratio. The system's underlying microcontroller uses the reference cycle as the time base and calculates the pulse on-time and pulse off-time in the high-frequency pulse charging parameters through product and difference calculations. The time segmentation algorithm formula is expressed as follows:

[0089] ;

[0090] ;

[0091] in This indicates the pulse conduction time allocated to the specific two-wheeled electric vehicle. Indicates the corresponding pulse disconnection time. This represents the preset high-frequency operating reference period of the supercharging pile system. In this scheme, the high-frequency operating reference period... The optimal parameter is 1000 milliseconds.

[0092] This step transforms abstract business and power requirements into pulse control parameters that can be directly executed by the underlying hardware, thereby achieving a reasonable allocation of charging time resources to high-priority order vehicles while ensuring the physical safety of the battery itself in each vehicle.

[0093] S3. Using the maximum available power of the distribution network as a constraint, and combining the pulse peak power, the pulse on-time and pulse off-time are phase-shifted and staggered on the time axis to limit the total actual output power superimposed at the same moment to the maximum available power of the distribution network.

[0094] Furthermore, step S3, the step of performing phase-shift interleaving scheduling on the time axis, includes:

[0095] Extract the charging urgency weights of each two-wheeled electric vehicle and generate a global dynamic scheduling sequence using a descending sorting algorithm;

[0096] Assign phase shift angles to each two-wheeled electric vehicle in the global dynamic scheduling sequence, and map the pulse conduction time to different time slots in the synchronization cycle;

[0097] Using a time axis alignment algorithm, the pulse turn-on time corresponding to the two-wheeled electric vehicles with lower charging urgency weight is inserted into the pulse turn-off time corresponding to the two-wheeled electric vehicles with higher charging urgency weight, forming a complementary staggered pulse waveform sequence.

[0098] Furthermore, step S3, which involves limiting the total actual output power superimposed at the same time to within the maximum available power of the distribution network, includes:

[0099] Discrete-time sampling calculations are performed on the pulse turn-on time and pulse turn-off time after phase-shift staggered scheduling to obtain the predicted superimposed total power at each discrete sampling point;

[0100] By comparing the predicted total power with the maximum available power of the distribution network, the potential overload time window exceeding the maximum available power of the distribution network can be located.

[0101] Within the potential overload time window, the pulse conduction time corresponding to two-wheeled electric vehicles with lower charging urgency weights is compressed in reverse order according to the charging urgency weights, so that the adjusted actual total output power is limited to the maximum available power of the distribution network.

[0102] Specifically, the charging urgency weights of each two-wheeled electric vehicle calculated in the aforementioned steps are extracted, and all connected vehicles are prioritized using a descending sorting algorithm to generate a global dynamic scheduling sequence within the supercharging station. The vehicle at the top of the sequence corresponds to the highest charging urgency weight, and so on, decreasing in that order, thus establishing a baseline execution queue for subsequent time-sharing off-peak allocation.

[0103] After generating the global dynamic scheduling sequence, the system assigns a specific phase shift time delay to each two-wheeled electric vehicle in the sequence, distributing the pulse turn-on times that might otherwise trigger at the same instant to different time slots within a global synchronization cycle. The system uses a time axis alignment algorithm to forcibly insert the pulse turn-on times of lower-ranked, low-weight vehicles in the sequence into the pulse disconnect times of higher-ranked, high-weight vehicles. The phase shift time delay algorithm formula is expressed as follows:

[0104] ;

[0105] in Represented as the first in the global dynamic scheduling sequence The phase shift time delay for each two-wheeled electric vehicle is assigned to a charging start point. Indicates the first position in the sequence. The pulse conduction time is allocated to each high-weight two-wheeled electric vehicle. Through this end-to-end cumulative delay mapping, the system forms a complementary and staggered pulse waveform sequence on the time axis.

