Energy management method and device of hybrid vehicle, controller and storage medium

Abstract

The invention relates to an energy management method and device of a hybrid vehicle, a controller and a storage medium. The method comprises the following steps: acquiring operation parameters of a vehicle in a preset historical time period and driving modes of the vehicle in each unit time; under the condition that the driving mode represents the pure electric mode, the minimum value of the battery power consumption rate in unit time is determined, under the condition that the driving mode represents the hybrid mode, a candidate working point meeting the requirement that the combination of the fuel consumption rate and the battery power consumption rate is minimum is determined, and on the basis of the battery power consumption rate optimal sequence and the candidate working point, the battery power consumption rate is determined. An optimal driving mode and a global optimal control sequence are obtained through a dynamic programming algorithm, and system-level performance index parameters of the vehicle are optimized based on the optimal driving mode and the global optimal control sequence; according to the method, the system-level performance index parameters of the vehicle are optimized, so that the system-level performance index parameters of the vehicle are better, and the economical efficiency and the high efficiency of vehicle operation are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of vehicles, in particular to an energy management method, device, controller and storage medium of a hybrid vehicle. BACKGROUND

[0002] Hybrid vehicles can be divided into series type, parallel type, power split type and series-parallel type according to the connection mode of the powertrain configuration. Energy management is to solve how to control the power components to make the fuel consumption rate and / or the battery power consumption rate minimum in a certain time period under the premise of ensuring the operation efficiency, wherein the power components include an engine and a motor. The energy transmission process of the hybrid vehicle includes mechanical system and electrical system energy transmission, and the vehicle is controlled by the engine and / or the motor. How to coordinate the two systems to make the engine meet the minimum fuel consumption rate and the motor meet the minimum battery power consumption rate under the premise of high-efficiency and stable operation of the vehicle transmission system is a current research hotspot.

[0003] Traditional energy management strategies often have difficulty in efficiently handling the increase in control dimensions and state coupling problems brought by multiple modes and multiple gears. There are limitations in accurately mining the global energy-saving potential of a specific configuration under real historical working conditions and quickly generating control sequences that meet the dual optimization goals (minimum fuel consumption rate + minimum battery power consumption rate), so as to make the system-level performance index parameters of the vehicle reach better, and it is difficult to control the power components of the vehicle through the optimized system-level performance index parameters of the vehicle in the subsequent actual operation process of the vehicle, so that the vehicle transmission system reaches the optimal operation efficiency or fuel economy in a certain operation time period. SUMMARY

[0004] Therefore, it is necessary to provide an energy management method, device, controller, computer readable storage medium and computer program product of a hybrid vehicle in the vehicle running process, which can quickly generate optimal driving modes and global optimal control sequences that meet the dual optimization goals.

[0005] In a first aspect, the present application provides an energy management method of a hybrid vehicle, comprising:

[0006] obtaining the running parameters of a preset historical time period of the vehicle, and the driving mode of each unit time in the preset historical time period of the vehicle;

[0007] For each of the unit times, in the case that the driving mode represents the pure electric mode, the minimum value of the battery power consumption rate of the unit time is determined based on the longitudinal dynamics model and the battery model constructed in advance, in combination with the corresponding operating parameters; and the optimal sequence of the battery power consumption rate is determined based on the minimum values of the battery power consumption rate corresponding to a plurality of the unit times;

[0008] For each of the unit times, in the case that the driving mode represents the hybrid mode, the combined minimum value of the fuel consumption rate and the battery power consumption rate of the unit time is determined based on the longitudinal dynamics model, the battery model and the engine universal characteristic curve, in combination with the corresponding operating parameters; and the candidate working point satisfying the combined minimum of the fuel consumption rate and the battery power consumption rate is determined based on the combined minimum values of the fuel consumption rate and the battery power consumption rate corresponding to a plurality of the unit times;

[0009] Based on the optimal sequence of the battery power consumption rate and a plurality of the candidate working points, the optimal driving mode for each of the unit times in the preset historical time period and the global optimal control sequence are obtained through a dynamic programming algorithm; the global optimal control sequence is the optimal working point that makes the combination of the fuel consumption rate and the battery power consumption rate optimal for each of the unit times in the entire preset historical time period;

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

[0011] In a second aspect, the application further provides an energy management device of a hybrid vehicle, comprising:

[0012] A parameter determination module is configured to acquire operating parameters of a preset historical time period of the vehicle and driving modes of the vehicle in each unit time in the preset historical time period;

[0013] A pure electric optimization module is configured to, for each of the unit times, in the case that the driving mode represents the pure electric mode, determine the minimum value of the battery power consumption rate of the unit time based on the longitudinal dynamics model and the battery model constructed in advance, in combination with the corresponding operating parameters; and determine the optimal sequence of the battery power consumption rate based on the minimum values of the battery power consumption rate corresponding to a plurality of the unit times;

[0014] a hybrid optimization module configured to, for each unit time, determine a combined minimum value of fuel consumption rate and battery power consumption rate based on the longitudinal dynamics model, the battery model and the engine characteristic curve, in combination with the corresponding operating parameter, when the driving mode represents a hybrid mode; and determine a candidate working point 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 a plurality of unit times;

[0015] an optimal sequence determination module configured to obtain an optimal driving mode and a global optimal control sequence for each unit time in the preset historical time period based on the battery power consumption rate optimal sequence and a plurality of candidate working points by a dynamic programming algorithm; the global optimal control sequence being an optimal working point making 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;

[0016] an energy management module configured to optimize a system-level performance index parameter of the vehicle based on the optimal driving mode and the global optimal control sequence, so as to optimize energy distribution in the operation of the vehicle; the system-level performance index parameter including a transmission ratio of a transmission system, a torque distribution proportionality coefficient limit value of an electric machine, a battery charge and discharge power limit value, an electric machine power limit value and an engine power limit value.

[0017] In a third aspect, the present application further provides a controller comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of any of the energy management methods for hybrid vehicles when executing the computer program.

[0018] In a fourth aspect, the present application further provides a computer readable storage medium having a computer program stored thereon, wherein the computer program implements the steps of any of the energy management methods for hybrid vehicles when executed by a processor.

[0019] In a fifth aspect, the present application further provides a computer program product comprising a computer program, wherein the computer program implements the steps of any of the energy management methods for hybrid vehicles when executed by a processor.

[0020] The energy management method, device, controller, computer readable storage medium and computer program product of the hybrid vehicle described above, by obtaining the running parameters of the vehicle in each unit time in a preset historical time period and the state of charge of the battery, obtains the driving mode of the vehicle in the historical time period; then, for each unit time, when the driving mode of the vehicle is the pure electric mode, the minimum value of the battery power consumption rate of the relevant unit time is determined by combining the relevant running parameters and the pre-constructed longitudinal dynamics model and battery model, and then the optimal sequence of the battery power consumption rate corresponding to the preset historical time period is determined based on the minimum value of the battery power consumption rate of each unit time; for each unit time, when the driving mode of the vehicle is the hybrid mode, the combined minimum value of the fuel consumption rate and the battery power consumption rate of the unit time is screened out by combining the relevant running parameters and the pre-constructed longitudinal dynamics model, battery model and engine universal characteristic curve; and further, based on the combined minimum value of the fuel consumption rate and the battery power consumption rate of each unit time, the candidate working point that meets the combined minimum of the fuel consumption rate and the battery power consumption rate is determined; then, based on the optimal sequence of the battery power consumption rate determined for the historical running time period of the vehicle and the plurality of candidate working points, the optimal driving mode for each unit time in the preset historical time period and the globally optimal control sequence are obtained by a dynamic programming algorithm; wherein the globally optimal control sequence is the 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; finally, the system-level performance index parameters of the vehicle can be optimized based on the optimal driving mode and the globally optimal control sequence determined, and thus the optimization of energy distribution in the subsequent actual running process of the vehicle can be realized; wherein the system-level performance index parameters include: the transmission ratio of the transmission system, the torque distribution proportion coefficient limit value of the motor, the battery charge and discharge power limit value, the motor power limit value and the engine power limit value. By optimizing the system-level performance index parameters of the vehicle in this way, the system-level performance index parameters of the vehicle can be made more optimal, thereby helping to ensure that the vehicle has a running time period with optimal combination of fuel consumption rate and battery power consumption rate in the subsequent running process, and thus helping to improve the economy and efficiency of the vehicle running. BRIEF DESCRIPTION OF DRAWINGS

[0021] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the related art, the drawings needed to be used in the description of the embodiments of the present application or the related art will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other related drawings can be obtained by those skilled in the art without creating any inventive labor.

