A hybrid electric vehicle power control method and system based on multi-objective optimization

Abstract

The application relates to the technical field of power control of hybrid electric vehicles, and relates to a hybrid electric vehicle power control method and system based on multi-target optimization, which comprises the following steps: collecting the current vehicle speed of a hybrid electric vehicle to be analyzed to obtain a current driving speed, acquiring a battery power distribution coefficient and a super capacitor power distribution coefficient, monitoring the hybrid electric vehicle to be analyzed in real time to obtain a current power and a last-time power, confirming adjacent cycle distribution parameters, acquiring optimized battery output power and optimized super capacitor output power, delivering the optimized battery output power and the optimized super capacitor output power to a drive motor to obtain a drive motor to be controlled, performing power control on the drive motor to be controlled to obtain a drive motor that has been controlled, and completing the hybrid electric vehicle power control based on multi-target optimization based on the drive motor that has been controlled. The application can simultaneously improve the comprehensive working efficiency of the vehicle and the stable composite power output power.

Description

Technical Field

[0001] This invention relates to the field of hybrid electric vehicle power control technology, and in particular to a hybrid electric vehicle power control method and system based on multi-objective optimization. Background Technology

[0002] Hybrid electric vehicles (HEVs) are cars that simultaneously utilize two or more power sources (typically an internal combustion engine and an electric motor). The electric motor is powered by a battery (rechargeable battery) and a supercapacitor, enabling coordinated operation between the internal combustion engine and the electric motor to reduce fuel consumption, decrease emissions, and improve performance. Multi-objective optimization is a mathematical optimization problem that seeks a set of decision variables to optimize all objective functions given multiple conflicting or mutually restrictive objective functions.

[0003] Existing methods often focus on a single control objective (such as focusing solely on power following or voltage stability) without fully considering the synergistic optimization of the battery and supercapacitor, resulting in significant energy consumption in the vehicle. Secondly, under transient conditions such as rapid acceleration and deceleration, existing methods struggle to respond quickly to changes in power demand, leading to large fluctuations in the output power of the composite power supply, impacting vehicle smoothness and component lifespan. Therefore, simultaneously improving the overall operating efficiency of the vehicle and stabilizing the output power of the composite power supply is a pressing technical challenge. Summary of the Invention

[0004] This invention provides a power control method for hybrid electric vehicles based on multi-objective optimization and a computer-readable storage medium. Its main purpose is to simultaneously improve the overall operating efficiency of the vehicle and stabilize the output power of the composite power supply.

[0005] To achieve the above objectives, the present invention provides a power control method for hybrid electric vehicles based on multi-objective optimization, comprising: The hybrid electric vehicle to be analyzed has been identified, which includes: vehicle speed sensor, battery management system, drive motor and power bus; The current vehicle speed of the hybrid vehicle to be analyzed is collected by the vehicle speed sensor to obtain the current driving speed and the current capacitor voltage. The battery power allocation coefficient and the supercapacitor power allocation coefficient are obtained based on the battery management system, current driving speed and current capacitor voltage. The hybrid electric vehicle to be analyzed is monitored in real time using a preset sampling period to obtain the power at the current moment and the power at the previous moment. The absolute difference between the current power and the previous power is calculated to obtain the power fluctuation rate, and the second-level power fluctuation threshold is obtained based on the preset power fluctuation upper limit. The adjacent cycle allocation parameters are determined based on the power bus, power fluctuation rate, battery power allocation coefficient, supercapacitor power allocation coefficient and second-level power fluctuation threshold. Optimize the output power of the battery and the supercapacitor by obtaining the parameters allocated between adjacent cycles; The optimized battery output power and the optimized supercapacitor output power are sent to the drive motor to obtain the drive motor to be controlled. Power control is performed on the drive motor to be controlled to obtain the controlled drive motor, and power control of the hybrid vehicle based on multi-objective optimization is completed based on the controlled drive motor.

[0006] Optionally, the step of obtaining the battery power allocation coefficient and the supercapacitor power allocation coefficient based on the battery management system, current driving speed, and current capacitor voltage includes: The ideal charge value is calculated based on the current driving speed and the current capacitor voltage, and the actual charge value of the supercapacitor is obtained from the battery management system. The reference state of charge value is calculated based on the ideal charge value and the actual charge value of the supercapacitor, and the state of charge deviation value is calculated based on the reference state of charge value and the actual charge value of the supercapacitor. The state of charge deviation value is fuzzified to obtain the power allocation coefficient of the battery and the power allocation coefficient of the supercapacitor.

[0007] Optionally, the formula for calculating the ideal charge value is as follows:

[0008] in, Indicates the ideal charge value, Indicates the current capacitor voltage. This indicates the preset maximum capacitor voltage. Indicates the current driving speed. This indicates the preset maximum speed of the car.

[0009] Optionally, the calculation of the reference state of charge value based on the ideal charge value and the actual charge value of the supercapacitor includes: If the ideal charge value is greater than the actual charge value of the supercapacitor, the charging compensation amount is calculated based on the ideal charge value and the preset charging compensation coefficient. If the ideal charge value is less than the actual charge value of the supercapacitor, the discharge compensation amount is calculated based on the ideal charge value and the preset discharge compensation coefficient. If the ideal charge value is equal to the actual charge value of the supercapacitor, then the preset zero value will be used as the compensation amount. Based on the charging compensation amount, discharging compensation amount, or compensation amount, the charge compensation amount is determined. The charge compensation amount and the actual charge value of the supercapacitor are summed to obtain the reference state of charge value.

[0010] Optionally, the fuzzification processing of the state-of-charge deviation value to obtain the battery power allocation coefficient and the supercapacitor power allocation coefficient includes: Construct a membership function parameter table, and use the membership function parameter table to map the charge state deviation values ​​to obtain a fuzzy subset membership set; The fuzzy subset membership degrees are extracted sequentially from the fuzzy subset membership degree set, and the target quantization value is confirmed from the pre-constructed fuzzy rule table based on the extracted fuzzy subset membership degrees. The target quantized value and the extracted fuzzy subset membership degree are weighted and summed to obtain the weighted output value; Sum the weighted output values ​​to obtain a weighted output value set, and sum the weighted output value set to obtain the weighted sum; Calculate the sum of membership degrees of the fuzzy subset membership degree set, and calculate the adjustment amount of the supercapacitor power allocation coefficient based on the weighted sum and the sum of membership degrees; Obtain the historical capacitor power allocation coefficient, and sum the historical capacitor power allocation coefficient and the adjustment amount of the supercapacitor power allocation coefficient to obtain the initial capacitor power allocation coefficient. The initial capacitor power allocation coefficient is limited to obtain the supercapacitor power allocation coefficient, and the battery power allocation coefficient is calculated based on the supercapacitor power allocation coefficient.

[0011] Optionally, the construction of the membership function parameter table includes: Obtain the theoretical range of the state of charge deviation value, confirm the boundary points of the theoretical range of the state of charge deviation value, and obtain the set of interval boundary points; Based on the interval boundary point set, the theoretical interval of the state of charge deviation value is divided into fuzzy subsets to obtain the fuzzy subset interval set. The set of fuzzy subset interval center values ​​is determined based on the fuzzy subset interval set, wherein the fuzzy subset interval center value corresponds one-to-one with the fuzzy subset interval; Construct a membership function parameter table based on the fuzzy subset interval set and the fuzzy subset interval center value set.

[0012] Optionally, the step of determining the adjacent cycle allocation parameters based on the power bus, power fluctuation rate, battery power allocation coefficient, supercapacitor power allocation coefficient, and second-level power fluctuation threshold includes: If the power fluctuation rate is less than the upper limit of power fluctuation, then the qualified allocation parameters are determined based on the battery power allocation coefficient and the supercapacitor power allocation coefficient. If the power fluctuation rate is greater than the upper limit of power fluctuation and less than or equal to the second level power fluctuation threshold, then the step size of the battery power allocation coefficient is reduced to obtain the updated allocation parameters. If the power fluctuation rate is greater than the second-level power fluctuation threshold, then multi-objective optimization is performed on the hybrid vehicle under analysis based on the power bus to obtain the optimized allocation parameters, which include: optimized battery power allocation coefficient and optimized supercapacitor allocation coefficient. The allocation parameters for adjacent periods are determined based on qualified allocation parameters, updated allocation parameters, or optimized allocation parameters.

[0013] Optionally, the step-size reduction operation on the battery power allocation coefficient to obtain updated allocation parameters includes: Obtain the historical battery power allocation coefficient, and calculate the coefficient change step size based on the battery power allocation coefficient and the historical battery power allocation coefficient; The battery power allocation coefficient is calculated and updated based on the preset step reduction coefficient, coefficient change step size and historical battery power allocation coefficient. The updated supercapacitor allocation coefficient is calculated based on the updated battery power allocation coefficient, and the updated allocation parameters are confirmed based on the updated battery power allocation coefficient and the updated supercapacitor allocation coefficient.

