Energy distribution method and device, controller and storage medium

Abstract

The invention relates to an energy distribution method and device, a controller and a storage medium. The method comprises the steps that driving modes and operation parameters of a vehicle in each unit time in a preset historical time period are obtained, and a battery electricity consumption rate optimal sequence in a pure electric mode is determined in combination with a pre-constructed longitudinal kinetic model, a pre-constructed power division kinetic model, a pre-constructed battery model and a pre-constructed engine universal characteristic curve; a candidate working point, meeting the minimum combination of the fuel consumption rate and the battery power consumption rate, of the hybrid power mode is determined; screening the candidate working points to obtain a more balanced target working point; based on the battery power consumption rate optimal sequence and the multiple target working points, obtaining each optimal driving mode and a global optimal control sequence for a preset historical time period through a dynamic programming algorithm; and system-level performance index parameters of the vehicle are optimized based on the optimal driving mode and the global optimal control sequence, so that optimization of energy distribution in the vehicle running process is realized, and the vehicle economy is improved.

Description

Technical Field

[0001] The present application relates to the field of vehicle technology, and in particular to an energy distribution method, device, controller and storage medium. Background Art

[0002] Multi-mode, multi-speed hybrid vehicles (MMVs) offer significant energy-saving potential due to their integration of multiple hybrid drive modes. However, this also presents challenges in configuration design and control. The core challenge lies in quickly and accurately exploiting the energy-saving potential of complex and diverse MMV configurations. Energy management, a key component in addressing this challenge, aims to optimize the vehicle's powertrain efficiency or fuel economy over a specific timeframe by controlling the powertrain components.

[0003] The energy transfer process of hybrid vehicles involves the complex coupling and conversion of mechanical systems (engine drive) and electrical systems (motor drive / recovery). Coordinating these two systems to achieve minimum fuel consumption for the engine and minimum power consumption for the motor / battery system (or equivalently, optimal energy efficiency) while ensuring efficient and stable operation of the vehicle's transmission system remains a key and challenging task. Traditional energy management strategies often struggle to effectively handle the increased control dimensions and state coupling associated with multiple modes and gears. They are limited in accurately exploring the global energy-saving potential of a specific configuration under real-world historical operating conditions and in rapidly generating control sequences that meet the dual optimization objectives of minimum fuel consumption and minimum battery power consumption. Consequently, these strategies fail to optimize the vehicle's system-level performance parameters, making it difficult to control the vehicle's powertrain components using optimized system-level performance parameters during subsequent actual operation to achieve optimal operating efficiency or fuel economy within a specific operating timeframe. Summary of the Invention

[0004] Based on this, it is necessary to provide an energy distribution method, device, controller, computer-readable storage medium and computer program product that can quickly generate an optimal driving mode and a global optimal control sequence that meet the dual optimization goals, and based on this, realize the optimization of energy distribution during vehicle operation.

[0005] In a first aspect, the present application provides an energy distribution method, the method comprising:

[0006] Obtaining the driving mode and operating parameters of the vehicle at each unit time in a preset historical time period;

[0007] For each unit time, when the driving mode represents a pure electric mode, determining a minimum value of the battery power consumption rate per unit time based on a pre-established longitudinal dynamics model, a power split dynamics model, and a battery model in combination with the corresponding operating parameters; and determining an optimal sequence of battery power consumption rates based on a plurality of minimum values ​​of the battery power consumption rates corresponding to the unit times;

[0008] For each unit time, when the driving mode represents a hybrid mode, determining a combined minimum value of the fuel consumption rate and the battery power consumption rate for the unit time based on the longitudinal dynamic model, the power split dynamic model, the battery model, and the engine universal characteristic curve, in combination with the corresponding operating parameters; and determining a candidate operating point that satisfies the minimum combined value of the fuel consumption rate and the battery power consumption rate based on multiple combined minimum values ​​of the fuel consumption rate and the battery power consumption rate corresponding to the unit time; and screening the candidate operating points to obtain a target operating point in which the fuel consumption rate and the battery power consumption rate are evenly distributed.

[0009] Based on the optimal sequence of battery power consumption rates and the plurality of target operating points, a dynamic programming algorithm is used to determine an optimal driving mode and a global optimal control sequence for each unit time in the preset historical time period; the global optimal control sequence is an optimal operating point that optimizes the combination of the fuel consumption rate and the battery power consumption rate for each unit time in the entire preset historical time period;

[0010] Based on the optimal driving mode and the global optimal control sequence, the system-level performance index parameters of the vehicle are optimized to optimize the energy distribution during the operation of the vehicle; the system-level performance index parameters include: the transmission system transmission ratio, the motor torque distribution ratio coefficient limit value, the battery charge and discharge power limit value, the motor power limit value and the engine power limit value.

[0011] In a second aspect, the present application further provides an energy distribution device, comprising:

[0012] A data acquisition module is used to obtain the driving mode and operating parameters of the vehicle at each unit time in a preset historical time period;

[0013] a pure electric optimization module, configured to determine, for each unit time, a minimum battery power consumption rate per unit time based on a pre-established longitudinal dynamics model, a power split dynamics model, and a battery model, when the driving mode represents a pure electric mode; and determine an optimal sequence of battery power consumption rates based on the minimum battery power consumption rates corresponding to a plurality of unit times;

[0014] a hybrid optimization module configured to, for each unit time, determine, when the driving mode characterizes a hybrid mode, based on the longitudinal dynamic model, the power split dynamic model, the battery model, and the engine universal characteristic curve, a minimum combined value of the fuel consumption rate and the battery power consumption rate for the unit time; determine, based on a plurality of minimum combined values ​​of the fuel consumption rate and the battery power consumption rate corresponding to the unit time, a candidate operating point that satisfies the minimum combined value of the fuel consumption rate and the battery power consumption rate; and screen the candidate operating points to obtain a target operating point in which the fuel consumption rate and the battery power consumption rate are evenly distributed;

[0015] a global optimization module for determining, based on the optimal sequence of battery power consumption rates and the plurality of target operating points, an optimal driving mode and a global optimal control sequence for each unit time in the preset historical time period using a dynamic programming algorithm; the global optimal control sequence being an optimal operating point that optimizes the combination of the fuel consumption rate and the battery power consumption rate for each unit time in the entire preset historical time period;

[0016] An energy distribution module is configured to optimize system-level performance index parameters of the vehicle based on the optimal drive mode and the global optimal control sequence to optimize energy distribution during vehicle operation; the system-level performance index parameters include: a transmission system transmission ratio, a motor torque distribution ratio coefficient limit value, a battery charge and discharge power limit value, a motor power limit value, and an engine power limit value.

[0017] In a third aspect, the present application further provides a controller comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps in the first aspect when executing the computer program.

[0018] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, which implements the steps in the first aspect when executed by a processor.

[0019] In a fifth aspect, the present application further provides a computer program product, comprising a computer program, which implements the steps in the first aspect when executed by a processor.

[0020] In the energy distribution method, device, controller, computer-readable storage medium and computer program product provided by the present application, the energy distribution method obtains the driving mode and operating parameters of the vehicle at each unit time in a preset historical time period to obtain the operating data of the vehicle in the historical time period; then, for each unit time, when the vehicle driving mode is pure electric mode, the pre-constructed longitudinal dynamic model, power split dynamic model and battery model are combined with relevant operating parameters to determine the minimum value of the battery power consumption rate of the relevant unit time, and then based on the minimum value of the battery power consumption rate of each unit time, the optimal sequence of battery power consumption rate corresponding to the preset historical time period is determined; for each unit time, when the vehicle driving mode is hybrid mode, the pre-constructed longitudinal dynamic model, power split dynamic model, battery model and engine universal characteristic curve are combined with relevant operating parameters to screen out the combined minimum value of the fuel consumption rate and the battery power consumption rate per unit time; and further based on the combined minimum value of the fuel consumption rate and the battery power consumption rate per unit time, the optimal sequence of battery power consumption rate corresponding to the preset historical time period is determined. The candidate operating point with the smallest combination of fuel consumption rate and battery power consumption rate is selected; the operating points among the candidate operating points are further screened to determine the target operating point that makes the distribution of fuel consumption rate and battery power consumption rate balanced, and the target operating point is conducive to making the power distribution instruction smoother; thereafter, based on the optimal sequence of battery power consumption rate determined for the historical operating time period of the vehicle and multiple target operating points, the optimal driving mode and the global optimal control sequence for each unit time in the preset historical time period can be obtained through a dynamic programming algorithm; wherein, the global optimal control sequence is the optimal operating point that makes the combination of fuel consumption rate and battery power consumption rate optimal for each unit time in the entire preset historical time period; finally, the system-level performance index parameters of the vehicle can be optimized based on the determined optimal driving mode and global optimal control sequence, thereby realizing the optimization of energy distribution in the subsequent actual operation process of the vehicle; wherein, the system-level performance index parameters include: transmission system transmission ratio, motor torque distribution ratio coefficient limit value, battery charge and discharge power limit value, motor power limit value and engine power limit value. By optimizing the system-level performance index parameters of the vehicle in this way, the system-level performance index parameters of the vehicle can be made better, which is beneficial to ensure that the vehicle can have an optimal operating time period with an optimal combination of fuel consumption rate and battery power consumption rate during subsequent operation, thereby improving the economy and efficiency of vehicle operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the following briefly introduces the drawings required for use in the embodiments of the present application or related technical descriptions. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying any creative work.