[0022] Figure 1 Flowchart of the energy management method of the hybrid vehicle in one embodiment;

[0023] Figure 2 a schematic diagram of working points in an embodiment;

[0024] Figure 3 a schematic diagram of a flow of a method for determining a minimum value of battery power consumption rate in an embodiment;

[0025] Figure 4 a schematic diagram of a flow of a method for determining candidate working points in an embodiment;

[0026] Figure 5 a structural block diagram of an energy management device of a hybrid vehicle in an embodiment;

[0027] Figure 6 an internal structural diagram of a controller in an embodiment. DETAILED DESCRIPTION

[0028] In order to make the purpose, technical scheme and advantages of the present application clearer, the present application will be further described in detail below with reference to the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and do not limit the present application.

[0029] It should be noted that the terms "first", "second", and the like used in the present 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 "include" and "have" and any variations thereof used in the present application are intended to cover non-exclusive inclusion. The term "a plurality of" used in the present application means two or more. The term "and / or" used in the present application means one of the options or any combination of multiple options.

[0030] The hybrid configuration scheme of the hybrid vehicle usually contains two electric machines and a multi-degree-of-freedom planetary gear structure. Compared with the series and parallel hybrid vehicles, the control process of such a transmission system is more complex, requiring more state variables and control variables, resulting in a higher control dimension. In the global optimization process using the dynamic programming algorithm, the calculation load and time will exponentially increase with the increase of the dimension, making it difficult for the traditional dynamic programming algorithm to meet the real-time calculation efficiency requirements.

[0031] In addition, in the configuration design process, as the complexity of the transmission system increases, such as increasing the number of planetary gear structures or clutches, the number of possible configuration schemes will increase exponentially. Therefore, the evaluation process of the configuration scheme puts higher challenges on the calculation efficiency of the algorithm.

[0032] In order to quickly evaluate the fuel consumption of a large number of design schemes, and realize efficient calculation of fuel consumption under different working conditions, the application provides an energy management method for a power-split hybrid vehicle based on dynamic programming (DP).

[0033] In an exemplary embodiment, as shown in Figure 1 An energy management method for a hybrid vehicle is provided, which is described by taking a controller in the vehicle as an example. The controller can be used to control the operation of the vehicle, and includes the following steps S102 to S110. Wherein:

[0034] Step 102, obtaining the running parameters of the vehicle in a preset historical time period, and the driving mode of the vehicle in each unit time in the preset historical time period.

[0035] The preset historical time period is a past time window for analyzing the running state of the vehicle, such as the past 10 seconds, 1 minute, etc. The unit time is the minimum time unit for optimization control, such as 1 second, 0.1 second, which affects the granularity of the control sequence.

[0036] The running parameter is a physical quantity that affects the dynamic performance of the vehicle, which is used to calculate the real-time dynamic demand of the vehicle, and can specifically include at least one of the following: the curb weight is the self-weight of the vehicle, such as 1800kg (kilograms), which affects the acceleration and climbing demand; the rolling resistance coefficient is the rolling resistance between the tire and the ground, such as 0.015, which affects the energy consumption of driving on flat roads; the road slope is the road slope during the driving process of the vehicle, such as 5% uphill, which directly affects the driving force demand; the air resistance coefficient is a dimensionless parameter, which is used to describe the size of the resistance suffered by the vehicle when moving in the air, which mainly depends on the shape design and surface smoothness of the vehicle; the windward area is the projection area of the vehicle facing the airflow direction, that is, the cross-sectional area of the vehicle from the front; the air resistance coefficient and the windward area jointly determine the wind resistance when driving at high speed, for example, the air resistance coefficient can be 0.3, and the windward area can be 2.5m² (square meters); the vehicle speed is the driving speed of the vehicle, such as 60km / h (kilometers / hour), which is used to calculate the real-time power demand; the rotational mass conversion coefficient is the equivalent inertia of the rotating parts of the transmission system; the moment of inertia is a physical quantity that describes the ability of an object to resist angular acceleration changes, similar to the "mass" in translation, including the moment of inertia of the wheel end of the vehicle and the moment of inertia of the flywheel of the vehicle, which is used to affect the acceleration response; the wheel radius is the effective radius when the wheel rolls; the transmission efficiency is the ratio of the actual available power to the input power in the process of power transmission from the engine or motor to the wheel; the transmission ratio of the transmission is the ratio of the input shaft speed to the output shaft speed of the transmission, which reflects the torque amplification or speed regulation capability; the main reducer transmission ratio is the ratio of the transmission output shaft speed to the wheel speed, which further amplifies the torque and adapts the vehicle speed.

[0037] The driving mode is the current or expected power source mode of the vehicle, including a pure electric mode and a hybrid mode, wherein the pure electric mode is driven by an electric motor, and the hybrid mode is driven by an engine and an electric motor.

[0038] In one embodiment, a hybrid vehicle is driving on a highway, and the running parameters of the vehicle in a preset historical time period are obtained, for example, the running parameters of the vehicle in the past 30 seconds are obtained, including that the vehicle speed is increased from 80 km / h to 100 km / h, the road slope is 2%, the air resistance coefficient is 0.28, and the SOC is 65%; the dynamics demand of the vehicle can be calculated according to the road slope, the acceleration demand, etc., for example, the current required wheel end torque is 500 Nm (Newton meter); the driving mode can also be determined according to the SOC and the demand torque, for example, the pure electric mode is selected because the SOC is sufficient and the demand torque does not exceed the upper limit of the electric motor; if the demand torque suddenly increases, such as overtaking, the hybrid mode is switched to; and the driving mode sequence and the corresponding dynamics demand per unit time (such as every 1 second) are output for subsequent optimization.

[0039] In the above embodiment, the historical running parameters and the vehicle driving mode are obtained, which provides a data basis for subsequent energy management.

[0040] In step 104, for each unit time, when the driving mode represents the pure electric mode, the minimum value of the battery power consumption rate per unit time is determined based on the pre-constructed longitudinal dynamics model and the battery model in combination with the corresponding running parameters; and the optimal sequence of the battery power consumption rate is determined based on the minimum values of the battery power consumption rate corresponding to a plurality of unit times.

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

[0042] In one embodiment, the longitudinal dynamics model can be constructed according to the classical longitudinal dynamics formula that the driving force is equal to the sum of the friction resistance, the slope resistance, the air resistance and the acceleration resistance, according to the curb weight of the vehicle, the rolling resistance coefficient, the road slope angle, the air resistance coefficient, the air density, the wind area, the vehicle speed, the rotational mass conversion coefficient, the rotational inertia of the vehicle wheel end, the rotational inertia of the vehicle flywheel, the wheel radius, the transmission efficiency, the transmission ratio and the main reducer transmission ratio.

