Power compensation methods, devices and computer equipment in direct drive mode

Abstract

This application relates to a power compensation method, apparatus, computer equipment, storage medium, and computer program product in direct-drive mode, applied to a hybrid vehicle equipped with a high-voltage superstructure. The method includes: obtaining the current driving mode of the hybrid vehicle; when the current driving mode is direct-drive mode, obtaining the state of charge (SOC) of the hybrid vehicle battery; when the SOC is below a first preset threshold, obtaining the real-time power of the high-voltage superstructure; when the real-time power is above a second preset threshold, obtaining a current compensation power demand message based on a Kalman filter; and controlling the generator to perform power compensation based on the current compensation power demand message. This method enables power compensation that can be flexibly adjusted according to the current compensation power demand in direct-drive mode.

Description

Technical Field

[0001] This application relates to the field of vehicle control technology, and in particular to a power compensation method, apparatus, computer equipment, storage medium and computer program product in direct drive mode. Background Technology

[0002] With the development of hybrid technology, large vehicles equipped with high-voltage superstructures are gradually adopting hybrid systems. Hybrid vehicles equipped with high-voltage superstructures add components such as generators, drive motors, and power batteries to the existing engine. When the vehicle is in direct drive mode, the engine directly drives the vehicle, and the generator and drive motor do not participate in power generation. Simultaneously, to avoid wasting engine energy, the generator and drive motor are always on standby, promptly intervening when there is excess engine power to convert energy into electrical energy and store it in the battery system, thereby improving energy utilization throughout the entire driving mode.

[0003] When a hybrid vehicle equipped with a high-voltage superstructure is driving in direct drive mode, the vehicle operates purely on the engine. If the superstructure remains operational for an extended period, it will continuously deplete the high-voltage battery. To avoid unnecessary economic losses due to interruptions in the superstructure's operation, the engine's output power is typically increased to control the generator to charge the battery.

[0004] Currently, when hybrid vehicles perform power compensation in direct drive mode, they typically output a preset compensation charging power based on the remaining battery charge, and cannot flexibly adjust it according to the real-time power consumption of the superstructure. Summary of the Invention

[0005] Therefore, it is necessary to provide a power compensation method, device, computer equipment, computer-readable storage medium, and computer program product in direct drive mode that can flexibly adjust the power consumption of the upper device according to the real-time power consumption of the upper device, in order to address the above-mentioned technical problems.

[0006] Firstly, this application provides a power compensation method in direct drive mode, applied to a hybrid vehicle equipped with a high-voltage superstructure. The method includes:

[0007] Obtain the current driving mode of the hybrid vehicle;

[0008] When the current driving mode is direct drive mode, obtain the state of charge of the hybrid vehicle battery;

[0009] When the state of charge is lower than the first preset state threshold, the real-time power of the high-voltage superstructure is obtained;

[0010] When the real-time power is higher than the second preset state threshold, the current compensation power demand message is obtained based on the Kalman filter method;

[0011] Based on the current power compensation demand message, control the generator to perform power compensation.

[0012] In one embodiment, when the state of charge is below a first preset threshold, obtaining the real-time power of the high-voltage superstructure includes:

[0013] The real-time power of the hybrid vehicle's battery management system and motor control system is obtained separately.

[0014] The real-time power of the high-voltage superstructure is calculated based on the real-time power of the battery management system and the motor control system.

[0015] In one embodiment, the current compensation power demand message is obtained based on the Kalman filter method, including:

[0016] Construct a discrete linear physical model of the work done by the battery and the work done by the motor in a hybrid vehicle;

[0017] The work done by the battery and motor of the hybrid vehicle is used as the state variables, and the difference between the work done by the battery and motor is used as the observation, to establish a state-space model.

[0018] Based on the state-space model, establish the time update equation and the measurement update equation;

[0019] The optimal estimate of the state variables is obtained by iterative calculation based on the time update equation and the measurement update equation.

[0020] Obtain the current compensation power demand message based on the optimal estimate of the state variables.

[0021] In one embodiment, the optimal estimate of the state variables is obtained by iterative calculation based on the time update equation and the measurement update equation, including:

[0022] Obtain the current state variable at the current moment and use it as the target state variable. Based on the time update equation, obtain the prior estimate of the target state variable.

[0023] Input the prior estimate of the target state quantity into the measurement update equation to obtain the posterior estimate of the target state quantity;

[0024] Input the posterior estimate of the target state variable into the time update equation, obtain the prior estimate of the next state variable at the next time step, use the next state variable as the target state variable, return to the step of inputting the prior estimate of the target state variable into the measurement update equation and continue to execute. Repeat the above process until the total number of iterations reaches the preset number of iterations, and output the optimal estimates of the work done by the hybrid vehicle battery and the work done by the motor.

[0025] In one embodiment, obtaining the current compensation power demand message based on the optimal estimate of the state variables includes:

[0026] Based on the optimal estimate of the state variables, calculate the difference between the work done by the battery and the work done by the motor in the hybrid vehicle.

[0027] The average power consumed by the high-voltage superstructure is calculated based on the difference between the power output of the hybrid vehicle battery and the power output of the motor.

[0028] Based on the average power consumed by the high-voltage upper structure, obtain the current compensation power demand message.

[0029] In one embodiment, controlling the generator to perform power compensation based on the current compensation power demand message further includes:

[0030] Calculate the engine's requested torque based on the current compensation power demand information;

[0031] Based on the requested torque from the engine, the hybrid vehicle's generator is controlled to produce a corresponding negative torque to charge the battery.

