Energy distribution method, apparatus, controller and storage medium

Abstract

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

Description

Technical Field

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

[0002] Multi-mode, multi-gear hybrid vehicles exhibit significant energy-saving potential due to their integration of multiple hybrid drive modes, but they also present challenges in configuration design and control. The core challenge lies in how to quickly and accurately unlock the energy-saving potential of the complex and diverse configurations of multi-mode, multi-gear hybrid vehicles. Energy management, as a key element in addressing this challenge, aims to optimize the vehicle's drivetrain efficiency or fuel economy within a given timeframe through the control of power components.

[0003] The energy transfer process in hybrid vehicles involves a complex coupling and conversion between mechanical systems (engine-driven) and electrical systems (motor-driven / regenerative braking). Coordinating these two systems to achieve minimum fuel consumption of the engine and minimum energy consumption (or equivalent optimal energy efficiency) of the motor / battery system, while ensuring the efficient and stable operation of the vehicle's drivetrain, is a key research focus and challenge. Traditional energy management strategies often struggle to efficiently handle the increased control dimensions and state coupling issues brought about by multiple modes and gears. They have limitations in accurately identifying the global energy-saving potential of specific configurations under real-world operating conditions and rapidly generating control sequences that meet dual optimization objectives (minimum fuel consumption + minimum battery energy consumption). Consequently, they cannot achieve optimal system-level performance parameters for the vehicle, making it difficult to control the vehicle's power components through optimized system-level performance parameters during subsequent actual operation, thus hindering the optimal operating efficiency or fuel economy of the vehicle's drivetrain within a specific operating period. Summary of the Invention

[0004] Therefore, it is necessary to provide an energy distribution method, device, controller, computer-readable storage medium, and computer program product that can quickly generate the optimal driving mode and the globally optimal control sequence that satisfy the dual optimization objectives, and thereby optimize the energy distribution during vehicle operation.

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

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

[0007] For each unit time, when the driving mode represents the pure electric mode, the minimum battery power consumption rate for each unit time is determined based on the pre-built longitudinal dynamics model, power shunt dynamics model and battery model, combined with the corresponding operating parameters; and the optimal sequence of battery power consumption rate is determined based on the minimum battery power consumption rate corresponding to multiple unit times.

[0008] For each unit time, under the condition that the driving mode represents the hybrid mode, based on the longitudinal dynamics model, the power split dynamics model, the battery model, and the engine universal characteristic curve, and in combination with the corresponding operating parameters, the minimum combined value of fuel consumption rate and battery charge consumption rate per unit time is determined; and based on the minimum combined value of fuel consumption rate and battery charge consumption rate corresponding to multiple unit times, candidate operating points that satisfy the minimum combination of fuel consumption rate and battery charge consumption rate are determined; and the candidate operating points are screened to obtain the target operating point where the distribution of fuel consumption rate and battery charge consumption rate is balanced;

[0009] Based on the optimal battery power consumption rate sequence 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 makes the combination of fuel consumption rate and battery power consumption rate optimal for each unit time in the entire preset historical time period.

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

[0011] Secondly, this application also provides an energy distribution device, the device comprising:

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

[0013] The pure electric optimization module is used to determine the minimum battery power consumption rate for each unit time, under the condition that the driving mode represents the pure electric mode, based on a pre-built longitudinal dynamics model, power shunt dynamics model and battery model; and to determine the optimal sequence of battery power consumption rate based on the minimum battery power consumption rate corresponding to multiple unit times.

[0014] The hybrid optimization module is used to determine, for each unit time, the minimum combined value of fuel consumption rate and battery charge consumption rate based on the longitudinal dynamics model, the power split dynamics model, the battery model, and the engine universal characteristic curve, under the condition that the driving mode represents the hybrid mode; and based on the minimum combined value of fuel consumption rate and battery charge consumption rate corresponding to multiple unit times, determine the candidate operating point that satisfies the minimum combination of fuel consumption rate and battery charge consumption rate; and filter the candidate operating points to obtain the target operating point where the distribution of fuel consumption rate and battery charge consumption rate is balanced.

[0015] A global optimization module is used to obtain 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 rate and multiple target operating points, using a dynamic programming algorithm; 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.

[0016] The energy distribution module is used to optimize the system-level performance parameters of the vehicle based on the optimal driving mode and the global optimal control sequence, so as to optimize the energy distribution during the operation of the vehicle; the system-level performance parameters include: transmission ratio of the transmission system, limit value of motor torque distribution ratio coefficient, limit value of battery charging and discharging power, limit value of motor power and limit value of engine power.

[0017] Thirdly, this application also provides a controller, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the first aspect.

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

[0019] Fifthly, this application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps in the first aspect.

[0020] In the energy distribution method, apparatus, controller, computer-readable storage medium, and computer program product provided in this application, the energy distribution method obtains the vehicle's operating data for the historical time period by acquiring the driving mode and operating parameters of the vehicle at each unit time within a preset historical time period. Then, for each unit time, in pure electric driving mode, the method determines the minimum battery power consumption rate for that unit time by using a pre-built longitudinal dynamics model, power shunt dynamics model, and battery model, combined with relevant operating parameters. Based on the minimum battery power consumption rate for each unit time, the optimal sequence of battery power consumption rates for the preset historical time period is determined. For each unit time, in hybrid driving mode, the method selects the minimum combined value of fuel consumption rate and battery power consumption rate for each unit time by using a pre-built longitudinal dynamics model, power shunt dynamics model, battery model, and engine universal characteristic curve, combined with relevant operating parameters. Furthermore, based on the minimum combined value of fuel consumption rate and battery power consumption rate for each unit time, the method determines the optimal sequence of battery power consumption rates for the preset historical time period. The candidate operating point is selected based on the minimum combination of fuel consumption rate and battery charge consumption rate. Further screening of these candidate operating points determines the target operating point that balances the distribution of fuel consumption rate and battery charge consumption rate, which facilitates smoother power distribution commands. Then, based on the optimal battery charge consumption rate sequence determined for the vehicle's historical operating time period and multiple target operating points, a dynamic programming algorithm is used to obtain the optimal driving mode and the globally optimal control sequence for each unit time within the preset historical time period. The globally optimal control sequence is the optimal operating point that optimizes the combination of fuel consumption rate and battery charge consumption rate for each unit time within the entire preset historical time period. Finally, based on the determined optimal driving mode and globally optimal control sequence, the vehicle's system-level performance parameters are optimized, thereby optimizing energy distribution during subsequent actual vehicle operation. These system-level performance parameters include: transmission ratio, motor torque distribution ratio limit, battery charging / discharging power limit, motor power limit, and engine power limit. This method optimizes the vehicle's system-level performance parameters, enabling them to reach a higher level. This helps ensure that the vehicle operates during periods with the optimal combination of fuel consumption and battery charge consumption, thereby improving the vehicle's economy and efficiency. Attached Figure Description

[0021] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments of this application or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 This is a flowchart illustrating an energy distribution method in one embodiment;

[0023] Figure 2 This is a schematic diagram of the operating points in one embodiment;

[0024] Figure 3 This is a flowchart illustrating a method for updating the globally optimal control sequence in one embodiment;

[0025] Figure 4 This is a schematic diagram of the SOC grid density in one embodiment;

[0026] Figure 5 This is a flowchart illustrating a method for determining the target operating point in one embodiment;

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

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

[0029] Figure 8 This is a diagram of the internal structure of the controller in one embodiment. Detailed Implementation

[0030] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0031] It should be noted that the terms "first," "second," etc., used in this application can 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 "comprising" and "having," and any variations thereof, used in this application, are intended to cover non-exclusive inclusion. The term "multiple" used in this application refers to two or more. The term "and / or" used in this application refers to one of the embodiments, or any combination of multiple embodiments.