[0043]

[0044]

[0045] F t is the dynamics demand of the vehicle (N=kg·m / s 2), m represents the kerb mass of the vehicle (kg), g is the acceleration due to gravity (m / s2), f represents the rolling resistance coefficient, a represents the road slope angle during the vehicle driving process, C D represents the air resistance coefficient during the vehicle driving process, is the air density (kg / m 3 ), A represents the frontal area during the vehicle driving process (m2), u represents the vehicle speed during the vehicle driving process (m / s), t is the unit time, and d is the rotational mass conversion coefficient of the vehicle. The above calculation equation is dimensionless calculation, I w is the moment of inertia of the vehicle wheel end (kg·m2), I f is the moment of inertia of the vehicle flywheel (kg·m2), r is the wheel radius (m), is the transmission efficiency, i g is the transmission ratio, and i0 is the main reducer transmission ratio.

[0046] In one embodiment, the controller determines the dynamic demand of the vehicle in each unit time based on the longitudinal dynamics model and the operating parameters, and the dynamic demand is the driving force or braking force required by the vehicle, such as the increase in demand torque when climbing. Then the optimal driving mode of the vehicle can be determined according to the dynamic demand of each unit time and the SOC. For example, for a historical time period of a hybrid vehicle driving on a highway, the operating parameters of the vehicle in the preset historical time period of the highway driving stage are obtained, for example, the operating parameters of the vehicle in the 30 seconds of the highway driving stage are obtained, including the vehicle speed from 80 km / h to 100 km / h, the road slope is 2%, the air resistance coefficient is 0.28, and the SOC is 65%; the dynamic demand of the vehicle can be calculated according to the road slope, the acceleration condition and the like, for example, the required wheel end torque of the vehicle in this driving stage is 500 Nm (Newton meter); the driving mode can also be determined according to the SOC and the demand torque, for example, in the highway driving stage, there is a running time period with sufficient SOC and demand torque not exceeding the upper limit of the motor, and the pure electric mode is selected in this time period; if there is a sudden increase in demand torque in the highway driving stage, such as overtaking, the time period of overtaking driving is switched to the hybrid mode; and the driving mode sequence and the corresponding dynamic demand of each unit time (such as every 1 second) are output.

[0047] In the above embodiment, the dynamic demand and the optimal driving mode of the vehicle in the historical running time period are determined by the longitudinal dynamics model and the state of charge of the battery of the vehicle, which provides a data basis for optimizing the system-level performance index parameters of the vehicle.

[0048] The pre-constructed battery model is a mathematical model for quantifying the dynamic characteristics of the battery. For example, the battery model can be determined according to the open-circuit voltage, charge-discharge internal resistance, battery charge-discharge power, and current capacity of the battery. The open-circuit voltage is the terminal voltage of the battery without load, the charge-discharge internal resistance is the internal resistance of the battery, the battery charge-discharge power is the maximum allowed charge-discharge power of the battery, and the current capacity is the percentage of the remaining capacity. For example, the battery model can be represented as:

[0049]

[0050] wherein SOC(k+1) is the SOC at time (k+1) per unit time, SOC(k) is the SOC at time k per unit time, represents the open-circuit voltage (volts, V) of the battery, represents the corresponding charge-discharge internal resistance (ohms, Ω) of the battery, represents the corresponding charge-discharge power (watts, W) of the battery, represents the current capacity (coulombs, C) of the battery, represents the duration (seconds, s) of the first unit of time.

[0051] The battery capacity consumption rate is the amount of battery energy reduced per unit time.

[0052] For example, a certain hybrid vehicle is running in electric mode, and the historical time period is 30 seconds. The controller takes the battery capacity consumption rate as the optimization target, determines the battery capacity consumption rate of the vehicle per unit time by the open-circuit voltage, charge-discharge internal resistance, battery charge-discharge power, and current capacity of the battery within 30 seconds, and then determines the optimal sequence of battery capacity consumption rate that minimizes the battery capacity consumption rate.

[0053] For example, in the case of driving mode representing electric mode, in the case of two motors driving the vehicle at the same time, the battery capacity consumption rate under different power distribution of the two motors is determined by the pre-set battery model; and based on the battery capacity consumption rate, the motor power sequence of the two motors that minimizes the battery capacity consumption rate in the pre-set historical time period is determined; in the case of pure electric mode of the vehicle with only one motor driving, the motor power and the battery capacity consumption rate are uniquely determined, and there is only one motor power sequence that minimizes the battery capacity consumption rate.

[0054] In the above embodiment, in the case of the vehicle running in electric mode, the battery capacity consumption rate is taken as the optimization target, the battery capacity consumption rate of the vehicle per unit time is determined by the battery model, and the optimal sequence of battery capacity consumption rate that minimizes the battery capacity consumption rate is determined, which is beneficial to improve the energy utilization efficiency of the vehicle.

[0055] In summary, since the historical time period includes multiple unit times, and the vehicle has its corresponding driving mode in each unit time, for each unit time in which the driving mode is pure electric mode, the minimum battery power consumption rate of each unit time can be determined by combining the operating parameters of the unit time with the above-mentioned pre-constructed longitudinal dynamics model and battery model. Then, based on the minimum battery power consumption rate corresponding to each unit time in which the driving mode is pure electric mode, the optimal sequence of battery power consumption rates corresponding to the pure electric mode in the historical time period can be determined. The determined optimal sequence of battery power consumption rates is beneficial in principle to improving the energy utilization efficiency of the vehicle in the pure electric mode in the historical time period.

[0056] In the above embodiment, by combining the longitudinal dynamics model and the battery model, a comprehensive modeling of the vehicle operating state can be achieved, which can more accurately reflect the dynamic characteristics of the complex hybrid power system, thereby providing a more reliable optimization basis for the energy management strategy.

[0057] In step 106, for each unit time, when the driving mode represents a hybrid mode, the combined minimum fuel consumption rate and battery power consumption rate of the unit time are determined based on the longitudinal dynamics model, the battery model, and the engine universal characteristic curve, in combination with the corresponding operating parameters; and based on the combined minimum fuel consumption rate and battery power consumption rate corresponding to multiple unit times, a candidate working point that satisfies the combined minimum fuel consumption rate and battery power consumption rate is determined.

[0058] The hybrid mode is a state in which the engine and the motor work together to drive the vehicle. The engine universal characteristic diagram is a three-dimensional contour diagram describing the change of the engine fuel consumption rate (Brake Specific Fuel Consumption, BSFC) with the speed (rpm) and torque (Nm). The fuel consumption rate of the engine is the fuel consumption rate of the engine in a unit time; the battery power consumption rate is the amount of reduction of battery energy in a unit time; the minimum fuel consumption rate is the lowest fuel consumption rate that the engine can achieve under the current working condition, and the minimum battery power consumption rate is the lowest battery power consumption rate that the motor can achieve under the current working condition. For example, the engine fuel consumption rate is calculated based on the engine universal characteristic diagram (BSFC diagram) and the engine output power, engine fuel consumption rate = BSFC x Pe x Δt, where Pe is the engine output power (kW), and Δt is a unit time; for example, the BSFC of a certain engine is 220 g / kWh (grams / kilowatt hour) at 2000 rpm (rpm) and 100 Nm, and the fuel consumption rate of the engine is 220 g / kWh x 10 kW x 1 / 3600 h = 0.61 g / s (grams / second) when the output power is 10 kW.