[0014] Optionally, the multi-objective optimization based on the power bus to obtain optimized allocation parameters includes: Power data is acquired based on the power bus to obtain the total demand power. Discrete wavelet multi-scale decomposition is then performed on the total demand power to obtain low-frequency and high-frequency components. The low-frequency component is multiplied by the battery power distribution coefficient to obtain the battery output power, and the high-frequency component is multiplied by the supercapacitor power distribution coefficient to obtain the supercapacitor output power. The current sharing ratio of the supercapacitor is obtained by calculating the sharing ratio based on the output power of the battery and the output power of the supercapacitor. Set the supercapacitor sharing ratio increment, add the current supercapacitor sharing ratio and the supercapacitor sharing ratio increment to obtain the adjusted current sharing ratio; The minimum value is extracted from the adjusted current sharing ratio and the preset upper limit of the supercapacitor sharing ratio to obtain the updated supercapacitor sharing ratio. The updated supercapacitor power sharing ratio is used as the optimized battery power allocation coefficient, and the optimized supercapacitor allocation coefficient is calculated based on the optimized battery power allocation coefficient. The optimized allocation parameters were determined based on the optimized battery power allocation coefficient and the optimized supercapacitor allocation coefficient.

[0015] To achieve the above objectives, the present invention also provides a hybrid electric vehicle power control system based on multi-objective optimization, comprising: The vehicle data acquisition module is used to identify the hybrid vehicle to be analyzed. The hybrid vehicle to be analyzed includes: vehicle speed sensor, battery management system, drive motor and power bus. The vehicle speed sensor collects the current vehicle speed of the hybrid vehicle to be analyzed to obtain the current driving speed and obtain the current capacitor voltage. The power allocation coefficient calculation module is used to obtain the battery power allocation coefficient and the supercapacitor power allocation coefficient based on the battery management system, current driving speed and current capacitor voltage. The power optimization parameter generation module is used to monitor the hybrid vehicle under analysis in real time using a preset sampling period, obtain the power at the current moment and the power at the previous moment, calculate the absolute difference between the power at the current moment and the power at the previous moment to obtain the power fluctuation rate, and obtain the second-level power fluctuation threshold based on the preset power fluctuation upper limit. The vehicle power control module is used to determine the adjacent cycle allocation parameters based on the power bus, power fluctuation rate, battery power allocation coefficient, supercapacitor power allocation coefficient, and second-level power fluctuation threshold. Based on the adjacent cycle allocation parameters, it obtains the optimized battery output power and the optimized supercapacitor output power, and sends the optimized battery output power and the optimized supercapacitor output power to the drive motor to obtain the drive motor to be controlled. It then performs power control on the drive motor to obtain the controlled drive motor, and completes the hybrid vehicle power control based on multi-objective optimization based on the controlled drive motor.

[0016] To address the above problems, the present invention also provides an electronic device, the electronic device comprising: Memory, storing at least one instruction; The processor executes the instructions stored in the memory to implement the multi-objective optimization-based hybrid vehicle power control method described above.

[0017] To address the aforementioned problems, the present invention also provides a computer-readable storage medium storing at least one instruction, which is executed by a processor in an electronic device to implement the aforementioned multi-objective optimization-based hybrid vehicle power control method.

[0018] To address the problems described in the background art, this invention identifies the hybrid electric vehicle to be analyzed, which includes a vehicle speed sensor, a battery management system, a drive motor, and a power bus. By pre-identifying the vehicle's structural components, this invention enables the control method to be adaptively adjusted for different types and configurations of hybrid electric vehicles, improving the method's versatility and applicability. The invention collects the current vehicle speed from the vehicle speed sensor to obtain the current driving speed and acquire the current capacitor voltage. By collecting the current driving speed in real time using the vehicle speed sensor, this invention can accurately obtain the vehicle's real-time operating status, providing dynamic input parameters for subsequent calculation of the ideal charge value. Simultaneously, by acquiring the current capacitor voltage, it can... This invention provides real-time feedback on the electrical state of the supercapacitor, offering accurate data for state-of-charge calculation and power allocation. This avoids control malfunctions caused by unknown capacitor states. Based on the battery management system, current driving speed, and current capacitor voltage, the invention obtains the battery power allocation coefficient and the supercapacitor power allocation coefficient. By incorporating vehicle speed and capacitor voltage information into the calculation process of the allocation coefficient, the control strategy can anticipate changes in power demand, achieving an organic combination of feedforward and feedback control. This improves response speed and adaptability. The invention utilizes a preset sampling period to monitor the hybrid vehicle under analysis in real time, obtaining the current power and the power at the previous moment. The absolute difference between the current power and the previous power is calculated to obtain the power fluctuation rate. Based on a preset upper limit for power fluctuation, a second-level power fluctuation threshold is obtained. This invention calculates the power fluctuation rate by taking the absolute difference between the current power and the power at the previous moment. This quantitatively assesses the stability of the composite power supply's output power, providing an objective basis for determining whether subsequent optimization adjustments are needed. By obtaining the second-level power fluctuation threshold based on the upper limit, a graded judgment mechanism is established, enabling the differentiation of the severity of power fluctuations and thus adopting differentiated optimization strategies. This avoids the problems of over-intervention or under-intervention that may result from single threshold control. Based on the power bus, power fluctuation rate, battery power allocation coefficient, supercapacitor power allocation coefficient, and the second-level power fluctuation threshold, adjacent cycle allocation parameters are determined. This invention introduces power fluctuation... The power grading judgment mechanism maintains the original allocation coefficient when power fluctuations are normal, reduces the step size when there is a slight exceedance, and performs multi-objective optimization when there is a severe exceedance. This achieves refined graded control, avoiding unnecessary computational overhead and ensuring timely and effective intervention under abnormal operating conditions. Based on the allocation parameters of adjacent cycles, the optimized battery output power and optimized supercapacitor output power are obtained. This invention fully leverages the complementary advantages of the high energy density of the battery and the high power density of the supercapacitor by distributing the optimized power output command to the battery and supercapacitor respectively, achieving optimal synergistic operation of the two energy storage components. The optimized battery output power and optimized supercapacitor output power are then transmitted to the drive motor to obtain the drive motor to be controlled.This invention performs power control on the drive motor to be controlled, resulting in a controlled drive motor. This power control enables the drive motor to output corresponding torque and speed according to the optimized power command, achieving precise control of the vehicle's power output. Based on the controlled drive motor, multi-objective optimization-based power control for hybrid vehicles is completed. Therefore, this invention can simultaneously improve the overall operating efficiency of the vehicle and stabilize the output power of the hybrid power supply. Attached Figure Description

[0019] Figure 1 This is a flowchart illustrating a hybrid electric vehicle power control method based on multi-objective optimization according to an embodiment of the present invention. Figure 2 A functional block diagram of a hybrid electric vehicle power control system based on multi-objective optimization provided in an embodiment of the present invention; Figure 3 This is a schematic diagram of the structure of an electronic device for implementing the multi-objective optimization-based hybrid electric vehicle power control method according to an embodiment of the present invention.

[0020] Explanation of reference numerals in the attached figures: 10. Electronic device; 11. Processor; 12. Memory; 13. Bus.

[0021] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0022] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0023] This application provides a hybrid electric vehicle power control method based on multi-objective optimization. The execution entity of the multi-objective optimization-based hybrid electric vehicle power control method includes, but is not limited to, at least one of the following electronic devices that can be configured to execute the method provided in this application: a server, a terminal, etc. In other words, the multi-objective optimization-based hybrid electric vehicle power control method can be executed by software or hardware installed on a terminal device or a server device, and the software can be a blockchain platform. The server includes, but is not limited to, a single server, a server cluster, a cloud server, or a cloud server cluster.

[0024] Reference Figure 1 The diagram shown is a flowchart illustrating a hybrid electric vehicle power control method based on multi-objective optimization according to an embodiment of the present invention. In this embodiment, the hybrid electric vehicle power control method based on multi-objective optimization includes: S1. Identify the hybrid vehicle to be analyzed, which includes: vehicle speed sensor, battery management system, drive motor and power bus.

[0025] It should be explained that the hybrid vehicle under analysis is a hybrid vehicle equipped with an idle start-stop system. The vehicle speed sensor is used to collect the current driving speed of the hybrid vehicle under analysis in real time. The battery management system is used to monitor and manage the operating status of the vehicle's power battery in real time. The drive motor is an electric motor used to convert the electrical energy provided by the hybrid power source into mechanical energy. The hybrid power source is a power source composed of two or more different types of energy storage elements connected in parallel or series through a power electronic converter. The power bus is a wire used to transmit electrical energy between the hybrid power source and the drive motor, while simultaneously collecting the total power demand required by the hybrid power source.