[0032] Multi-mode hybrid power systems are the operating systems of hybrid vehicles. The energy transfer process in multi-mode hybrid power systems involves the coordinated operation of mechanical and electrical systems. How to coordinate the two systems through actuators such as clutches and synchronizers to achieve efficient and stable operation of the transmission system is a core research issue. While energy management strategies based on dynamic programming (DP) algorithms can theoretically achieve globally optimal fuel economy (often used as an energy-saving benchmark for other control strategies), in practical applications, traditional dynamic programming algorithms suffer from the curse of dimensionality, interpolation errors, and Markov property failure when solving the global optimum problem of multi-mode hybrid power systems. This makes it difficult to quickly, accurately, and reasonably evaluate the energy consumption economy of the topology configuration. A single complete DP optimization can take several hours, which cannot meet the rapid iteration requirements of the configuration design stage. Furthermore, multi-mode, multi-gear vehicles have two motors and an engine, as well as multiple gears / modes, resulting in numerous state and control variables and a high control dimension, further increasing the computational load and leading to a slow solution process. Optimization efficiency and results cannot simultaneously reach an ideal state. 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 in this application can determine the optimal battery energy consumption rate sequence in pure electric mode and the candidate operating point that minimizes the combination of fuel consumption rate and battery energy consumption rate in hybrid mode based on data such as the driving mode and operating parameters of the vehicle in each unit time during a preset historical time period, as well as pre-constructed longitudinal dynamics model, power split dynamics 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 energy consumption rate more balanced is obtained. Furthermore, by combining the optimal battery energy consumption rate sequence and multiple target operating points using a dynamic programming algorithm, the globally optimal control sequence corresponding to the preset historical time period and the optimal driving mode for each unit time are obtained. The determined globally optimal control sequence within the preset historical time period 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 corresponding actual operating data of the vehicle is obtained based on the actual operating data. The actual control sequence (the actual control sequence corresponding to the actual operating points of fuel consumption rate and battery power consumption rate at each unit time during the actual operating period of the vehicle); combined with the driving mode of the vehicle at each unit time during the actual operating period, the comparison results are obtained by comparing the actual control sequence of the vehicle with the adapted operating points in the global optimal control sequence; if the comparison results indicate that the difference between the actual control sequence of the vehicle and the adapted operating points in the global optimal control sequence exceeds a preset threshold, it indicates that the difference between the actual control sequence of the vehicle and the adapted operating points in the global optimal control sequence is relatively large, which means that there is still room for optimization of the system-level performance index parameters of the vehicle; then, at least some system-level performance index parameters of the vehicle can be optimized and adjusted according to the difference to make the system-level performance index parameters of the vehicle more optimal, thereby helping to ensure that the vehicle can have the optimal combination of fuel consumption rate and battery power consumption rate during subsequent actual operation, thus improving the economy and efficiency of vehicle operation.

[0034] Therefore, the energy allocation method provided in this application can be used to optimize the system-level performance parameters of a vehicle offline. That is, the step of "optimizing the system-level performance parameters of a vehicle based on the optimal driving mode and the globally optimal control sequence" provided in this application can be executed offline, without using online control. This method of optimizing the system-level performance parameters of a vehicle offline does not affect the actual normal operation of the vehicle and is beneficial to ensuring vehicle driving safety. Specifically, the process of determining the globally optimal control sequence of the vehicle and the optimal driving mode for each unit of time in the historical time period in the energy allocation method provided in this application is a process of simulating and evaluating energy consumption and fuel consumption based on the driving mode and operating parameters of the vehicle in a preset historical time period, for at least one known standard test condition. Standard test conditions include, but are not limited to, the Worldwide Light-duty Test Cycle (WLTC), the New European Driving Cycle (NEDC), and the China Light-duty Vehicle Test Cycle (CLTC).

[0035] In one exemplary embodiment, such as Figure 1 The diagram illustrates a flow chart of an energy distribution method. Taking a vehicle controller as an example, the method is used to control vehicle operation and includes steps 102 to 110. Wherein:

[0036] Step 102: Obtain the driving mode and operating parameters of the vehicle at each unit of time within a preset historical time period.

[0037] 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, 20 minutes, 1 hour, etc.

[0038] Operating parameters are physical quantities that affect the dynamic performance of a vehicle and can be used to calculate the real-time dynamic requirements of the vehicle. Specifically, they may include at least one of the following: vehicle curb weight, rolling resistance coefficient, road slope angle, air drag coefficient, air density, frontal area, vehicle speed, rotational mass conversion factor, moment of inertia at the wheel ends, moment of inertia at the flywheel, wheel radius, transmission efficiency, transmission ratio, and final drive ratio. The vehicle's curb weight, such as 1800 kg, affects acceleration and hill-climbing requirements; the rolling resistance coefficient, such as 0.015, affects energy consumption on flat roads; the road gradient, such as the angle of inclination of the road during vehicle movement, directly affects driving force requirements; the drag coefficient, a dimensionless parameter, describes the magnitude of the resistance a vehicle experiences when moving through the air, primarily depending on the vehicle's shape and surface smoothness; the frontal area, the projected area of ​​the vehicle facing the airflow direction, i.e., the cross-sectional area of ​​the vehicle viewed from directly in front; the drag coefficient and frontal area together determine wind resistance at high speeds; for example, the drag coefficient can be 0.3, and the frontal area can be 2.5 m² (square meters); vehicle speed is the vehicle's travel speed. The following parameters are used to calculate real-time power demand: speed (e.g., 60 km / h); rotational mass conversion factor (equivalent inertia of rotating components in the transmission system); moment of inertia (MoI) is a physical quantity describing an object's ability to resist changes in angular acceleration, similar to "mass" in translation, including the moment of inertia at the vehicle's wheel ends and the moment of inertia at the vehicle's flywheel, which affects acceleration response; wheel radius is the effective radius of the wheel when it is rolling; transmission efficiency is the ratio of actual usable power to input power during the transmission of power from the engine or motor to the wheels; transmission ratio is the ratio of the transmission's input shaft speed to its output shaft speed, reflecting torque amplification or speed regulation capability; final drive ratio is the ratio of the transmission's output shaft speed to the wheel speed, further amplifying torque and adapting to vehicle speed.

[0039] The driving modes may include, for example, pure electric mode and hybrid mode; in pure electric mode, the vehicle is driven only by the electric motor, while in hybrid mode, the vehicle is driven by both the engine and the electric motor.

[0040] Step 104: For each unit time, under the condition that the driving mode represents the pure electric mode, the minimum battery power consumption rate per unit time is determined based on the pre-built longitudinal dynamics model, power shunt dynamics model and battery model, combined with the corresponding operating parameters; and the optimal sequence of battery power consumption rate is determined based on the minimum battery power consumption rate corresponding to multiple unit times.

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

[0042] In one embodiment, based on the vehicle's curb weight, rolling resistance coefficient, road slope angle, air drag coefficient, air density, frontal area, vehicle speed, rotational mass conversion factor, moment of inertia at the vehicle's wheel ends, moment of inertia at the vehicle's flywheel, wheel radius, transmission efficiency, transmission ratio, and final drive ratio, and according to the classical longitudinal dynamics formula, the driving force equals the sum of frictional resistance, slope resistance, air resistance, and acceleration resistance. The resulting longitudinal dynamics model is as follows:

[0043]

[0044]

[0045] Among them, F t For the vehicle's dynamic requirements (N=kg·m / s) 2 ), m represents the vehicle's curb weight (kg), g is the acceleration due to gravity (m / s²), f represents the rolling resistance coefficient, α represents the road gradient angle during vehicle movement, and C D This represents the air resistance coefficient during vehicle operation. It is the density of air (kg / m³) 3 A represents the vehicle's frontal area (m²) during travel, u represents the vehicle's speed (m / s), t is the unit time, and δ is the vehicle's rotational mass conversion factor. The above calculations... The equation is calculated using dimensionless methods, I w I is the moment of inertia at the wheel end of the vehicle (kg·m²). f Let r be the moment of inertia at the vehicle's flywheel (kg·m²), and r be the wheel radius (m). For transmission efficiency, i g i0 is the gear ratio of the transmission, and i0 is the gear ratio of the main reducer.