[0022] Figure 1 1 is a flow chart of an energy distribution method according to an embodiment;

[0023] Figure 2 is a schematic diagram of a working point in one embodiment;

[0024] Figure 3 1 is a flow chart of a method for updating a global optimal control sequence in one embodiment;

[0025] Figure 4 Schematic diagram of SOC grid density in one embodiment;

[0026] Figure 5 Schematic diagram of a flow chart of a method for determining a target operating point in one embodiment;

[0027] Figure 6 is a schematic diagram of a target operating point in one embodiment;

[0028] Figure 7 is a structural block diagram of an energy distribution device in one embodiment;

[0029] Figure 8 FIG. 4 is a diagram showing the internal structure of a controller in one embodiment. DETAILED DESCRIPTION

[0030] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0031] It should be noted that the terms "first", "second", etc. used in this application may be used to describe various elements, but these elements are not limited by these terms. These terms are only used to distinguish the first element from the second element. The terms "including" and "having" used in this application and any variations thereof are intended to cover non-exclusive inclusions. The term "plurality" used in this application refers to two or more. The term "and / or" used in this application refers to one of the solutions or any combination of multiple solutions.

[0032] A multi-mode hybrid powertrain (MHP) is the operating system of a hybrid vehicle. The energy transfer process in a MHP system involves the coordinated operation of mechanical and electrical systems. Coordinating these two systems through actuators such as clutches and synchronizers to achieve efficient and stable transmission operation remains a core research issue. While energy management strategies based on dynamic programming (DP) algorithms can theoretically achieve globally optimal fuel economy (often serving as a benchmark for energy conservation for other control strategies), in practice, traditional DP algorithms struggle to quickly, accurately, and rationally evaluate the energy efficiency of topological configurations when solving the global optimization problem for MHP systems due to the curse of dimensionality, interpolation errors, and Markov property failure. A single complete DP optimization run can take hours, failing to meet the rapid iteration requirements of the configuration design phase. Furthermore, MHP vehicles, with two motors and an engine, as well as multiple gears / modes, have numerous state and control variables, resulting in a high control dimensionality. This further increases the computational load, slowing the solution process and preventing both optimal optimization efficiency and results. Therefore, in order to accelerate the design optimization and parameter calibration of multi-mode configurations, it is urgent to develop an energy allocation method that takes into account both computational efficiency and energy management.

[0033] The energy distribution method provided by the present application can determine the optimal sequence of battery power consumption rate in pure electric mode and the candidate operating point that satisfies the minimum combination of fuel consumption rate and battery power consumption rate in hybrid mode based on the driving mode and operating parameters and other data of each unit time in the preset historical time period of the vehicle, as well as the pre-constructed longitudinal dynamic model, power split dynamic model, battery model and engine universal characteristic curve; then, by screening the candidate operating points, a target operating point that makes the distribution of fuel consumption rate and battery power consumption rate more balanced is obtained; further, through the dynamic programming algorithm, the optimal sequence of battery power consumption rate and multiple target operating points are combined to obtain the global optimal control sequence corresponding to the preset historical time period and the optimal driving mode for each unit time; the determined global optimal control sequence within the preset historical time period of the vehicle can be used as a benchmark; then, the actual operating data of the vehicle in a recent (latest) time period (actual operating time period) is collected, and the actual operating data corresponding to the vehicle is obtained based on the actual operating data. an actual control sequence (an actual control sequence corresponding to the actual operating point of the fuel consumption rate and the battery power consumption rate corresponding to each unit time of the vehicle during the actual operating time period); in combination with the driving mode of the vehicle at each unit time during the actual operating time period, a comparison result is obtained by comparing the actual control sequence of the vehicle with the adapted operating point in the global optimal control sequence; when the comparison result indicates that the difference between the actual control sequence of the vehicle and the adapted operating point in the global optimal control sequence exceeds a preset threshold, it means that the difference between the actual control sequence of the vehicle and the adapted operating point in the global optimal control sequence is relatively large, which means that there is still room for optimization of the system-level performance indicator parameters of the vehicle; then, at least part of the system-level performance indicator parameters of the vehicle can be optimized according to the difference, so that the system-level performance indicator parameters of the vehicle are more optimized, which is conducive to ensuring that the vehicle can have an operating time period with an optimal combination of fuel consumption rate and battery power consumption rate in the subsequent actual operation process, thereby facilitating improvement of the economy and efficiency of vehicle operation.

[0034] It can be seen that the energy distribution method provided by the application can be used for offline optimization of the system-level performance index parameter of the vehicle, that is, the step of "optimizing the system-level performance index parameter of the vehicle based on the optimal driving mode and the global optimal control sequence" provided by the application can be executed offline, and the online control mode is not used. The method of offline optimization of the system-level performance index parameter of the vehicle does not affect the actual normal operation of the vehicle, and is beneficial to ensuring the driving safety of the vehicle. In the energy distribution method provided by the application, the process of determining the global optimal control sequence of the vehicle and the optimal driving mode of each unit time in the historical time period is a process of simulating and evaluating the electricity consumption and fuel consumption based on the driving mode and the operation parameters of the vehicle in the preset historical time period and other data for at least one known standard test condition of the vehicle. The standard test condition includes but is not limited to Worldwide Light-duty Test Cycle (WLTC), New European Driving Cycle (NEDC) and China Light-duty Vehicle Test Cycle (CLTC) test conditions.

[0035] In an exemplary embodiment, as shown in Figure 1 A flowchart of an energy distribution method is provided, and the controller applied in the vehicle is taken as an example for illustration, which can be used to control the operation of the vehicle, including the following steps 102 to 110. Wherein:

[0036] Step 102, obtaining the driving mode and operation parameters of the vehicle in each unit time in the preset historical time period.

[0037] The preset historical time period is a past time window for analyzing the running state of the vehicle, such as the past 10 seconds, 1 minute, 20 minutes, 1 hour, etc.

[0038] The operating parameters are physical quantities that affect the vehicle's dynamic performance and can at least be used to calculate the vehicle's real-time dynamic requirements. Specifically, they may include at least one of the following: the vehicle's curb mass, rolling resistance coefficient, road slope angle, air resistance coefficient, air density, frontal area, vehicle speed, rotational mass conversion factor, moment of inertia of the vehicle's wheel end, moment of inertia at the vehicle's flywheel, wheel radius, transmission efficiency, transmission ratio, and final drive ratio. The vehicle's curb mass is the vehicle's own weight, such as 1800 kg, which affects acceleration and climbing requirements; the rolling resistance coefficient is the rolling resistance between the tire and the ground, such as 0.015, which affects the energy consumption of driving on flat roads; the road slope is the road slope angle during vehicle driving, which directly affects the driving force requirement; the air resistance coefficient is a dimensionless parameter used to describe the magnitude of the resistance encountered by the vehicle when moving in the air, which mainly depends on the vehicle's appearance design and surface smoothness; the frontal area is the projected area of ​​the vehicle facing the direction of the airflow, that is, the cross-sectional area of ​​the vehicle when viewed from the front; the air resistance coefficient and the frontal area jointly determine the wind resistance when driving at high speeds. For example, the air resistance coefficient can be 0.3 and the frontal area can be 2.5m² (square meters); the vehicle speed is the vehicle's driving speed. The speed is expressed as 60 km / h (kilometers per hour), which is used to calculate real-time power demand. The rotational mass conversion factor is the equivalent inertia of the rotating components of the transmission system. The moment of inertia is a physical quantity that describes the ability of an object to resist changes in angular acceleration, similar to the "mass" in translation. It includes the moment of inertia of the vehicle's wheel ends and the moment of inertia at the vehicle's flywheel, and is used to influence acceleration response. The wheel radius is the effective radius of the wheel when rolling. The transmission efficiency refers to the ratio of the actual available power to the input power during the process of power being transmitted from the engine or motor to the wheels. The transmission ratio is the ratio of the transmission input shaft speed to the output shaft speed, reflecting the torque amplification or speed regulation capability. The final reducer ratio is the ratio of the transmission output shaft speed to the wheel speed, further amplifying the torque and adapting to the vehicle speed.

[0039] The driving mode may include, for example, a pure electric mode and a hybrid mode; wherein a vehicle operating in the pure electric mode is driven only by the motor, and a vehicle operating in the hybrid mode is driven jointly by the engine and the motor.

[0040] Step 104 , for each unit time, when the driving mode represents a pure electric mode, based on the pre-built longitudinal dynamics model, the power split dynamics model, and the battery model, combined with the corresponding operating parameters, determines the minimum value of the battery power consumption rate per unit time; and determines the optimal sequence of the battery power consumption rates based on the minimum values ​​of the battery power consumption rates corresponding to multiple unit times.

[0041] Among them, the longitudinal dynamics model is used to describe the force and motion relationship of the vehicle along the driving direction, and is used to calculate the real-time driving force / braking force requirements, that is, the vehicle's dynamic requirements.

[0042] In one embodiment, the longitudinal dynamics model constructed based on the vehicle's curb mass, rolling resistance coefficient, road slope angle, air resistance coefficient, air density, frontal area, vehicle speed, rotational mass conversion factor, moment of inertia of the vehicle wheel end, moment of inertia at the vehicle flywheel, wheel radius, transmission efficiency, transmission ratio and final drive ratio can be obtained based on the classical longitudinal dynamics formula, where the driving force is equal to the sum of friction resistance, slope resistance, air resistance and acceleration resistance.

[0043]

[0044]

[0045] Among them, F t is the vehicle's dynamics requirement (N=kg·m / s 2 ), m represents the vehicle's curb mass (kg), g is the acceleration of gravity (m / s²), f represents the rolling resistance coefficient, α represents the road slope angle during vehicle driving, C D Represents the air resistance coefficient of the vehicle during driving. is the air density (kg / m 3 ), A represents the frontal area of ​​the vehicle during driving (m²), u represents the speed of the vehicle during driving (m / s), t is the unit time, δ is the vehicle's rotational mass conversion coefficient, the above calculation The equation uses dimensionless calculation, I w is the moment of inertia of the vehicle wheel end (kg·m²), I f is the moment of inertia of the vehicle's flywheel (kg·m²), r is the wheel radius (m), is the transmission efficiency, i g is the transmission ratio of the transmission, and i0 is the main reducer ratio.