[0032] Secondly, this application also provides a power compensation device in direct drive mode, applied to a hybrid vehicle equipped with a high-voltage superstructure. The device includes:

[0033] The mode monitoring module is used to obtain the current driving mode of the hybrid vehicle;

[0034] The battery monitoring module is used to obtain the state of charge of the hybrid vehicle battery when the current driving mode is direct drive mode.

[0035] The power monitoring module is used to obtain the real-time power of the high-voltage superstructure when the state of charge of the hybrid vehicle battery is lower than a first preset state threshold.

[0036] The power acquisition module is used to acquire the current compensation power demand message based on the Kalman filter method when the real-time power of the high-voltage installation is higher than the second preset state threshold.

[0037] The compensation control module is used to control the generator to perform power compensation based on the current compensation power demand message.

[0038] Thirdly, this application also provides a computer device. The computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to perform the following steps:

[0039] Obtain the current driving mode of the hybrid vehicle;

[0040] When the current driving mode is direct drive mode, obtain the state of charge of the hybrid vehicle battery;

[0041] When the state of charge is lower than the first preset state threshold, the real-time power of the high-voltage superstructure is obtained;

[0042] When the real-time power is higher than the second preset state threshold, the current compensation power demand message is obtained based on the Kalman filter method;

[0043] Based on the current power compensation demand message, control the generator to perform power compensation.

[0044] Fourthly, this application also provides a computer-readable storage medium. The computer-readable storage medium stores a computer program thereon, which, when executed by a processor, performs the following steps:

[0045] Obtain the current driving mode of the hybrid vehicle;

[0046] When the current driving mode is direct drive mode, obtain the state of charge of the hybrid vehicle battery;

[0047] When the state of charge is lower than the first preset state threshold, the real-time power of the high-voltage superstructure is obtained;

[0048] When the real-time power is higher than the second preset state threshold, the current compensation power demand message is obtained based on the Kalman filter method;

[0049] Based on the current power compensation demand message, control the generator to perform power compensation.

[0050] Fifthly, this application also provides a computer program product. The computer program product includes a computer program that, when executed by a processor, performs the following steps:

[0051] Obtain the current driving mode of the hybrid vehicle;

[0052] When the current driving mode is direct drive mode, obtain the state of charge of the hybrid vehicle battery;

[0053] When the state of charge is lower than the first preset state threshold, the real-time power of the high-voltage superstructure is obtained;

[0054] When the real-time power is higher than the second preset state threshold, the current compensation power demand message is obtained based on the Kalman filter method;

[0055] Based on the current power compensation demand message, control the generator to perform power compensation.

[0056] The aforementioned power compensation method, device, computer equipment, storage medium, and computer program product in direct-drive mode are applied to hybrid vehicles equipped with high-voltage superstructures. The method involves obtaining the current driving mode of the hybrid vehicle; when the current driving mode is direct-drive, obtaining the state of charge (SOC) of the hybrid vehicle battery; when the SOC is below a first preset threshold, obtaining the real-time power of the high-voltage superstructure; when the real-time power is above a second preset threshold, obtaining the current compensation power demand message based on a Kalman filter; and controlling the generator to perform power compensation based on the current compensation power demand message. Throughout the process, the conditions for power compensation are determined, the current compensation power demand message is obtained based on a Kalman filter, and the generator is controlled to operate according to the current compensation power demand. In direct-drive mode, power compensation can be flexibly adjusted according to the current compensation power demand. Attached Figure Description

[0057] Figure 1 This is a diagram illustrating the application environment of a power compensation method in direct drive mode in one embodiment.

[0058] Figure 2 This is a flowchart illustrating a power compensation method in direct drive mode in one embodiment.

[0059] Figure 3 This is a schematic diagram of the process for obtaining the current compensation power demand message based on the Kalman filter method in another embodiment;

[0060] Figure 4 This is a flowchart illustrating the calculation of the optimal estimate based on the Kalman filter method in another embodiment;

[0061] Figure 5 This is a structural block diagram of a power compensation device in direct drive mode in one embodiment;

[0062] Figure 6 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0063] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0064] The power compensation method in direct drive mode provided in this application embodiment can be applied to, for example... Figure 1In the application environment shown, terminal 102 communicates with generator 104 via a network. A data storage system can store the data that terminal 102 needs to process. The data storage system can be integrated onto terminal 102 or placed in the cloud or on other network servers. Terminal 102 obtains the current driving mode of the hybrid vehicle; when the current driving mode is direct drive mode, it obtains the state of charge (SOC) of the hybrid vehicle battery; when the SOC is lower than a first preset threshold, it obtains the real-time power of the high-voltage superstructure; when the real-time power is higher than a second preset threshold, it obtains the current compensation power demand message based on the Kalman filter method; based on the current compensation power demand message, it controls generator 104 to perform power compensation. Terminal 102 can be, but is not limited to, various personal computers, laptops, smartphones, tablets, IoT devices, and portable wearable devices. IoT devices can be smart speakers, smart TVs, smart air conditioners, smart in-vehicle devices, etc. Portable wearable devices can be smartwatches, smart bracelets, head-mounted devices, etc.

[0065] In one embodiment, such as Figure 2 As shown, a power compensation method in direct drive mode is provided, which is applied to... Figure 1 Taking terminal 102 as an example, the explanation includes the following steps:

[0066] S100: Obtain the current driving mode of the hybrid vehicle.

[0067] The current driving modes of the hybrid vehicle include: pure electric mode, hybrid mode, and direct drive mode. When driving in pure electric mode, the hybrid vehicle is driven solely by the electric motor; when driving in hybrid mode, the engine drives the generator, the generator supplies electricity to the electric motor, and the electric motor drives the wheels forward; when driving in direct drive mode, the hybrid vehicle is driven solely by the engine.