[0106] The system then performs discrete-time sampling calculations on the interleaved pulse waveform sequence according to a set microsecond-level sampling step size, extracting the instantaneous pulse overlap state at each discrete sampling point, thereby obtaining the predicted superimposed total power at any time within a future global synchronization cycle. The formula for calculating the predicted superimposed total power is expressed as follows:

[0107] ;

[0108] in Indicates the sampling points in discrete time. The predicted total power is superimposed on the above. This indicates the total number of two-wheeled electric vehicles connected to the supercharging station. Represented as the first The maximum safe current corresponding to the peak pulse power set for a two-wheeled electric vehicle. This indicates the constant output voltage of the internal DC bus of the supercharging station. Indicates the first One vehicle The pulse waveform state function takes a value of one when it is within the pulse conduction time period and otherwise takes a value of zero. In this scheme, the internal DC bus has a constant output voltage. The preferred parameter is 84V.

[0109] The system performs a real-time difference comparison between the predicted total power calculated at each discrete sampling point and the currently acquired maximum available power of the distribution network. When the predicted total power exceeds the maximum available power of the distribution network, the system identifies the interval between these consecutive sampling points as a potential overload time window. Within this potential overload time window, the system activates a peak shaving mechanism, compressing the pulse conduction time of each vehicle in reverse order of charging urgency weight, starting from the lowest priority vehicle at the end of the global dynamic scheduling sequence. The pulse conduction time compression algorithm is expressed as follows:

[0110] ;

[0111] in This indicates the pulse conduction time after peak clipping adjustment. This indicates the upper limit threshold of the maximum available power in the distribution network. This represents the base step size for discrete-time sampling. In this scheme, the maximum available power threshold of the distribution network is... The preferred parameters are 30kW and the basic step size for discrete-time sampling. The parameter is preferably 1 millisecond. This instruction is executed until the actual total output power at all discrete sampling points is limited to the maximum available power of the distribution network.

[0112] This step utilizes underlying control logic that combines time-sharing peak queuing with dynamic peak shaving and compression. Without increasing the physical expansion cost of external transformers, it effectively avoids the risk of grid over-limit tripping caused by multiple high-power vehicles charging concurrently, thus ensuring the overall operational safety of the distribution network.

[0113] S4. When performing phase shift interleaving scheduling, during the zero current period corresponding to the pulse disconnection time, the open circuit voltage of the two-wheeled electric vehicle is collected, and the battery polarization degree of the two-wheeled electric vehicle is calculated in combination with the charging terminal voltage of the supercharging pile.

[0114] Further, step S4, the step of calculating the battery polarization degree of the two-wheeled electric vehicle, includes:

[0115] During the zero-current period, the voltage drop slope of the battery terminals is continuously monitored, and the actual open-circuit voltage value is extracted when the voltage drop slope is lower than the preset slope threshold.

[0116] Record the peak output voltage before the trigger pulse disconnection time and define it as the charging terminal voltage;

[0117] Calculate the voltage difference between the charging terminal voltage and the actual open-circuit voltage, and input it into the equivalent circuit model to separate the polarization voltage drop and quantify the degree of battery polarization.

[0118] Specifically, during the phase-shift interleaved scheduling process, when the underlying control module enters the zero-current period corresponding to the pulse disconnection time, the system's high-frequency voltage sampling module continuously monitors the voltage drop slope of the battery terminals of the two-wheeled electric vehicle. Since the underlying ohmic voltage drop disappears instantaneously after the large-current pulse is cut off, the terminal voltage will exhibit a gradually flattening non-linear decreasing curve. The system calculates the voltage change rate between adjacent discrete sampling points in real time, and at a stable moment when the voltage drop slope is lower than the system's preset slope threshold, extracts the static voltage value at that moment as the true open-circuit voltage value. The voltage drop slope monitoring algorithm formula is expressed as follows:

[0119] ;

[0120] in This represents the voltage drop slope in microseconds, calculated in real time during the zero-current period. This represents the real-time battery terminal voltage read at the current discrete sampling moment. This represents the battery terminal voltage read at the previous adjacent sampling time. This represents the basic sampling period of the high-frequency voltage set at the system's underlying level. In this scheme, the basic sampling period of the high-frequency voltage... The preferred parameter is 10 milliseconds, and the preferred system preset slope threshold parameter is 5 mV / s. When the real-time calculated... When the voltage remains below the preset slope threshold, the system locks and outputs the currently read terminal voltage as the actual open-circuit voltage value.