[0046] The construction parameters of the power split dynamic model may include at least one of the following: the ring gear of the first planetary gear mechanism of the vehicle, the planet carrier of the first planetary gear mechanism, and the rotational inertia and gear radius of the sun gear of the first planetary gear mechanism; the ring gear of the second planetary gear mechanism, the planet carrier of the second planetary gear mechanism, and the rotational inertia and gear radius of the sun gear of the second planetary gear mechanism; the engine inertia, the inertia of the first motor, the inertia of the second motor, the inertia of the wheel output end, the engine torque, the torque of the first motor, the torque of the second motor, the torque of the wheel output end, the engine angular acceleration, the angular acceleration of the first motor, the angular acceleration of the second motor, and the angular acceleration of the wheel output end.

[0047] The power-split dynamics model is one type of multi-mode dynamics model. This model describes the dynamic behavior of a multi-mode hybrid system in different operating modes, including the drive mode switching process. Operating modes include pure electric vehicle (EV), series, two-speed parallel, and power-split.

[0048] In one embodiment, the power-split dynamics model corresponding to the power-split operating mode is used as an example for illustration. The power-split dynamics model is used to describe the distribution relationship between mechanical power and electrical power in a power-split hybrid system (e.g., a planetary gear configuration). The power-split dynamics model is a type of dynamics model for a multi-mode configuration. When a mode in the multi-mode configuration is a power-split mode, the dynamics behavior in that mode is described by the power-split dynamics model. Exemplarily, the controller acquires dynamics parameters within a 30-second historical period and constructs the power-split dynamics model based on these dynamics parameters. For example, a power split dynamic model is constructed based on a series of parameters including the internal force requirement of the first planetary gear mechanism of the vehicle, the internal force requirement of the second planetary gear mechanism, the inertia of the planetary carrier of the first planetary gear, the engine inertia, the gear radius of the ring gear of the first planetary gear, the gear radius of the ring gear of the second planetary gear, the gear radius of the sun gear of the first planetary gear, the gear radius of the sun gear of the second planetary gear, the rotational inertia of the first motor, the rotational inertia of the sun gear of the first planetary gear, the rotational inertia of the sun gear of the second planetary gear, the rotational inertia of the ring gear of the first planetary gear, the rotational inertia of the ring gear of the second planetary gear, the wheel end inertia, the engine angular acceleration, the angular acceleration of the first motor, the angular acceleration of the second motor, the angular acceleration of the wheel output end, the engine torque, the torque of the first motor, the torque of the second motor, and the torque of the wheel output end.

[0049]

[0050] in, is the internal force requirement of the first planetary gear mechanism, F2 is the internal force requirement of the second planetary gear mechanism, represents the inertia of the first planetary gear carrier, Indicates the engine inertia, R1 indicates the gear radius of the ring gear of the first planetary gear row, R2 indicates the gear radius of the ring gear of the second planetary gear row, S1 indicates the gear radius of the sun gear of the first planetary gear row, S2 indicates the gear radius of the sun gear of the second planetary gear row, represents the moment of inertia of the first motor, represents the moment of inertia of the second motor, represents the moment of inertia of the sun gear of the first planetary gear, represents the moment of inertia of the sun gear of the second planetary gear, represents the moment of inertia of the ring gear of the second planetary gear set, is the wheel end inertia, Indicates the moment of inertia of the ring gear of the first planetary gear set;

[0051] is the engine angular acceleration, represents the angular acceleration of the first motor, represents the angular acceleration of the second motor, represents the angular acceleration of the wheel output end, represents the engine torque, represents the first motor torque, represents the second motor torque, Indicates the wheel output torque.

[0052] In the above embodiment, by combining the longitudinal dynamics model with the power split dynamics model, a comprehensive modeling of the vehicle operating state can be achieved, which can more accurately reflect the dynamic characteristics of the complex hybrid system, thereby providing a more reliable optimization basis for the energy management strategy.

[0053] The preset historical time period is a past time window used to analyze the vehicle's operating status, such as the past 10 seconds, 1 minute, etc. The unit time is the minimum time unit for optimized control, such as 1 second, 0.1 second, which affects the granularity of the control sequence. The battery state of charge (SOC) is the percentage of the current battery remaining charge. For example, SOC=70% means that the current battery remaining charge is 70%. SOC is used to determine the drivable range in pure electric mode or the energy distribution strategy in hybrid mode. The driving mode is the current or expected power source of the vehicle, including pure electric mode and hybrid mode. The pure electric mode is driven by the motor, and the hybrid mode is driven by the engine and motor together.

[0054] In one embodiment, the controller determines the vehicle's dynamic demand per unit time based on the longitudinal dynamics model and operating parameters. Dynamic demand refers to the required driving or braking force, such as the required torque increase when climbing a hill. The controller then determines the optimal driving mode for the vehicle based on the dynamic demand per unit time and the SOC. For example, for a hybrid vehicle traveling on a highway, operating parameters for a preset historical time period during the highway driving phase are obtained. For example, operating parameters for 30 seconds of the vehicle during the highway driving phase are obtained, including a speed increase from 80 km / h to 100 km / h, a 2% road gradient, an air resistance coefficient of 0.28, and a SOC of 65%. The vehicle's dynamic requirements can be calculated based on the road gradient, acceleration, etc., for example, the required wheel-end torque for the driving phase is calculated to be 500 Nm (Newton-meters). The driving mode can also be determined based on the SOC and required torque. For example, during the highway driving phase, if there is a period of operation when the SOC is sufficient and the required torque does not exceed the upper limit of the motor, the pure electric mode is selected during this period. If there is a sudden increase in the required torque during the highway driving phase, such as when overtaking, the vehicle is switched to hybrid mode during the overtaking period. The driving mode sequence and the corresponding dynamic requirements are also output per unit time (e.g., every 1 second).

[0055] In the above embodiment, the longitudinal dynamics model and the battery state of charge of the vehicle are used to determine the dynamics requirements and optimal driving mode of the vehicle during the historical operating period, providing a data basis for optimizing the system-level performance index parameters of the vehicle.

[0056] Among them, the pure electric mode is the operating state of a hybrid vehicle that is completely driven by an electric motor.

[0057] The pre-built battery model is a mathematical model used to quantify the dynamic characteristics of the battery. For example, the battery model can be determined based on the battery's open circuit voltage, charge and discharge internal resistance, battery charge and discharge power, and current charge. The open circuit voltage is the battery's terminal voltage when it is unloaded, the charge and discharge internal resistance is the battery's internal resistance, the battery charge and discharge power is the battery's maximum allowable charge and discharge power, and the current charge is the percentage of remaining charge. For example, the battery model can be expressed as:

[0058]

[0059] Among them, SOC(k+1) is the SOC at the unit time (k+1), SOC(k) is the SOC at the unit time k, represents the open circuit voltage of the battery (volts, V), Indicates the corresponding charge and discharge internal resistance of the battery (ohm, Ω), Indicates the corresponding charging and discharging power of the battery (watt, W), Indicates the current charge of the battery (Coulomb, C), Indicates the The duration of a unit of time (seconds, s).

[0060] The battery consumption rate is the reduction of battery energy per unit time.

[0061] For example, a hybrid vehicle is traveling in pure electric mode with a historical time period of 30 seconds. The controller uses the battery power consumption rate as the optimization target and determines the vehicle's battery power consumption rate per unit time through the battery's open circuit voltage, charge and discharge internal resistance, battery charge and discharge power, and current power within 30 seconds, and then determines the optimal battery power consumption rate sequence that minimizes the battery power consumption rate.

[0062] Exemplarily, when the driving mode represents a pure electric mode, when the vehicle has two motors driven simultaneously, the battery power consumption rate under different power distributions of the two motors is determined by a preset battery model; and based on each battery power consumption rate, the motor power sequence of the two motors that minimizes the battery power consumption rate in a preset historical time period is determined; in the case of a pure electric mode in which the vehicle is driven by only one motor, the motor power and the battery power consumption rate are uniquely determined, and there is a unique motor power sequence with the minimum battery power consumption rate.

[0063] In the above embodiment, when the vehicle is operating in pure electric mode, the battery power consumption rate is used as the optimization target, the battery power consumption rate of the vehicle per unit time is determined by the preset parameters of the battery, and the optimal battery power consumption rate sequence that minimizes the battery power consumption rate is determined. This optimal battery power consumption rate sequence is conducive to improving the energy utilization efficiency of the entire vehicle.

[0064] In summary, since the historical time period includes multiple unit times, and the vehicle has a corresponding drive mode during each unit time, the minimum battery power consumption rate for each unit time when the drive mode is pure electric mode can be determined by combining the operating parameters of that unit time with the pre-established longitudinal dynamic model, power split dynamic model, and battery model. Subsequently, based on the minimum battery power consumption rate corresponding to each unit time when the drive mode is pure electric mode, the optimal sequence of battery power consumption rates corresponding to pure electric mode in the historical time period can be determined. In principle, the determined optimal sequence of battery power consumption rates is beneficial for targeted improvement of the energy utilization efficiency of the vehicle in pure electric mode during the historical time period.

[0065] Step 106 , for each unit time, when the driving mode represents a hybrid mode, based on the longitudinal dynamics model, the power split dynamics model, the battery model, and the engine universal characteristic curve, in combination with the corresponding operating parameters, a minimum combined value of the fuel consumption rate and the battery power consumption rate per unit time is determined; and based on the minimum combined values ​​of the fuel consumption rate and the battery power consumption rate corresponding to multiple unit times, a candidate operating point is determined that satisfies the minimum combined value of the fuel consumption rate and the battery power consumption rate; and the candidate operating points are screened to obtain a target operating point in which the fuel consumption rate and the battery power consumption rate are evenly distributed.

[0066] The longitudinal dynamic model, power split dynamic model and battery model have been provided in the above content and will not be repeated here.