[0068] Optionally, the driver can select the vehicle driving mode according to the current driving conditions, and the terminal obtains the driving mode selected by the driver as the current driving mode. Pure electric mode can be selected when starting or driving at low speed, hybrid mode can be selected when strong acceleration is required, and direct drive mode can be selected when high-speed driving is required.

[0069] S200: When the current driving mode is direct drive mode, obtain the state of charge of the hybrid vehicle battery.

[0070] In hybrid vehicles, the State of Charge (SOC) of the battery refers to the battery's state of charge, reflecting its actual usable power. Also known as remaining capacity, the SOC represents the ratio of the battery's remaining dischargeable capacity to its full charge capacity, usually expressed as a percentage.

[0071] Optionally, when the current driving mode is direct drive mode, the terminal obtains the battery's SOC. When SOC = 0, the battery is fully discharged; when SOC = 100%, the battery is fully charged.

[0072] S300: When the state of charge is lower than the first preset state threshold, obtain the real-time power of the high-voltage superstructure.

[0073] The first preset state threshold is set according to actual needs, for example, it can be calibrated by computer software.

[0074] Optionally, the first preset state threshold can be set to 35% by computer software. When the state of charge is detected to be lower than 35%, it indicates that the high-voltage battery of the hybrid vehicle is at risk of being depleted. It is necessary to obtain the real-time power of the superstructure and further determine whether power compensation is necessary based on the real-time power of the superstructure.

[0075] S400: When the real-time power is higher than the second preset state threshold, obtain the current compensation power demand message based on the Kalman filter method.

[0076] The second preset state threshold refers to the power of the hybrid vehicle's superstructure under normal operating conditions, which can be set according to actual conditions, such as by calibration through computer software; the current compensation power requirement refers to the power requested by the terminal to balance the power consumed by the superstructure.

[0077] Optionally, when the real-time power of the superstructure is detected to be higher than the second preset state threshold, it indicates that the superstructure of the hybrid vehicle is currently operating. To ensure that the charging power of the battery is consistent with the power consumption of the superstructure, compensation is required based on the real-time power of the superstructure. Specifically, the real-time power of the superstructure can be calculated based on the collected current and voltage values. Considering that the current and voltage of the system may experience instantaneous jumps, and these jump values ​​will affect the real-time power value of the superstructure, thus affecting the solution of the compensation power, the real-time power of the superstructure is obtained based on the Kalman filter method, which is the current compensation power demand message.

[0078] S500: Based on the current compensation power demand message, control the generator to perform power compensation.

[0079] Specifically, controlling the generator to perform power compensation means increasing the output power of the hybrid vehicle's engine, thereby driving the generator to charge the battery.

[0080] Optionally, the terminal communicates with the generator via a communication network based on the current compensation power demand message. The generator generates a corresponding negative torque to charge the battery according to the increased torque of the engine. At the same time, the positive torque generated by the engine and the negative torque generated by the motor should be able to cancel each other out when they are delivered to the output front axle of the hybrid vehicle, so that the vehicle can still respond to the torque demand of the driver.

[0081] The aforementioned power compensation method in direct-drive mode is applied to hybrid vehicles equipped with a high-voltage superstructure. The method involves obtaining the current driving mode of the hybrid vehicle; if the current driving mode is direct-drive, obtaining the state of charge (SOC) of the hybrid vehicle battery; if the SOC is below a first preset threshold, obtaining the real-time power of the high-voltage superstructure; if the real-time power is above a second preset threshold, obtaining the current compensation power demand message based on a Kalman filter; and controlling the generator to perform power compensation based on the current compensation power demand message. Throughout the process, the conditions for power compensation are determined, the current compensation power demand message is obtained based on a Kalman filter, and the generator is controlled to operate according to the current compensation power demand. In direct-drive mode, power compensation can be flexibly adjusted according to the current compensation power demand.

[0082] The aforementioned power compensation method in direct-drive mode is applied to hybrid vehicles equipped with a high-voltage superstructure. The method involves obtaining the current driving mode of the hybrid vehicle; if the current driving mode is direct-drive, obtaining the state of charge (SOC) of the hybrid vehicle battery; if the SOC is below a first preset threshold, obtaining the real-time power of the high-voltage superstructure; if the real-time power is above a second preset threshold, obtaining the current compensation power demand message based on a Kalman filter; and controlling the generator to perform power compensation based on the current compensation power demand message. Throughout the process, the conditions for power compensation are determined, the current compensation power demand message is obtained based on a Kalman filter, and the generator is controlled to operate according to the current compensation power demand. In direct-drive mode, power compensation can be flexibly adjusted according to the current compensation power demand.

[0083] In one embodiment, when the state of charge is below a first preset threshold, obtaining the real-time power of the high-voltage superstructure includes:

[0084] The real-time power of the hybrid vehicle's battery management system and motor control system is obtained separately.

[0085] The real-time power of the high-voltage superstructure is calculated based on the real-time power of the battery management system and the motor control system.

[0086] When the battery's remaining power is insufficient, the real-time power of the high-voltage superstructure is further acquired, and its operational status is determined based on this real-time power. Specifically, this can be achieved by acquiring the real-time power of the hybrid vehicle's Battery Management System (BMS) and Motor Control Unit (MCU). Subtracting the real-time power of the MCU from the BMS's real-time power yields the electrical power consumed by the superstructure and other electrical equipment. In this application, the power consumption of other electrical equipment is relatively small compared to the superstructure's power and can be ignored.

[0087] In this embodiment, when the power of other electrical devices is negligible compared to the power of the upper device, the power is calculated by subtracting the power of the MCU from the power of the BMS in real time, and the calculated power is approximated as the real-time power consumed by the upper device, thereby realizing a rapid determination of whether the upper device is in working state.