[0059] The candidate working point is determined based on a value combination of engine working parameters and motor working parameters of the vehicle in a preset historical time period, such as a value combination of parameters such as rotation speed, torque, power, etc., to achieve optimal energy consumption under the premise of meeting power demand, specifically to meet the combination of minimum fuel consumption rate and minimum battery power consumption rate.

[0060] In summary, for each unit time in the historical time period, the combination minimum value of the fuel consumption rate and the battery power consumption rate in each unit time can be determined by combining the running parameters of each unit time, through the pre-constructed longitudinal dynamics model, the battery model and the engine universal characteristic curve, and then the candidate working point meeting the combination minimum of the fuel consumption rate and the battery power consumption rate can be determined based on the combination minimum values of the fuel consumption rate and the battery power consumption rate corresponding to a plurality of unit times.

[0061] The candidate working point is located in a two-dimensional coordinate system formed by the fuel consumption rate and the battery power consumption rate, the fuel consumption rate can be the horizontal coordinate in the two-dimensional coordinate system, and the battery power consumption rate can be the vertical coordinate in the two-dimensional coordinate system.

[0062] For example, the controller constructs a multi-objective optimization model based on the engine fuel consumption rate and the battery power consumption rate of the vehicle to determine the candidate working point meeting the combination of the minimum fuel consumption rate and the minimum battery power consumption rate in the preset historical time period. The multi-objective optimization model is a mathematical model for simultaneously optimizing the engine fuel consumption rate and the battery power consumption rate, and usually adopts Pareto optimization or weighted summation method; for example, the objective function of the multi-objective optimization model can be: min(w1fuel consumption rate+w2power consumption rate), wherein w1 and w2 are weight coefficients, for example, in the case of w1=0.7 and w2=0.3, the fuel economy is more important. For example, the candidate working point can be a value combination of engine rotation speed (rpm), engine torque (Nm), motor power (kW), fuel consumption rate (g / s) and power consumption rate (kW), and the specific values can be as shown in Table 1:

[0063] Table 1: Candidate working point value combination table

[0064]

[0065] Figure 2 For a working point in an embodiment; in an embodiment, the candidate working point can be a working point set in which all working points coincide with the Pareto working point as shown in Figure 2 .

[0066] In one embodiment, the hybrid vehicle cruises at 80 km / h, the demand power is 40 kW, and the preset historical time period is the past 10 seconds. The input parameters of the hybrid vehicle can be: engine BSFC map, motor efficiency map; battery SOC = 60%, internal resistance = 0.1 Ω; the power demand can be: 40 kW constant. The objective function of the multi-objective optimization model can be: min (0.6 fuel consumption rate + 0.4 electric quantity consumption rate), the variables can be: engine power Pe, motor power Pm, and Pe + Pm = 40 kW. The candidate working points generated by the controller can be as shown in Table 2:

[0067] Table 2 Candidate working point value combination table

[0068]

[0069] If fuel economy is prioritized, point B can be selected as the preferred candidate working point (fuel consumption rate 0.65 g / s, electric quantity consumption rate 15 kW); if the battery needs to be prioritized, point C can be selected as the preferred candidate working point, which can make the electric quantity consumption lower.

[0070] In the above embodiment, a multi-objective optimization model is constructed, the engine fuel consumption and the battery electric quantity consumption are considered at the same time, the optimal candidate working point is screened out, the vehicle is ensured to meet the power demand at the same time, the fuel consumption and the battery energy loss in the hybrid mode are reduced as much as possible, the endurance mileage is prolonged, and the use cost is reduced.

[0071] Step 108, based on the optimal sequence of battery electric quantity consumption rate and the plurality of candidate working points, the optimal driving mode and the global optimal control sequence for each unit time in the preset historical time period are obtained by a dynamic programming algorithm; the global optimal control sequence is the optimal working point that makes the combination of the fuel consumption rate and the battery electric quantity consumption rate optimal for each unit time in the entire preset historical time period.

[0072] The entire preset historical time period is a past time window for analyzing the vehicle running state, such as a short period of 10 seconds, which is suitable for urban congestion conditions and frequent start-stop situations; a long period of 60 seconds, which is suitable for high-speed cruising and needs to predict slope changes.

[0073] The optimal sequence of battery electric quantity consumption rate is determined based on the minimum value of the battery electric quantity consumption rate corresponding to each unit time in the pure electric mode in the historical time period; the plurality of candidate working points are the combined minimum working points determined based on the combined minimum value of the fuel consumption rate and the battery electric quantity consumption rate corresponding to each unit time in the hybrid mode in the historical time period, and are obtained after further distribution balancing screening.

[0074] Exemplarily, after the above-mentioned battery power consumption rate optimal sequence corresponding to each unit time in the historical time period and the plurality of candidate working points are obtained, the dynamic programming algorithm can be further used to optimize the battery power consumption rate optimal sequence and the plurality of candidate working points, so as to obtain an optimal driving mode more suitable for each unit time in the historical time period and a global optimal control sequence. In other words, if the vehicle is actually operated in the related optimal driving mode and the combination of the fuel consumption rate and the battery power consumption rate in the global optimal control sequence for each unit time in the historical time period, the operation of the vehicle in the historical time period can achieve the optimal operation efficiency or fuel economy effect.

[0075] It can be seen that, in the energy distribution method provided by the present application, based on the driving mode and the operation parameter of the vehicle in each unit time in the preset historical time period, in combination with the pre-constructed longitudinal dynamics model, the battery model and the engine universal characteristic curve, the related steps of determining the optimal driving mode of the vehicle in each unit time in the preset historical time period and the global optimal control sequence corresponding to the preset historical time period are equivalent to the process of offline solving the optimal operation mode of the vehicle in the preset historical time period. In the method provided by the present application, after the candidate working point satisfying the combination of the fuel consumption rate and the battery power consumption rate in the hybrid mode of the vehicle in the preset historical time period is obtained, the operation amount of determining the optimal driving mode and the global optimal control sequence corresponding to the preset historical time period in the subsequent step can be reduced, the operation efficiency can be improved, and the effect of the optimal driving mode and the global optimal control sequence obtained by operation can be closer to the ideal state. Moreover, the method can accelerate the convergence of the DP algorithm and avoid traversing all possible working points. Therefore, the step of determining the optimal driving mode and the global optimal control sequence corresponding to the preset historical time period provided by the present application is beneficial to reducing the calculation compliance, improving the solving speed of the solving process, and making the optimization efficiency and the optimization result reach the ideal state at the same time, which is a super-fast energy management method considering the calculation efficiency and the optimization performance.

[0076] In an exemplary embodiment, based on the battery power consumption rate optimal sequence and the plurality of candidate working points, the dynamic programming algorithm can be used to obtain the optimal driving mode and the global optimal control sequence for each unit time in the preset historical time period under the constraint condition of the preset component working state of the vehicle.

[0077] The preset component working state constraint condition (preset constraint condition) is a hard boundary condition that must be met in global optimization, covering the safety and performance limits of the vehicle power system. The global optimal control sequence is a sequence that optimizes the combination of fuel consumption rate and battery power consumption rate within the entire preset historical time period, based on which the optimal sequence of combined operation of engine power and motor power of the vehicle within the preset historical time period can be obtained, i.e., the time sequence of operation instructions related to engine power and motor power generated by comprehensively considering all constraint conditions and optimization objectives.