[0067] Hybrid mode is when the engine and electric motor work together to propel the vehicle. The engine universal characteristic diagram (EUD) is a three-dimensional contour plot depicting the engine's brake-specific fuel consumption (BSFC) as a function of speed (rpm) and torque (Nm). The engine's BSFC is the fuel consumption rate per unit time, with the minimum fuel consumption rate being the lowest achievable under the engine's current operating conditions. For example, the engine's BSFC is calculated based on the EUD and engine output power as follows: BSFC = BSFC × Pe × Δt, where Pe is the engine output power (kW) and Δt is the unit time. For example, if an engine has a BSFC of 220 g / kWh (grams per kilowatt-hour) at 2000 rpm and a torque of 100 Nm, then the BSFC at an engine output of 10 kW is 220 g / kWh × 10 kW × 1 / 3600 h, which is approximately 0.61 g / s (grams per second). The battery consumption rate is the reduction of battery energy per unit time; the minimum battery consumption rate is the lowest battery consumption rate that the motor can achieve under the current working conditions.

[0068] The candidate operating points are determined based on a combination of the vehicle's engine operating parameters and motor operating parameters within a preset historical time period, such as a combination of speed, torque, power and other parameters, to achieve optimal energy consumption while meeting power requirements. Specifically, they are a combination that meets the minimum fuel consumption rate and the minimum battery power consumption rate.

[0069] In summary, for each unit time of the vehicle in hybrid mode during the historical time period, the operating parameters of each unit time can be combined with the pre-built longitudinal dynamic model, power split dynamic model, battery model and engine universal characteristic curve to determine the combined minimum value of the fuel consumption rate and the battery power consumption rate under each unit time. Then, based on the combined minimum values ​​of the fuel consumption rate and the battery power consumption rate corresponding to multiple unit times, the candidate operating point that meets the minimum combination of the fuel consumption rate and the battery power consumption rate can be determined.

[0070] Furthermore, the candidate operating points may be further screened to eliminate some operating points that are not effective in reducing energy consumption, so as to obtain a target operating point with a balanced distribution of fuel consumption rate and battery power consumption rate among the candidate operating points.

[0071] The candidate operating point is located in a two-dimensional coordinate system formed by the fuel consumption rate and the battery power consumption rate. The fuel consumption rate can be the horizontal coordinate in the two-dimensional coordinate system, and the battery power consumption rate can be the vertical coordinate in the two-dimensional coordinate system.

[0072] In one exemplary embodiment, the slopes between adjacent candidate operating points among the candidate operating points can be determined in a two-dimensional coordinate system. Along the positive direction of the abscissa, for every three adjacent candidate operating points, if the absolute value of the first slope is greater than or equal to the absolute value of the second slope, the candidate operating point located in the middle of the three adjacent candidate operating points is determined as the target operating point with a balanced distribution of fuel consumption and battery power consumption. In other words, the candidate operating point that protrudes toward the origin among every three adjacent candidate operating points is selected as the target operating point.

[0073] The method of determining the target operating point from the candidate operating points is equivalent to using the Pareto frontier method to select a candidate operating point that optimizes both fuel economy and electrical system efficiency. The selected candidate operating point is also the target operating point.

[0074] Exemplarily, the controller constructs a multi-objective optimization model based on the vehicle's engine fuel consumption rate and battery power consumption rate, and determines candidate operating points that satisfy the combination of minimum fuel consumption rate and minimum battery power consumption rate within a preset historical time period. A multi-objective optimization model is a mathematical model that optimizes the combination of engine fuel consumption rate and battery power consumption rate, typically employing a Pareto optimality or weighted summation method. Exemplarily, the objective function of the multi-objective optimization model can be: min(w1 fuel consumption rate + w2 power consumption rate), where w1 and w2 are weight coefficients. For example, when w1 = 0.7 and w2 = 0.3, fuel economy is prioritized. For example, a candidate operating point can be determined by combining engine speed (rpm) and engine torque (Nm), motor power (kW), fuel consumption rate (g / s), and power consumption rate (kW). Specific values ​​can be shown in Table 1 below.

[0075] Table 1 Candidate working point value combination table

[0076]

[0077] Figure 2 is a schematic diagram of a working point in one embodiment; in one embodiment, the candidate working point can be as follows Figure 2 The set of operating points where all operating points coincide with Pareto operating points is shown.

[0078] In one embodiment, a hybrid vehicle cruised at 80 km / h and demanded 40 kW of power during a preset historical time period, which is the past 10 seconds. The hybrid vehicle's input parameters may include: an engine universal characteristic diagram, a motor efficiency diagram; a battery SOC of 60%, an internal resistance of 0.1 Ω; and a constant power demand of 40 kW. The objective function of the multi-objective optimization model may be: min(0.6 fuel consumption rate + 0.4 power consumption rate), and the variables may be: engine power Pe, motor power Pm, where Pe + Pm = 40 kW. The candidate operating points generated by the controller may be as shown in Table 2 below.

[0079] Table 2 Candidate working point value combination table

[0080]

[0081] If fuel economy is the priority, point B can be selected as the preferred candidate operating point (fuel consumption rate 0.65g / s, power consumption rate 15kW); if battery protection is the priority, point C can be selected as the preferred candidate operating point, which can make the power consumption rate lower.

[0082] In the above embodiment, a multi-objective optimization model is constructed, taking into account both engine fuel consumption and battery power consumption, and the optimal candidate operating point is screened out, so that the vehicle can meet the power requirements while minimizing fuel consumption and battery energy loss, thereby extending the cruising range and reducing the cost of use.

[0083] In an exemplary embodiment, the method of determining the target operating point from the candidate operating points is equivalent to a slope convexity screening. Slope convexity screening is an optimization method based on the geometric characteristics of the Pareto front. By analyzing the slope of the marginal rate of substitution of adjacent operating points (the ratio of the fuel consumption rate to the power consumption rate), the set of operating points that meet the global convexity condition is screened out, that is, the candidate operating points that protrude toward the origin among every three adjacent candidate operating points are screened out. The target operating point is the optimal combination of engine power and motor power that is finally selected according to the optimization weight after passing the convexity screening. For example, Figure 2 The slope screening point shown is the target operating point after slope convexity screening.

[0084] In the above embodiment, by screening the operating points among the candidate operating points based on slope convexity, an efficient balance between fuel consumption and electricity consumption is achieved, further dimensionality reduction and solution of the dynamic programming algorithm are achieved, calculation time is reduced, calculation efficiency is greatly improved, simulation accuracy is enhanced, and overall energy efficiency is significantly improved.

[0085] Step 108, based on the optimal sequence of battery power consumption rate and multiple target operating points, a dynamic programming algorithm is used to obtain the optimal driving mode and the global optimal control sequence for each unit time in the preset historical time period; the global optimal control sequence is the optimal operating point that optimizes the combination of fuel consumption rate and battery power consumption rate for each unit time in the entire preset historical time period.

[0086] Among them, the optimal sequence of battery power consumption rate is determined by the minimum value of battery power consumption rate corresponding to each unit time of pure electric mode operation in the historical time period; multiple target operating points are the combined minimum candidate operating points obtained by determining the combined minimum value of fuel consumption rate and battery power consumption rate corresponding to each unit time of hybrid mode operation in the historical time period, and are further obtained after distribution balance screening.

[0087] For example, after obtaining the above-mentioned optimal sequence of battery consumption rates and multiple target operating points corresponding to each unit time of the vehicle in the historical time period, the optimal sequence of battery consumption rates and multiple target operating points can be further optimized using a dynamic programming algorithm (DP) to obtain an optimized driving mode and a global optimal control sequence that are more suitable for each unit time in the historical time period. This means that if the vehicle were to operate in actual operation using the relevant optimal driving mode for each unit time in the historical time period and the combination of fuel consumption rate and battery consumption rate for each unit time in the global optimal control sequence, the vehicle's operation in the historical time period would achieve optimal operating efficiency or fuel economy.

[0088] As can be seen, in the energy allocation method provided by the present application, the steps of determining the optimal driving mode for each unit time of the vehicle in the preset historical time period and the global optimal control sequence corresponding to the preset historical time period based on the driving mode and operating parameters of the vehicle at each unit time in the preset historical time period, combined with the pre-constructed longitudinal dynamic model, power split dynamic model, battery model, and engine universal characteristic curve, and further combined with slope convexity screening, are equivalent to the process of offline solving the optimal operating mode of the vehicle in the preset historical time period. In the method provided by the present application, after obtaining the candidate operating point that satisfies the minimum combination of fuel consumption rate and battery power consumption rate in the hybrid mode of the vehicle in the preset historical time period, the target operating point with a more balanced distribution of fuel consumption rate and battery power consumption rate is further selected from the multiple candidate operating points through slope screening. The execution of this step not only reduces the number of operating points, but also obtains an operating point that promotes a more balanced distribution of fuel consumption rate and battery power consumption rate. This not only reduces the computational complexity of determining the optimal driving mode and global optimal control sequence corresponding to the preset historical time period in the subsequent steps, thereby improving computational efficiency, but also helps to make the calculated optimal driving mode and global optimal control sequence closer to the ideal state. This method can also accelerate the convergence of the DP algorithm and avoid traversing all possible operating points. Based on this, the steps provided in this application for determining the optimal drive mode and global optimal control sequence corresponding to a preset historical time period are beneficial for reducing computational complexity, improving the solution speed, and achieving ideal optimization efficiency and results. This is an ultra-fast energy management method that balances computational efficiency and optimization performance.

[0089] In an exemplary embodiment, an optimal sequence based on the battery power consumption rate and multiple target operating points can be selected, and the optimal driving mode and the global optimal control sequence for each unit time in a preset historical time period can be obtained by a dynamic programming algorithm while satisfying the constraints of the working status of the preset components of the vehicle.

[0090] The constraints on the operating states of pre-set components (pre-set constraints) are hard boundary conditions that must be met during global optimization, encompassing the safety and performance limits of the vehicle's powertrain. The global optimal control sequence is the sequence that optimizes the combination of fuel consumption and battery power consumption over the entire pre-set historical time period. Based on this sequence, the optimal operating sequence for the vehicle's engine and motor power combinations over the pre-set historical time period can be derived. This sequence is the time series of engine and motor power-related operating instructions generated by integrating all constraints with the optimization objective.