[0088] In one embodiment, such as Figure 3 As shown, the current compensation power demand message is obtained based on the Kalman filter method, including:

[0089] S410: Construct a discrete linear physical model of the work done by the battery and the work done by the motor in a hybrid vehicle;

[0090] S420: The work done by the battery and motor of the hybrid vehicle is used as the state variable, and the difference between the work done by the battery and motor is used as the observation, to establish a state-space model.

[0091] S430: Based on the state-space model, establish the time update equation and the measurement update equation;

[0092] S440: Iterative calculations are performed based on the time update equation and the measurement update equation to obtain the optimal estimate of the state variables;

[0093] S450: Obtain the current compensation power demand message based on the optimal estimate of the state variables.

[0094] The Kalman filter (KF) is a highly efficient recursive filter that can estimate the state of a dynamic system from a series of incomplete and noisy observations. The Kalman filter considers the joint distribution of the values ​​of each observation at different times to generate an estimate of the unknown variable, thus making it more accurate than estimations based on a single observation.

[0095] When the battery power is determined to be insufficient and the high-voltage superstructure is in operation, the average power consumed by the superstructure over a period of time is calculated based on the Kalman filter method, which is then used as the power that needs to be compensated. Based on the current and voltage values ​​collected by the battery management system and the motor control system at discrete moments, and combined with the battery work and motor work, a discrete linear physical model of the battery work and motor work of the hybrid vehicle is constructed, as shown in the following equation (1):

[0096]

[0097] Where T is the sampling period, U 1，k-1 Let I be the voltage of the battery at time k-1. 1，k-1 U is the battery current at time k-1; 2，k-1 Let I be the voltage of the motor at time k-1.2，k-1 W_B represents the motor current at time k-1. k M_B represents the amount of work done by the battery at time k. k This represents the amount of work done by the motor at time k.

[0098] Based on the discrete linear physical model represented by equation (1) above, a state-space model is abstracted for the work done by the battery and the work done by the motor. Let the system state variables be x. k =[W_B k W_M k ] T The state transition matrix is ​​A = [1, 0; 0, 1]; the control variable is u. k =[U 1，k ·I 1，k U 2，k ·I 2，k ] T The input control matrix is ​​B = [T, T]; the observation is z. k =W_B k -W_M k The observation model matrix is ​​C = [1, -1]. For this embodiment, its state-space model can be expressed as equation (2):

[0099]

[0100] Among them, process noise w k The noise v follows a Gaussian distribution with mean 0 and variance Q. k It follows a Gaussian noise distribution with a mean of 0 and a variance of R, and the process noise and measurement noise are independent of each other.

[0101] Based on the state-space model represented by equation (2) above, Kalman filtering is performed according to the time update equation and the measurement update equation. The time update equation is used for state prediction. By forward estimating the state variables and the error covariance, a priori estimate of the state at the next moment is constructed. For this embodiment, the mathematical representation of the time update equation is shown in equation (3) below:

[0102]

[0103] in, This is the state estimate at time k-1. To be Substituting into the time update equation, we obtain the prior estimate of the state at time k; the uncertainty of the state at each time step is represented by the system's covariance matrix, P. k-1 It is the covariance matrix corresponding to the state at time k-1. Let be the prior estimate covariance matrix corresponding to the state at time k.

[0104] Based on the time update equation expressed in equation (3) above, a measurement update equation is used for prediction correction, that is, combining the prior estimate with the new measurement variable to construct an improved posterior estimate. The mathematical representation of the measurement update equation is shown in equation (4) below:

[0105]

[0106] Among them, K k The Kalman gain at time k, Let P be the posterior estimate of the state at time k. k Let K be the posterior estimated covariance matrix corresponding to the state at time k. The measurement update equation first calculates the Kalman gain K. k Then, based on the observation z k Calculate the posterior estimate of the state at time k. Finally, the posterior estimated covariance matrix corresponding to the state at time k is calculated.

[0107] After calculating the time update equation and the measurement update equation, the entire process is repeated, using the posterior estimate obtained in the previous step as input to calculate the prior estimate for the next time step. Throughout the Kalman filtering process, the current state estimate is recursively calculated based on the observations from the previous time step, iterating multiple times to obtain the optimal estimates of the battery work and motor work from time k0 to time k1.

[0108] Based on the optimal estimates of battery work and motor work Calculate the average power consumed by the superstructure from time k0 to time k1, and use it as the power that needs to be compensated now, as shown in the following formula (5):

[0109]

[0110] In this embodiment, to avoid the calculated compensation power being affected by measurement noise, the Kalman filter method is used to iterate the prior prediction value obtained from the information of the previous moment, and finally obtain the optimal estimate of the battery work and the motor work. Thus, the average power consumed by the superstructure over a period of time is calculated as the power that needs to be compensated at the present time, thereby achieving more accurate power compensation.

[0111] In one embodiment, such as Figure 4 As shown, the optimal estimate of the state variables is obtained through iterative calculation based on the time update equation and the measurement update equation, including:

[0112] Obtain the current state variable at the current moment and use it as the target state variable. Based on the time update equation, obtain the prior estimate of the target state variable.

[0113] Input the prior estimate of the target state quantity into the measurement update equation to obtain the posterior estimate of the target state quantity;

[0114] Input the posterior estimate of the target state variable into the time update equation, obtain the prior estimate of the next state variable at the next time step, use the next state variable as the target state variable, return to the step of inputting the prior estimate of the target state variable into the measurement update equation and continue to execute. Repeat the above process until the total number of iterations reaches the preset number of iterations, and output the optimal estimates of the work done by the hybrid vehicle battery and the work done by the motor.