[0046] The parameters for constructing the power shunt dynamics model may include at least one of the following: the rotational inertia and gear radius of the ring gear of the first planetary gear set, the planet carrier of the first planetary gear set, and the sun gear of the first planetary gear set; the rotational inertia and gear radius of the ring gear of the second planetary gear set, the planet carrier of the second planetary gear set, and the sun gear of the second planetary gear set; 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 dynamics model for multi-mode configurations. Multi-mode dynamics models describe the dynamic behavior of a multi-mode hybrid system under different operating modes, including the drive mode switching process. Operating modes can include four modes: 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 power system (such as a planetary gear configuration). The power-split dynamics model is a type of dynamics model for multi-mode configurations. When one of the modes in a multi-mode configuration is the power-split mode, the dynamic behavior under that mode is described by the power-split dynamics model. For example, the controller acquires dynamic parameters within a 30-second historical time period and constructs the power-split dynamics model based on these parameters. For example, the power splitting dynamics model is constructed from a series of parameters, including the internal force requirements of the first planetary gear mechanism, the internal force requirements of the second planetary gear mechanism, the inertia of the planet carrier of the first planetary gear set, the engine inertia, the gear radius of the ring gear of the first planetary gear set, the gear radius of the ring gear of the second planetary gear set, the gear radius of the sun gear of the first planetary gear set, the gear radius of the sun gear of the second planetary gear set, the rotational inertia of the first motor, the rotational inertia of the second motor, the rotational inertia of the sun gear of the first planetary gear set, the rotational inertia of the ring gear of the first planetary gear set, the rotational inertia of the ring gear of the second planetary gear set, 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 at the wheel output end, the engine torque, the torque of the first motor, the torque of the second motor, and the torque at the wheel output end.

[0049]

[0050] in, F1 represents the internal force requirement of the first planetary gear mechanism, and F2 represents the internal force requirement of the second planetary gear mechanism. This represents the moment of inertia of the first planetary carrier. R1 represents the gear radius of the ring gear of the first planetary gear set, R2 represents the gear radius of the ring gear of the second planetary gear set, S1 represents the gear radius of the sun gear of the first planetary gear set, and S2 represents the gear radius of the sun gear of the second planetary gear set. This represents the moment of inertia of the first motor. This represents the moment of inertia of the second motor. This represents the moment of inertia of the sun gear in the first planetary array. This represents the moment of inertia of the sun gear in the second planetary array. This represents the moment of inertia of the gear ring of the second planetary set. Indicates the wheel end inertia. This represents the moment of inertia of the gear ring of the first planetary set;

[0051] Indicates the engine's angular acceleration. Indicates the angular acceleration of the first motor. This indicates the angular acceleration of the second motor. This represents the angular acceleration at the wheel's output end. Indicates engine torque. This indicates the torque of the first motor. This indicates the torque of the second motor. This indicates the torque output at the wheel.

[0052] In the above embodiments, by combining the longitudinal dynamics model and the power split dynamics model, a comprehensive model of the vehicle's operating state can be achieved, which can more accurately reflect the dynamic characteristics of complex hybrid power systems, thereby providing a more reliable optimization basis for energy management strategies.

[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 or 1 minute. The unit time is the smallest time unit for optimized control, such as 1 second or 0.1 seconds, affecting the granularity of the control sequence. The battery state of charge (SOC) is the current percentage of remaining battery charge. For example, SOC=70% means the battery currently has 70% charge remaining. SOC is used to determine the driving range in pure electric mode or the energy distribution strategy in hybrid mode. The drive mode is the vehicle's current or expected power source, including pure electric mode and hybrid mode. In pure electric mode, the vehicle is driven by an electric motor, while in hybrid mode, it is driven by a combination of an engine and an electric motor.

[0054] In one embodiment, the controller determines the vehicle's dynamic requirements at each unit time based on a longitudinal dynamics model and operating parameters. These dynamic requirements are the driving or braking forces the vehicle needs, such as the increased torque required when climbing a hill. The optimal driving mode for the vehicle can then be determined based on the dynamic requirements at each unit time and the State of Charge (SOC). For example, for a hybrid vehicle's historical driving time on a highway, the system obtains the operating parameters of a preset historical time period during which the vehicle is driving on the highway. For instance, it obtains the operating parameters of the vehicle during 30 seconds of highway driving, including the vehicle speed increasing from 80km / h to 100km / h, road gradient of 2%, air drag coefficient of 0.28, and SOC of 65%. The system can calculate the vehicle's dynamic requirements based on road gradient, acceleration, etc. For example, it calculates that the wheel-end torque required for this driving phase is 500Nm (Newton-meters). The system can also determine the driving mode based on SOC and required torque. For example, during highway driving, if there is a period of time when SOC is sufficient and the required torque does not exceed the motor's upper limit, the pure electric mode is selected during this period. If there is a sudden increase in required torque during highway driving, such as when overtaking, the system switches to hybrid mode during the overtaking period. Simultaneously, the system outputs the driving mode sequence and corresponding dynamic requirements per unit time (e.g., every 1 second).

[0055] In the above embodiments, the dynamic requirements and optimal driving mode of the vehicle during historical operating periods are determined by using the longitudinal dynamics model and the vehicle's battery state of charge, providing a data foundation for optimizing the vehicle's system-level performance parameters.

[0056] Among them, pure electric mode is the operating state of a hybrid vehicle that is completely driven by the 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, internal resistance during charging and discharging, charging and discharging power, and current charge level. Here, the open-circuit voltage is the battery's terminal voltage when there is no load, the internal resistance during charging and discharging is the battery's internal resistance, the charging and discharging power is the battery's maximum allowable charging and discharging power, and the current charge level is the percentage of remaining charge. For example, the battery model can be represented as:

[0058]

[0059] Where SOC(k+1) is the SOC at time (k+1) and SOC(k) is the SOC at time k. This indicates the open-circuit voltage of the battery (volts, V). This indicates the corresponding internal resistance of the battery during charging and discharging (ohms, Ω). This indicates the corresponding charging and discharging power of the battery (watts, W). This indicates the current charge level of the battery (coulombs, C). Indicates the first The duration of a unit of time (seconds).

[0060] Battery power consumption rate is the amount of battery energy reduced per unit time.

[0061] For example, when a hybrid vehicle is driving in pure electric mode, the historical time period is 30 seconds. The controller uses the battery power consumption rate as the optimization target. It determines the battery power consumption rate of the vehicle in a unit time by using the battery's open circuit voltage, charging and discharging internal resistance, battery charging and discharging power and current power within 30 seconds, and then determines the optimal sequence of battery power consumption rates that minimizes the battery power consumption rate.

[0062] For example, when the driving mode represents the pure electric mode, and the vehicle has two motors driving simultaneously, the battery power consumption rate under different power distributions of the two motors is determined by a preset battery model; and the motor power sequence of the two motors that minimize the battery power consumption rate in a preset historical time period is determined based on each battery power consumption rate; in the case of the pure electric mode where the vehicle is driven by only one motor, the motor power and 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 embodiments, 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. The optimal sequence of battery power consumption rates that minimizes the battery power consumption rate is determined. This optimal sequence of battery power consumption rates is beneficial to improving the energy utilization efficiency of the whole vehicle.

[0064] In summary, since the historical time period includes multiple time units, each with its corresponding driving mode, the minimum battery consumption rate can be determined for each time unit in pure electric driving mode by combining the operating parameters of that time unit with the pre-constructed longitudinal dynamics model, power shunt dynamics model, and battery model. Then, based on the minimum battery consumption rate for each time unit in pure electric driving mode, the optimal sequence of battery consumption rates for pure electric mode in the historical time period can be determined. The determined optimal sequence of battery consumption rates, in principle, is beneficial for specifically improving the energy utilization efficiency of vehicles in pure electric mode during the historical time period.