[0091] In one embodiment, a global optimal control sequence can be solved within a historical time period based on a dynamic programming algorithm or a model predictive control algorithm. For example, the global optimal control sequence can also be a vehicle engine and motor power sequence that optimizes the combination of fuel consumption rate and battery power consumption rate, determined based on a combination of different values ​​of time, engine power, motor power, and drive mode. Specifically, the sequence can be shown in Table 3 below:

[0092] Table 3 Combinations of working parameter values ​​corresponding to the global optimal control sequence

[0093]

[0094] In one embodiment, the controller obtains the hybrid vehicle's motor power sequence, target operating point, battery state of charge, vehicle driving mode, and dynamic demand data for each unit time period over a 30-second historical period encompassing urban congestion and high-speed acceleration scenarios. The motor power sequence can be 0-10 seconds, with motor power of [20, 18, 15, ...] kW. The target operating point can be operating point A1: engine power 25 kW + motor power 5 kW, fuel consumption 0.6 g / s; operating point B1: engine power 30 kW + motor power 0 kW, fuel consumption 0.7 g / s; and SOC = 65%. The controller then divides the historical period into 30 1-second unit times. For each unit time period, it selects the operating point that minimizes the combination of battery power consumption and engine fuel consumption, while also considering SOC balance to avoid deep discharge. The controller then outputs the globally optimal control sequence corresponding to the operating parameter value combination, as shown in Table 4 below:

[0095] Table 4 Combinations of working parameter values ​​corresponding to the global optimal control sequence

[0096]

[0097] In the above embodiment, a global optimal control sequence is generated based on the motor power sequence that minimizes the battery power consumption rate in pure electric mode and the target operating point in hybrid mode, combined with SOC, drive mode and dynamic requirements. The global optimal control sequence can be used to maintain the vehicle in the optimal operating state throughout the entire operating cycle, avoid the overall efficiency reduction caused by short-term optimization, and improve the robustness and adaptability of the control.

[0098] Step 110 , based on the optimal driving mode and the global optimal control sequence, optimizes the system-level performance index parameters of the vehicle to optimize the energy distribution during vehicle operation; the system-level performance index parameters include: transmission system transmission ratio, motor torque distribution ratio coefficient limit value, battery charge and discharge power limit value, motor power limit value, and engine power limit value.

[0099] System-level performance index parameters include, but are not limited to, transmission system transmission ratio, motor torque distribution ratio coefficient limit, battery charge and discharge power limit, motor power limit, and engine power limit. The system-level performance index parameters shown in this application are merely examples and are not intended to limit the system-level performance index parameters of a vehicle.

[0100] Exemplarily, after the controller obtains the global optimal control sequence of the vehicle within a preset historical time period and the optimal driving mode (optimal driving mode) corresponding to each unit time within the preset historical time period, it can collect the actual operating data of the vehicle in a recent (latest) time period (actual operating time period), and obtain the actual control sequence corresponding to the vehicle (the actual operating points of the fuel consumption rate and battery power consumption rate corresponding to each unit time within the actual operating time period) based on the actual operating data; combined with the driving mode of the vehicle at each unit time in the actual operating time period, a comparison result is obtained by comparing the actual control sequence of the vehicle with the adapted operating point in the global optimal control sequence; when the comparison result indicates that the difference between the actual control sequence of the vehicle and the adapted operating point in the global optimal control sequence exceeds a preset threshold, it means that the difference between the actual control sequence of the vehicle and the adapted operating point in the global optimal control sequence is relatively large, which means that the system-level performance index parameters of the vehicle still have room for optimization; then, at least part of the system-level performance index parameters of the vehicle can be optimized and adjusted according to the difference, so that the system-level performance index parameters of the vehicle are better. The optimized system-level performance indicator parameters are used to optimize energy distribution during vehicle operation, which is beneficial to ensuring that the vehicle has an optimal operating time period with an optimal combination of fuel consumption rate and battery power consumption rate during subsequent actual operation, thereby improving the economy and efficiency of the vehicle during actual operation.

[0101] That is, taking the global optimal control sequence and the related optimal driving mode for each unit time as a reference group (benchmark), obtain a group of actual global control sequences and actual driving modes of vehicles under actual operating conditions. The actual global optimal control sequence can be a sequence that optimizes the combination of fuel consumption rate and battery power consumption rate within the operating time period of the entire actual operating condition of the vehicle; then compare the actual global control sequence and the driving mode for each unit time corresponding to the actual global control sequence with the reference group to obtain a comparison result. If the comparison result shows that the fuel economy and battery power economy of the actual global control sequence are worse than those of the reference group, it indicates that the system-level performance index parameters corresponding to the actual global control sequence need to be optimized. In this case, the system-level performance index parameters of the vehicle can be optimized based on the reference group.

[0102] In the above embodiment, based on the global optimal control sequence and the related optimal driving mode for each unit time, the system-level performance index parameters of the vehicle are optimized, which can significantly improve the comprehensive energy efficiency and system robustness of the multi-mode hybrid vehicle.

[0103] The energy distribution method obtains the driving mode and operation parameters of the vehicle in each unit time in a preset historical time period to obtain operation data of the vehicle in the historical time period; then, for each unit time, when the driving mode of the vehicle is the pure electric mode, the minimum value of the battery power consumption rate of the relevant unit time is determined by the pre-constructed longitudinal dynamics model, power split dynamics model and battery model in combination with the relevant operation parameters, and then the optimal sequence of the battery power consumption rate corresponding to the preset historical time period is determined based on the minimum value of the battery power consumption rate of each unit time; for each unit time, when the driving mode of the vehicle is the hybrid mode, the combined minimum value of the fuel consumption rate and the battery power consumption rate of the unit time is screened out by the pre-constructed longitudinal dynamics model, power split dynamics model, battery model and engine universal characteristic curve in combination with the relevant operation parameters; and further, the candidate working point that meets the combined minimum of the fuel consumption rate and the battery power consumption rate is determined based on the combined minimum value of the fuel consumption rate and the battery power consumption rate of each unit time; further, the working points in the candidate working points are screened to determine the target working point that makes the fuel consumption rate and the battery power consumption rate distributed evenly, and the target working point is beneficial to make the power distribution instruction smoother; then, the optimal driving mode and the global optimal control sequence for each unit time in the preset historical time period can be obtained by the dynamic programming algorithm based on the battery power consumption rate optimal sequence and the plurality of target working points determined for the historical operation time period of the vehicle; wherein the global optimal control sequence is the optimal working point that makes the combined optimal of the fuel consumption rate and the battery power consumption rate in each unit time in the entire preset historical time period; finally, the system-level performance index parameters of the vehicle can be optimized based on the determined optimal driving mode and global optimal control sequence, and then the optimization of the energy distribution in the subsequent actual operation process of the vehicle can be realized; wherein the system-level performance index parameters include: transmission ratio of the transmission system, motor torque distribution proportion coefficient limit value, battery charge and discharge power limit value, motor power limit value and engine power limit value. By this way, the system-level performance index parameters of the vehicle are optimized, so that the system-level performance index parameters of the vehicle are better, thereby being beneficial to ensure that the vehicle has an operation time period with the combined optimal of the fuel consumption rate and the battery power consumption rate in the subsequent operation process, thereby being beneficial to improve the economy and efficiency of the vehicle operation.

[0104] In an exemplary embodiment, the above steps include: determining the minimum battery power consumption rate per unit time based on a pre-constructed longitudinal dynamic model, a power split dynamic model, and a battery model in combination with corresponding operating parameters, which can be specifically performed as follows: determining the vehicle's dynamic requirements per unit time based on the pre-constructed longitudinal dynamic model in combination with the operating parameters per unit time; determining the internal force requirements of the first planetary gear mechanism and the second planetary gear mechanism in the vehicle per unit time based on the dynamic requirements; determining the first motor torque and the second motor torque of the vehicle per unit time based on the pre-constructed power split dynamic model in combination with the internal force requirements of the first planetary gear mechanism and the second planetary gear mechanism per unit time; and determining the motor power requirement per unit time based on the first motor torque and the second motor torque; determining the corresponding battery charging and discharging power based on the motor power requirement, and determining the minimum battery power consumption rate per unit time based on the pre-constructed battery model in combination with the battery charging and discharging power.

[0105] In an exemplary embodiment, the above steps of: determining a combined minimum value of a fuel consumption rate and a battery power consumption rate per unit time based on the longitudinal dynamics model, the power split dynamics model, the battery model, and the engine universal characteristic curve in combination with corresponding operating parameters, may be specifically performed as follows: determining a vehicle dynamics demand per unit time based on a pre-established longitudinal dynamics model in combination with the operating parameters per unit time; determining an internal force demand of a first planetary gear mechanism and an internal force demand of a second planetary gear mechanism in the vehicle per unit time based on the dynamics demand; determining a first motor torque, a second motor torque, and an engine torque of the vehicle per unit time based on the pre-established power split dynamics model in combination with the internal force demand of the first planetary gear mechanism and the internal force demand of the second planetary gear mechanism per unit time; and determining a motor power demand and an engine power demand per unit time based on the first motor torque, the second motor torque, and the engine torque; determining a corresponding battery charge and discharge power based on the motor power demand, and determining a combined minimum value of a fuel consumption rate and a battery power consumption rate per unit time based on the pre-established battery model and the engine universal characteristic curve in combination with the battery charge and discharge power and the engine power demand.

[0106] In an exemplary embodiment, Figure 3 FIG. 1 is a flow chart of a method for updating a global optimal control sequence in one embodiment. Figure 3 The method for updating the global optimal control sequence shown in the figure is a specific implementation method for updating the global optimal control sequence of the entire preset historical time period by using the battery state of charge grid density, including the following steps 302 to 304. Among them:

[0107] Step 302, determining the target battery state of charge grid density based on the accuracy level and time consumption of the global optimal control sequence under multiple preset battery state of charge grid densities.

