An energy management method, an energy management system, a terminal device, and a storage medium

Abstract

The application provides an energy management method, an energy management system, a terminal device and a storage medium. The method comprises the following steps: monitoring a driving state of a target vehicle in real time, and generating driving information of the target vehicle within a preset time length; performing optimization on the driving information with the lowest equivalent fuel consumption as an optimization target, so as to obtain an SOC threshold update threshold corresponding to the driving information; wherein the SOC threshold update threshold comprises an upper SOC threshold update threshold and a lower SOC threshold update threshold; selecting an energy supply source based on a comparison result of a real-time SOC value of the target vehicle and the SOC threshold update threshold; and calling a corresponding energy management strategy according to the energy supply source. The application can improve the energy utilization efficiency of a series hybrid power system and reduce energy waste.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of hybrid power system energy management, and particularly relates to an energy management method, an energy management system, a terminal device and a storage medium. BACKGROUND

[0002] With the increase of global energy consumption, new energy vehicle technology has become a research hotspot. Hybrid power system is not limited by battery endurance and charging problems, and can flexibly switch or work simultaneously between internal combustion engine and electric motor, thereby providing higher driving flexibility and convenience. According to the power connection mode, the hybrid power system is divided into three types of series, parallel and series-parallel. Among them, the series hybrid power system is a kind of auxiliary power unit composed of internal combustion engine and generator combined with energy storage battery system, which drives the wheels through the driving motor. The internal combustion engine does not directly drive the wheels, but converts mechanical energy into electrical energy through the generator, which can be stored in the battery or directly supplied to the driving motor. The internal combustion engine and the driving system are completely decoupled, which is simple in structure and convenient for design and maintenance. And the internal combustion engine can work at a fixed speed and load, always running in the medium and high efficiency area, thereby improving fuel economy and reducing emissions. The series hybrid power system has a broader market prospect, especially for users who need to balance endurance mileage and environmental performance.

[0003] The series hybrid power system involves APU (Auxiliary Power Unit), energy storage battery system and driving motor and other power sources. Through reasonable energy management, the hybrid power system can flexibly switch power sources under different driving conditions and optimize energy utilization. In the prior art, the energy management is usually carried out through a rule-based switching energy management strategy. Specifically, the start-stop of the internal combustion engine and the charging and discharging of the battery are controlled by setting logical threshold values. When the SOC (State of Charge) is lower than the lower threshold value of SOC, the internal combustion engine starts to generate electricity to charge the energy storage battery. When the SOC is higher than the upper threshold value of SOC, the internal combustion engine is turned off and the vehicle enters pure electric mode. However, the above method does not consider the influence of speed and working condition changes during driving on energy management, which is easy to cause energy waste. SUMMARY

[0004] The present application aims to provide an energy management method, an energy management system, a terminal device and a storage medium to solve the above technical problems and improve the energy utilization efficiency of the series hybrid power system and reduce energy waste.

[0005] In order to solve the above technical problems, the present application provides an energy management method, comprising:

[0006] Real-time monitoring of the driving state of the target vehicle, generating the driving information of the target vehicle within a preset time length;

[0007] Optimizing the driving information with the lowest equivalent fuel consumption as the optimization target, obtaining the SOC threshold update threshold under the driving information; wherein the SOC threshold update threshold includes an SOC upper limit update threshold and an SOC lower limit update threshold;

[0008] Based on the comparison result of the real-time SOC value of the target vehicle and the SOC threshold update threshold, selecting an energy supply source, and calling a corresponding energy management strategy according to the energy supply source.

[0009] In the above scheme, the driving information is generated by real-time monitoring of the driving state of the target vehicle, which enables the energy management strategy to be adjusted according to the current driving conditions. The driving information is optimized with the lowest equivalent fuel consumption as the optimization target, and a dynamic SOC threshold update threshold is obtained. This dynamic adjustment can better adapt to different driving conditions and improve the flexibility and adaptability of the system. Based on the real-time SOC value and the dynamically updated SOC threshold value, a suitable energy supply source is selected, and this reasonable energy distribution strategy can maximize the use of energy and reduce waste.