[0026] S2. Based on the vehicle speed sensor, collect the current vehicle speed of the hybrid vehicle to be analyzed, obtain the current driving speed, and acquire the current capacitor voltage.

[0027] It should be explained that the current driving speed is the instantaneous speed of the hybrid vehicle being analyzed at its current location, obtained in real time by the vehicle speed sensor. The current capacitor voltage is the terminal voltage of the supercapacitor at the current moment, obtained in real time by the battery management system. A supercapacitor is an energy storage element that falls between a traditional capacitor and a rechargeable battery. This energy storage element achieves rapid storage and release of charge through the double-layer effect or Faraday quasi-capacitance effect at the electrode-electrolyte interface.

[0028] S3. Obtain the battery power allocation coefficient and supercapacitor power allocation coefficient based on the battery management system, current driving speed and current capacitor voltage.

[0029] Specifically, the process of obtaining the battery power allocation coefficient and the supercapacitor power allocation coefficient based on the battery management system, current driving speed, and current capacitor voltage includes: The ideal charge value is calculated based on the current driving speed and the current capacitor voltage, and the actual charge value of the supercapacitor is obtained from the battery management system. The reference state of charge value is calculated based on the ideal charge value and the actual charge value of the supercapacitor, and the state of charge deviation value is calculated based on the reference state of charge value and the actual charge value of the supercapacitor. The state of charge deviation value is fuzzified to obtain the power allocation coefficient of the battery and the power allocation coefficient of the supercapacitor.

[0030] It should be explained that the detailed steps for calculating the ideal charge value based on the current driving speed and current capacitor voltage, the detailed steps for calculating the reference state of charge value based on the ideal charge value and the actual charge value of the supercapacitor, and the detailed steps for fuzzifying the state of charge deviation value to obtain the battery power allocation coefficient and the supercapacitor power allocation coefficient will all be given later and will not be repeated here. The step of obtaining the actual charge value of the supercapacitor from the battery management system is as follows: the terminal voltage of the supercapacitor is collected in real time, and the actual charge value of the supercapacitor is obtained by dividing the terminal voltage by the maximum voltage of the capacitor. The maximum voltage of the capacitor is obtained from the supercapacitor's design drawings. The state of charge deviation value is the difference obtained by subtracting the actual charge value of the supercapacitor from the reference state of charge value.

[0031] In detail, the formula for calculating the ideal charge value is as follows:

[0032] in, Indicates the ideal charge value, Indicates the current capacitor voltage. This indicates the preset maximum capacitor voltage. Indicates the current driving speed. This indicates the preset maximum speed of the car.

[0033] It should be explained that the maximum voltage of the capacitor is the maximum terminal voltage applied by the supercapacitor under normal operating conditions. The maximum vehicle speed is the highest driving speed that the hybrid vehicle under analysis can achieve in its design. The formula for calculating the ideal charge value mentioned above in this invention calculates a dimensionless value between 0 and 1 by nonlinearly combining the ratio of the current capacitor voltage to the maximum capacitor voltage and the ratio of the current driving speed to the maximum vehicle speed. This value represents the optimal state of charge that the supercapacitor should maintain under the current vehicle speed and current capacitor voltage conditions. The formula for calculating the ideal charge value... This reflects the relative level of the capacitor voltage; the higher the voltage, the larger this value, indicating that when the supercapacitor already has a significant amount of stored energy, its charge target should be reduced to avoid overcharging. The ideal charge value is calculated using the formula... This reflects the relative level of vehicle speed; the faster the speed, the larger this value, indicating that the vehicle has a higher demand for auxiliary power from the supercapacitor at high speeds and needs to maintain a high charge target to meet the power requirements at high speeds. In summary, the purpose of this formula is to dynamically calculate an optimal charge value for the supercapacitor that balances energy supply and energy recovery based on the vehicle's real-time operating state (current vehicle speed) and the supercapacitor's real-time electrical state (current capacitor voltage). This provides a scientific basis for subsequent power allocation and achieves coordinated and optimized control between the battery and the supercapacitor.

[0034] In detail, the calculation of the reference state of charge value based on the ideal charge value and the actual charge value of the supercapacitor includes: If the ideal charge value is greater than the actual charge value of the supercapacitor, the charging compensation amount is calculated based on the ideal charge value and the preset charging compensation coefficient. If the ideal charge value is less than the actual charge value of the supercapacitor, the discharge compensation amount is calculated based on the ideal charge value and the preset discharge compensation coefficient. If the ideal charge value is equal to the actual charge value of the supercapacitor, then the preset zero value will be used as the compensation amount. Based on the charging compensation amount, discharging compensation amount, or compensation amount, the charge compensation amount is determined. The charge compensation amount and the actual charge value of the supercapacitor are summed to obtain the reference state of charge value.

[0035] It should be explained that if the ideal charge value is greater than the actual charge value of the supercapacitor, it means that the current stored charge of the supercapacitor is insufficient to meet the expected optimal operating state, and external power supplementation is required. The charging compensation coefficient is a pre-set coefficient used to control the magnitude and adjustment speed of the compensation amount when the supercapacitor needs to be charged. The charging compensation coefficient is set as follows: First, based on the rated capacity and maximum charging current capability of the supercapacitor, determine the maximum charging amount that the supercapacitor can safely absorb within a unit sampling period. This serves as the upper limit constraint for setting the charging compensation coefficient, avoiding overcurrent charging of the supercapacitor due to excessive compensation. Second, through offline simulation experiments, under typical urban operating conditions (such as WLTC operating conditions or actual urban public transport operating conditions), with the optimization objectives of minimizing the supercapacitor's state of charge tracking error and minimizing energy loss during the charging process, different charging compensation coefficients (within the range of 0.1 to 0) are applied. 9. Perform an iterative optimization with a step size of 0.1, recording the state of charge overshoot, settling time, and charging energy loss for each set of parameters. Then, plot the relationship curve between the charging compensation coefficient and the comprehensive performance index based on the iterative results. Select the charging compensation coefficient with the best comprehensive performance index (the optimal combination of the smallest state of charge overshoot, shortest settling time, and lowest charging energy loss) as the preferred value. Finally, verify the preferred value on the actual vehicle platform and fine-tune it according to the actual temperature rise of the supercapacitor (the temperature rise during charging should be controlled within the normal range) to obtain the charging compensation coefficient.

[0036] Importantly, the step of calculating the charging compensation amount based on the ideal charge value and the preset charging compensation coefficient is as follows: First, calculate the difference between the ideal charge value and the actual charge value of the supercapacitor. Then, multiply this difference by the charging compensation coefficient, and the resulting product is the charging compensation amount. If the ideal charge value is less than the actual charge value of the supercapacitor, it indicates that the current stored charge of the supercapacitor exceeds the expected optimal operating state, and excess energy needs to be released. The discharge compensation coefficient is a preset coefficient used to control the magnitude and adjustment speed of the compensation amount when the supercapacitor needs to discharge. The setting method of the discharge compensation coefficient is as follows: First, based on the rated capacity and maximum discharge current capability of the supercapacitor, determine the maximum discharge charge that the supercapacitor can safely release within a unit sampling period. This serves as the upper limit constraint for setting the discharge compensation coefficient, avoiding overcurrent discharge of the supercapacitor due to excessive compensation. Second, through offline simulation experiments, under typical urban operating conditions (such as WLTC operating conditions or actual urban public transport operating conditions), with the optimization objectives of minimizing the supercapacitor's state of charge tracking error and minimizing energy loss during the discharge process, different discharge compensation coefficients (within the range of 0.1 to 0.1) are applied. 9. Perform an iterative optimization with a step size of 0.1, recording the state-of-charge overshoot, settling time, and discharge energy loss for each set of parameters. Then, based on the iterative results, plot the relationship curve between the discharge compensation coefficient and the comprehensive performance index. Select the discharge compensation coefficient with the optimal comprehensive performance index (the minimum state-of-charge overshoot, the shortest settling time, and the lowest discharge energy loss, weighted optimal) as the preferred value. Finally, verify the preferred value on a real vehicle platform and fine-tune it according to the actual temperature rise of the supercapacitor (the temperature rise during discharge should be controlled within the normal range) to obtain the discharge compensation coefficient. The steps for calculating the discharge compensation amount based on the ideal charge value and the preset discharge compensation coefficient are as follows: First, calculate the difference between the ideal charge value and the actual charge value of the supercapacitor. Then, multiply this difference by the discharge compensation coefficient. The product is the discharge compensation amount. If the ideal charge value equals the actual charge value of the supercapacitor, it means that the supercapacitor is already in an ideal working state and no charging or discharging adjustment is required. Zero value is 0. The charge compensation amount can be the charging compensation amount, the discharging compensation amount, or the compensation amount. The reference state of charge is the sum of the charge compensation and the actual charge value of the supercapacitor.