[0108] wherein the battery state of charge grid density is the accuracy level in discretizing the continuous state variable of battery state of charge, and the SOC grid density directly affects the calculation complexity and optimization accuracy. Exemplarily, the preset SOC grid density can be but is not limited to 1%, 2%, 5% or 10%; high-density SOC grid: small SOC interval (such as 1% step), high accuracy but large calculation amount; low-density SOC grid: large SOC interval (such as 5% step), fast calculation but possible loss of optimal solution; the mathematical expression of the SOC grid density can be: SOC grid density = {0%, Δs, 2Δs,..., 100%} (Δs is the grid step). For example, Figure 4 Figure 4 is a schematic diagram of the SOC grid density in an embodiment, Figure 4 The state variable sv1 or sv2 in the above formula (1) can be the SOC or the driving mode.

[0109] In an exemplary embodiment, the trade-off relationship between the accuracy and the time consumption under different SOC grid densities can be as shown in Table 5:

[0110] Table 5

[0111]

[0112] As can be seen from Table 5 above, the calculation time consumption is approximately linearly related to the number of grid points, and the fuel consumption error increases exponentially with the grid step (due to the nonlinear battery efficiency characteristic).

[0113] In an exemplary embodiment, the controller can generate a high-precision solution under the SOC grid with a step of 1% as a reference, and then test the fuel consumption error and time consumption of different SOC grids (such as 2%, 5%, 10%), and select the grid density with a fuel consumption error ≤1% and the shortest time consumption as the target SOC grid density.

[0114] Step 304, updating the global optimal control sequence for the entire preset historical time period based on the target battery state of charge grid density under the preset constraint conditions of the longitudinal dynamics model and the dynamics model of the multi-mode configuration (power split dynamics model).

[0115] ​Exemplarily, the initial global optimal control sequence is generated by a 1% high-density SOC grid, and the target SOC grid density is a 3% low-density SOC grid. The constraints of operating parameters such as engine power, motor power, and battery discharge power are determined through the longitudinal dynamic model and the dynamic model of the multi-mode configuration, and the SOC trajectory is re-discretized according to 3%. If the new SOC point and the original sequence power do not meet the constraints, the operating parameters such as engine power, motor power, and battery discharge power are adjusted. After the adjustment, the updated global optimal control sequence is output through the dynamic programming algorithm and the Pareto boundary method.

[0116] In the above embodiment, based on the accuracy and time consumption of the global optimal control sequence at different SOC grid densities, the grid density with a fuel consumption rate error less than a specified value and the shortest time consumption is determined as the target SOC grid density. The global optimal control sequence is updated according to the target SOC grid density, which can significantly reduce the calculation time consumption and improve the calculation performance of the controller.

[0117] In some embodiments, Figure 5 FIG. 1 is a flow chart of a method for determining a target operating point in one embodiment; FIG. Figure 5 The target operating point determination method shown includes the following steps 502 to 504. In which:

[0118] Step 502: Determine the slopes between adjacent working points among the candidate working points.

[0119] For example, it can be achieved by Figure 6 For the working points on the Pareto boundary curve in the fuel consumption-electricity consumption working space shown, the slope θ between adjacent working points is calculated according to the calculation formula of the slope θ. The calculation formula of the slope θ may include:

[0120]

[0121] in, represents the slope between candidate working point k and candidate working point (k-1), Indicates the change in battery state of charge SOC, and also represents the change in power consumption. represents the change in SOC of candidate operating point k, represents the change in SOC of the candidate operating point (k-1), Indicates the change in fuel consumption rate, represents the fuel consumption rate of candidate operating point k, represents the fuel consumption rate of the candidate operating point (k-1).

[0122] Step 504: Determine the operating point whose slope meets the preset condition as the target operating point.

[0123] For example, a candidate working point k whose slope between adjacent working points satisfies the following preset conditions may be used as a target working point:

[0124]

[0125] Among them, taking any candidate working point k as the center, represents the slope between the left candidate working point (k-1) and the adjacent candidate working point k, Represents the slope between candidate operating point k and another adjacent candidate operating point on the right (k+1). Compare the slopes of each candidate operating point k and its adjacent points on the Pareto frontier in turn, and select the operating point with the slope closest to the Pareto convex edge, specifically the operating point convex to the center point of the two-dimensional coordinate circle of power consumption rate and fuel consumption rate. Figure 6 As shown, Figure 6 Schematic diagram of a target operating point in one embodiment.

[0126] In the above embodiment, by determining the slopes between adjacent operating points among the candidate operating points, selecting the operating points whose slopes meet preset conditions as target operating points, and screening the operating points among the candidate operating points for slope convexity, an efficient balance between fuel and electricity consumption is achieved, further dimensionality reduction is achieved in the dynamic programming algorithm, and computation time is reduced.

[0127] In some embodiments, generating a candidate operating point that satisfies a combination of minimum fuel consumption rate and minimum battery power consumption rate within a preset historical time period based on the vehicle's engine universal characteristic diagram can be: based on the dynamic requirements determined by the longitudinal dynamic model, coupling the vehicle's engine universal characteristic diagram and the transmission system efficiency to establish a multi-objective optimization model, and determining the candidate operating point that satisfies a combination of minimum fuel consumption rate and minimum battery power consumption rate within a preset historical time period through the Pareto boundary method.

[0128] The universal characteristic diagram is a three-dimensional map that describes engine performance. The horizontal axis is speed (rpm), the vertical axis is torque (Nm), and the contour lines are fuel consumption (g / kWh) or efficiency (%). Its core function is to visualize the engine's efficient operating range and guide the parameter optimization of the hybrid system. For example, a two-dimensional optimization space is established with the engine fuel consumption rate and battery power consumption rate as the coordinate axes to visualize all possible operating point combinations; for example, the two-dimensional optimization space can be as follows: Figure 2 As shown, the horizontal axis is the fuel consumption rate (g / s), and the vertical axis is the battery power consumption rate (W). For the dynamic requirements of a hybrid vehicle, all feasible power distribution combinations of its engine and motor are enumerated, that is, Figure 2 All working points in .

[0129] Note that g / kWh is the fuel mass (gram) consumed per 1 kWh of mechanical work produced, used to evaluate engine thermal efficiency, for map and long-term energy consumption optimization, g / s is the instantaneous fuel consumption rate.

[0130] Exemplarily, the non-inferior solution set is determined by eliminating inefficient working points in the two-dimensional optimization space through the Pareto boundary method, and the Pareto boundary point coinciding with the engine optimal working curve is selected as the candidate working point. The Pareto boundary method is a core tool in multi-objective optimization, which is used to find the best trade-off solution set between conflicting objectives, i.e. without sacrificing any objective, the other objective cannot be further optimized; the inefficient working point is the working point in the two-dimensional optimization space, which has other working points that can be better in at least one objective (engine fuel consumption rate or battery power consumption rate); the non-inferior solution set is the working point set on the Pareto boundary, such as the Pareto working point in Figure 2 , the optimization of any objective must sacrifice the other objective, representing the optimal trade-off solution.

[0131] Exemplarily, if the fuel consumption rate and battery power consumption rate of working point X are both ≤ working point Y, and at least one is strictly better, then working point X dominates working point Y, and working point Y is an inefficient working point. Exemplarily, working point F (1.2 g / s, 30 kW), working point D (1.8 g / s, 20 kW), working point F has lower fuel consumption rate but higher battery power consumption rate, and working point D and F do not dominate each other; working point G (2.5 g / s, 0 kW) is dominated by working points D, E, and F, because the fuel consumption rate and battery power consumption rate of working point G are both worse, and the working point not dominated by any working point, such as the working point D-E-F connection line, represents the non-inferior solution set.

[0132] The optimal working curve is the continuous trajectory of the lowest fuel consumption rate working point in the map.

[0133] Exemplarily, the engine map: the horizontal axis is the engine speed (rpm); the vertical axis is the torque (Nm); the contour line is the fuel consumption rate (g / kWh);

[0134] The optimal working curve is the trajectory connecting the lowest fuel consumption rate points, for example:

[0135] The speed and torque of the lowest fuel consumption rate point 1 are 2000 rpm and 120 Nm, respectively, and its fuel consumption rate = 210 g / kWh;

[0136] The speed and torque of the lowest fuel consumption rate point 2 are 2500 rpm and 150 Nm, respectively, and its fuel consumption rate = 205 g / kWh.

[0137] The candidate points to be screened may be points that coincide with the optimal working curve from the Pareto frontier of the non-inferior solution set, such as the working point Q (1.8 g / s, 30 kW).

[0138] In the above embodiment, based on dynamic requirements, a two-dimensional optimization space of the engine fuel consumption rate and the battery power consumption rate is established on the universal characteristic diagram, the multi-objective problem is visualized, and the inefficient working points in the two-dimensional optimization space are eliminated through the Pareto boundary method, the non-inferior solution set is determined, the effective trade-off options are retained, and the Pareto boundary points that coincide with the optimal operating curve of the engine are selected as candidate working points, which can ensure that the engine always operates in the high-efficiency range.

[0139] In some embodiments, the method of controlling vehicle operation according to the global optimal control sequence may include: controlling vehicle operation under the constraint that the vehicle's engine speed, motor speed, engine torque, motor torque, battery charge and discharge power, and battery state of charge are in an effective working state, that is, controlling vehicle operation under the constraint that each component during the operation of the vehicle transmission system is in an effective working state.

[0140] Among them, the engine speed is the rotation speed of the engine crankshaft; the motor speed is the rotation speed of the motor rotor; the engine torque is the rotational torque output by the engine; the motor torque is the driving or braking torque output by the motor; and the battery charge and discharge power is the instantaneous charge and discharge capacity of the battery.

[0141] The energy allocation method of this application is applied to hybrid vehicles. To ensure the normal operation of the hybrid vehicle's powertrain and the life of the battery, the battery charge must be the same or similar at the beginning and end of operation, and fluctuate within a suitable dynamic range during operation. To maintain a stable state of charge (SOC), the initial battery charge must be equal to the battery charge at the end of operation, and the vehicle's engine speed, motor speed, engine torque, motor torque, and battery charge and discharge power must be in an effective operating state.