[0010] In an implementation manner, the real-time monitoring of the driving state of the target vehicle, generating the driving information of the target vehicle within a preset time length, specifically includes:

[0011] Real-time monitoring of the driving state of the target vehicle, calculating the vehicle speed information of the target vehicle within a preset time length; wherein the vehicle speed information includes real-time driving speed, maximum driving speed, minimum driving speed and average driving speed within the preset time length;

[0012] Calculating the first speed difference value between the maximum driving speed and the average driving speed, the second speed difference value between the minimum driving speed and the average driving speed, and selecting the maximum value of the first speed difference value and the second speed difference value as the maximum speed difference value;

[0013] When the maximum speed difference value is less than or equal to a preset speed threshold, it is determined that the target vehicle is in a stable driving state, and the driving information is generated based on the vehicle speed information and the time when the vehicle speed information is collected.

[0014] In the scheme, the speed fluctuation of the vehicle is accurately calculated by monitoring the vehicle speed information in real time, the first speed difference value and the second speed difference value are generated, and the maximum value is selected as the maximum speed difference value. When the maximum speed difference value is less than or equal to the preset speed threshold value, the system determines that the vehicle is in a stable driving state, and ensures accurate judgment of the vehicle state. The driving information is generated based on the vehicle speed information when the vehicle is in a stable driving state, and the energy distribution strategy is optimized. The energy management in the stable state is more predictable and efficient, and unnecessary energy waste is reduced.

[0015] In an implementation manner, the driving information is optimized with the lowest equivalent fuel consumption as the optimization target to obtain an SOC threshold update threshold under the driving information, and the method specifically comprises the following steps.

[0016] A target function is constructed with the lowest equivalent fuel consumption as the optimization target, and an expression of the target function is as follows:

[0017]

[0018] In the formula, m eqv (t) is the equivalent fuel consumption at time t; m fuel (t) is the actual fuel consumption at time t; s(t) is an equivalent fuel consumption factor; m ess (t) is the battery energy consumption at time t; P batt (t) is the power output by the battery at time t; Q lhv is the low heat value of fuel;

[0019] The equivalent fuel consumption factor is optimized based on a preset constraint condition to obtain an equivalent fuel consumption factor optimization value, the target function is solved based on the equivalent fuel consumption factor optimization value, and the lowest equivalent fuel consumption is obtained.

[0020] The SOC upper limit threshold of the equivalent fuel consumption factor optimization value is taken as the SOC upper limit update threshold, and the SOC lower limit threshold is taken as the SOC lower limit update threshold; and an expression of the equivalent fuel consumption factor is as follows:

[0021]

[0022] In the formula, m are average efficiencies of the motor, the motor controller, the battery and the engine respectively; SOC low is the SOC lower limit threshold; SOC high is the SOC upper limit threshold; SOC ref is a reference SOC value; and SOC(t) is an actual SOC value of the battery at time t.

[0023] In the above scheme, by constructing a target function with the minimum equivalent fuel consumption as the optimization target and performing optimization based on a particle swarm algorithm, the system can effectively reduce the overall fuel consumption of the vehicle and improve fuel economy.

[0024] The target function comprehensively considers the actual fuel consumption and battery energy consumption and converts the battery energy consumption into equivalent fuel consumption through an equivalent fuel consumption factor to minimize the comprehensive fuel consumption. A particle swarm optimization algorithm is used to optimize the equivalent fuel consumption factor, which has the characteristics of global search capability and fast convergence speed and can effectively find the optimal solution in a complex optimization space. Through the particle swarm algorithm, the optimal SOC threshold update threshold can be quickly determined, thereby optimizing the energy management strategy. By dynamically optimizing the equivalent fuel consumption factor, the system can dynamically adjust the SOC threshold update threshold according to the real-time driving state and battery state of the vehicle, realizing real-time optimization of the energy management strategy. This dynamic adjustment capability enables the system to automatically adapt to different conditions, improving the flexibility and adaptability of the entire system. Further, the target function considers the average efficiency of the motor, motor controller, battery, and engine, as well as the upper and lower limits and reference value of the SOC, ensuring that the energy management strategy comprehensively considers the efficiency of each part of the system and the health status of the battery. This comprehensive consideration enables the energy management to not only focus on fuel economy but also consider the service life and performance of the battery.