[0037] In detail, the process of fuzzifying the state-of-charge deviation value to obtain the battery power allocation coefficient and the supercapacitor power allocation coefficient includes: Construct a membership function parameter table, and use the membership function parameter table to map the charge state deviation values ​​to obtain a fuzzy subset membership set; The fuzzy subset membership degrees are extracted sequentially from the fuzzy subset membership degree set, and the target quantization value is confirmed from the pre-constructed fuzzy rule table based on the extracted fuzzy subset membership degrees. The target quantized value and the extracted fuzzy subset membership degree are weighted and summed to obtain the weighted output value; Sum the weighted output values ​​to obtain a weighted output value set, and sum the weighted output value set to obtain the weighted sum; Calculate the sum of membership degrees of the fuzzy subset membership degree set, and calculate the adjustment amount of the supercapacitor power allocation coefficient based on the weighted sum and the sum of membership degrees; Obtain the historical capacitor power allocation coefficient, and sum the historical capacitor power allocation coefficient and the adjustment amount of the supercapacitor power allocation coefficient to obtain the initial capacitor power allocation coefficient. The initial capacitor power allocation coefficient is limited to obtain the supercapacitor power allocation coefficient, and the battery power allocation coefficient is calculated based on the supercapacitor power allocation coefficient.

[0038] It should be explained that the detailed steps for constructing the membership function parameter table will be given later. In the step of mapping the charge state deviation values ​​using the membership function parameter table to obtain the fuzzy subset membership set, the fuzzy subset membership degree is calculated using the following formula:

[0039] in, The first fuzzy subset in the membership set represents the fuzzy subset. fuzzy subset membership degree Indicates the deviation value of the state of charge. Indicates the first The minimum value of the interval membership degree of a fuzzy subset. Indicates the first The center point of the interval of membership degree of a fuzzy subset. Indicates the first The maximum value of the membership degree of a fuzzy subset within an interval; By summing the membership degrees of fuzzy subsets, we obtain the fuzzy subset membership degree set.

[0040] Understandably, the fuzzy subset membership set is a collection of fuzzy subset memberships. The fuzzy rule table is a table describing the logical correspondence between fuzzy subsets of charge state deviation and supercapacitor allocation coefficient adjustment amounts. Each rule is in the form of "if the input belongs to a certain fuzzy subset, then the output is a certain adjustment amount". The target quantization value is the specific numerical value of the supercapacitor power allocation coefficient adjustment amount corresponding to a certain fuzzy subset in the fuzzy rule table. The weighted output value is the product obtained by multiplying the extracted fuzzy subset membership degree with its corresponding target quantization value. The weighted output value set is a collection of weighted output values ​​of all fuzzy subsets. The weighted sum is the value obtained by summing all weighted output values ​​in the weighted output value set. The membership degree sum is the value obtained by summing all fuzzy subset membership degrees in the fuzzy subset membership degree set. The supercapacitor power allocation coefficient adjustment amount is the value obtained by dividing the weighted sum by the membership degree sum. The historical capacitor power allocation coefficient is the supercapacitor power allocation coefficient calculated in the previous sampling period. The initial capacitor power allocation coefficient is the sum of the historical capacitor power allocation coefficient and the adjustment amount of the supercapacitor power allocation coefficient. The step of limiting the initial capacitor power allocation coefficient to obtain the supercapacitor power allocation coefficient is as follows: extract the minimum value from the initial capacitor power allocation coefficient and a preset upper limit for the supercapacitor power allocation coefficient (e.g., 0.9), and then extract the maximum value from the minimum value and a preset lower limit for the supercapacitor power allocation coefficient (e.g., 0.1). The maximum value is taken as the supercapacitor power allocation coefficient. The step of calculating the battery power allocation coefficient based on the supercapacitor power allocation coefficient is as follows: subtract the supercapacitor power allocation coefficient from 1; the difference is the battery power allocation coefficient.

[0041] Importantly, the state of charge (SCC) deviation is a continuously changing, precise value, while power allocation between the battery and the supercapacitor is a typical nonlinear decision-making problem, difficult to fully describe using a precise mathematical model. Therefore, this invention uses fuzzification to transform the precise SCC deviation value into fuzzy subsets with clear physical meanings, such as negative large, negative small, zero, positive small, and positive large, along with their membership degrees. This allows for reasoning based on a fuzzy rule base constructed from expert experience, making the power allocation decision more closely resemble the control logic in actual engineering. Simultaneously, the fuzzification method effectively suppresses interference from sensor measurement noise and system parameter fluctuations, preventing drastic fluctuations in the power allocation coefficient caused by small deviations. Furthermore, combining fuzzy reasoning and defuzzification processes enables a smooth mapping from precise input to fuzzy reasoning and then to precise output, resulting in a continuous and smooth nonlinear change in the power allocation coefficient with respect to the SCC deviation. This approach ensures stable operation while adaptively adjusting the allocation intensity according to the deviation magnitude: fine-tuning for small deviations and significant adjustments for large deviations, ultimately achieving a more rational and efficient energy allocation between the battery and the supercapacitor.

[0042] In detail, the construction of the membership function parameter table includes: Obtain the theoretical range of the state of charge deviation value, confirm the boundary points of the theoretical range of the state of charge deviation value, and obtain the set of interval boundary points; Based on the interval boundary point set, the theoretical interval of the state of charge deviation value is divided into fuzzy subsets to obtain the fuzzy subset interval set; The set of fuzzy subset interval center values ​​is determined based on the fuzzy subset interval set, wherein the fuzzy subset interval center value corresponds one-to-one with the fuzzy subset interval; Construct a membership function parameter table based on the fuzzy subset interval set and the fuzzy subset interval center value set.

[0043] It should be explained that the theoretical range of the state of charge deviation (SCD) value is the range of all possible values ​​for the SCD. The process of identifying the boundary points within the theoretical SCD range involves selecting several key numerical points within that range as boundaries for dividing the fuzzy subsets. These boundary points divide the theoretical range into multiple consecutive sub-intervals, each corresponding to a fuzzy subset. These key numerical points are symmetrically selected by experts within the theoretical range based on engineering experience and control accuracy requirements. The interval boundary point set is a collection of these interval boundary points. The fuzzy subset interval set is a collection of the value intervals corresponding to each fuzzy subset obtained after dividing the theoretical SCD range based on the interval boundary point set. The steps for determining the center value set of fuzzy subset intervals based on the fuzzy subset interval set are as follows: First, it is necessary to clarify the physical meaning represented by each fuzzy subset interval: the negative large interval represents the case where the charge state deviation is in the negative direction and the absolute value is large; the negative small interval represents the case where the deviation is in the negative direction and the absolute value is small; the zero interval represents the case where the deviation is close to zero; the positive small interval represents the case where the deviation is in the positive direction and the absolute value is small; and the positive large interval represents the case where the deviation is in the positive direction and the absolute value is large. Then, based on the symmetrical distribution characteristics of each interval on the number axis, a numerical point that best represents the characteristics of the fuzzy subset is selected as the center value in each interval. The specific selection rule is as follows: for the negative large interval [-1, -0.4], because its coverage is wide and For extreme negative values, -0.5, representing a typical large negative deviation, is selected as the center value. For the small negative interval [-0.4, -0.1], -0.2 is selected as the center value. For the zero interval [-0.1, 0.1], the geometric center point 0 is selected as the center value. For the small positive interval [0.1, 0.4], 0.2 is selected as the center value. For the large positive interval [0.4, 1], 0.5, representing a typical large positive deviation, is selected as the center value. Finally, the selected center values ​​are arranged in the order of large negative, small negative, zero, small positive, large positive, resulting in the fuzzy subset interval center value set {-0.5, -0.2, 0, 0.2, 0.5}. The construction of the membership function parameter table based on the fuzzy subset interval set and the fuzzy subset interval center value set involves mapping each fuzzy subset interval in the fuzzy subset interval set to a one-to-one correspondence with each fuzzy subset interval center value, thus constructing the membership function parameter table.

[0044] S4. Use the preset sampling period to monitor the hybrid vehicle under analysis in real time to obtain the power at the current moment and the power at the previous moment.

[0045] It should be explained that the sampling period is a pre-set time interval for continuously collecting the operating parameters of the hybrid vehicle to be analyzed. The current power is the product of the DC bus voltage and current of the power bus at the current sampling moment. The previous power is the product of the DC bus voltage and current of the power bus at the previous sampling moment.

[0046] S5. Calculate the absolute difference between the power at the current moment and the power at the previous moment to obtain the power fluctuation rate, and obtain the second-level power fluctuation threshold based on the preset power fluctuation upper limit.