[0142] The specific constraints for constraining the working states of various components in the hybrid vehicle transmission system are:

[0143]

[0144] Among them, the appropriate dynamic range of SOC is 0.4-0.7. Indicates the initial state of charge of the battery. Indicates the battery charge at the end of the run; Indicates the speed of the power component, which is the engine or motor. , i=e represents the engine speed, i=em represents the motor speed; represents the engine speed at unit time k, Indicates the minimum engine speed. Indicates the maximum engine speed. represents the speed of the motor at unit time k, Indicates the minimum speed of the motor. Indicates the maximum speed of the motor; Indicates the torque of the power component, which is the engine or motor. , i=e represents the torque of the engine, i=em represents the torque of the motor, represents the engine torque per unit time k, Indicates the minimum torque of the engine, Indicates the maximum torque of the engine, represents the torque of the motor at unit time k, Indicates the minimum torque of the motor, Indicates the maximum torque of the motor;

[0145] It represents the charge and discharge power of the battery per unit time k, Indicates the minimum charge and discharge power of the battery. Indicates the maximum charge and discharge power of the battery.

[0146] In the above embodiment, by constraining the working state of each component, the combination of the engine meeting the minimum fuel consumption rate and the motor meeting the minimum battery power consumption rate is minimized while ensuring efficient and stable operation of the vehicle.

[0147] It should be noted that the energy distribution method of the present application can be applied to a variety of standard test conditions, verify the effectiveness of the working point through dynamic simulation, and generate a global optimal control sequence corresponding to the engine-motor collaborative operation. The various working conditions include the aforementioned standard test conditions.

[0148] It should be understood that, although the various steps in the flowcharts involved in the various embodiments described above are displayed in sequence according to the instructions of the arrows, these steps are not necessarily performed in sequence in the order indicated by the arrows. Unless clearly stated herein, the execution of these steps is not strictly limited in order, and these steps can be performed in other orders. Moreover, at least a portion of the steps in the flowcharts involved in the various embodiments described above may include multiple steps or multiple stages, and these steps or stages are not necessarily performed at the same time, but can be performed at different times, and the execution order of these steps or stages is not necessarily performed in sequence, but can be performed in turn or alternately with at least a portion of the steps or stages in other steps or other steps. It is understandable that the various steps in different embodiments can be freely combined as needed, and the various non-contradictory schemes formed by the combination all fall within the scope of protection of this application.

[0149] Based on the same inventive concept, the embodiments of the present application also provide an energy distribution device for implementing the energy distribution method described above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme described in the above method, so the specific limitations in one or more energy distribution device embodiments provided below can refer to the limitations of the energy distribution method described above, which will not be repeated here.

[0150] In one exemplary embodiment, as shown in Figure 7 An energy distribution device 700 is provided, comprising a data acquisition module 710, a pure electric optimization module 720, a hybrid optimization module 730, a global optimization module 740, and an energy distribution module 750, wherein:

[0151] The data acquisition module 710 is configured to acquire driving modes and operating parameters of the vehicle in each unit time in a preset historical time period.

[0152] The pure electric optimization module 720 is configured to, for each unit time, determine a minimum value of a battery power consumption rate in the unit time based on a longitudinal dynamics model, a power split dynamics model, and a battery model pre-constructed in a case where the driving mode represents a pure electric mode; and determine an optimal sequence of the battery power consumption rate based on the minimum values of the battery power consumption rate corresponding to a plurality of unit times.

[0153] The hybrid optimization module 730 is configured to, for each unit time, determine a combined minimum value of a fuel consumption rate and a battery power consumption rate in the unit time based on the longitudinal dynamics model, the power split dynamics model, the battery model, and an engine universal characteristic curve in a case where the driving mode represents a hybrid mode; determine candidate working points that satisfy the combined minimum of the fuel consumption rate and the battery power consumption rate based on the combined minimum values of the fuel consumption rate and the battery power consumption rate corresponding to a plurality of unit times; and screen the candidate working points to obtain target working points in which the fuel consumption rate and the battery power consumption rate are distributed evenly.

[0154] The global optimization module 740 is configured to obtain an optimal driving mode and a globally optimal control sequence for each unit time in the preset historical time period by a dynamic programming algorithm based on the optimal sequence of the battery power consumption rate and a plurality of target working points; and the globally optimal control sequence is an optimal working point that optimizes the combination of the fuel consumption rate and the battery power consumption rate in each unit time in the entire preset historical time period.

[0155] Energy distribution module 750 is used to optimize the vehicle's system-level performance index parameters based on the optimal driving mode and the global optimal control sequence to optimize the energy distribution during vehicle operation; the system-level performance index parameters include: transmission system transmission ratio, motor torque distribution ratio coefficient limit value, battery charge and discharge power limit value, motor power limit value and engine power limit value.

[0156] In some embodiments, the candidate operating point is located in a two-dimensional coordinate system composed of fuel consumption rate and battery power consumption rate, the fuel consumption rate is the horizontal coordinate in the two-dimensional coordinate system, and the battery power consumption rate is the vertical coordinate in the two-dimensional coordinate system; the hybrid optimization module 730 is used to screen the candidate operating points to obtain a target operating point in which the fuel consumption rate and the battery power consumption rate are evenly distributed, specifically for: determining the slopes between adjacent candidate operating points in the candidate operating point in the two-dimensional coordinate system; along the positive direction of the horizontal coordinate, for the two slopes corresponding to every three adjacent candidate operating points, if the absolute value of the previous slope is greater than or equal to the absolute value of the latter slope, the candidate operating point located in the middle position among the three adjacent candidate operating points is determined as the target operating point with a balanced distribution of fuel consumption rate and battery power consumption rate.

[0157] In an exemplary embodiment, the pure electric optimization module 720 is used to determine the minimum battery power consumption rate per unit time based on a pre-built longitudinal dynamic model, a power split dynamic model and a battery model, combined with corresponding operating parameters. Specifically, it is used to: determine the vehicle's dynamic requirements per unit time based on the pre-built longitudinal dynamic model combined with the operating parameters per unit time; determine the vehicle's motor power requirements per unit time based on the pre-built power split dynamic model combined with the dynamic requirements per unit time; and determine the minimum battery power consumption rate per unit time based on the pre-built battery model in response to the motor power requirements.

[0158] In an exemplary embodiment, the global optimization module 740 is used to determine the combined minimum value of the fuel consumption rate and the battery power consumption rate per unit time based on the longitudinal dynamic model, the power split dynamic model, the battery model and the engine universal characteristic curve in combination with the corresponding operating parameters. Specifically, it is used to: determine the dynamic requirements of the vehicle per unit time based on the pre-built longitudinal dynamic model in combination with the operating parameters per unit time; determine the motor power requirements and engine power requirements of the vehicle per unit time based on the pre-built power split dynamic model in combination with the dynamic requirements per unit time; and determine the combined minimum value of the fuel consumption rate and the battery power consumption rate per unit time based on the pre-built battery model and the engine universal characteristic curve for the motor power requirements and the engine power requirements.

[0159] In an exemplary embodiment, the global optimization module 740 is used to obtain the optimal driving mode and the global optimal control sequence for each unit time in a preset historical time period based on the optimal sequence of battery power consumption rate and multiple target operating points through a dynamic programming algorithm. Specifically, it is used to: based on the optimal sequence of battery power consumption rate and multiple target operating points, obtain the optimal driving mode and the global optimal control sequence for each unit time in a preset historical time period through a dynamic programming algorithm while satisfying the constraints of the working status of the preset components of the vehicle.

[0160] Each module in the energy distribution device described above may be implemented in whole or in part through software, hardware, or a combination thereof. Each module may be embedded in or independent of a processor in the controller in hardware form, or may be stored in a memory in the controller in software form, so that the processor can call and execute the corresponding operations of each module.

[0161] In an exemplary embodiment, a controller is provided, the internal structure of which can be shown as follows: Figure 8 As shown. The controller includes a processor, a memory, an input / output interface (Input / Output, abbreviated as I / O) and a communication interface. The processor, memory and input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the controller is used to provide computing and control capabilities. The memory of the controller includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The input / output interface of the controller is used to exchange information between the processor and an external device. The communication interface of the controller is used to communicate with an external terminal in a wired or wireless manner. The wireless manner can be achieved through WIFI, a mobile cellular network, near field communication (NFC) or other technologies. When the computer program is executed by the processor, a vehicle driving control method is implemented.

[0162] Those skilled in the art will understand that Figure 8 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the controller to which the solution of the present application is applied. The specific controller may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0163] In an exemplary embodiment, a controller is provided, including a memory and a processor. The memory stores a computer program, and the processor implements the steps in the above method embodiments when executing the computer program.

[0164] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0165] In an exemplary embodiment, a computer program product is provided, including a computer program. When the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0166] Those skilled in the art will understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. In particular, any reference to memory, database, or other media used in the embodiments provided in this application can include at least one of non-volatile memory 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 various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The databases involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the various embodiments provided herein may be, but are not limited to, general-purpose processors, central processing units (CPUs), graphics processing units (GPUs), digital signal processors (DSPs), programmable logic devices (PLDs), quantum computing-based data processing logic devices, artificial intelligence (AI) processors, and the like.

[0167] The technical features of the above embodiments can be combined arbitrarily. In order to make the description concise, 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 application.

[0168] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present application. It should be noted that a person of ordinary skill in the art may make various modifications and improvements without departing from the spirit of the present application, and these modifications and improvements fall within the scope of protection of the present application. Therefore, the scope of protection of the present application shall be determined by the appended claims.