[0025] In an implementation manner, the particle swarm optimization of the equivalent fuel consumption factor based on the preset constraint condition specifically includes:

[0026] The constraint condition is obtained, wherein the constraint factors of the constraint condition include motor angular velocity, engine angular velocity, battery state of charge, and required torque;

[0027] The working model of the target vehicle is constructed according to the constraint factors; wherein the working model includes a required torque model, an engine fuel consumption model, a motor model, and a battery model;

[0028] The particle swarm optimization of the preset constraint condition is performed according to the expression of each working model to obtain the optimized value of the equivalent fuel consumption factor; wherein the expression of the preset constraint condition is:

[0029]

[0030] In the formula, w mot,max is the maximum allowable angular velocity of the motor; w eng,min is the minimum allowable angular velocity of the engine; w eng,max is the maximum allowable angular velocity of the engine; T eng,min , T eng,maxTmin and Tmax are the minimum and maximum allowable engine torques for a given engine angular velocity; T mot,min , T mot,max min and Tmax are the minimum and maximum allowable motor torques for a given motor angular velocity.

[0031] In an implementation, the particle swarm optimization of the preset constraint condition according to the expression of each working model to obtain the optimized value of the equivalent fuel consumption factor includes:

[0032] The expression of the demand torque model is:

[0033]

[0034] wherein, T 需求 is the demand torque; m is the vehicle mass; g is the gravitational acceleration; f r is the rolling resistance coefficient; cosα is the cosine value of the road inclination; p α is the air density; C D is the air resistance coefficient; A f is the vehicle frontal area; v is the vehicle speed; sinα is the sine value of the road inclination; s is the rotational mass conversion coefficient; R w is the vehicle radius;

[0035] The expression of the engine fuel consumption model is:

[0036]

[0037] wherein, is the fuel consumption rate; T eng is the engine torque; w eng is the engine angular velocity; is the function rule;

[0038] The expression of the motor model is:

[0039]

[0040] wherein, P mot is the motor power; T mot is the motor torque; w mot is the motor angular velocity; sgn(T mot ) is a sign function for determining the direction of the motor torque; ηmo t is the motor efficiency;

[0041] The expression of the battery model is:

[0042]

[0043] wherein SOC is a battery state of charge; U oc is an open circuit voltage of the battery; R int is an internal resistance of the battery; P batt is a battery power; Q batt is a battery capacity;

[0044] solving the constraint factors according to the expression of each working model, obtaining a plurality of constraint factor values, performing particle swarm optimization on the constraint condition based on the constraint factor values, and obtaining an optimized value of the equivalent fuel consumption factor.

[0045] In the above scheme, through the accurate vehicle working model, comprehensive constraint condition and efficient particle swarm optimization algorithm, dynamic optimization of the equivalent fuel consumption factor is realized.

[0046] In an implementation manner, the energy supply source is selected based on a comparison result of the real-time SOC value of the target vehicle and the SOC threshold value, and specifically includes:

[0047] obtaining a driving mode and a real-time SOC value of the target vehicle;

[0048] when the driving mode is the pure electric driving mode and the real-time SOC value is greater than the SOC lower limit update threshold, switching the auxiliary power unit to an off state and selecting the energy storage battery system as the energy supply source;

[0049] when the driving mode is the pure electric driving mode and the real-time SOC value is less than the SOC lower limit update threshold, or the driving mode is the hybrid driving mode and the real-time SOC value is less than the SOC upper limit update threshold, selecting the energy storage battery system and the auxiliary power unit as the energy supply source; when the driving mode is the hybrid driving mode, the auxiliary power unit is in an on state.