[0047] It should be explained that power fluctuation rate is the absolute difference between the power at the current moment and the power at the previous moment. The upper limit of power fluctuation is the maximum fluctuation value of the output power of the composite power supply set in advance. The second-level power fluctuation threshold obtained based on the preset upper limit of power fluctuation is the value obtained by multiplying the upper limit of power fluctuation by a preset second-level threshold coefficient (for example, the second-level threshold coefficient is 1.5), which is the second-level power fluctuation threshold. The power fluctuation upper limit is set as follows: First, based on the rated power of the drive motor of the hybrid electric vehicle to be analyzed, the maximum current change rate of the battery, and the transient response capability of the supercapacitor, the maximum power change range that the hybrid power supply can tolerate under normal operating conditions is determined. Second, through real vehicle calibration experiments, the output power data of the hybrid power supply is collected under typical operating conditions (such as rapid acceleration, rapid deceleration, idle start-stop, etc.), and the distribution characteristics of the power fluctuation rate are statistically analyzed. The critical value at which the smoothness of the whole vehicle decreases significantly after the power fluctuation rate exceeds a certain threshold is used as a reference. Then, taking into account the battery health management requirements (the current change rate caused by power fluctuation should be controlled within the range of the battery) and the power buffering capability of the supercapacitor (the power fluctuation amplitude that the supercapacitor can suppress is limited), a preliminary threshold is determined through multi-objective trade-offs. Finally, different thresholds are verified on a real vehicle or hardware-in-the-loop simulation platform. With the optimization objectives of minimizing the energy consumption of the whole vehicle, the best power fluctuation suppression effect, and the minimum battery temperature rise, the preliminary threshold is iteratively optimized to determine the upper limit of power fluctuation.

[0048] S6. Based on the power bus, power fluctuation rate, battery power allocation coefficient, supercapacitor power allocation coefficient and second-level power fluctuation threshold, the allocation parameters for adjacent cycles are determined.

[0049] In detail, the determination of adjacent cycle allocation parameters based on power bus, power fluctuation rate, battery power allocation coefficient, supercapacitor power allocation coefficient, and second-level power fluctuation threshold includes: If the power fluctuation rate is less than the upper limit of power fluctuation, then the qualified allocation parameters are determined based on the battery power allocation coefficient and the supercapacitor power allocation coefficient. If the power fluctuation rate is greater than the upper limit of power fluctuation and less than or equal to the second level power fluctuation threshold, then the step size of the battery power allocation coefficient is reduced to obtain the updated allocation parameters. If the power fluctuation rate is greater than the second-level power fluctuation threshold, then multi-objective optimization is performed on the hybrid vehicle under analysis based on the power bus to obtain the optimized allocation parameters, which include: optimized battery power allocation coefficient and optimized supercapacitor allocation coefficient. The allocation parameters for adjacent periods are determined based on qualified allocation parameters, updated allocation parameters, or optimized allocation parameters.

[0050] It should be explained that if the power fluctuation rate is less than the upper limit of power fluctuation, it means that the output power variation of the composite power supply within the current sampling period is within the normal range, that is, the power output of the composite power supply is relatively stable and there are no abnormal fluctuations. No adjustment is needed to the battery power allocation coefficient or the supercapacitor power allocation coefficient. Qualified allocation parameters include both the battery power allocation coefficient and the supercapacitor power allocation coefficient. If the power fluctuation rate is greater than the upper limit of power fluctuation, and the power fluctuation rate is less than or equal to the second-level power fluctuation threshold, it means that the output power variation of the composite power supply within the current sampling period is in a slightly excessive state, that is, the power fluctuation has exceeded the normal range, but has not yet reached a serious level. Moderate optimization adjustment of the battery power allocation coefficient is required. The detailed steps for decreasing the step size of the battery power allocation coefficient to obtain the updated allocation parameters will be given later and will not be repeated here. If the power fluctuation rate is greater than the second-level power fluctuation threshold, it means that the output power variation of the composite power supply within the current sampling period is in a severely excessive state, that is, the power fluctuation has seriously exceeded the normal range, which may adversely affect the smoothness of the vehicle, battery life, and the health of the supercapacitor. Strong optimization adjustment is required. The detailed steps for multi-objective optimization of the hybrid vehicle under analysis based on the power bus to obtain the optimized allocation parameters will be given later and will not be repeated here. The adjacent period allocation parameters are a set of power allocation coefficients selected from qualified allocation parameters, updated allocation parameters, or optimized allocation parameters based on the power fluctuation rate judgment result of the current sampling period.

[0051] Specifically, the step-size reduction operation on the battery power allocation coefficient to obtain updated allocation parameters includes: Obtain the historical battery power allocation coefficient, and calculate the coefficient change step size based on the battery power allocation coefficient and the historical battery power allocation coefficient; The battery power allocation coefficient is calculated and updated based on the preset step reduction coefficient, coefficient change step size and historical battery power allocation coefficient. The updated supercapacitor allocation coefficient is calculated based on the updated battery power allocation coefficient, and the updated allocation parameters are confirmed based on the updated battery power allocation coefficient and the updated supercapacitor allocation coefficient.

[0052] It needs to be explained that the historical battery power allocation coefficient is the battery power allocation coefficient calculated in the previous sampling period. The coefficient change step size is the coefficient obtained by subtracting the historical battery power allocation coefficient from the battery power allocation coefficient. The step size reduction coefficient is a pre-set step size used to reduce the power allocation coefficient when the power fluctuation is slightly excessive. The step size reduction coefficient is set as follows: First, the battery power change rate should not exceed 10% of its rated power per second, and the supercapacitor power change rate should not exceed 30% of its rated power per second, which serves as the constraint boundary for setting the step size reduction coefficient. Second, through offline simulation experiments, under typical urban operating conditions (such as WLTC, NEDC, or actual urban bus operating conditions), with the optimization objectives of optimal power fluctuation suppression and minimum vehicle energy consumption, different step size reduction coefficients (within the range of 0.1 to 0.9, step size 0.1) are iterated. To optimize, the number of times power fluctuations exceeded the standard, the peak value of power fluctuations, and the total energy consumption were recorded for each set of parameters. Then, the relationship curve between the step reduction coefficient and the comprehensive performance index was plotted based on the traversal results. The step reduction coefficient with the best comprehensive performance index (the minimum number of times power fluctuations exceeded the standard, the minimum peak value of power fluctuations, and the minimum energy consumption) was selected as the optimal value. Finally, the optimal value was verified on the actual vehicle platform and fine-tuned based on the driver's actual experience (such as subjective evaluation indicators such as acceleration smoothness and braking comfort) to finally obtain the step reduction coefficient.

[0053] Understandably, the step of calculating the updated battery power allocation coefficient based on the preset step reduction coefficient, coefficient change step size, and historical battery power allocation coefficient is as follows: The updated battery power allocation coefficient is obtained by multiplying the step reduction coefficient by the coefficient change step size and then adding the historical battery power allocation coefficient. The method for calculating the updated supercapacitor allocation coefficient based on the updated battery power allocation coefficient is the same as the method for calculating the battery power allocation coefficient based on the supercapacitor power allocation coefficient, and will not be repeated here. The updated allocation parameters include both the updated battery power allocation coefficient and the updated supercapacitor allocation coefficient.

[0054] In detail, the multi-objective optimization based on the power bus to obtain optimized allocation parameters includes: Power data is acquired based on the power bus to obtain the total demand power. Discrete wavelet multi-scale decomposition is then performed on the total demand power to obtain low-frequency and high-frequency components. The low-frequency component is multiplied by the battery power distribution coefficient to obtain the battery output power, and the high-frequency component is multiplied by the supercapacitor power distribution coefficient to obtain the supercapacitor output power. The current sharing ratio of the supercapacitor is obtained by calculating the sharing ratio based on the output power of the battery and the output power of the supercapacitor. Set the supercapacitor sharing ratio increment, add the current supercapacitor sharing ratio and the supercapacitor sharing ratio increment to obtain the adjusted current sharing ratio; The minimum value is extracted from the adjusted current sharing ratio and the preset upper limit of the supercapacitor sharing ratio to obtain the updated supercapacitor sharing ratio. The updated supercapacitor power sharing ratio is used as the optimized battery power allocation coefficient, and the optimized supercapacitor allocation coefficient is calculated based on the optimized battery power allocation coefficient. The optimized allocation parameters were determined based on the optimized battery power allocation coefficient and the optimized supercapacitor allocation coefficient.