[0115] Kalman filtering uses feedback control to estimate the state of a dynamic system. Specifically, it estimates the system's state at a given moment and then obtains feedback based on noisy observed variables. Therefore, Kalman filtering establishes two equations: a time update equation and a measurement update equation. The time update equation continuously advances the values ​​of the current state variables and the error covariance estimate to construct a prior estimate for the state at the next time step. The measurement update equation provides feedback based on this, combining the prior estimate with the new observed variables to construct an improved posterior estimate. The time update equation can also be viewed as a prediction equation for state forecasting; the measurement update equation can be viewed as a correction equation for prediction and correction.

[0116] In this embodiment, the time update equation projects the current state variable as a prior estimate forward to the measurement update equation in a timely manner. The measurement update equation then corrects the prior estimate to obtain the posterior estimate of the state. After completing one round of updates for both the time update equation and the measurement update equation, this process is repeated, using the posterior estimate obtained in the previous calculation as the prior estimate for the next calculation, until the total number of iterations reaches the preset number of iterations. Finally, the optimal estimates of the work done by the hybrid vehicle's battery and motor are output. The Kalman filter method is used to achieve more accurate power compensation.

[0117] In one embodiment, obtaining the current compensation power demand message based on the optimal estimate of the state variables includes:

[0118] Based on the optimal estimate of the state variables, calculate the difference between the work done by the battery and the work done by the motor in the hybrid vehicle.

[0119] The average power consumed by the high-voltage superstructure is calculated based on the difference between the power output of the hybrid vehicle battery and the power output of the motor.

[0120] Based on the average power consumed by the high-voltage upper structure, obtain the current compensation power demand message.

[0121] Obtaining the optimal estimate x of the state variable k =[W_B k W_M k ] TIn this case, based on the optimal estimate of the state variables, calculate the difference W_B between the work done by the battery and the work done by the motor in the hybrid vehicle. k -W_M k Based on this difference in work done, the average power P0 consumed by the superstructure from time k0 to time k1 is calculated as the current compensation power requirement.

[0122] In this embodiment, after obtaining the optimal estimates of the battery's work and the motor's work, the average power consumed by the superstructure over a period of time is calculated as the power that needs to be compensated at present, thus realizing power compensation that can be flexibly adjusted according to the current compensation power demand.

[0123] In one embodiment, controlling the generator to perform power compensation based on the current compensation power demand message further includes:

[0124] Calculate the engine's requested torque based on the current compensation power demand information;

[0125] Based on the requested torque from the engine, the hybrid vehicle's generator is controlled to produce a corresponding negative torque to charge the battery.

[0126] Power compensation is performed based on the average power P0 consumed by the upper body. The compensation power P1 can be slightly greater than P0. Specifically, it can be the average power P0 plus or multiplied by a compensation coefficient, for example: P1 = P0 + C or P1 = P0 × fac. The compensation coefficient (C or fac) can be calibrated and modified by computer software.

[0127] Based on the compensation power P1, the increased torque T1 of the engine is calculated according to P1 and the current engine speed n0, as shown in the following formula (6); the generator generates a corresponding negative torque to charge the battery (reverse drag generation) according to the increased torque of the engine, as shown in the following formula (7).

[0128] T1=P1×9550 / n0 (6)

[0129] T2 = -T1 (7)

[0130] In this embodiment, the increased torque T1 from the engine and the negative torque T2 generated by the motor are equal in magnitude and opposite in direction. The torque transmitted to the front drive axle of the hybrid vehicle should cancel each other out, and the vehicle's response should be the torque demanded by the driver. By compensating for the real-time changes in the hybrid vehicle's power demand, the engine's output power is increased, and the generator is controlled to generate electricity, ensuring a gradual and continuous charging process for the battery. This avoids situations where the battery is charged at high power at a particular time, and also avoids a reduction in vehicle power caused by charging the battery at high power.

[0131] To illustrate the technical solution of the power compensation method in direct drive mode of this application in detail, the following specific application example will be used to explain the entire process, which includes the following steps:

[0132] 1. Determine whether the current driving mode of the hybrid vehicle is direct drive mode. If the current driving mode is direct drive mode, obtain the state of charge of the hybrid vehicle battery.

[0133] 2. When the state of charge is below the first preset threshold, the approximate real-time power of the high-voltage superstructure is obtained by subtracting the real-time power of the battery management system from the real-time power of the motor control system; when the real-time power is above the second preset threshold, the current compensation power demand message is obtained based on the Kalman filter method, specifically:

[0134] a) Construct a discrete linear physical model of the work done by the battery and the work done by the motor in a hybrid vehicle.

[0135] b) Based on the discrete linear physical model, a state-space model is abstracted for the work done by the battery and the work done by the motor.

[0136] c) Based on the state-space model, establish time update equations and measurement update equations. The time update equations project the current state variables as prior estimates to the measurement update equations in a timely manner. The measurement update equations correct the prior estimates to obtain the posterior estimates of the state. After completing one round of updates for the time update equations and measurement update equations, this process is repeated, using the posterior estimate obtained in the previous calculation as the prior estimate for the next calculation, until the total number of iterations reaches the preset number of iterations, and the optimal estimates of the work done by the hybrid vehicle battery and the work done by the motor are output.

[0137] d) Given the optimal estimates of the battery work and motor work, calculate the difference between the battery work and motor work based on the optimal estimates of the state variables, and calculate the average power consumed by the superstructure over a period of time based on this difference.

[0138] e) Add or multiply a compensation coefficient to the average power to obtain the power value that needs to be compensated at the current moment.

[0139] 3. Based on the compensation power value and the current engine speed, calculate the increased torque of the engine. The generator generates a corresponding negative torque to charge the battery based on the increased engine torque (reverse drag generation).