[0065] Step 106: For each unit time, under the condition that the driving mode represents the hybrid mode, based on the longitudinal dynamics model, power split dynamics model, battery model and engine universal characteristic curve, combined with the corresponding operating parameters, determine the minimum combined value of fuel consumption rate and battery charge consumption rate per unit time; and based on the minimum combined value of fuel consumption rate and battery charge consumption rate corresponding to multiple unit times, determine the candidate operating point that satisfies the minimum combination of fuel consumption rate and battery charge consumption rate; and screen the candidate operating points to obtain the target operating point among which the distribution of fuel consumption rate and battery charge consumption rate is balanced.

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

[0067] Hybrid mode is the state in which the engine and electric motor work together to drive the vehicle. The engine universal characteristic diagram is a three-dimensional contour plot describing the change in engine fuel consumption rate (BSFC) with engine speed (rpm) and torque (Nm). The engine fuel consumption rate is the fuel consumption rate of the engine per unit time, and the minimum fuel consumption rate is the lowest fuel consumption rate the engine can achieve under current operating conditions. For example, based on the engine universal characteristic diagram and engine output power, the engine fuel consumption rate is calculated as: Engine fuel consumption rate = BSFC × Pe × Δt, where Pe is the engine output power (kW) and Δt is the unit time. For instance, if an engine has a BSFC of 220 g / kWh at 2000 rpm and 100 Nm, then the fuel consumption rate when the engine output power is 10 kW is approximately 220 g / kWh × 10 kW × 1 / 3600h ≈ 0.61 g / s. The battery power consumption rate is the amount of battery energy reduced per unit time; the minimum battery power consumption rate is the lowest battery power consumption rate that the motor can achieve under the current operating conditions.

[0068] The candidate operating point is determined based on the combination of engine and motor operating parameters of the vehicle within a preset historical time period, such as the combination of parameters such as speed, torque, and power. It achieves optimal energy consumption while meeting power requirements, specifically by satisfying the combination of minimum fuel consumption rate and minimum battery power consumption rate.

[0069] In summary, for each unit time of the vehicle's hybrid mode in a historical period, the minimum combined value of fuel consumption rate and battery charge consumption rate can be determined by combining the operating parameters of each unit time with the pre-constructed longitudinal dynamics model, power split dynamics model, battery model, and engine universal characteristic curve. Then, based on the minimum combined value of fuel consumption rate and battery charge consumption rate corresponding to multiple unit times, the candidate operating point that satisfies the minimum combined value of fuel consumption rate and battery charge consumption rate can be determined.

[0070] Furthermore, the candidate operating points can be further screened to eliminate some operating points that are not effective in reducing energy consumption, so as to obtain the 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 composed of fuel consumption rate and battery power consumption rate. Fuel consumption rate can be the horizontal axis of the two-dimensional coordinate system, and battery power consumption rate can be the vertical axis of the two-dimensional coordinate system.

[0072] In an exemplary embodiment, the slope between adjacent candidate operating points can be determined in a two-dimensional coordinate system. Along the positive direction of the horizontal axis, among the two slopes corresponding to every three adjacent candidate operating points, if the absolute value of the former 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. That is, the candidate operating point protruding towards 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 boundary method to select candidate operating points that simultaneously optimize fuel economy and electrical system efficiency. The selected candidate operating points are also the target operating points.

[0074] For example, the controller constructs a multi-objective optimization model based on the vehicle's engine fuel consumption rate and battery charge consumption rate to determine candidate operating points that satisfy the combination of minimum fuel consumption rate and minimum battery charge consumption rate within a preset historical time period. The multi-objective optimization model is a mathematical model for optimizing the combination of engine fuel consumption rate and battery charge consumption rate, typically employing Pareto optimality or weighted summation methods. For example, the objective function of this multi-objective optimization model can be: min(w1 fuel consumption rate + w2 charge consumption rate), where w1 and w2 are weighting coefficients. For instance, w1=0.7 and w2=0.3 indicates a greater emphasis on fuel economy. For example, candidate operating points can be determined by combinations of engine speed (rpm), engine torque (Nm), motor power (kW), fuel consumption rate (g / s), and charge consumption rate (kW), as shown in Table 1 below.

[0075] Table 1. Candidate Operating Point Value Combinations

[0076]

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

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

[0079] Table 2 Candidate Operating Point Value Combinations

[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 result in a lower power consumption rate.

[0082] In the above embodiments, a multi-objective optimization model is constructed, which simultaneously considers engine fuel consumption and battery power consumption, and selects the optimal candidate operating point to obtain a vehicle that meets power requirements while minimizing fuel consumption and battery energy loss, extending driving range and reducing operating costs.

[0083] In an exemplary embodiment, the method for determining the target operating point from candidate operating points is essentially a slope convexity screening. Slope convexity screening is an optimization method based on the geometric characteristics of the Pareto front. By analyzing the marginal substitution rate slope (the ratio of fuel consumption rate to electricity consumption rate) of adjacent operating points, it filters out a set of operating points that satisfy the global convexity condition, that is, it filters out the candidate operating points that bulge towards the origin among every three adjacent candidate operating points. The target operating point is the optimal combination of engine power and motor power finally selected according to the optimization weights after passing the convexity screening. For example, as shown... Figure 2 The slope screening point shown is the target working point after slope convexity screening.

[0084] In the above embodiments, by performing slope convexity screening on the operating points among the candidate operating points, a highly efficient balance between fuel and electricity consumption is achieved, further reducing the dimensionality of the dynamic programming algorithm, reducing computation time, significantly improving computational efficiency and simulation accuracy, and significantly improving overall energy efficiency.

[0085] Step 108: Based on the optimal battery power consumption rate sequence 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 are obtained through dynamic programming algorithm; the global optimal control sequence is the optimal operating point that makes the combination of fuel consumption rate and battery power consumption rate optimal in each unit time in the entire preset historical time period.

[0086] Among them, the optimal battery power consumption rate sequence is determined by the minimum battery power consumption rate corresponding to each unit time of pure electric mode operation in the historical period; the multiple target operating points are determined by the minimum combination of fuel consumption rate and battery power consumption rate corresponding to each unit time of hybrid mode operation in the historical period, and then further filtered by distribution equilibrium.

[0087] For example, after obtaining the optimal sequence of battery power consumption rate and multiple target operating points for the vehicle at each unit time within a historical time period, a dynamic programming algorithm (DP) can be used to further optimize the optimal sequence of battery power consumption rate and multiple target operating points to obtain an optimized optimal driving mode and a globally optimal control sequence that are more suitable for each unit time within the historical time period. In other words, if the vehicle actually operates using the relevant optimal driving mode and the combination of fuel consumption rate and battery power consumption rate for each unit time within the globally optimal control sequence, the vehicle can achieve optimal operating efficiency or fuel economy during the historical time period.

[0088] As can be seen, the energy allocation method provided in this application, based on the vehicle's driving mode and operating parameters at each unit time within a preset historical time period, combined with pre-constructed longitudinal dynamics models, power split dynamics models, battery models, and engine universal characteristic curves, and further combined with slope convexity screening, determines the optimal driving mode of the vehicle at each unit time within the preset historical time period, as well as the relevant steps of determining the globally optimal control sequence corresponding to the preset historical time period. This is equivalent to an offline solution process for the optimal operating mode of the vehicle within the preset historical time period. In the method provided in this application, after obtaining the candidate operating point that minimizes the combination of fuel consumption rate and battery charge consumption rate in the vehicle's hybrid mode within the preset historical time period, the method further filters out the target operating point with a more balanced distribution of fuel consumption rate and battery charge consumption rate from multiple candidate operating points through slope screening. This step not only reduces the number of operating points but also obtains operating points that are conducive to a more balanced distribution of fuel consumption rate and battery charge consumption rate. This not only reduces the computational load in subsequent steps for determining the optimal driving mode and globally optimal control sequence corresponding to the preset historical time period, improving computational efficiency, but also helps to make the calculated optimal driving mode and globally optimal control sequence closer to the ideal state. Furthermore, this method can accelerate the convergence of the dynamic programming algorithm and avoid traversing all possible operating points. Based on this, the steps provided in this application for determining the optimal driving mode and the globally optimal control sequence corresponding to a preset historical time period are beneficial for reducing computational complexity, improving the solution speed, and enabling both optimization efficiency and optimization results to reach an ideal state simultaneously. It is an ultra-fast energy management method that balances computational efficiency and optimization performance.