[0078] In one embodiment, the global optimal control sequence is the engine power and motor power sequence of the vehicle within the entire preset historical time period, which can be solved within the historical time period based on a dynamic programming algorithm or a model predictive control algorithm. Exemplarily, the global optimal control sequence can be a combination of different values of time, engine power, motor power and driving mode, and is determined to be the engine and motor power sequence of the vehicle that optimizes the combination of fuel consumption rate and battery power consumption rate, which can be as shown in Table 3 as follows:

[0079] Table 3: Value combination table of global optimal control sequence

[0080]

[0081] In one embodiment, the controller obtains the local optimal control sequence, candidate working points, battery state of charge, driving mode and dynamics demand data of the hybrid vehicle within a 30-second historical time period including an urban congestion scenario and a high-speed acceleration scenario; the local optimal control sequence can be 0-10 seconds, motor power [20, 18, 15,...] kW; the candidate working points can be working point A1: engine 25 kW + motor 5 kW, fuel consumption rate 0.6 g / s, working point B1: engine 30 kW + motor 0 kW, fuel consumption rate 0.7 g / s; SOC = 65%. Then the controller divides the historical time period into 30 unit times each with a length of 1 second, selects the working point that minimizes the battery power consumption and engine fuel consumption in each unit time, and considers SOC balancing to avoid deep discharge, and finally the global optimal control sequence output by the controller can be as shown in Table 4 as follows:

[0082] Table 4: Value table of global optimal control sequence

[0083]

[0084] In the above embodiments, based on the local optimal control sequence in pure electric mode and the candidate working points in hybrid mode, combined with SOC, driving mode and dynamics demand, the global optimal control sequence is generated, so that the vehicle maintains the optimal working state within the entire operation cycle, avoids the overall efficiency decline caused by short-term optimization, and improves the robustness and adaptability of control.

[0085] In step 110, the system-level performance index parameters of the vehicle are optimized based on the optimal driving mode and the global optimal control sequence to achieve optimization of energy distribution during operation of the vehicle.

[0086] The system-level performance index parameters include, but are not limited to, transmission ratio of the transmission system, torque distribution proportionality coefficient limit value of the motor, battery charge and discharge power limit value, motor power limit value, and engine power limit value. That is, the system-level performance index parameters shown in the present application are only several examples provided by the present application, and are not used to limit the system-level performance index parameters of the vehicle. For example, after the controller obtains the global optimal control sequence of the vehicle in a preset historical time period and the optimal driving mode corresponding to each unit time in the preset historical time period, the actual operation data of the vehicle in a certain time period (actual operation time period) in the recent (latest) time period can be collected, and the actual control sequence (actual working point of fuel consumption rate and battery power consumption rate corresponding to each unit time in the actual operation time period) of the vehicle is obtained based on the actual operation data; by comparing the actual control sequence of the vehicle with the adaptive working point in the global optimal control sequence, a comparison result is obtained in combination with the driving mode of the vehicle in each unit time in the actual operation time period; if the comparison result represents that the difference between the actual control sequence of the vehicle and the adaptive working point 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 adaptive working point in the global optimal control sequence is relatively large, which means that there is still optimization space for the system-level performance index parameters of the vehicle; then, at least part of the 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. The optimized system-level performance index parameters are used to achieve optimization of energy distribution during operation of the vehicle, which is beneficial to ensure that the vehicle has an operation time period with optimal combination of fuel consumption rate and battery power consumption rate in the subsequent actual operation process, thereby improving the economy and efficiency of the vehicle in the actual operation process.

[0087] That is, in the case of taking the global optimal control sequence and the optimal driving mode of each unit time related to the global optimal control sequence as a reference group (benchmark), a set of actual global control sequences of a vehicle under actual operating conditions and actual driving modes are obtained. The actual global optimal control sequence can be a sequence that optimizes the combination of fuel consumption rate and battery power consumption rate within the entire actual operating time period of the vehicle. Then, the actual global control sequence and the driving mode of each unit time corresponding to the actual global control sequence are compared with the reference group to obtain a comparison result. If the comparison result shows that the fuel economy and battery power economy of the actual global control sequence are worse than those of the reference group, it indicates that the system-level performance index parameters corresponding to the actual global control sequence need to be optimized. In this case, the system-level performance index parameters of the vehicle can be optimized based on the reference group.

[0088] The energy management method of the hybrid vehicle, by obtaining the running parameters of the vehicle in each unit time in a preset historical time period and the driving mode of the vehicle in the historical time period; then for each unit time, when the driving mode of the vehicle is pure electric mode, the minimum value of the battery power consumption rate of the relevant unit time is determined by combining the relevant running parameters through the pre-constructed longitudinal dynamics model and the battery model, and then the optimal sequence of the battery power consumption rate corresponding to the preset historical time period is determined based on the minimum value of the battery power consumption rate of each unit time; for each unit time, when the driving mode of the vehicle is hybrid mode, the combined minimum value of the fuel consumption rate and the battery power consumption rate of the unit time is screened out by combining the relevant running parameters through the pre-constructed longitudinal dynamics model, the battery model and the engine universal characteristic curve; and further based on the combined minimum value of the fuel consumption rate and the battery power consumption rate of each unit time, the candidate working point that satisfies the combined minimum of the fuel consumption rate and the battery power consumption rate is determined; then, based on the optimal sequence of the battery power consumption rate determined for the historical running time period of the vehicle and the multiple candidate working points, the optimal driving mode for each unit time in the preset historical time period and the global optimal control sequence can be obtained through the dynamic programming algorithm; wherein the global optimal control sequence is the 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; finally, the system-level performance index parameters of the vehicle can be optimized based on the determined optimal driving mode and the global optimal control sequence, and then the optimization of energy distribution in the subsequent actual running process of the vehicle can be realized; wherein the system-level performance index parameters include: transmission ratio of the transmission system, torque distribution proportion coefficient limit value of the motor, battery charge and discharge power limit value, motor power limit value and engine power limit value. By optimizing the system-level performance index parameters of the vehicle in this way, the system-level performance index parameters of the vehicle can be made more optimal, thereby helping to ensure that the vehicle has a running time period with optimal combination of fuel consumption rate and battery power consumption rate in the subsequent running process, thereby helping to improve the economy and efficiency of the vehicle operation.

[0089] In one exemplary embodiment, Figure 3 The flowchart of the method for determining the minimum value of the battery power consumption rate in one embodiment is shown in Figure 3 The kinetic demand and driving mode determination method involves a specific implementation of determining the minimum value of the battery power consumption rate of each unit time based on the pre-constructed longitudinal dynamics model and the battery model in combination with the corresponding running parameters, which is based on the embodiment shown in Figure 1 The description of the embodiment not described in detail can be referred to in Figure 1 , including the following steps 302 to 306. Among them:

[0090] At step 302, based on the pre-constructed longitudinal dynamics model, the dynamics demand of the vehicle in a unit time is determined in combination with the running parameters in the unit time.

[0091] The dynamics demand is the driving force or braking force required by the vehicle, which is calculated from the running parameters, such as the demand torque increase when climbing a slope. The longitudinal dynamics model is used to describe the force and motion relationship of the vehicle in the driving direction, and is used to calculate the real-time driving force / braking force demand; any working condition covers all possible running states of the vehicle, such as starting, accelerating, cruising, decelerating, climbing, etc., so that the longitudinal dynamics model needs to have universality and adaptability.