[0140] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0141] The inventive concept of a power compensation method based on direct drive mode, such as Figure 5 As shown, this application embodiment also provides a power compensation device in direct drive mode for implementing the power compensation method in direct drive mode described above. The device includes:

[0142] The mode monitoring module 501 is used to obtain the current driving mode of the hybrid vehicle;

[0143] The power monitoring module 502 is used to obtain the state of charge of the hybrid vehicle battery when the current driving mode is direct drive mode.

[0144] The power monitoring module 503 is used to obtain the real-time power of the high-voltage superstructure when the state of charge of the hybrid vehicle battery is lower than a first preset state threshold.

[0145] The power acquisition module 504 is used to acquire the current compensation power demand message based on the Kalman filter method when the real-time power installed on the high voltage is higher than the second preset state threshold.

[0146] The compensation control module 505 is used to control the generator to perform power compensation based on the current compensation power demand message.

[0147] The aforementioned power compensation device in direct-drive mode is applied to hybrid vehicles equipped with a high-voltage superstructure. It acquires the current driving mode of the hybrid vehicle; when the current driving mode is direct-drive, it acquires the state of charge (SOC) of the hybrid vehicle battery; when the SOC is below a first preset threshold, it acquires the real-time power of the high-voltage superstructure; when the real-time power is above a second preset threshold, it acquires the current compensation power demand message based on a Kalman filter; and based on the current compensation power demand message, it controls the generator to perform power compensation. Throughout the process, the conditions for power compensation are determined, the current compensation power demand message is acquired based on a Kalman filter, and the generator is controlled to operate according to the current compensation power demand. In direct-drive mode, power compensation can be flexibly adjusted according to the current compensation power demand.

[0148] In one embodiment, the power monitoring module 503 is further configured to acquire the real-time power of the hybrid vehicle's battery management system and motor control system respectively; and to calculate the real-time power of the high-voltage superstructure based on the real-time power of the battery management system and motor control system respectively.

[0149] In one embodiment, the power acquisition module 504 is further configured to construct a discrete linear physical model of the work done by the hybrid vehicle battery and the work done by the motor; establish a state space model by taking the work done by the hybrid vehicle battery and the motor as state variables and the difference between the work done by the battery and the motor as an observation; establish a time update equation and a measurement update equation based on the state space model; perform iterative calculations based on the time update equation and the measurement update equation to obtain the optimal estimate of the state variables; and obtain the current compensation power demand message based on the optimal estimate of the state variables.

[0150] In one embodiment, the power acquisition module 504 is further configured to acquire the current state quantity at the current moment and use it as the target state quantity; according to the time update equation, acquire the prior estimate of the target state quantity; input the prior estimate of the target state quantity into the measurement update equation to acquire the posterior estimate of the target state quantity; input the posterior estimate of the target state quantity into the time update equation to acquire the prior estimate of the next state quantity at the next moment; use the next state quantity as the target state quantity; return to the step of inputting the prior estimate of the target state quantity into the measurement update equation to continue execution; repeat the above process until the total number of iterations reaches the preset number of iterations; and output the optimal estimate of the work done by the hybrid vehicle battery and the work done by the motor.

[0151] In one embodiment, the power acquisition module 504 is further configured to calculate the difference between the work done by the hybrid vehicle battery and the work done by the motor based on the optimal estimate of the state quantity; calculate the average power consumed by the high-voltage superstructure based on the difference between the work done by the hybrid vehicle battery and the work done by the motor; and obtain the current compensation power demand message based on the average power consumed by the high-voltage superstructure.

[0152] In one embodiment, the compensation control module 505 is further configured to calculate the requested torque of the engine based on the current compensation power demand message; and control the hybrid vehicle generator to generate a corresponding negative torque to charge the battery based on the requested torque of the engine.

[0153] The modules in the power compensation device in the direct-drive mode described above can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in the processor of a computer device in hardware form or independent of it, or stored in the memory of the computer device in software form, so that the processor can call and execute the operations corresponding to each module.

[0154] In one embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as follows: Figure 6 As shown, the computer device includes a processor, memory, input / output interfaces, a communication interface, a display unit, and an input device. The processor, memory, and input / output interfaces are connected via a system bus, and the communication interface, display unit, and input device are also connected to the system bus via the input / output interfaces. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The input / output interfaces are used for exchanging information between the processor and external devices. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, NFC (Near Field Communication), or other technologies. When the computer program is executed by the processor, it implements a power compensation method in direct-drive mode. The display unit is used to form a visually visible image and can be a display screen, a projection device, or a virtual reality imaging device. The display screen can be an LCD screen or an e-ink screen. The input device of the computer device can be a touch layer covering the display screen, or buttons, trackballs, or touchpads set on the casing of the computer device, or external keyboards, touchpads, or mice, etc.

[0155] Those skilled in the art will understand that Figure 6 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0156] In one embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to perform the following steps:

[0157] Obtain the current driving mode of the hybrid vehicle;

[0158] When the current driving mode is direct drive mode, obtain the state of charge of the hybrid vehicle battery;

[0159] When the state of charge is lower than the first preset state threshold, the real-time power of the high-voltage superstructure is obtained;

[0160] When the real-time power is higher than the second preset state threshold, the current compensation power demand message is obtained based on the Kalman filter method;

[0161] Based on the current power compensation demand message, control the generator to perform power compensation.

[0162] In one embodiment, when the processor executes the computer program, it also performs the following steps: acquiring the real-time power of the hybrid vehicle battery management system and the motor control system respectively; and calculating the real-time power of the high-voltage superstructure based on the real-time power of the battery management system and the motor control system respectively.