[0089] In an exemplary embodiment, based on the optimal sequence of battery power consumption rate and multiple target operating points, a dynamic programming algorithm can be used to obtain the optimal driving mode and the globally optimal control sequence for each unit time in a preset historical time period, while satisfying the constraints of the vehicle's preset component operating states.

[0090] Among them, the constraints on the working state of the preset components (preset constraints) are hard boundary conditions that must be met in the global optimization, covering the safety and performance limits of the vehicle powertrain system. The global optimal control sequence is the sequence that optimizes the combination of fuel consumption rate and battery charge consumption rate within the entire preset historical time period. Based on this sequence, the optimal combination of engine power and motor power of the vehicle within the preset historical time period can be obtained, that is, the time series of engine power and motor power related operating commands generated by combining all constraints and optimization objectives.

[0091] In one embodiment, the globally optimal control sequence can be solved over a historical time period based on dynamic programming or model predictive control algorithms. For example, the globally optimal control sequence can also be a sequence of engine and motor power for the vehicle that optimizes the combination of fuel consumption rate and battery charge consumption rate, determined based on different combinations of time, engine power, motor power, and drive mode values. This can be illustrated in Table 3 below.

[0092] Table 3. Combination of operating parameters corresponding to the globally optimal control sequence

[0093]

[0094] In one embodiment, the controller acquires the motor power sequence, target operating point, battery state of charge (SOC), driving mode, and dynamic requirements of the hybrid vehicle within a 30-second historical time period encompassing urban congestion and high-speed acceleration scenarios. The motor power sequence can be from 0 to 10 seconds, with motor power ranging from [20, 18, 15, ...] kW. The target operating point can be operating point A1: engine power 25 kW + motor power 5 kW, fuel consumption rate 0.6 g / s; operating point B1: engine power 30 kW + motor power 0 kW, fuel consumption rate 0.7 g / s; SOC = 65%. The controller then divides the historical time period into 30 1-second units. Within each unit, it selects the operating point that minimizes the combination of battery power consumption rate and engine fuel consumption rate, while also considering SOC balance to avoid deep discharge. The final combination of operating parameters corresponding to the globally optimal control sequence output by the controller is shown in Table 4 below.

[0095] Table 4. Combination of operating parameter values ​​corresponding to the globally optimal control sequence

[0096]

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

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

[0099] The system-level performance parameters include, but are not limited to: transmission ratio, motor torque distribution ratio limit, battery charging and discharging power limit, motor power limit, and engine power limit. In other words, the system-level performance parameters shown in this application are merely examples provided by this application and are not intended to limit the system-level performance parameters of the vehicle.

[0100] For example, after the controller acquires the vehicle's globally optimal control sequence 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 vehicle's actual operating data for a recent (latest) time period (actual operating time period) and obtain the corresponding actual control sequence for the vehicle based on the actual operating data (the actual operating points of the vehicle's fuel consumption rate and battery charge consumption rate corresponding to each unit time within the actual operating time period). Combining the vehicle's driving mode for each unit time within the actual operating time period, a comparison result is obtained by comparing the vehicle's actual control sequence with the adapted operating points in the globally optimal control sequence. If the comparison result indicates that the difference between the vehicle's actual control sequence and the adapted operating points in the globally optimal control sequence exceeds a preset threshold, it indicates that the difference between the vehicle's actual control sequence and the adapted operating points in the globally optimal control sequence is relatively large, which means that there is still room for optimization of the vehicle's system-level performance parameters. Then, at least some of the vehicle's system-level performance parameters can be optimized and adjusted according to the difference to make the vehicle's system-level performance parameters even better. The optimized system-level performance parameters are used to optimize energy distribution during vehicle operation, which helps ensure that the vehicle can have the optimal combination of fuel consumption rate and battery power consumption rate during subsequent actual operation, thereby improving the economy and efficiency of the vehicle in actual operation.

[0101] In other words, using the globally optimal control sequence and the corresponding optimal driving modes for each unit of time as a reference group (benchmark), a set of actual global control sequences and actual driving modes of the vehicle under actual operating conditions is obtained. The actual globally optimal control sequence can be the sequence that optimizes the combination of fuel consumption rate and battery charge consumption rate during the entire operating period of the vehicle under actual operating conditions. Then, the actual global control sequence and the corresponding driving modes for each unit of time are compared with the reference group to obtain the comparison results. If the comparison results show that the fuel economy and battery charge 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 embodiments, by optimizing the system-level performance parameters of the vehicle based on the globally optimal control sequence and the optimal driving mode for each unit of time, the overall energy efficiency and system robustness of the multi-mode hybrid vehicle can be significantly improved.

[0103] The aforementioned energy allocation method obtains the vehicle's operating data for each unit of time within a preset historical time period by acquiring the vehicle's driving mode and operating parameters. Then, for each unit of time, in pure electric driving mode, the minimum battery consumption rate is determined using pre-built longitudinal dynamics, power split dynamics, and battery models, combined with relevant operating parameters. Based on the minimum battery consumption rate for each unit of time, the optimal battery consumption rate sequence for the preset historical time period is determined. For each unit of time, in hybrid driving mode, the minimum combination of fuel consumption rate and battery consumption rate is selected using pre-built longitudinal dynamics, power split dynamics, battery models, and engine universal characteristic curves, combined with relevant operating parameters. Furthermore, based on the minimum combination of fuel consumption rate and battery consumption rate for each unit of time, candidate models satisfying the minimum combination of fuel consumption rate and battery consumption rate are determined. The process begins with selecting an operating point. Further screening of candidate operating points reveals a target operating point that balances fuel consumption and battery charge consumption, facilitating smoother power distribution commands. Then, based on the optimal battery charge consumption sequence determined from the vehicle's historical operating timeframe and multiple target operating points, a dynamic programming algorithm is used to obtain the optimal driving mode and globally optimal control sequence for each unit of time within the preset historical timeframe. The globally optimal control sequence represents the optimal operating point that optimizes the combination of fuel consumption and battery charge consumption for each unit of time within the entire preset historical timeframe. Finally, based on the determined optimal driving mode and globally optimal control sequence, the vehicle's system-level performance parameters are optimized, thereby optimizing energy distribution during subsequent actual vehicle operation. These system-level performance parameters include: transmission ratio, motor torque distribution ratio limit, battery charging / discharging power limit, motor power limit, and engine power limit. This method optimizes the vehicle's system-level performance parameters, enabling them to reach a higher level. This helps ensure that the vehicle operates during periods with the optimal combination of fuel consumption and battery charge consumption, thereby improving the vehicle's economy and efficiency.

[0104] In an exemplary embodiment, the steps described above, namely: determining the minimum battery power consumption rate per unit time based on a pre-built longitudinal dynamics model, a power shunt dynamics model, and a battery model, combined with corresponding operating parameters, can be specifically executed as follows: determining the vehicle's dynamic requirements per unit time based on the pre-built longitudinal dynamics model and the operating parameters per unit time; determining the internal force requirements of the first planetary gear mechanism and the second planetary gear mechanism within 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-built power shunt dynamics model and the internal force requirements of the first and second planetary gear mechanisms per unit time; determining the motor power requirements 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 requirements; and determining the minimum battery power consumption rate per unit time based on the pre-built battery model and the battery charging and discharging power.