[0121] After obtaining the actual open-circuit voltage value, the system's underlying memory synchronously retrieves the peak output voltage data recorded by the supercharger just before triggering the pulse disconnect command, and defines it as the charging terminal voltage for the current scheduling cycle. The system then calculates the total voltage difference between the charging terminal voltage and the actual open-circuit voltage value. To accurately assess the internal concentration polarization effect, the system inputs the total voltage difference into a preset equivalent circuit model, eliminating the ohmic voltage drop component generated by the resistance of purely physical conductors, thereby separating the polarization voltage drop and quantifying the dimensionless battery polarization degree parameter. The battery polarization degree quantification algorithm formula is expressed as follows:

[0122] ;

[0123] ;

[0124] in This represents the total voltage difference calculated between the charging terminal voltage and the actual open-circuit voltage. This represents the charging terminal voltage extracted moment before the trigger pulse disconnect command. This indicates that the actual open-circuit voltage value is extracted and recorded at the zero-current steady-state moment. This represents the actual output current corresponding to the peak power of the pulse that the vehicle is currently executing in the interleaved scheduling sequence. This indicates that the system identifies and extracts the battery's ohmic internal resistance online through an equivalent circuit model. This represents the final quantized output, used to characterize the degree of battery polarization, which is used to represent the electrochemical concentration gradient.

[0125] This step cleverly utilizes the inherent disconnection gap of high-frequency pulse power supply as a physical idle monitoring window, transforming the electrochemical polarization state inside the closed black box into an external voltage calculation characteristic that can be accurately quantified, thus providing the system with safe and reliable closed-loop feedback data.

[0126] S5. Based on the battery polarization, the pulse on-time and pulse off-time are dynamically corrected, and the phase shift interleaving scheduling is performed cyclically using the corrected high-frequency pulse charging parameters.

[0127] Furthermore, step S5, the step of dynamically correcting the pulse on-time and pulse off-time, includes:

[0128] The polarization degree of the battery is compared with a preset safety benchmark threshold to calculate the polarization deviation rate;

[0129] When the battery polarization exceeds the safety benchmark threshold, the pulse disconnection time is extended proportionally based on the polarization deviation rate, while the pulse conduction time is reduced simultaneously.

[0130] The corrected pulse off-time and pulse on-time are recombined into a duty cycle, which serves as the basic input parameter for the next cycle's phase-shift interleaved scheduling.

[0131] Specifically, the system acquires the quantized battery polarization level from the previous execution cycle and performs a real-time differential comparison with a preset safety benchmark threshold within the underlying system, which characterizes the maximum concentration polarization boundary the battery can withstand. When the system determines that the current battery polarization level exceeds the set safety benchmark threshold, it indicates that the electrochemical reaction rate inside the battery has significantly lagged behind the external electron input rate. The system immediately triggers depolarization control logic and calculates the polarization deviation rate. The polarization deviation rate algorithm formula is expressed as:

[0132] ;

[0133] in This represents the polarization deviation rate calculated in real time. This indicates the degree of battery polarization in the current cycle, which is quantified and extracted in the preceding steps. This indicates the system's preset safety benchmark threshold for this battery model. In this solution, the safety benchmark threshold... The optimal parameter is 0.05.

[0134] Based on the calculated polarization deviation rate, the system introduces an adaptive gain control variable to dynamically correct the current pulse timing allocation proportionally. This is achieved by extending the idle time without current, accelerating the natural diffusion and equilibrium of ion concentration within the battery. The system calculates the time difference requiring compensation according to the deviation ratio, then extends the original pulse disconnection time and simultaneously reduces the original pulse conduction time by an equal amount, ensuring that the total duration of the underlying high-frequency operating reference cycle remains absolutely constant. The dynamic time correction algorithm formula is expressed as follows:

[0135] ;

[0136] ;

[0137] in This indicates the disconnection time of the new round of pulses generated after dynamic correction. This indicates the conduction time of the new round of pulses generated after synchronous reduction. and These correspond to the original pulse disconnection time and the original pulse conduction time that are currently being executed within the current cycle, respectively. This indicates that the system adaptively adjusts the gain coefficient for different cell chemistry systems. This represents the total duration of the fixed high-frequency operating reference cycle of the underlying hardware. In this scheme, the gain coefficient is adaptively adjusted. The optimal parameter is 0.2.