[0163] In one embodiment, when the processor executes the computer program, it further performs the following steps: constructing a discrete linear physical model of the work done by the hybrid vehicle battery and the work done by the motor; establishing a state-space model by taking the work done by the hybrid vehicle battery and the motor as state variables and the difference between the work done by the battery and the motor as an observation; establishing time update equations and measurement update equations based on the state-space model; performing iterative calculations based on the time update equations and measurement update equations to obtain the optimal estimate of the state variables; and obtaining the current compensation power demand message based on the optimal estimate of the state variables.

[0164] In one embodiment, when the processor executes the computer program, it further implements the following steps: obtaining the current state quantity at the current moment and using it as the target state quantity; obtaining the prior estimate of the target state quantity according to the time update equation; inputting the prior estimate of the target state quantity into the measurement update equation to obtain the posterior estimate of the target state quantity; inputting the posterior estimate of the target state quantity into the time update equation to obtain the prior estimate of the next state quantity at the next moment; using the next state quantity as the target state quantity; returning to the step of inputting the prior estimate of the target state quantity into the measurement update equation to continue execution; repeating the above process until the total number of iterations reaches the preset number of iterations; and outputting the optimal estimates of the work done by the hybrid vehicle battery and the work done by the motor.

[0165] In one embodiment, when the processor executes the computer program, it further performs the following steps: calculating the difference between the work done by the hybrid vehicle battery and the work done by the motor based on the optimal estimate of the state variables; calculating the average power consumed by the high-voltage superstructure based on the difference between the work done by the hybrid vehicle battery and the work done by the motor; and obtaining the current compensation power demand message based on the average power consumed by the high-voltage superstructure.

[0166] In one embodiment, when the processor executes the computer program, it also performs the following steps: calculating the requested torque of the engine based on the current compensation power demand message; and controlling the hybrid vehicle generator to generate a corresponding negative torque to charge the battery based on the requested torque of the engine.

[0167] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, the computer program performing the following steps when executed by a processor:

[0168] Obtain the current driving mode of the hybrid vehicle;

[0169] When the current driving mode is direct drive mode, obtain the state of charge of the hybrid vehicle battery;

[0170] When the state of charge is lower than the first preset state threshold, the real-time power of the high-voltage superstructure is obtained;

[0171] When the real-time power is higher than the second preset state threshold, the current compensation power demand message is obtained based on the Kalman filter method;

[0172] Based on the current power compensation demand message, control the generator to perform power compensation.

[0173] In one embodiment, when the computer program is executed by the processor, it further performs the following steps: acquiring the real-time power of the hybrid vehicle's battery management system and motor control system respectively; and calculating the real-time power of the high-voltage superstructure based on the real-time power of the battery management system and motor control system respectively.

[0174] In one embodiment, when the computer program is executed by the processor, it further performs the following steps: constructing a discrete linear physical model of the work done by the hybrid vehicle battery and the work done by the motor; establishing a state-space model by taking the work done by the hybrid vehicle battery and the motor as state variables and the difference between the work done by the battery and the motor as an observation; establishing time update equations and measurement update equations based on the state-space model; performing iterative calculations based on the time update equations and measurement update equations to obtain the optimal estimate of the state variables; and obtaining the current compensation power demand message based on the optimal estimate of the state variables.

[0175] In one embodiment, when the computer program is executed by the processor, it further implements the following steps: obtaining the current state quantity at the current moment and using it as the target state quantity; obtaining the prior estimate of the target state quantity according to the time update equation; inputting the prior estimate of the target state quantity into the measurement update equation to obtain the posterior estimate of the target state quantity; inputting the posterior estimate of the target state quantity into the time update equation to obtain the prior estimate of the next state quantity at the next moment; using the next state quantity as the target state quantity; returning to the step of inputting the prior estimate of the target state quantity into the measurement update equation to continue execution; repeating the above process until the total number of iterations reaches the preset number of iterations; and outputting the optimal estimates of the work done by the hybrid vehicle battery and the work done by the motor.

[0176] In one embodiment, when the computer program is executed by the processor, it further performs the following steps: calculating the difference between the work done by the hybrid vehicle battery and the work done by the motor based on the optimal estimate of the state variables; calculating the average power consumed by the high-voltage superstructure based on the difference between the work done by the hybrid vehicle battery and the work done by the motor; and obtaining the current compensation power demand message based on the average power consumed by the high-voltage superstructure.

[0177] In one embodiment, when the computer program is executed by the processor, it further performs the following steps: calculating the requested torque of the engine based on the current compensation power demand message; and controlling the hybrid vehicle generator to generate a corresponding negative torque to charge the battery based on the requested torque of the engine.

[0178] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, performs the following steps:

[0179] Obtain the current driving mode of the hybrid vehicle;

[0180] When the current driving mode is direct drive mode, obtain the state of charge of the hybrid vehicle battery;

[0181] When the state of charge is lower than the first preset state threshold, the real-time power of the high-voltage superstructure is obtained;

[0182] When the real-time power is higher than the second preset state threshold, the current compensation power demand message is obtained based on the Kalman filter method;

[0183] Based on the current power compensation demand message, control the generator to perform power compensation.

[0184] In one embodiment, when the computer program is executed by the processor, it further performs the following steps: acquiring the real-time power of the hybrid vehicle's battery management system and motor control system respectively; and calculating the real-time power of the high-voltage superstructure based on the real-time power of the battery management system and motor control system respectively.

[0185] In one embodiment, when the computer program is executed by the processor, it further performs the following steps: constructing a discrete linear physical model of the work done by the hybrid vehicle battery and the work done by the motor; establishing a state-space model by taking the work done by the hybrid vehicle battery and the motor as state variables and the difference between the work done by the battery and the motor as an observation; establishing time update equations and measurement update equations based on the state-space model; performing iterative calculations based on the time update equations and measurement update equations to obtain the optimal estimate of the state variables; and obtaining the current compensation power demand message based on the optimal estimate of the state variables.