[0092] For example, in the city commuting working condition, for each unit time, the vehicle speed (low and frequent change), road slope (gentle) and other running parameters in the unit time are obtained first, and these parameters are substituted into the longitudinal dynamics model. The longitudinal dynamics model combines the inherent parameters of the vehicle, such as the curb weight, rolling resistance coefficient, air resistance coefficient, etc., to calculate the size of the driving force required by the vehicle to overcome the driving resistance in the unit time, so as to determine the dynamics demand of the unit time in this working condition.

[0093] At step 304, based on the dynamics demand of the vehicle in a unit time, the battery charging and discharging power of the vehicle in the unit time is determined.

[0094] In one embodiment, in a unit time of the city congestion working condition, the controller determines the dynamics demand (relatively gentle and frequent fluctuation of power demand) of the vehicle to overcome the rolling resistance and short-time acceleration resistance by combining the longitudinal dynamics model with the running parameters of low speed and frequent start-stop in the period. Based on this demand, it is determined that the motor needs to be driven alone to match the working condition characteristics, and then the corresponding power of the battery is determined to meet the power supply of the vehicle low-speed running and short-time acceleration, while combining the current state of charge of the battery to exclude the risk of overcharging and overdischarging, and finally determine the discharging power of the battery in the unit time. This power is the battery charging and discharging power that meets the dynamics demand in the period.

[0095] At step 306, based on the pre-constructed battery model in combination with the battery charging and discharging power in a unit time, the minimum value of the battery power consumption rate in the unit time is determined.

[0096] In one embodiment, in a unit time of the city commuting working condition in the pure electric mode, it is known that the battery needs to output a certain discharging power to meet the dynamics demand of the vehicle starting and accelerating. The controller inputs the discharging power into the pre-constructed battery model (including parameters such as battery internal resistance and capacity attenuation characteristics), the battery model simulates the energy loss under different discharging currents, and by calculating the power consumption rate under different discharging strategies, the discharging scheme with the lowest energy loss under the discharging power constraint is selected, thereby determining the minimum value of the battery power consumption rate in the unit time.

[0097] In the above embodiments, by "determining the vehicle dynamics demand based on the pre-constructed longitudinal dynamics model combined with the running parameters per unit time", the real-time power supply demand of the vehicle under different working conditions can be accurately matched, the energy waste or power shortage problem caused by power estimation deviation can be avoided, and a scientific basis for subsequent energy distribution is provided; on this basis, "determining the battery charging and discharging power according to the dynamics demand", the power output or received by the battery can be highly adapted to the actual power demand of the vehicle, so that the battery is neither overloaded nor affected in vehicle driving performance due to insufficient power, effectively balancing power supply and battery protection; combined with the pre-constructed battery model, "determining the minimum value of the battery power consumption rate per unit time" according to the determined battery charging and discharging power, the battery power consumption can be maximally reduced under the premise of meeting the vehicle power demand, the battery energy utilization efficiency is improved, the vehicle range after single charging is prolonged, the influence of unnecessary battery power consumption on battery life is reduced, and the synergistic optimization of vehicle power demand, battery power control and efficient use of battery power is realized, providing efficient and accurate technical support for energy management of hybrid electric vehicles in pure electric mode.

[0098] In an exemplary embodiment, the specific implementation of the combined minimum value of the fuel consumption rate and the battery power consumption rate per unit time based on the longitudinal dynamics model, the battery model and the engine universal characteristic curve, combined with the corresponding running parameters can further include:

[0099] Based on the pre-constructed longitudinal dynamics model, combined with the relevant running parameters per unit time, the dynamics demand of the vehicle per unit time is determined; based on the dynamics demand of the vehicle per unit time, the battery charging and discharging power and the engine power demand of the vehicle per unit time are determined; based on the pre-constructed battery model and the engine universal characteristic curve, combined with the battery charging and discharging power and the engine power demand, the combined minimum value of the fuel consumption rate and the battery power consumption rate per unit time is determined.

[0100] For example, during a certain unit time of driving on urban roads, the controller first substitutes the operating parameters such as vehicle speed and road slope during this period into the longitudinal dynamics model, and calculates the power required for the vehicle to overcome the driving resistance in combination with parameters such as the vehicle curb weight and air resistance coefficient, and determines the dynamic demand at this time; based on this demand, combined with the power distribution logic in the hybrid mode, it is determined that the battery needs to output a certain discharge power to assist in driving, and at the same time the engine needs to provide corresponding power to meet the main power demand; the determined battery discharge power is then input into the battery model, combined with the engine power demand and mapped to the engine universal characteristic curve, and the energy consumption under different power distribution ratios is analyzed, and finally a combination with a low fuel consumption rate and a low battery power consumption rate is selected under the premise of meeting the power demand per unit time, that is, the minimum combined value of the two is obtained.

[0101] In some embodiments, the method for determining the candidate operating point can be based on dynamic requirements, coupling the vehicle's engine universal characteristic curve and transmission efficiency to establish a multi-objective optimization model, and determining the candidate operating point that meets the combination of minimum fuel consumption rate and minimum battery power consumption rate within a preset historical time period through the Pareto boundary method. Figure 4 FIG. 1 is a flow chart of a method for determining a candidate operating point in one embodiment; FIG. Figure 4 The candidate working point determination method shown in the above Figure 1 Based on the embodiment shown, for details not described in this embodiment, please refer to Figure 1 The method includes the following steps S402 to S406.

[0102] Step 402 : Based on the dynamic requirements of each unit time, a two-dimensional optimization space of fuel consumption rate and battery power consumption rate is established on the engine universal characteristic curve.

[0103] The universal characteristic curve is a three-dimensional graph describing engine performance, with the horizontal axis representing speed (rpm), the vertical axis representing torque (Nm), and the contour lines representing fuel consumption (g / kWh) or efficiency (%). Its core function is to visualize the engine's efficient operating range and guide the energy distribution of the hybrid system. The two-dimensional optimization space is a plane space with the engine fuel consumption rate and battery power consumption rate as coordinate axes, used to visualize the initial operating points composed of all possible combinations of fuel consumption rate and battery power consumption rate. For example, the two-dimensional optimization space can be as follows: Figure 2 As shown, the horizontal axis is the fuel consumption rate (g / s), and the vertical axis is the battery power consumption rate (kW). For the dynamic requirements of a hybrid vehicle, all feasible power distribution combinations of its engine and motor are enumerated, that is, Figure 2 All working points in .

[0104] Note that g / kWh is the fuel mass (gram) consumed per 1 kWh of mechanical work produced, used to evaluate engine thermal efficiency, for map and long-term energy consumption optimization, and g / s is the instantaneous fuel consumption rate.

[0105] Step 404, determine the non-inferior solution set of all initial working points in the two-dimensional optimization space by the Pareto boundary method.

[0106] Wherein, the Pareto boundary method is a core tool in multi-objective optimization, used to find the best trade-off solution set between conflicting objectives, i.e. without sacrificing any objective, other objectives cannot be further optimized; inefficient working points are working points in the two-dimensional optimization space, for which there are other working points that can be more optimal in at least one objective (engine fuel consumption rate or battery power consumption rate); the non-inferior solution set is the working point set on the Pareto boundary, such as the Pareto working points in Figure 2 , the optimization of any objective must sacrifice the other, representing the optimal trade-off solution. Illustratively, the non-inferior solution set is determined by eliminating inefficient working points in the two-dimensional optimization space by the Pareto boundary method.