Claims

1. An energy distribution method, characterized in that: The method comprises: Obtaining the driving mode and operating parameters of the vehicle at each unit time in a preset historical time period; For each unit time, when the driving mode represents a pure electric mode, determining a minimum value of the battery power consumption rate per unit time based on a pre-established longitudinal dynamics model, a power split dynamics model, and a battery model in combination with the corresponding operating parameters; and determining an optimal sequence of battery power consumption rates based on a plurality of minimum values ​​of the battery power consumption rates corresponding to the unit times; For each unit time, when the driving mode represents a hybrid mode, determining a combined minimum value of the fuel consumption rate and the battery power consumption rate for the unit time based on the longitudinal dynamic model, the power split dynamic model, the battery model, and the engine universal characteristic curve, in combination with the corresponding operating parameters; and determining a candidate operating point that satisfies the minimum combined value of the fuel consumption rate and the battery power consumption rate based on multiple combined minimum values ​​of the fuel consumption rate and the battery power consumption rate corresponding to the unit time; and screening the candidate operating points to obtain a target operating point in which the fuel consumption rate and the battery power consumption rate are evenly distributed. Based on the optimal sequence of battery power consumption rates and the plurality of target operating points, a dynamic programming algorithm is used to determine an optimal driving mode and a global optimal control sequence for each unit time in the preset historical time period; the global optimal control sequence is an optimal operating point that optimizes the combination of the fuel consumption rate and the battery power consumption rate for each unit time in the entire preset historical time period; Based on the optimal driving mode and the global optimal control sequence, the system-level performance index parameters of the vehicle are optimized to optimize the energy distribution during the operation of the vehicle; the system-level performance index parameters include: the transmission system transmission ratio, the motor torque distribution ratio coefficient limit value, the battery charge and discharge power limit value, the motor power limit value and the engine power limit value.

2. The method according to claim 1, characterized in that The candidate operating point is located in a two-dimensional coordinate system formed by the fuel consumption rate and the battery power consumption rate, the fuel consumption rate is the abscissa in the two-dimensional coordinate system, and the battery power consumption rate is the ordinate in the two-dimensional coordinate system; The screening of the candidate operating points to obtain a target operating point in which the fuel consumption rate and the battery power consumption rate are balanced includes: Determining, in the two-dimensional coordinate system, a slope between adjacent candidate working points among the candidate working points; Along the positive direction of the horizontal coordinate, when the two slopes corresponding to every three adjacent candidate operating points satisfy the condition that the absolute value of the previous slope is greater than or equal to the absolute value of the latter slope, the candidate operating point located in the middle position among the three adjacent candidate operating points is determined as the target operating point with a balanced distribution of the fuel consumption rate and the battery power consumption rate.

3. The method according to claim 1, characterized in that The determining of the minimum value of the battery power consumption rate per unit time based on the pre-built longitudinal dynamic model, the power split dynamic model, and the battery model in combination with the corresponding operating parameters includes: Determining a dynamics requirement of the vehicle per unit time based on a pre-built longitudinal dynamics model and the operating parameters per unit time; determining, based on the dynamics requirement, an internal force requirement of a first planetary gear mechanism and an internal force requirement of a second planetary gear mechanism in the vehicle per unit time; determining a first motor torque and a second motor torque of the vehicle per unit time based on a pre-established power split dynamics model and in combination with an internal force requirement of the first planetary gear mechanism and an internal force requirement of the second planetary gear mechanism per unit time; and determining a motor power requirement per unit time based on the first motor torque and the second motor torque; The corresponding battery charging and discharging power is determined based on the motor power demand, and the minimum value of the battery power consumption rate per unit time is determined based on a pre-built battery model combined with the battery charging and discharging power.

4. The method according to claim 1, wherein The determining of the combined minimum value of the fuel consumption rate and the battery power consumption rate per unit time based on the longitudinal dynamic model, the power split dynamic model, the battery model, and the engine universal characteristic curve in combination with the corresponding operating parameters includes: Determining a dynamics requirement of the vehicle per unit time based on a pre-built longitudinal dynamics model and the operating parameters per unit time; determining, based on the dynamics requirement, an internal force requirement of a first planetary gear mechanism and an internal force requirement of a second planetary gear mechanism in the vehicle per unit time; determining, based on a pre-established power split dynamics model and in combination with the internal force requirements of the first planetary gear mechanism and the internal force requirements of the second planetary gear mechanism per unit time, a first motor torque, a second motor torque, and an engine torque of the vehicle per unit time; and determining, based on the first motor torque, the second motor torque, and the engine torque, a motor power requirement and an engine power requirement per unit time; Based on the motor power requirement, the corresponding battery charging and discharging power is determined, and based on a pre-built battery model and an engine universal characteristic curve, the combined minimum value of the fuel consumption rate and the battery power consumption rate per unit time is determined in combination with the battery charging and discharging power and the engine power requirement.

5. The method according to claim 1, wherein The method of obtaining the optimal driving mode and the global optimal control sequence for each unit time in the preset historical time period based on the optimal sequence of battery power consumption rates and the plurality of target operating points through a dynamic programming algorithm includes: Based on the optimal sequence of battery power consumption rates and the plurality of target operating points, an optimal driving mode and a global optimal control sequence for each unit time in the preset historical time period are obtained by a dynamic programming algorithm while satisfying constraints on the operating states of preset components of the vehicle; The constraints on the working states of the preset components of the vehicle are: Among them, SOC represents the battery state of charge per unit time. Indicates the initial state battery power per unit time, Indicates the battery capacity at the end of the unit time; Indicates the minimum engine speed. represents the engine speed per unit time k, Indicates the maximum speed of the engine. Indicates the minimum torque of the engine, represents the engine torque per unit time k, Indicates the maximum torque of the engine, Indicates the minimum speed of the motor. represents the speed of the motor per unit time k, Indicates the maximum speed of the motor. Indicates the minimum torque of the motor, represents the torque of the motor per unit time k, Indicates the maximum torque of the motor, Indicates the minimum charge and discharge power of the battery. It represents the charge and discharge power of k battery per unit time, Indicates the maximum charge and discharge power of the battery.

6. The method according to claim 1, characterized in that The pre-built longitudinal dynamic model is: Among them, F t is the vehicle's dynamics requirement, m is the vehicle's curb mass, g is the acceleration of gravity, f is the rolling resistance coefficient, α is the road slope angle during vehicle driving, and C D is the air resistance coefficient of the vehicle during driving, is the air density during vehicle movement, A is the frontal area during vehicle movement, u represents the vehicle speed during vehicle movement, t is the unit time, δ is the vehicle's rotational mass conversion coefficient; the equation for calculating δ uses dimensionless calculation, I w is the moment of inertia of the vehicle wheel end, I f is the moment of inertia of the vehicle flywheel, r is the wheel radius, is the transmission efficiency, i g is the transmission ratio of the transmission, and i0 is the main reducer ratio.

7. The method according to claim 1, characterized in that The pre-built power split dynamic model is: in, represents the internal force requirement of the first planetary gear mechanism, F2 represents the internal force requirement of the second planetary gear mechanism, represents the engine inertia, represents the inertia of the planetary carrier of the first planetary row, R1 represents the gear radius of the ring gear of the first planetary row, R2 represents the gear radius of the ring gear of the second planetary row, S1 represents the gear radius of the sun gear of the first planetary row, S2 represents the gear radius of the sun gear of the second planetary row, represents the moment of inertia of the first motor, represents the moment of inertia of the second motor, represents the moment of inertia of the sun gear of the first planetary gear, represents the moment of inertia of the sun gear of the second planetary gear, represents the moment of inertia of the ring gear of the first planetary gear set, represents the moment of inertia of the ring gear of the second planetary gear set, Indicates the wheel end inertia; represents the engine angular acceleration, represents the angular acceleration of the first motor, represents the angular acceleration of the wheel output end, represents the angular acceleration of the second motor, represents the engine torque, represents the first motor torque, represents the wheel output torque, Represents the torque of the second motor.

8. The method according to claim 1, characterized in that The pre-built battery model is: ; Wherein, SOC(k+1) is the SOC per unit time (k+1), SOC(k) is the SOC per unit time k, and SOC represents the battery state of charge per unit time; represents the open circuit voltage of the battery, Indicates the corresponding charge and discharge internal resistance of the battery, Indicates the corresponding charging and discharging power of the battery, Indicates the current battery charge. Indicates the The duration of a unit of time.

9. An energy distribution device, characterized in that: The device comprises: A data acquisition module is used to obtain the driving mode and operating parameters of the vehicle at each unit time in a preset historical time period; a pure electric optimization module, configured to determine, for each unit time, a minimum battery power consumption rate per unit time based on a pre-established longitudinal dynamics model, a power split dynamics model, and a battery model, when the driving mode represents a pure electric mode; and determine an optimal sequence of battery power consumption rates based on the minimum battery power consumption rates corresponding to a plurality of unit times; a hybrid optimization module configured to, for each unit time, determine, when the driving mode characterizes a hybrid mode, based on the longitudinal dynamic model, the power split dynamic model, the battery model, and the engine universal characteristic curve, a minimum combined value of the fuel consumption rate and the battery power consumption rate for the unit time; determine, based on a plurality of minimum combined values ​​of the fuel consumption rate and the battery power consumption rate corresponding to the unit time, a candidate operating point that satisfies the minimum combined value of the fuel consumption rate and the battery power consumption rate; and screen the candidate operating points to obtain a target operating point in which the fuel consumption rate and the battery power consumption rate are evenly distributed; a global optimization module for determining, based on the optimal sequence of battery power consumption rates and the plurality of target operating points, an optimal driving mode and a global optimal control sequence for each unit time in the preset historical time period using a dynamic programming algorithm; the global optimal control sequence being an optimal operating point that optimizes the combination of the fuel consumption rate and the battery power consumption rate for each unit time in the entire preset historical time period; An energy distribution module is configured to optimize system-level performance index parameters of the vehicle based on the optimal drive mode and the global optimal control sequence to optimize energy distribution during vehicle operation; the system-level performance index parameters include: a transmission system transmission ratio, a motor torque distribution ratio coefficient limit value, a battery charge and discharge power limit value, a motor power limit value, and an engine power limit value.

10. A controller comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 8 are implemented.

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

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