[0105] In an exemplary embodiment, the steps described above, namely: determining the minimum combined value of fuel consumption rate and battery charge consumption rate per unit time based on the longitudinal dynamics model, power shunt dynamics model, battery model, and engine universal characteristic curve, combined with corresponding operating parameters, can be specifically executed as follows: determining the vehicle's dynamic requirements per unit time based on the pre-built longitudinal dynamics model and operating parameters per unit time; determining the internal force requirements of the first planetary gear mechanism and the second planetary gear mechanism per unit time based on the dynamic requirements; determining the first motor torque, second motor torque, and engine torque per unit time based on the pre-built power shunt dynamics model and the internal force requirements of the first and second planetary gear mechanisms per unit time; determining the motor power requirements and engine power requirements per unit time based on the first motor torque, second motor torque, and engine torque; determining the corresponding battery charging and discharging power based on the motor power requirements; and determining the minimum combined value of fuel consumption rate and battery charge consumption rate per unit time based on the pre-built battery model and engine universal characteristic curve, combined with battery charging and discharging power and engine power requirements.

[0106] In one exemplary embodiment, Figure 3 This is a flowchart illustrating a method for updating the globally optimal control sequence in one embodiment, as shown below. Figure 3 The method for updating the globally optimal control sequence shown involves a specific implementation of updating the globally optimal control sequence for the entire preset historical time period through battery state-of-charge grid density, including steps 302 to 304. Wherein:

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

[0108] The battery state-of-charge (SOC) grid density is the precision level used when discretizing the continuous state variable of the battery SOC. SOC grid density directly affects computational complexity and optimization accuracy. For example, the preset SOC grid density can be, but is not limited to, 1%, 2%, 5%, or 10%. High-density SOC grids have small SOC intervals (e.g., 1% step size), resulting in high accuracy but high computational cost; low-density SOC grids have large SOC intervals (e.g., 5% step size), resulting in fast computation but potentially missing optimal solutions. The mathematical expression for SOC grid density can be: SOC grid density = {0%, Δs, 2Δs, ..., 100%} (Δs is the grid step size). Figure 4 As shown, Figure 4 This is a schematic diagram of the SOC mesh density in one embodiment. Figure 4 The state variables sv1 or sv2 can be either SOC or driver mode.

[0109] In one exemplary embodiment, the trade-off between accuracy and time consumption under different SOC mesh densities can be shown in Table 5 below:

[0110] Table 5

[0111]

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

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

[0114] Step 304: Under the preset constraints of the longitudinal dynamic model and the dynamic model of the multi-mode configuration (power shunt dynamic model), update the global optimal control sequence for the entire preset historical time period based on the grid density of the target battery state of charge.

[0115] For example, 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 by the longitudinal dynamic model and the dynamic model of the multi-mode configuration. The SOC trajectory is re-discretized according to 3%. If the new SOC point does not meet the constraints of the original sequence power, the operating parameters such as engine power, motor power, and battery discharge power are adjusted. After adjustment, the updated global optimal control sequence is output by dynamic programming algorithm and Pareto boundary method.

[0116] In the above embodiments, based on the accuracy and time consumption of the globally optimal control sequence under different SOC grid densities, the grid density with the shortest fuel consumption rate error and the shortest time consumption is determined as the target SOC grid density. The globally optimal control sequence is updated according to the target SOC grid density, which can significantly reduce the computation time and improve the computing performance of the controller.

[0117] In some embodiments, Figure 5 This is a flowchart illustrating a method for determining the target operating point in one embodiment; such as... Figure 5 The method for determining the target operating point, as shown, includes steps 502 to 504. Wherein:

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

[0119] For example, it can be done through, as Figure 6 The working points on the Pareto boundary curve in the fuel-electricity consumption workspace shown are used to calculate the slope θ between adjacent working points according to the formula for calculating the slope θ. The formula for calculating the slope θ may include:

[0120]

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

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

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

[0124]

[0125] Where any candidate working point k is taken as the center, This represents the slope between the left candidate operating point (k-1) and the adjacent candidate operating point k. This represents the slope between candidate operating point k and another adjacent candidate operating point (k+1) on the right. The slopes of each candidate operating point k and its adjacent points on the Pareto boundary are compared sequentially, and the operating point with the slope closest to the Pareto convex edge is selected. Specifically, this is the operating point whose slope extends to the center point of the two-dimensional coordinate circle representing the energy consumption rate and fuel consumption rate. For example... Figure 6 As shown, Figure 6 This is a schematic diagram of the target operating point in one embodiment.

[0126] In the above embodiments, by determining the slope between adjacent working points in the candidate working points, the working points whose slopes meet the preset conditions are taken as the target working points, and the working points in the candidate working points are screened for slope convexity, so as to achieve an efficient balance between fuel and electricity consumption, realize further dimensionality reduction of dynamic programming algorithm, and reduce computation time.

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

[0128] The universal characteristic diagram is a three-dimensional graph describing engine performance. The horizontal axis represents engine speed (rpm), the vertical axis represents torque (Nm), and the contour lines represent fuel consumption rate (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 engine fuel consumption rate and battery charge consumption rate as coordinate axes to visualize all possible combinations of operating points. For example, the two-dimensional optimization space can be as follows: Figure 2 As shown, the horizontal axis represents fuel consumption rate (g / s), and the vertical axis represents battery power consumption rate (W). For a given hybrid vehicle's dynamic requirements, all feasible power distribution combinations of its engine and electric motor are enumerated. Figure 2 All work points in the system.

[0129] It should be noted that g / kWh is the mass (in grams) of fuel consumed to produce 1 kilowatt-hour of mechanical work, used to evaluate engine thermal efficiency, for universal characteristic diagrams and long-term energy consumption optimization, and g / s is the instantaneous fuel consumption rate.

[0130] For example, the Pareto boundary method is used to eliminate inefficient operating points in the two-dimensional optimization space, determine the non-dominated solution set, and select Pareto boundary points that coincide with the engine's optimal operating curve as candidate operating points. The Pareto boundary method is a core tool in multi-objective optimization, used to find the optimal trade-off between conflicting objectives, i.e., where no other objective can be further optimized without sacrificing any other objective. Inefficient operating points are those in the two-dimensional optimization space where other operating points are better at at least one objective (engine fuel consumption rate or battery charge consumption rate). The non-dominated solution set is the set of operating points on the Pareto boundary, such as... Figure 2 In Pareto working points, the optimization of one objective must sacrifice the other, representing the optimal trade-off solution.

[0131] For example, if the fuel consumption rate and battery power consumption rate of operating point X are both less than or equal to those of operating point Y, and at least one is strictly better, then operating point X dominates operating point Y, and operating point Y is an inefficient operating point. As another example, operating point F (1.2 g / s, 30 kW) and operating point D (1.8 g / s, 20 kW) have different fuel consumption rates; operating point F has a lower fuel consumption rate but a higher battery power consumption rate, so operating points D and F do not dominate each other. Operating point G (2.5 g / s, 0 kW) is dominated by operating points D, E, and F. Since operating point G has a worse fuel consumption rate and battery power consumption rate, the connection between operating points not dominated by any operating point, such as the connection between operating points DEF, represents a non-dominated solution set.

[0132] The optimal operating curve is the continuous trajectory of the operating point with the lowest fuel consumption rate in the universal characteristic diagram.

[0133] For example, the universal characteristic diagram of an engine: the horizontal axis is the engine speed (rpm); the vertical axis is the torque (Nm); the contour lines are the fuel consumption rate (g / kWh);

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

[0135] The lowest fuel consumption rate occurs at 2000 rpm and 120 Nm of torque, with a fuel consumption rate of 210 g / kWh.