[0107] Illustratively, if the fuel consumption rate and battery power consumption rate of working point X are both ≤ working point Y, and at least one is strictly better, then working point X dominates working point Y, and working point Y is an inefficient working point. Illustratively, working point F (1.2 g / s, -30 kW), working point D (1.8 g / s, -20 kW), working point F has lower fuel consumption rate but higher battery power consumption rate, and working point D and F do not dominate each other; working point G (2.5 g / s, 0 kW) is dominated by working points D, E (working points on the Pareto boundary), F, because the fuel consumption rate and battery power consumption rate of working point G are both worse, and the working point connection line, such as the working point D-E-F connection line, represents the non-inferior solution set, which is not dominated by any working point.

[0108] Step 406, select the Pareto boundary point that coincides with the pre-set engine optimal working curve as the candidate working point that satisfies the combined minimum fuel consumption rate and battery power consumption rate in the non-inferior solution set.

[0109] Wherein, the engine optimal working curve is the continuous trajectory of the lowest fuel consumption rate working point in the universal characteristic curve.

[0110] Illustratively, the engine universal characteristic curve: the horizontal axis is the engine speed (rpm); the vertical axis is the torque (Nm); the contour line is the fuel consumption rate (g / kWh);

[0111] The optimal working curve is the trajectory connecting the lowest fuel consumption rate points, for example:

[0112] The minimum fuel consumption point 1 has a rotation speed of 2000 rpm and a torque of 120 Nm, and the fuel consumption thereof is 210 g / kWh;

[0113] The minimum fuel consumption point 2 has a rotation speed of 2500 rpm and a torque of 150 Nm, and the fuel consumption thereof is 205 g / kWh.

[0114] The candidate point can be selected from the Pareto boundary in the non-inferior solution set and coincides with the optimal working curve, such as the working point Q (1.8 g / s, 30 kW).

[0115] In the above embodiment, based on the kinetic demand, a two-dimensional optimization space of the engine fuel consumption and the battery power consumption is established on the universal characteristic curve, the multi-objective problem is visualized, the low-efficiency working point in the two-dimensional optimization space is removed by the Pareto boundary method, the non-inferior solution set is determined, the effective trade-off options are reserved, the Pareto boundary point coinciding with the engine optimal working curve is selected as the candidate working point, and it can be ensured that the engine always operates in the high-efficiency interval.

[0116] In some embodiments, the manner of determining the global optimal control sequence can be: based on the battery power consumption optimal sequence and the plurality of candidate working points, the optimal driving mode for each unit time in the preset historical time period and the global optimal control sequence are obtained by a dynamic programming algorithm under the condition of satisfying the constraint condition of the preset component working state of the vehicle. For example, based on the battery power consumption optimal sequence, the plurality of candidate working points, the battery state of charge, the driving mode of the vehicle in each unit time and the kinetic demand, the decision branches with efficiency lower than a preset threshold are removed in the entire preset historical time period by a dynamic programming algorithm, and the optimal driving mode for each unit time and the global optimal control sequence are determined; the efficiency is determined based on the fuel consumption rate and the battery power consumption rate, and the preset threshold is determined based on the efficiency critical value of the fuel consumption rate and the efficiency critical value of the battery power consumption rate set in the dynamic programming algorithm optimization process.

[0117] The preset threshold is determined by the efficiency critical value set in the optimization process, specifically including the efficiency critical value of the fuel consumption rate (fuel consumption threshold) and the efficiency critical value of the battery power consumption rate (power consumption threshold), which is used to quickly remove obviously inefficient decision branches. For example, if the fuel consumption rate or the power consumption rate of a certain control sequence exceeds the corresponding preset threshold, it is directly excluded to reduce the amount of calculation. By removing the decision branches with efficiency lower than the preset threshold, the DP algorithm convergence can be accelerated, and all possible branches can be avoided.

[0118] For example, the preset threshold can be:

[0119] fuel consumption threshold, which can be but is not limited to greater than 250 g / kWh, and the high-efficiency engine threshold is usually set to 220-250 g / kWh;

[0120] power consumption threshold, the instantaneous discharge power is greater than the maximum allowable value of the battery, such as being limited to 30 kW when SOC=20%.

[0121] In some embodiments, the manner in which the vehicle is controlled to operate according to the globally optimal control sequence can be to control the vehicle to operate under the constraint that the engine speed, motor speed, engine torque, motor torque, battery charge and discharge power, and battery state of charge of the vehicle are in a preset working state, i.e., under the constraint that each component in the transmission system of the vehicle is in an effective working state during operation of the transmission system.

[0122] wherein the engine speed is the rotational speed of the engine crankshaft; the motor speed is the rotational speed of the motor rotor; the engine torque is the rotational torque output by the engine; the motor torque is the driving or braking torque output by the motor; and the battery charge and discharge power is the instantaneous charge and discharge capability of the battery.

[0123] The energy management method of the hybrid vehicle of the present application is applied to a hybrid vehicle. To ensure normal use of the transmission system of the hybrid vehicle and the service life of the battery, the battery charge needs to be the same or similar at the start and end of operation, and always fluctuate within a suitable dynamic range during operation. To maintain the SOC stable, the battery charge at the initial state therefore needs to be equal to the battery charge at the end of operation, and the engine speed, motor speed, engine torque, motor torque, and battery charge and discharge power of the vehicle need to be in an effective working state.

[0124] The constraint condition for constraining the working state of each component in the transmission system of the hybrid vehicle during operation is specifically:

[0125]

[0126] wherein the suitable dynamic range of the SOC is 0.4-0.7, represents the battery charge at the initial state, represents the battery charge at the end of operation; represents the speed of the power component, which is the engine or the motor, , i=e represents the speed of the engine, and i=em represents the speed of the motor; represents the speed of the engine at time k per unit time, represents the minimum speed of the engine, represents the maximum speed of the engine, represents the speed of the motor at time k per unit time, represents the minimum speed of the motor, represents the maximum rotation speed of the motor; represents the torque of the power component, the power component being the engine or the motor, , i = e represents the torque of the engine, i = em represents the torque of the motor; represents the engine torque at time k per unit time, represents the minimum torque of the engine, represents the maximum torque of the engine, represents the motor torque at time k per unit time, represents the minimum torque of the motor, represents the maximum torque of the motor;

[0127] represents the charge-discharge power of the battery at time k per unit time, represents the minimum charge-discharge power of the battery, represents the maximum charge-discharge power of the battery.

[0128] In the above embodiment, by constraining the working state of each component, the engine meets the minimum fuel consumption rate and / or the motor meets the minimum battery power consumption rate under the premise of ensuring efficient and stable operation of the vehicle.

[0129] It should be noted that the energy management method of the hybrid vehicle of the present application is applied to various driving cycle working conditions, the effectiveness of the working point is verified through dynamic simulation, and a globally optimal control sequence corresponding to the engine-motor cooperative working is generated, and the various working conditions include: Worldwide Harmonized Light Vehicles Test Cycle (WLTC) and Highway Fuel Economy Test (HWEET) and the like.

[0130] It should be understood that although each step in the flowchart involved in the embodiments described above is shown in sequence according to the arrow, these steps are not necessarily executed in the order indicated by the arrow. Unless otherwise specified herein, the execution of these steps is not strictly limited in sequence, and these steps can be executed in other orders. Moreover, at least part of the steps in the flowchart involved in the embodiments described above can include multiple steps or multiple stages, which are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily sequential, but can be alternately executed with other steps or steps or stages in other steps. It can be understood that the steps in different embodiments can be freely combined as needed, and various non-contradictory schemes formed by the combination are within the scope of protection of the present application.

[0131] Based on the same inventive concept, the embodiments of the present application also provide a hybrid vehicle energy management device for implementing the energy management method of the hybrid vehicle involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme described in the above method, so the specific limitations in one or more hybrid vehicle energy management device embodiments provided below can refer to the limitations of the hybrid vehicle energy management method in the above text, which will not be repeated here.