[0055] It should be explained that the step of acquiring power data based on the power bus to obtain the total demand power is as follows: The instantaneous voltage and current of the DC bus are measured in real time using voltage and current sensors installed on the power bus, and the total demand power required by the composite power supply at the current moment is calculated based on the instantaneous voltage and current. The discrete wavelet multi-scale decomposition of the total demand power is an operation of multi-scale decomposing the total demand power signal using wavelet transform. This operation of multi-scale decomposing the total demand power signal using wavelet transform is existing technology and will not be elaborated further here. The low-frequency component is the power with lower frequency and relatively smooth changes obtained after discrete wavelet multi-scale decomposition of the total demand power signal. The high-frequency component is the power with higher frequency and more drastic changes obtained after discrete wavelet multi-scale decomposition of the total demand power signal. The battery output power is the product of the low-frequency component and the battery power allocation coefficient. The supercapacitor output power is the product of the high-frequency component and the supercapacitor power allocation coefficient. The formula for calculating the current sharing ratio of the supercapacitor based on the output power of the battery and the output power of the supercapacitor is as follows:

[0056] in, This indicates the current proportion of the load shared by the supercapacitor. This indicates the output power of the supercapacitor. This represents the battery output power. The supercapacitor sharing ratio increment is used to increase the proportion of the supercapacitor when power fluctuations severely exceed limits. This ratio is used to suppress power fluctuations by utilizing the fast response characteristics of the supercapacitor. The adjusted current sharing ratio is the sum of the current supercapacitor sharing ratio and the supercapacitor sharing ratio increment. The updated supercapacitor sharing ratio is the minimum value between the adjusted current sharing ratio and the upper limit of the supercapacitor sharing ratio. The method for calculating the optimized supercapacitor allocation coefficient based on the optimized battery power allocation coefficient is the same as the method for calculating the battery power allocation coefficient based on the supercapacitor power allocation coefficient, and will not be repeated here.

[0057] S7. Optimize the output power of the battery and the output power of the supercapacitor based on the adjacent cycle allocation parameters.

[0058] It should be explained that optimizing the battery output power is achieved by multiplying the battery power allocation coefficient in the adjacent cycle allocation parameters by the current total demand power, and then performing discrete wavelet multi-scale decomposition to obtain the low-frequency component. Optimizing the supercapacitor output power is achieved by multiplying the supercapacitor power allocation coefficient in the adjacent cycle allocation parameters by the high-frequency component of the current total demand power.

[0059] S8. The optimized battery output power and the optimized supercapacitor output power are sent to the drive motor to obtain the drive motor to be controlled.

[0060] It should be explained that the drive motor to be controlled is a drive motor that has already delivered optimized battery output power and optimized supercapacitor output power.

[0061] S9. Perform power control on the drive motor to be controlled to obtain the controlled drive motor, and complete the power control of the hybrid vehicle based on multi-objective optimization based on the controlled drive motor.

[0062] It should be explained that the power control of the drive motor to be controlled involves using the optimized output power of the battery and the optimized output power of the supercapacitor as command values. This is achieved by controlling the discharge current and voltage of the battery, and by controlling the discharge current and voltage of the supercapacitor via a bidirectional DC / DC converter. This ensures that both output power in tandem according to the commanded power, jointly driving the drive motor to be controlled. The controlled drive motor is one that has received the optimized output power command and is operating normally according to the commanded power. It should be noted that this invention obtains the battery power allocation coefficient and supercapacitor power allocation coefficient based on the current driving speed and current capacitor voltage, enabling dynamic adjustment of power allocation according to real-time vehicle speed and capacitor status, thus improving energy utilization efficiency. By real-time monitoring of power fluctuation rate and setting a second-level power fluctuation threshold, combined with step-size reduction when power fluctuation is slightly exceeded and multi-objective optimization when it is severely exceeded, power fluctuation is effectively suppressed, achieving stable control of the composite power supply output power. By increasing the supercapacitor's share of power when it is severely exceeded and using wavelet decomposition to allocate high-frequency components to the supercapacitor, the supercapacitor is made to withstand transient high-current impacts, reducing the number of high-current discharges of the battery. The synergistic work of the above steps jointly achieves the multi-objective optimization control effect of improving the overall working efficiency of the vehicle, stabilizing the composite power supply output power, and extending the service life of the battery and supercapacitor.

[0063] To address the problems described in the background art, this invention identifies the hybrid electric vehicle to be analyzed, which includes a vehicle speed sensor, a battery management system, a drive motor, and a power bus. By pre-identifying the vehicle's structural components, this invention enables the control method to be adaptively adjusted for different types and configurations of hybrid electric vehicles, improving the method's versatility and applicability. The invention collects the current vehicle speed from the vehicle speed sensor to obtain the current driving speed and acquire the current capacitor voltage. By collecting the current driving speed in real time using the vehicle speed sensor, this invention can accurately obtain the vehicle's real-time operating status, providing dynamic input parameters for subsequent calculation of the ideal charge value. Simultaneously, by acquiring the current capacitor voltage, it can... This invention provides real-time feedback on the electrical state of the supercapacitor, offering accurate data for state-of-charge calculation and power allocation. This avoids control malfunctions caused by unknown capacitor states. Based on the battery management system, current driving speed, and current capacitor voltage, the invention obtains the battery power allocation coefficient and the supercapacitor power allocation coefficient. By incorporating vehicle speed and capacitor voltage information into the calculation process of the allocation coefficient, the control strategy can anticipate changes in power demand, achieving an organic combination of feedforward and feedback control. This improves response speed and adaptability. The invention utilizes a preset sampling period to monitor the hybrid vehicle under analysis in real time, obtaining the current power and the power at the previous moment. The absolute difference between the current power and the previous power is calculated to obtain the power fluctuation rate. Based on a preset upper limit for power fluctuation, a second-level power fluctuation threshold is obtained. This invention calculates the power fluctuation rate by taking the absolute difference between the current power and the power at the previous moment. This quantitatively assesses the stability of the composite power supply's output power, providing an objective basis for determining whether subsequent optimization adjustments are needed. By obtaining the second-level power fluctuation threshold based on the upper limit, a graded judgment mechanism is established, enabling the differentiation of the severity of power fluctuations and thus adopting differentiated optimization strategies. This avoids the problems of over-intervention or under-intervention that may result from single threshold control. Based on the power bus, power fluctuation rate, battery power allocation coefficient, supercapacitor power allocation coefficient, and the second-level power fluctuation threshold, adjacent cycle allocation parameters are determined. This invention introduces power fluctuation... The power grading judgment mechanism maintains the original allocation coefficient when power fluctuations are normal, reduces the step size when there is a slight exceedance, and performs multi-objective optimization when there is a severe exceedance. This achieves refined graded control, avoiding unnecessary computational overhead and ensuring timely and effective intervention under abnormal operating conditions. Based on the allocation parameters of adjacent cycles, the optimized battery output power and optimized supercapacitor output power are obtained. This invention fully leverages the complementary advantages of the high energy density of the battery and the high power density of the supercapacitor by distributing the optimized power output command to the battery and supercapacitor respectively, achieving optimal synergistic operation of the two energy storage components. The optimized battery output power and optimized supercapacitor output power are then transmitted to the drive motor to obtain the drive motor to be controlled.This invention performs power control on the drive motor to be controlled, resulting in a controlled drive motor. This power control enables the drive motor to output corresponding torque and speed according to the optimized power command, achieving precise control of the vehicle's power output. Based on the controlled drive motor, multi-objective optimization-based power control for hybrid vehicles is completed. Therefore, this invention can simultaneously improve the overall operating efficiency of the vehicle and stabilize the output power of the hybrid power supply.

[0064] like Figure 2 The diagram shown is a functional block diagram of a hybrid electric vehicle power control system based on multi-objective optimization provided in an embodiment of the present invention.

[0065] The hybrid electric vehicle power control system 100 based on multi-objective optimization described in this invention can be installed in an electronic device. Depending on the functions implemented, the hybrid electric vehicle power control system 100 may include a vehicle data acquisition module 101, a power distribution coefficient calculation module 102, a power optimization parameter generation module 103, and a vehicle power control module 104. The module described in this invention can also be called a unit, which refers to a series of computer program segments that can be executed by the processor of an electronic device and can perform a fixed function, and which are stored in the memory of the electronic device. The vehicle data acquisition module 101 is used to identify the hybrid vehicle to be analyzed, wherein the hybrid vehicle to be analyzed includes: a vehicle speed sensor, a battery management system, a drive motor and a power bus. The vehicle speed sensor is used to collect the current vehicle speed of the hybrid vehicle to be analyzed to obtain the current driving speed and the current capacitor voltage. The power allocation coefficient calculation module 102 is used to obtain the battery power allocation coefficient and the supercapacitor power allocation coefficient based on the battery management system, the current driving speed and the current capacitor voltage. The power optimization parameter generation module 103 is used to monitor the hybrid electric vehicle to be analyzed in real time using a preset sampling period, obtain the power at the current moment and the power at the previous moment, calculate the absolute difference between the power at the current moment and the power at the previous moment to obtain the power fluctuation rate, and obtain the second-level power fluctuation threshold based on the preset power fluctuation upper limit. The vehicle power control module 104 is used to determine the adjacent cycle allocation parameters based on the power bus, power fluctuation rate, battery power allocation coefficient, supercapacitor power allocation coefficient and second-level power fluctuation threshold, obtain the optimized battery output power and optimized supercapacitor output power based on the adjacent cycle allocation parameters, send the optimized battery output power and optimized supercapacitor output power to the drive motor to obtain the drive motor to be controlled, perform power control on the drive motor to be controlled to obtain the controlled drive motor, and complete the hybrid vehicle power control based on multi-objective optimization based on the controlled drive motor.