[0138] After completing the time series compensation calculation, the system recombines the corrected pulse off-time and pulse on-time in the time domain to update the physical configuration of the duty cycle in the high-frequency pulse charging parameters. The duty cycle update combination formula is expressed as:

[0139] ;

[0140] in This represents the corrected duty cycle parameter generated after recombination. The system directly sends this corrected high-frequency pulse charging parameter and feeds it back to the underlying microcontroller, using it as the basic input parameter for the next global synchronization cycle to execute phase shift interleaving scheduling, thereby initiating a new round of closed-loop control.

[0141] This step constructs a hardware-level closed-loop feedback link from polarization monitoring to parameter adaptive adjustment, effectively eliminating internal concentration accumulation caused by high-frequency continuous high current impact, reducing the risk of battery cycle life degradation and improving the safety boundary of the supercharging process.

[0142] Example 2

[0143] Existing power allocation strategies typically rely solely on remaining battery power or allocation based on the order of access. This allocation logic is completely detached from real-time delivery scenarios, making it difficult to identify the urgency of orders for different riders. This can easily lead to riders with orders nearing their due date facing penalties for slow charging, while riders with ample time occupy key supercharging resources, impacting overall operational efficiency. To address these issues, this invention provides a dynamic power adjustment system for supercharging stations for two-wheeled electric vehicles, the structure of which is as follows: Figure 7 As shown. The specific implementation process of this system is as follows:

[0144] The data sensing and computing module includes a smart meter, a controller local area network communication bus, and a microprocessor. When multiple two-wheeled electric vehicles are connected to the supercharging pile, it is used to obtain the maximum available power of the power distribution network, collect the battery status data and business status data of each two-wheeled electric vehicle in real time, and calculate the charging urgency weight of each two-wheeled electric vehicle based on the battery status data and business status data.

[0145] The parameter allocation module, including a main control chip and a memory, is used to allocate corresponding high-frequency pulse charging parameters according to the charging urgency weight of each of the two-wheeled electric vehicles. The high-frequency pulse charging parameters include the pulse peak power and the duty cycle composed of the pulse on time and the pulse off time.

[0146] The power dispatch control module, including a DC bus controller and a power electronic power converter, is used to use the maximum available power of the distribution network as a constraint condition, combined with the pulse peak power, to perform phase-shifted interleaving scheduling of the pulse on-time and pulse off-time on the time axis, so as to limit the actual total output power superimposed at the same time to within the maximum available power of the distribution network;

[0147] The polarization state monitoring module includes a high-frequency voltage sampling circuit and an analog-to-digital converter, which is used to collect the open-circuit voltage of the two-wheeled electric vehicle during the zero-current period corresponding to the pulse disconnection time when the phase shift interleaving scheduling is performed, and calculate the battery polarization degree of the two-wheeled electric vehicle in combination with the charging terminal voltage of the supercharging pile.

[0148] The closed-loop correction drive module includes a pulse width modulation generator, which is used to dynamically correct the pulse on-time and pulse off-time based on the battery polarization degree, and use the corrected high-frequency pulse charging parameters to drive the power electronic power converter to cyclically execute the phase shift interleaving scheduling.

[0149] Specifically, when multiple riders connect to the supercharging station, the microprocessor reads data such as the remaining battery power and temperature of each vehicle through the controller local area network communication bus, and simultaneously obtains business status data such as the delivery time of food delivery orders. It calculates the charging urgency weight with business attributes, and then the main control chip positively maps high-frequency pulse charging parameters such as duty cycle based on this weight. Then, the DC bus controller schedules the charging pulses of each vehicle in a staggered manner according to the time axis, under the constraint of meeting the maximum available power of the power grid, to prioritize power supply for urgent orders. During the pulse disconnection interval, the high-frequency voltage sampling circuit reads the open circuit voltage without loss and calculates the current polarization degree of the battery cell. Finally, the pulse width modulation generator dynamically corrects the pulse duty cycle of the next cycle according to the feedback polarization degree, thereby improving the charging efficiency of riders with urgent orders while effectively avoiding the risks of power grid overload and battery thermal runaway.