[0136] The lowest fuel consumption rate occurs at 2500 rpm and 150 Nm of torque, with a fuel consumption rate of 205 g / kWh.

[0137] Candidate points can be selected from the Pareto boundary of the non-dominated solution set, choosing points that coincide with the optimal operating curve, such as operating point Q (1.8 g / s, 30 kW).

[0138] In the above embodiments, based on dynamic requirements, a two-dimensional optimization space for engine fuel consumption rate and battery power consumption rate is established on the universal characteristic diagram to visualize the multi-objective problem. Inefficient operating points in the two-dimensional optimization space are eliminated by the Pareto boundary method to determine the non-dominated solution set, retain effective trade-off options, and select Pareto boundary points that coincide with the engine's optimal operating curve as candidate operating 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 globally optimal control sequence may include: controlling vehicle operation under the constraint that the vehicle's engine speed, motor speed, engine torque, motor torque, battery charging and discharging power, and battery state of charge are in an effective working state, that is, under the constraint that each component of the vehicle's transmission system is in an effective working state during operation.

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

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

[0142] The specific constraints on the operating states of each component in the powertrain system of a hybrid vehicle are as follows:

[0143]

[0144] The appropriate dynamic range for SOC is 0.4-0.7. This indicates the initial charge level of the battery. This indicates the battery level at the end of the run; This indicates the rotational speed of the power component, which can be an engine or an electric motor. i=e represents the engine speed, and i=em represents the motor speed; This represents the engine speed at time k, which is a unit of time. This indicates the engine's minimum speed. This indicates the engine's maximum speed. This represents the motor speed at time k, which is a unit of time. This indicates the minimum speed of the motor. Indicates the maximum speed of the motor; This indicates the torque of a power component, which may be an engine or an electric motor. Where i=e represents the engine torque, and i=em represents the motor torque. This represents the engine torque at time k, which is a unit of time. This indicates the engine's minimum torque. This indicates the engine's maximum torque. This represents the motor torque at time k, which is a unit of time. This indicates the minimum torque of the motor. This indicates the maximum torque of the motor;

[0145] This represents the charging and discharging power of the battery at time k, which is a unit of time. This indicates the battery's minimum charge and discharge power. This indicates the battery's maximum charge and discharge power.

[0146] In the above embodiments, by constraining the working state of each component, the combination of minimizing the engine's minimum fuel consumption rate and the motor's minimum battery power consumption rate is minimized under the premise of ensuring efficient and stable vehicle operation.

[0147] It should be noted that the energy distribution method of this application can be applied to various standard test conditions, verify the effectiveness of the operating point through dynamic simulation, and generate the corresponding globally optimal control sequence for engine-motor cooperative operation. These various conditions include the aforementioned standard test conditions.

[0148] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages in other steps. It is understood that the steps in different embodiments can be freely combined as needed, and all non-contradictory solutions formed by such combinations are within the scope of protection of this application.

[0149] Based on the same inventive concept, this application also provides an energy distribution device for implementing the energy distribution method described above. The solution provided by this device is similar to the solution described in the above method; therefore, the specific limitations in one or more energy distribution device embodiments provided below can be found in the limitations of the energy distribution method described above, and will not be repeated here.

[0150] In one exemplary embodiment, such as Figure 7 As shown, an energy distribution device 700 is provided, including: 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 used to acquire the driving mode and operating parameters of the vehicle at each unit of time within a preset historical time period;

[0152] The pure electric optimization module 720 is used to determine the minimum battery power consumption rate per unit time for each unit time, under the condition that the driving mode represents the pure electric mode, based on the pre-built longitudinal dynamics model, power shunt dynamics model and battery model; and to determine the optimal sequence of battery power consumption rate based on the minimum battery power consumption rate corresponding to multiple unit times.

[0153] The hybrid optimization module 730 is used to determine the minimum combined value of fuel consumption rate and battery charge consumption rate per unit time, based on the longitudinal dynamics model, power split dynamics model, battery model, and engine universal characteristic curve, under the condition that the driving mode represents the hybrid mode, for each unit time; and based on the minimum combined value of fuel consumption rate and battery charge consumption rate corresponding to multiple unit times, determine the candidate operating point that satisfies the minimum combination of fuel consumption rate and battery charge consumption rate; and filter the candidate operating points to obtain the target operating point with a balanced distribution of fuel consumption rate and battery charge consumption rate.

[0154] 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 battery power consumption rate sequence and multiple target operating points through a dynamic programming algorithm; 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.

[0155] The energy distribution module 750 is used to optimize the system-level performance parameters of the vehicle based on the optimal driving mode and the global optimal control sequence, so as to optimize the energy distribution during vehicle operation. The system-level performance parameters include: transmission ratio of the transmission system, limit value of motor torque distribution ratio coefficient, limit value of battery charging and discharging power, limit value of motor power and limit value of engine power.

[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, where fuel consumption rate is the horizontal coordinate and battery power consumption rate is the vertical coordinate. The hybrid optimization module 730 is used to filter the candidate operating points to obtain the target operating point where the distribution of fuel consumption rate and battery power consumption rate is balanced. Specifically, it is used to: determine the slope between each adjacent candidate operating point in the two-dimensional coordinate system; along the positive direction of the horizontal coordinate, if the absolute value of the previous slope is greater than or equal to the absolute value of the next slope, the candidate operating point located in the middle position among the three adjacent candidate operating points is determined as the target operating point where the distribution of fuel consumption rate and battery power consumption rate is balanced.

[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 dynamics model, a power shunt dynamics 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 dynamics model and the operating parameters per unit time; determine the vehicle's motor power requirements per unit time based on the pre-built power shunt dynamics model and the dynamic requirements per unit time; and determine the minimum battery power consumption rate per unit time based on the pre-built battery model for the motor power requirements.

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

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

[0160] Each module in the aforementioned energy distribution device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in the processor of the controller in hardware form or independent of it, or stored in the memory of the controller in software form, so that the processor can call and execute the corresponding operations of each module.

[0161] In one exemplary embodiment, a controller is provided, the internal structure of which can be shown in the following diagram. Figure 8 As shown, the controller includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides the environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The I / O interfaces are used for exchanging information between the processor and external devices. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, Near Field Communication (NFC), or other technologies. When the computer program is executed by the processor, it implements a vehicle driving control method.

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

[0163] In one exemplary embodiment, a controller is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments.

[0164] In one exemplary embodiment, a computer-readable storage medium is provided storing a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0165] In one exemplary embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0166] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile 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 many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, artificial intelligence (AI) processors, etc., and are not limited to these.

[0167] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0168] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. An energy distribution method, characterized by, The method comprises: acquiring driving modes and operating parameters of a vehicle in 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 a battery power consumption rate of the unit time based on a longitudinal dynamics model, a power split dynamics model and a battery model constructed in advance, in combination with the corresponding operating parameters; and determining an optimal sequence of the minimum values of the battery power consumption rate corresponding to a plurality of unit times; for each unit time, when the driving mode represents a hybrid mode, determining a combined minimum value of a fuel consumption rate and a battery power consumption rate of the unit time based on the longitudinal dynamics model, the power split dynamics model, the battery model and an engine universal characteristic curve, in combination with the corresponding operating parameters; determining a candidate working point that satisfies 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 screening the candidate working points to obtain a target working point in which the fuel consumption rate and the battery power consumption rate are distributed evenly; based on the optimal sequence of the battery power consumption rate and a plurality of target working points, obtaining an optimal driving mode and a global optimal control sequence for each unit time in the preset historical time period through a dynamic programming algorithm; the global optimal control sequence is an optimal working point that makes the combination of the fuel consumption rate and the battery power consumption rate optimal for each unit time in the entire preset historical time period; based on the optimal driving mode and the global optimal control sequence, optimizing a system-level performance index parameter of the vehicle to realize optimization of energy distribution in the operation process of the vehicle; the system-level performance index parameter includes a transmission ratio of a transmission system, a torque distribution proportion coefficient limit value of an electric motor, a battery charge and discharge power limit value, a motor power limit value and an engine power limit value.