[0066] In detail, the modules in the multi-objective optimization-based hybrid electric vehicle power control system 100 described in this embodiment of the invention employ the same methods as described above during use. Figure 1 The method uses the same techniques as the multi-objective optimization-based hybrid vehicle power control method described in the article and can produce the same technical effects, so it will not be repeated here.

[0067] like Figure 3 The diagram shown is a schematic representation of an electronic device for implementing a multi-objective optimization-based power control method for hybrid electric vehicles, according to an embodiment of the present invention.

[0068] The electronic device 1 may include a processor 10, a memory 11 and a bus 12, and may also include a computer program stored in the memory 11 and executable on the processor 10, such as a hybrid vehicle power control method program based on multi-objective optimization.

[0069] The memory 11 includes at least one type of readable storage medium, such as flash memory, portable hard drive, multimedia card, card-type memory (e.g., SD or DX memory), magnetic memory, magnetic disk, optical disk, etc. In some embodiments, the memory 11 can be an internal storage unit of the electronic device 1, such as a portable hard drive. In other embodiments, the memory 11 can be an external storage device of the electronic device 1, such as a plug-in portable hard drive, smart media card (SMC), secure digital card (SD), flash card, etc., equipped on the electronic device 1. Furthermore, the memory 11 includes both internal storage units and external storage devices of the electronic device 1. The memory 11 can be used not only to store application software and various types of data installed on the electronic device 1, such as the code of a hybrid vehicle power control method program based on multi-objective optimization, but also to temporarily store data that has been output or will be output.

[0070] In some embodiments, the processor 10 may be composed of integrated circuits, such as a single packaged integrated circuit or multiple integrated circuits with the same or different functions, including combinations of one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and various control chips. The processor 10 is the control unit of the electronic device, connecting various components of the entire electronic device through various interfaces and lines. It executes programs or modules stored in the memory 11 (e.g., a hybrid vehicle power control method program based on multi-objective optimization) and calls data stored in the memory 11 to perform various functions of the electronic device 1 and process data.

[0071] The bus 12 can be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus, etc. The bus 12 can be divided into an address bus, a data bus, a control bus, etc. The bus 12 is configured to realize the connection and communication between the memory 11 and at least one processor 10, etc.

[0072] Figure 3 Only electronic devices with components are shown; it will be understood by those skilled in the art that... Figure 3 The structure shown does not constitute a limitation on the electronic device 1, and may include fewer or more components than shown, or combine certain components, or have different component arrangements.

[0073] For example, although not shown, the electronic device 1 may also include a power supply (such as a battery) to power the various components. Preferably, the power supply can be logically connected to the at least one processor 10 through a power management device, thereby enabling functions such as charging management, discharging management, and power consumption management. The power supply may also include one or more DC or AC power supplies, recharging devices, power fault detection circuits, power converters or inverters, power status indicators, and other arbitrary components. The electronic device 1 may also include various sensors, Bluetooth modules, Wi-Fi modules, etc., which will not be described in detail here.

[0074] Furthermore, the electronic device 1 may also include a network interface. Optionally, the network interface may include a wired interface and / or a wireless interface (such as a Wi-Fi interface, a Bluetooth interface, etc.), which is typically used to establish communication connections between the electronic device 1 and other electronic devices.

[0075] Optionally, the electronic device 1 may further include a user interface, which may be a display, an input unit (such as a keyboard), and optionally, a standard wired interface or a wireless interface. Optionally, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, or an OLED (Organic Light-Emitting Diode) touchscreen, etc. The display may also be appropriately referred to as a screen or display unit, used to display information processed in the electronic device 1 and to display a visual user interface.

[0076] The hybrid vehicle power control method program based on multi-objective optimization stored in the memory 11 of the electronic device 1 is a combination of multiple instructions. When run in the processor 10, it can achieve the following: The hybrid electric vehicle to be analyzed has been identified, which includes: vehicle speed sensor, battery management system, drive motor and power bus; The current vehicle speed of the hybrid vehicle to be analyzed is collected by the vehicle speed sensor to obtain the current driving speed and the current capacitor voltage. The battery power allocation coefficient and the supercapacitor power allocation coefficient are obtained based on the battery management system, current driving speed and current capacitor voltage. The hybrid electric vehicle to be analyzed is monitored in real time using a preset sampling period to obtain the power at the current moment and the power at the previous moment. The absolute difference between the current power and the previous power is calculated to obtain the power fluctuation rate, and the second-level power fluctuation threshold is obtained based on the preset power fluctuation upper limit. The adjacent cycle allocation parameters are determined based on the power bus, power fluctuation rate, battery power allocation coefficient, supercapacitor power allocation coefficient and second-level power fluctuation threshold. Optimize the output power of the battery and the supercapacitor by obtaining the parameters allocated between adjacent cycles; The optimized battery output power and the optimized supercapacitor output power are sent to the drive motor to obtain the drive motor to be controlled. Power control is performed on the drive motor to be controlled to obtain the controlled drive motor, and power control of the hybrid vehicle based on multi-objective optimization is completed based on the controlled drive motor.

[0077] Specifically, the processor 10's implementation method for the above instructions can be found in [reference needed]. Figures 1 to 3 The descriptions of the relevant steps in the corresponding embodiments are not repeated here.

[0078] Furthermore, if the modules / units integrated in the electronic device 1 are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. The computer-readable storage medium can be volatile or non-volatile. For example, the computer-readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, or a read-only memory (ROM).

[0079] The present invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor of an electronic device, can perform the following: The hybrid electric vehicle to be analyzed has been identified, which includes: vehicle speed sensor, battery management system, drive motor and power bus; The current vehicle speed of the hybrid vehicle to be analyzed is collected by the vehicle speed sensor to obtain the current driving speed and the current capacitor voltage. The battery power allocation coefficient and the supercapacitor power allocation coefficient are obtained based on the battery management system, current driving speed and current capacitor voltage. The hybrid electric vehicle to be analyzed is monitored in real time using a preset sampling period to obtain the power at the current moment and the power at the previous moment. The absolute difference between the current power and the previous power is calculated to obtain the power fluctuation rate, and the second-level power fluctuation threshold is obtained based on the preset power fluctuation upper limit. The adjacent cycle allocation parameters are determined based on the power bus, power fluctuation rate, battery power allocation coefficient, supercapacitor power allocation coefficient and second-level power fluctuation threshold. Optimize the output power of the battery and the supercapacitor by obtaining the parameters allocated between adjacent cycles; The optimized battery output power and the optimized supercapacitor output power are sent to the drive motor to obtain the drive motor to be controlled. Power control is performed on the drive motor to be controlled to obtain the controlled drive motor, and power control of the hybrid vehicle based on multi-objective optimization is completed based on the controlled drive motor.

[0080] In the embodiments provided by this invention, it should be understood that the disclosed devices, systems, and methods can be implemented in other ways. For example, the system embodiments described above are merely illustrative, and actual implementations may have other classification methods.

[0081] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0082] Furthermore, the functional modules in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or in the form of hardware plus software functional modules.