[0150] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A dynamic power adjustment method for a supercharging station for two-wheeled electric vehicles, characterized in that, include: S1. When multiple two-wheeled electric vehicles are connected to the supercharging pile, the maximum available power of the power distribution network is obtained, the battery status data and business status data of each two-wheeled electric vehicle are collected in real time, and the charging urgency weight of each two-wheeled electric vehicle is calculated based on the battery status data and business status data. S2. Based on the charging urgency weight of each of the two-wheeled electric vehicles, allocate corresponding high-frequency pulse charging parameters, the high-frequency pulse charging parameters including pulse peak power and duty cycle composed of pulse on time and pulse off time; S3. Using the maximum available power of the distribution network as a constraint, and in combination with the pulse peak power, the pulse on-time and pulse off-time are phase-shifted and staggered on the time axis to limit the total actual output power superimposed at the same moment to the maximum available power of the distribution network. S4. When performing the phase shift interleaving scheduling, during the zero current period corresponding to the pulse disconnection time, the open circuit voltage of the two-wheeled electric vehicle is collected, and the battery polarization degree of the two-wheeled electric vehicle is calculated in combination with the charging terminal voltage of the supercharging pile. S5. Based on the battery polarization degree, dynamically correct the pulse on-time and pulse off-time, and use the corrected high-frequency pulse charging parameters to cyclically execute the phase shift interleaving scheduling.

2. The dynamic power adjustment method for a two-wheeled electric vehicle supercharging station according to claim 1, characterized in that, Step S1, the step of real-time collection of battery status data and business status data of each of the two-wheeled electric vehicles, includes: The remaining battery power, battery health, and temperature data of each of the two-wheeled electric vehicles are obtained through a communication protocol to construct the battery status data. By calling the business server interface, order delivery time, delivery distance, and rider tags are extracted to construct the business status data; The battery state data and the business state data are processed into dimensionless form using a normalization algorithm to generate standard state feature vectors for each of the two-wheeled electric vehicles.

3. The dynamic power adjustment method for a two-wheeled electric vehicle supercharging station according to claim 1, characterized in that, Step S1, the step of calculating the charging urgency weight of each of the two-wheeled electric vehicles, includes: Based on a preset priority scheduling logic, initial weight coefficients are assigned to the battery status data and the business status data, wherein the shorter the order delivery time and the lower the remaining battery power, the higher the initial weight coefficient. The battery status data, the business status data, and the initial weighting coefficients are input into a weighted evaluation function for combined calculation, and a baseline urgency value is output. The baseline urgency value is mapped and transformed by an activation function to generate the final charging urgency weight.

4. The dynamic power adjustment method for a two-wheeled electric vehicle supercharging station according to claim 1, characterized in that, Step S2, the step of allocating the corresponding high-frequency pulse charging parameters, includes: Combining the health and temperature data from the battery status data, the maximum safe current for output is calculated and set as the peak power of the pulse; Using a preset mapping model, the charging urgency weight is positively mapped to the target duty cycle value, and the charging urgency weight and the target duty cycle value are positively correlated. According to a preset reference period, the target duty cycle value is divided into on-time and off-time, and the pulse on-time and pulse off-time in the high-frequency pulse charging parameters are generated respectively.

5. The dynamic power adjustment method for a two-wheeled electric vehicle supercharging station according to claim 1, characterized in that, Step S3, the step of performing phase-shift interleaving scheduling on the time axis, includes: Extract the charging urgency weights of each of the two-wheeled electric vehicles, and generate a global dynamic scheduling sequence using a descending order sorting algorithm; A phase shift angle is assigned to each of the two-wheeled electric vehicles in the global dynamic scheduling sequence, and the pulse conduction time is mapped to different time slots of the synchronization cycle; Using a time axis alignment algorithm, the pulse turn-on time corresponding to the two-wheeled electric vehicle with a lower charging urgency weight is inserted into the pulse disconnect time corresponding to the two-wheeled electric vehicle with a higher charging urgency weight, forming a complementary staggered pulse waveform sequence.