2. The method of claim 1, wherein, The candidate working points are 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 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 screening of the candidate working points to obtain the target working point in which the fuel consumption rate and the battery power consumption rate are distributed evenly comprises: determining the slope between each adjacent candidate working point in the two-dimensional coordinate system; in the positive direction of the horizontal coordinate, when two slopes corresponding to every three adjacent candidate working points meet the condition that the absolute value of the previous slope is greater than or equal to the absolute value of the next slope, the candidate working point located in the middle position of the three adjacent candidate working points is determined as the target working point in which the fuel consumption rate and the battery power consumption rate are distributed evenly.

3. The method of claim 1, wherein The minimum value of the battery power consumption rate per unit time is determined based on the pre-constructed longitudinal dynamics model, the power split dynamics model and the battery model in combination with the corresponding operating parameters, and includes: The dynamics demand of the vehicle per unit time is determined based on the pre-constructed longitudinal dynamics model in combination with the operating parameters per unit time; The internal force demand of the first planetary gear mechanism and the internal force demand of the second planetary gear mechanism in the vehicle per unit time are determined based on the dynamics demand; The first motor torque and the second motor torque of the vehicle per unit time are determined based on the pre-constructed 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 the motor power demand per unit time is determined based on the first motor torque and the second motor torque; The battery charging and discharging power corresponding to the motor power demand is determined, and the minimum value of the battery power consumption rate per unit time is determined based on the pre-constructed battery model in combination with the battery charging and discharging power.

4. The method of claim 1, wherein the combined minimum value of the fuel consumption rate and the battery power consumption rate per unit time is determined 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, and includes: The dynamics demand of the vehicle per unit time is determined based on the pre-constructed longitudinal dynamics model in combination with the operating parameters per unit time; The internal force demand of the first planetary gear mechanism and the internal force demand of the second planetary gear mechanism in the vehicle per unit time are determined based on the dynamics demand; The first motor torque, the second motor torque and the engine torque of the vehicle per unit time are determined based on the pre-constructed 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 the motor power demand and the engine power demand per unit time are determined based on the first motor torque, the second motor torque and the engine torque; The battery charging and discharging power corresponding to the motor power demand is determined, and the combined minimum value of the fuel consumption rate and the battery power consumption rate per unit time is determined based on the pre-constructed battery model and the engine universal characteristic curve in combination with the battery charging and discharging power and the engine power demand.

5. The method of claim 1, wherein the optimal driving mode and the globally optimal control sequence for each unit time in the preset historical time period are obtained by a dynamic programming algorithm based on the optimal sequence of the battery power consumption rate and a plurality of target operating points, and include: ​ ​ Based on the optimal battery power consumption rate sequence and the plurality of target working 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 under the condition that the constraint condition of the preset component working state of the vehicle is met; The constraint condition of the preset component working state of the vehicle is: SOC(k) represents the state of charge of the battery at time k, SOC(k) represents the state of charge of the battery at time k, SOC(k) represents the state of charge of the battery at time k; Nmin represents the minimum engine speed, N(k) represents the engine speed at time k, Nmax represents the maximum engine speed, Tmin represents the minimum engine torque, T(k) represents the engine torque at time k, Tmax represents the maximum engine torque, Nmin represents the minimum engine speed, N(k) represents the engine speed at time k, Nmax represents the maximum engine speed, Tmin represents the minimum engine torque, T(k) represents the engine torque at time k, Tmax represents the maximum engine torque, Pmin represents the minimum battery power, P(k) represents the battery power at time k, Pmax represents the maximum battery power.

6. The method of claim 1, wherein, The pre-constructed longitudinal dynamics model is: Among them, F t For the vehicle's dynamic requirements, m is the vehicle's curb weight, g is the acceleration due to gravity, f is the rolling resistance coefficient, α is the road slope angle during vehicle movement, and C... D This refers to the air resistance coefficient during vehicle operation. Let I be the air density during vehicle movement, A be the frontal area of ​​the vehicle during movement, u be the vehicle speed during movement, t be the unit time, and δ be the vehicle's rotational mass conversion factor; the equation for calculating δ uses dimensionless calculation, I w Let I be the moment of inertia at the wheel end of the vehicle. f Let be the moment of inertia at the vehicle's flywheel, and r be the wheel radius. For transmission efficiency, i g i0 is the gear ratio of the transmission, and i0 is the gear ratio of the main reducer.

7. The method of claim 1, wherein, The pre-constructed power split dynamics model is: wherein, F1 represents a first planetary gear mechanism internal action force requirement, F2 represents a second planetary gear mechanism internal action force requirement, J1 represents an engine inertia, J1 represents a first planetary row carrier inertia, R1 represents a first planetary row ring gear radius, R2 represents a second planetary row ring gear radius, S1 represents a first planetary row sun gear radius, S2 represents a second planetary row sun gear radius, J1 represents a first motor rotational inertia, J1 represents a second motor rotational inertia, J1 represents a first planetary row sun gear rotational inertia, J1 represents a second planetary row sun gear rotational inertia, J1 represents a first planetary row ring gear rotational inertia, J1 represents a second planetary row ring gear rotational inertia, J1 represents a wheel end inertia; represents an engine angular acceleration, represents a first motor angular acceleration, represents a wheel output end angular acceleration, represents a second motor angular acceleration, represents an engine torque, represents a first motor torque, represents a wheel output end torque, represents a second motor torque.

8. The method of claim 1, wherein, The pre-constructed battery model is: ; SOC(k+1) = SOC(k) + (P(k) - P(k+1)) / SOC(k) * T(k+1) wherein SOC(k+1) is the SOC of the unit time (k+1), SOC(k) is the SOC of the unit time k, SOC represents the state of charge of the battery per unit time; represents the open circuit voltage of the battery, represents the corresponding charge and discharge internal resistance of the battery, represents the corresponding charge and discharge power of the battery, represents the current capacity of the battery, represents the duration of the unit time k. represents the duration of the unit time k.

9. An energy distribution apparatus, characterized by The device comprises: A data acquisition module configured to acquire driving modes and operating parameters of a vehicle in each unit time in a preset historical time period; An all-electric optimization module configured to, for each unit time, determine a minimum value of a battery power consumption rate of the unit time based on a pre-constructed longitudinal dynamics model, a power split dynamics model and a battery model under the condition that the driving mode represents an all-electric driving mode, and determine a battery power consumption rate optimal sequence based on the minimum values of the battery power consumption rates corresponding to the plurality of unit times; A hybrid optimization module configured to, for each unit time, determine a combined minimum value of a fuel consumption rate and a battery power consumption rate of the unit time based on the longitudinal dynamics model, the power split dynamics model, the battery model and an engine universal characteristic curve under the condition that the driving mode represents a hybrid driving mode, determine candidate working points satisfying 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 the 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; A global optimization module configured to, based on the battery power consumption rate optimal sequence and the plurality of target working points, obtain an optimal driving mode and a global optimal control sequence for each unit time in the preset historical time period by a dynamic programming algorithm; the global optimal control sequence is an optimal working point that makes the combination of the fuel consumption rate and the battery power consumption rate optimal for each unit time in the entire preset historical time period; An energy distribution module configured to optimize system-level performance indicator parameters of the vehicle based on the optimal driving mode and the global optimal control sequence to optimize energy distribution in the operation of the vehicle; the system-level performance indicator parameters include transmission system transmission ratio, motor torque distribution proportionality coefficient limit value, battery charge and discharge power limit value, motor power limit value and engine power limit value.

10. A controller comprising a memory and a processor, the memory storing a computer program, characterized in that, The processor executes the computer program to implement the steps of the method of any one of claims 1 to 8.

11. A computer readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to implement the steps of the method of any one of claims 1 to 8.

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