[0083] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0084] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A power control method for hybrid electric vehicles based on multi-objective optimization, characterized in that, The method includes: The hybrid electric vehicle to be analyzed has been identified, which includes: vehicle speed sensor, battery management system, drive motor and power bus; The current vehicle speed of the hybrid vehicle to be analyzed is collected by the vehicle speed sensor to obtain the current driving speed and the current capacitor voltage. The battery power allocation coefficient and the supercapacitor power allocation coefficient are obtained based on the battery management system, current driving speed and current capacitor voltage. The hybrid electric vehicle to be analyzed is monitored in real time using a preset sampling period to obtain the power at the current moment and the power at the previous moment. The absolute difference between the current power and the previous power is calculated to obtain the power fluctuation rate, and the second-level power fluctuation threshold is obtained based on the preset power fluctuation upper limit. The adjacent cycle allocation parameters are determined based on the power bus, power fluctuation rate, battery power allocation coefficient, supercapacitor power allocation coefficient and second-level power fluctuation threshold. The step of determining adjacent cycle allocation parameters based on power bus, power fluctuation rate, battery power allocation coefficient, supercapacitor power allocation coefficient, and second-level power fluctuation threshold includes: If the power fluctuation rate is less than the upper limit of power fluctuation, then the qualified allocation parameters are determined based on the battery power allocation coefficient and the supercapacitor power allocation coefficient. If the power fluctuation rate is greater than the upper limit of power fluctuation and less than or equal to the second level power fluctuation threshold, then the step size of the battery power allocation coefficient is reduced to obtain the updated allocation parameters. If the power fluctuation rate is greater than the second-level power fluctuation threshold, then multi-objective optimization is performed on the hybrid vehicle under analysis based on the power bus to obtain the optimized allocation parameters, which include: optimized battery power allocation coefficient and optimized supercapacitor allocation coefficient. The allocation parameters for adjacent periods are determined based on qualified allocation parameters, updated allocation parameters, or optimized allocation parameters. Optimize the output power of the battery and the supercapacitor by obtaining the parameters allocated between adjacent cycles; The optimized battery output power and the optimized supercapacitor output power are sent to the drive motor to obtain the drive motor to be controlled. Power control is performed on the drive motor to be controlled to obtain the controlled drive motor, and power control of the hybrid vehicle based on multi-objective optimization is completed based on the controlled drive motor.

2. The hybrid electric vehicle power control method based on multi-objective optimization as described in claim 1, characterized in that, The process of obtaining the battery power allocation coefficient and supercapacitor power allocation coefficient based on the battery management system, current driving speed, and current capacitor voltage includes: The ideal charge value is calculated based on the current driving speed and the current capacitor voltage, and the actual charge value of the supercapacitor is obtained from the battery management system. The reference state of charge value is calculated based on the ideal charge value and the actual charge value of the supercapacitor, and the state of charge deviation value is calculated based on the reference state of charge value and the actual charge value of the supercapacitor. The state of charge deviation value is fuzzified to obtain the power allocation coefficient of the battery and the power allocation coefficient of the supercapacitor.

3. The hybrid electric vehicle power control method based on multi-objective optimization as described in claim 2, characterized in that, The formula for calculating the ideal charge value is as follows: ； in, Indicates the ideal charge value, Indicates the current capacitor voltage. This indicates the preset maximum capacitor voltage. Indicates the current driving speed. This indicates the preset maximum speed of the car.

4. The hybrid electric vehicle power control method based on multi-objective optimization as described in claim 3, characterized in that, The calculation of the reference state of charge value based on the ideal charge value and the actual charge value of the supercapacitor includes: If the ideal charge value is greater than the actual charge value of the supercapacitor, the charging compensation amount is calculated based on the ideal charge value and the preset charging compensation coefficient. If the ideal charge value is less than the actual charge value of the supercapacitor, the discharge compensation amount is calculated based on the ideal charge value and the preset discharge compensation coefficient. If the ideal charge value is equal to the actual charge value of the supercapacitor, then the preset zero value will be used as the compensation amount. Based on the charging compensation amount, discharging compensation amount, or compensation amount, the charge compensation amount is determined. The charge compensation amount and the actual charge value of the supercapacitor are summed to obtain the reference state of charge value.

5. The hybrid electric vehicle power control method based on multi-objective optimization as described in claim 4, characterized in that, The process of fuzzifying the state-of-charge deviation value to obtain the battery power allocation coefficient and the supercapacitor power allocation coefficient includes: Construct a membership function parameter table, and use the membership function parameter table to map the charge state deviation values ​​to obtain a fuzzy subset membership set; The fuzzy subset membership degrees are extracted sequentially from the fuzzy subset membership degree set, and the target quantization value is confirmed from the pre-constructed fuzzy rule table based on the extracted fuzzy subset membership degrees. The target quantized value and the extracted fuzzy subset membership degree are weighted and summed to obtain the weighted output value; Sum the weighted output values ​​to obtain a weighted output value set, and sum the weighted output value set to obtain the weighted sum; Calculate the sum of membership degrees of the fuzzy subset membership degree set, and calculate the adjustment amount of the supercapacitor power allocation coefficient based on the weighted sum and the sum of membership degrees; Obtain the historical capacitor power allocation coefficient, and sum the historical capacitor power allocation coefficient and the adjustment amount of the supercapacitor power allocation coefficient to obtain the initial capacitor power allocation coefficient. The initial capacitor power allocation coefficient is limited to obtain the supercapacitor power allocation coefficient, and the battery power allocation coefficient is calculated based on the supercapacitor power allocation coefficient.

6. The hybrid electric vehicle power control method based on multi-objective optimization as described in claim 5, characterized in that, The construction of the membership function parameter table includes: Obtain the theoretical range of the state of charge deviation value, confirm the boundary points of the theoretical range of the state of charge deviation value, and obtain the set of interval boundary points; Based on the interval boundary point set, the theoretical interval of the state of charge deviation value is divided into fuzzy subsets to obtain the fuzzy subset interval set. The set of fuzzy subset interval center values ​​is determined based on the fuzzy subset interval set, wherein the fuzzy subset interval center value corresponds one-to-one with the fuzzy subset interval; Construct a membership function parameter table based on the fuzzy subset interval set and the fuzzy subset interval center value set.

7. The hybrid electric vehicle power control method based on multi-objective optimization as described in claim 6, characterized in that, The step-size reduction operation on the battery power allocation coefficient to obtain updated allocation parameters includes: Obtain the historical battery power allocation coefficient, and calculate the coefficient change step size based on the battery power allocation coefficient and the historical battery power allocation coefficient; The battery power allocation coefficient is calculated and updated based on the preset step reduction coefficient, coefficient change step size and historical battery power allocation coefficient. The updated supercapacitor allocation coefficient is calculated based on the updated battery power allocation coefficient, and the updated allocation parameters are confirmed based on the updated battery power allocation coefficient and the updated supercapacitor allocation coefficient.

8. The hybrid electric vehicle power control method based on multi-objective optimization as described in claim 7, characterized in that, The multi-objective optimization of the hybrid electric vehicle under analysis based on the power bus yields optimized allocation parameters, including: Power data is acquired based on the power bus to obtain the total demand power. Discrete wavelet multi-scale decomposition is then performed on the total demand power to obtain low-frequency and high-frequency components. The low-frequency component is multiplied by the battery power distribution coefficient to obtain the battery output power, and the high-frequency component is multiplied by the supercapacitor power distribution coefficient to obtain the supercapacitor output power. The current sharing ratio of the supercapacitor is obtained by calculating the sharing ratio based on the output power of the battery and the output power of the supercapacitor. Set the supercapacitor sharing ratio increment, add the current supercapacitor sharing ratio and the supercapacitor sharing ratio increment to obtain the adjusted current sharing ratio; The minimum value is extracted from the adjusted current sharing ratio and the preset upper limit of the supercapacitor sharing ratio to obtain the updated supercapacitor sharing ratio. The updated supercapacitor power sharing ratio is used as the optimized battery power allocation coefficient, and the optimized supercapacitor allocation coefficient is calculated based on the optimized battery power allocation coefficient. The optimized allocation parameters were determined based on the optimized battery power allocation coefficient and the optimized supercapacitor allocation coefficient.

9. A system applied to the multi-objective optimization-based power control method for hybrid electric vehicles as described in claim 1, characterized in that, The system includes: The vehicle data acquisition module is used to identify the hybrid vehicle to be analyzed. The hybrid vehicle to be analyzed includes: vehicle speed sensor, battery management system, drive motor and power bus. The vehicle speed sensor collects the current vehicle speed of the hybrid vehicle to be analyzed to obtain the current driving speed and obtain the current capacitor voltage. The power allocation coefficient calculation module is used to obtain the battery power allocation coefficient and the supercapacitor power allocation coefficient based on the battery management system, current driving speed and current capacitor voltage. The power optimization parameter generation module is used to monitor the hybrid vehicle under analysis in real time using a preset sampling period, obtain the power at the current moment and the power at the previous moment, calculate the absolute difference between the power at the current moment and the power at the previous moment to obtain the power fluctuation rate, and obtain the second-level power fluctuation threshold based on the preset power fluctuation upper limit. The vehicle power control module is used to determine the adjacent cycle allocation parameters based on the power bus, power fluctuation rate, battery power allocation coefficient, supercapacitor power allocation coefficient, and second-level power fluctuation threshold. Based on the adjacent cycle allocation parameters, it obtains the optimized battery output power and the optimized supercapacitor output power, and sends the optimized battery output power and the optimized supercapacitor output power to the drive motor to obtain the drive motor to be controlled. It then performs power control on the drive motor to obtain the controlled drive motor, and completes the hybrid vehicle power control based on multi-objective optimization based on the controlled drive motor.

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