[0186] In one embodiment, when the computer program is executed by the processor, it further implements the following steps: obtaining the current state quantity at the current moment and using it as the target state quantity; obtaining the prior estimate of the target state quantity according to the time update equation; inputting the prior estimate of the target state quantity into the measurement update equation to obtain the posterior estimate of the target state quantity; inputting the posterior estimate of the target state quantity into the time update equation to obtain the prior estimate of the next state quantity at the next moment; using the next state quantity as the target state quantity; returning to the step of inputting the prior estimate of the target state quantity into the measurement update equation to continue execution; repeating the above process until the total number of iterations reaches the preset number of iterations; and outputting the optimal estimates of the work done by the hybrid vehicle battery and the work done by the motor.

[0187] In one embodiment, when the computer program is executed by the processor, it further performs the following steps: calculating the difference between the work done by the hybrid vehicle battery and the work done by the motor based on the optimal estimate of the state variables; calculating the average power consumed by the high-voltage superstructure based on the difference between the work done by the hybrid vehicle battery and the work done by the motor; and obtaining the current compensation power demand message based on the average power consumed by the high-voltage superstructure.

[0188] In one embodiment, when the computer program is executed by the processor, it further performs the following steps: calculating the requested torque of the engine based on the current compensation power demand message; and controlling the hybrid vehicle generator to generate a corresponding negative torque to charge the battery based on the requested torque of the engine.

[0189] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data shall comply with the relevant laws, regulations and standards of the relevant countries and regions.

[0190] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0191] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0192] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A power compensation method in direct drive mode, characterized in that, Applied to hybrid vehicles equipped with high-voltage superstructures, the method includes: Obtain the current driving mode of the hybrid vehicle; When the current driving mode is direct drive mode, obtain the state of charge of the hybrid vehicle battery; When the state of charge is lower than a first preset state threshold, the real-time power of the high-voltage superstructure is obtained; When the real-time power is higher than the second preset state threshold, the current compensation power demand message is obtained based on the Kalman filter method; Based on the current compensation power demand message, control the generator to perform power compensation; The process of obtaining the current compensation power demand message based on the Kalman filter method includes: Construct a discrete linear physical model of the work done by the battery and the work done by the motor in the hybrid vehicle. The work done by the battery and motor of the hybrid vehicle is used as the state variables, and the difference between the work done by the battery and motor is used as the observation, to establish a state-space model. Based on the state-space model, establish the time update equation and the measurement update equation; The optimal estimate of the state variable is obtained by iterative calculation based on the time update equation and the measurement update equation. The current compensation power demand message is obtained based on the optimal estimate of the state variables.

2. The method according to claim 1, characterized in that, When the state of charge is lower than a first preset threshold, obtaining the real-time power of the high-voltage superstructure includes: The real-time power of the hybrid vehicle battery management system and motor control system are obtained respectively. The real-time power of the high-voltage superstructure is calculated based on the real-time power of the battery management system and the motor control system.

3. The method according to claim 1, characterized in that, The step of iteratively calculating based on the time update equation and the measurement update equation to obtain the optimal estimate of the state variable includes: Obtain the current state quantity at the current moment and use it as the target state quantity. Based on the time update equation, obtain the prior estimate of the target state quantity. The prior estimate of the target state quantity is input into the measurement update equation to obtain the posterior estimate of the target state quantity. The posterior estimate of the target state quantity is input into the time update equation to obtain the prior estimate of the next state quantity at the next time step. The next state quantity is used as the target state quantity. The process is repeated until the total number of iterations reaches the preset number of iterations. The optimal estimates of the work done by the hybrid vehicle battery and the work done by the motor are then output.

4. The method according to claim 1, characterized in that, The step of obtaining the current compensation power demand message based on the optimal estimate of the state variable includes: Based on the optimal estimate of the state variables, calculate the difference between the work done by the hybrid vehicle battery and the work done by the motor. The average power consumed by the high-voltage superstructure is calculated based on the difference between the power output of the hybrid vehicle battery and the power output of the motor. Based on the average power consumed by the high-voltage superstructure, obtain the current compensation power demand message.

5. The method according to claim 1, characterized in that, The step of controlling the generator to perform power compensation based on the current compensation power demand message further includes: Calculate the engine's requested torque based on the current compensation power demand message; Based on the requested torque of the engine, the hybrid vehicle generator is controlled to generate a corresponding negative torque to charge the battery.

6. A power compensation device in direct drive mode, characterized in that, The device, applicable to hybrid vehicles equipped with high-voltage superstructures, includes: The mode monitoring module is used to obtain the current driving mode of the hybrid vehicle; The battery monitoring module is used to obtain the state of charge of the hybrid vehicle battery when the current driving mode is direct drive mode; The power monitoring module is used to obtain the real-time power of the high-voltage superstructure when the state of charge of the hybrid vehicle battery is lower than a first preset state threshold. The power acquisition module is used to acquire the current compensation power demand message based on the Kalman filter method when the real-time power of the high voltage device is higher than the second preset state threshold. The compensation control module is used to control the generator to perform power compensation based on the current compensation power demand message; The power acquisition module is further configured to construct a discrete linear physical model of the work done by the hybrid vehicle battery and the work done by the motor; establish a state-space model by taking the work done by the hybrid vehicle battery and the motor as state variables and the difference between the work done by the battery and the motor as an observation; establish a time update equation and a measurement update equation based on the state-space model; perform iterative calculations based on the time update equation and the measurement update equation to obtain the optimal estimate of the state variables; and obtain the current compensation power demand message based on the optimal estimate of the state variables.

7. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 5.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 5.

9. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 5.

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