6. The dynamic power adjustment method for a two-wheeled electric vehicle supercharging station according to claim 1, characterized in that, Step S3, the step of limiting the total actual output power superimposed at the same time within the maximum available power of the distribution network, includes: Discrete-time sampling calculations are performed on the pulse on-time and pulse off-time after the phase-shift interleaving scheduling to obtain the predicted superimposed total power at each discrete sampling point; The predicted total power is compared with the maximum available power of the distribution network to locate the potential overload time window that exceeds the maximum available power of the distribution network; Within the potential overload time window, the pulse conduction time corresponding to the two-wheeled electric vehicle with a lower charging urgency weight is compressed in reverse order according to the charging urgency weight, so that the adjusted actual total output power is limited to the maximum available power of the power distribution network.

7. The dynamic power adjustment method for a two-wheeled electric vehicle supercharging station according to claim 1, characterized in that, Step S4, the step of calculating the battery polarization degree of the two-wheeled electric vehicle, includes: During the zero-current period, the voltage drop slope of the battery terminals is continuously monitored, and the actual open-circuit voltage value is extracted when the voltage drop slope is lower than a preset slope threshold. Obtain the peak output voltage record before the pulse disconnection time is triggered, and define it as the charging terminal voltage; Calculate the voltage difference between the charging terminal voltage and the actual open-circuit voltage value, and input it into the equivalent circuit model to separate the polarization voltage drop, and quantify the degree of battery polarization.

8. The dynamic power adjustment method for a two-wheeled electric vehicle supercharging station according to claim 1, characterized in that, Step S5, the step of dynamically correcting the pulse on-time and the pulse off-time, includes: The polarization degree of the battery is compared with a preset safety benchmark threshold to calculate the polarization deviation rate; When the degree of battery polarization is greater than the safety benchmark threshold, the pulse disconnection time is extended proportionally based on the polarization deviation rate, and the pulse conduction time is reduced simultaneously. The corrected pulse off-time and pulse on-time are recombined into the duty cycle, which serves as the basic input parameter for executing the phase shift interleaving schedule in the next cycle.

9. A dynamic power adjustment system for a supercharging station for two-wheeled electric vehicles, characterized in that, A dynamic power adjustment method for a two-wheeled electric vehicle supercharging pile according to any one of claims 1-8, the system comprising: The data sensing and computing module includes a smart meter, a controller local area network communication bus, and a microprocessor. When multiple two-wheeled electric vehicles are connected to the supercharging pile, it is used to obtain the maximum available power of the power distribution network, collect the battery status data and business status data of each two-wheeled electric vehicle in real time, and calculate the charging urgency weight of each two-wheeled electric vehicle based on the battery status data and business status data. The parameter allocation module, including a main control chip and a memory, is used to allocate corresponding high-frequency pulse charging parameters according to the charging urgency weight of each of the two-wheeled electric vehicles. The high-frequency pulse charging parameters include the pulse peak power and the duty cycle composed of the pulse on time and the pulse off time. The power dispatch control module, including a DC bus controller and a power electronic power converter, is used to use the maximum available power of the distribution network as a constraint condition, combined with the pulse peak power, to perform phase-shifted interleaving scheduling of the pulse on-time and pulse off-time on the time axis, so as to limit the actual total output power superimposed at the same time to within the maximum available power of the distribution network; The polarization state monitoring module includes a high-frequency voltage sampling circuit and an analog-to-digital converter, which is used to collect the open-circuit voltage of the two-wheeled electric vehicle during the zero-current period corresponding to the pulse disconnection time when the phase shift interleaving scheduling is performed, and calculate the battery polarization degree of the two-wheeled electric vehicle in combination with the charging terminal voltage of the supercharging pile. The closed-loop correction drive module includes a pulse width modulation generator, which is used to dynamically correct the pulse on-time and pulse off-time based on the battery polarization degree, and use the corrected high-frequency pulse charging parameters to drive the power electronic power converter to cyclically execute the phase shift interleaving scheduling.

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