[0132] In one exemplary embodiment, as shown in Figure 5 An energy management device 500 of a hybrid vehicle is provided, comprising: a parameter determination module 510, a pure electric optimization module 520, a hybrid optimization module 530, an optimal sequence determination module 540, and an energy management module 550, wherein:

[0133] The parameter determination module is configured to obtain running parameters of a preset historical time period of the vehicle, and driving modes of each unit time in the preset historical time period of the vehicle;

[0134] The pure electric optimization module 510 is configured to, for each unit time, when the driving mode represents a pure electric mode, determine a minimum value of a battery power consumption rate of the unit time based on a pre-constructed longitudinal dynamics model and a battery model in combination with the corresponding running parameters; and determine an optimal sequence of battery power consumption rates based on the minimum values of the battery power consumption rates corresponding to a plurality of unit times.

[0135] The hybrid optimization module 520 is configured to, for each unit time, determine a combined minimum value of fuel consumption rate and battery power consumption rate of the unit time based on the longitudinal dynamics model, the battery model and the engine universal characteristic curve in combination with the corresponding operating parameter when the driving mode represents the hybrid mode; and determine a candidate working point 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 a plurality of unit times.

[0136] The optimal sequence determination module 530 is configured to 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 based on the battery power consumption rate optimal sequence and a plurality of candidate working points; 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.

[0137] The energy management module 540 is configured to optimize a system-level performance index parameter of the vehicle based on the optimal driving mode and the global optimal control sequence to realize optimization of energy distribution in the operation 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 machine, a battery charging and discharging power limit value, an electric machine power limit value and an engine power limit value.

[0138] In some embodiments, the pure electric optimization module 520 is further configured to determine a dynamic demand of the vehicle at the unit time based on a pre-constructed longitudinal dynamics model in combination with the operating parameter of the relevant unit time.

[0139] Determine the battery charging and discharging power of the vehicle at the unit time based on the dynamic demand of the vehicle at the unit time.

[0140] Determine the minimum value of the battery power consumption rate at the unit time based on the pre-constructed battery model in combination with the battery charging and discharging power at the unit time.

[0141] Each module in the energy management device of the hybrid electric vehicle described above can be realized by software, hardware and a combination thereof in whole or in part. Each module described above can be embedded in or independent of the processor in the controller in hardware form, or can be stored in the memory in the controller in software form so as to be called and executed by the processor to perform the operations corresponding to each module.

[0142] In an exemplary embodiment, a controller is provided, and its internal structure diagram can be as shown in Figure 6The controller includes a processor, a memory, an input / output interface (I / O) and a communication interface. The processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the controller is configured to provide computing and control capabilities. The memory of the controller includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operating system and the computer program in the non-volatile storage medium to run. The input / output interface of the controller is configured to exchange information between the processor and external devices. The communication interface of the controller is configured to perform wired or wireless communication with external terminals. The wireless communication can be achieved through WIFI, mobile cellular network, near field communication (NFC) or other technologies. The computer program is executed by the processor to implement a vehicle driving control method.

[0143] Those skilled in the art can understand that, Figure 6 The structure shown in the figure is only a block diagram of part of the structure related to the scheme of the present application, and does not constitute a limitation on the controller to which the scheme of the present application is applied. The specific controller can include more or fewer components than those shown in the figure, or combine certain components, or have a different component arrangement.

[0144] In an exemplary embodiment, a controller is provided, including a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the steps in the above method embodiments.

[0145] In an exemplary embodiment, a computer readable storage medium is provided, storing a computer program, and the computer program is executed by a processor to implement the steps in the above method embodiments.

[0146] In an exemplary embodiment, a computer program product is provided, including a computer program, and the computer program is executed by a processor to implement the steps in the above method embodiments.

[0147] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer readable storage medium, and when executed, can include the processes of the above-mentioned embodiment methods. Any reference to memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile memory and volatile memory. The non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical storage, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. The volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration but not limitation, the RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc. The database involved in the embodiments provided in the present application can include at least one of a relational database and a non-relational database. The non-relational database can include a distributed database based on a block chain, etc., without being limited thereto. The processor involved in the embodiments provided in the present application can be a general-purpose processor, a central processing unit, a graphics processing unit, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, an artificial intelligence (AI) processor, etc., without being limited thereto.

[0148] The technical features of the above embodiments can be combined in any manner. To make the description concise, all possible combinations of the technical features in the above embodiments are not described, but as long as the combinations of the technical features do not exist contradictions, they should be considered as the scope of the present application.

[0149] The above-described embodiments are merely illustrative of several embodiments of the present application, and the description is relatively specific and detailed, but should not be understood as a limitation on the scope of the patent. It should be noted that for those skilled in the art, without departing from the concept of the present application, a number of modifications and improvements can be made, which are all within the scope of the present application. Therefore, the scope of protection of the present application should be subject to the appended claims.

Claims

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

2. The method according to claim 1, characterized in that The determining the minimum value of the battery power consumption rate per unit time based on the pre-built longitudinal dynamics model and battery model in combination with the corresponding operating parameters includes: Determining the dynamics requirements of the vehicle per unit time based on a pre-built longitudinal dynamics model and in combination with the operating parameters associated with the unit time; determining a battery charging and discharging power of the vehicle per unit time based on the dynamic demand of the vehicle per unit time; Based on a pre-built battery model and the battery charging and discharging power per unit time, a minimum value of the battery power consumption rate per unit time is determined.

3. The method according to claim 1, characterized in that The determining of the combined minimum value of the fuel consumption rate and the battery power consumption rate per unit time based on the longitudinal dynamics model, the battery model, and the engine universal characteristic curve in combination with the corresponding operating parameters includes: Determining the dynamics requirements of the vehicle per unit time based on a pre-built longitudinal dynamics model and in combination with the operating parameters associated with the unit time; determining a battery charging and discharging power and an engine power requirement of the vehicle per unit time based on the dynamic requirement of the vehicle per unit time; Based on a pre-built battery model and an engine universal characteristic curve, and in combination with the battery charging and discharging power and the engine power requirement, a minimum combined value of the fuel consumption rate and the battery power consumption rate per unit time is determined.

4. The method according to claim 3, characterized in that The determining, based on the minimum combined values ​​of the fuel consumption rates and the battery power consumption rates corresponding to the plurality of unit times, a candidate operating point that satisfies the minimum combined value of the fuel consumption rates and the battery power consumption rates includes: Based on the dynamic requirements of each unit time, a two-dimensional optimization space of the fuel consumption rate and the battery power consumption rate is established on the engine universal characteristic curve; the two-dimensional optimization space is a plane space with the fuel consumption rate and the battery power consumption rate as coordinate axes, and is used to visualize an initial operating point formed by a combination of the fuel consumption rate and the battery power consumption rate; Determine a non-inferior solution set of all the initial operating points in the two-dimensional optimization space by using the Pareto boundary method; the initial operating point in the non-inferior solution set is a Pareto boundary point formed by a combined minimum value of the fuel consumption rate and the battery power consumption rate; The Pareto boundary point that coincides with the preset engine optimal operating curve is selected from the non-inferior solution set as the candidate operating point that satisfies the minimum combination of the fuel consumption rate and the battery power consumption rate; the engine optimal operating curve is a continuous trajectory of the lowest fuel consumption rate operating point in the universal characteristic curve.

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

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

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

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

9. A controller comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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