[0050] In the above scheme, when the vehicle is in the pure electric driving mode and the SOC value is higher than the lower limit update threshold, the system switches off the auxiliary power unit and only uses the energy storage battery system for power supply, realizes zero-emission driving and improves energy efficiency. When the SOC value is lower than the lower limit threshold or the SOC value is lower than the upper limit update threshold in the hybrid driving mode, the system automatically turns on the auxiliary power unit, combines the battery and the auxiliary power unit for power supply, ensures continuous operation of the vehicle and maintains the battery SOC in a reasonable range. By monitoring the SOC value and timely adjusting the energy supply source, problems such as vehicle unable to drive due to low battery power can be prevented.

[0051] In an implementation, the calling of the corresponding energy management strategy according to the energy supply source specifically comprises: acquiring the maximum supply power of the energy supply source, comparing the maximum supply power with the demand power, and calling the corresponding energy management strategy according to the power comparison result; wherein,

[0052] When the energy supply source is the energy storage battery system, if the maximum discharge power of the energy storage battery system is greater than or equal to the demand power, the energy storage battery system is controlled to adjust the discharge power to the demand power.

[0053] When the energy supply source is the energy storage battery system and the auxiliary power unit, the maximum power generation of the auxiliary power unit is compared with the demand power.

[0054] If the maximum power generation is greater than or equal to the demand power, the auxiliary power unit is controlled to adjust the output power to the demand power.

[0055] If the maximum power generation is less than the demand power, the output power of the auxiliary power unit is adjusted to the maximum power generation, and the discharge power of the energy storage battery system is adjusted to a first discharge power, which is the power difference between the demand power and the maximum power generation.

[0056] In the above scheme, when only the energy storage battery system is used and the maximum discharge power thereof meets the demand, the battery discharge power is directly adjusted to the demand value, which is simple and direct and ensures stable power supply. When the energy storage battery system and the auxiliary power unit are used, intelligent power distribution is performed according to the relationship between the maximum power generation of the auxiliary power unit and the demand power, so as to ensure that the power demand is met. According to different energy supply sources and power demands, different management strategies are adopted, so that the system can flexibly adapt to various driving conditions and load demands. When the demand power changes, the power distribution can be adjusted in real time, so as to ensure that the system is always in an optimal working state.

[0057] In an implementation, before the calling of the corresponding energy management strategy according to the energy supply source, the method further comprises:

[0058] acquiring average driving speed information used for generating a previous SCO threshold update threshold value, comparing the average driving speed information with real-time vehicle speed information, if the deviation between the two is within a preset range, taking the energy management strategy as the energy management result of the target vehicle and executing the energy management strategy.

[0059] In the above scheme, by comparing the current real-time vehicle speed information with the information used when the SOC threshold update threshold is generated last time, it is ensured that the vehicle speed change is within the preset range. This can avoid frequent adjustment of energy management strategy due to frequent change of vehicle speed, and improve the stability of the system.

[0060] In an implementation manner, the energy management method further comprises adjusting power of the auxiliary power unit and the energy storage battery system based on a real-time battery power capability, in particular:

[0061] The temperature information and the SOC information of the energy storage battery system are monitored in real time, and the real-time battery power capability is generated based on the temperature information and the SOC information; wherein the real-time battery power capability comprises a maximum charging power and a maximum discharging power;

[0062] When the target vehicle is in the hybrid mode and the real-time discharging power of the energy storage battery is greater than the maximum discharging power, a first power adjustment strategy is executed; wherein the first power adjustment strategy is to adjust the real-time discharging power to the maximum discharging power;

[0063] When the target vehicle is in the hybrid mode and the real-time charging power of the energy storage battery is greater than the maximum charging power, a second power adjustment strategy is executed; wherein the second power adjustment strategy is to reduce the output power of the auxiliary power unit until the real-time charging power is less than the maximum charging power;

[0064] When the target vehicle is in the pure electric driving mode and the real-time discharging power of the energy storage battery is greater than the maximum discharging power, a third power adjustment strategy is executed; wherein the third power adjustment strategy is to adjust the real-time discharging power to the maximum discharging power.

[0065] In the above scheme, according to the real-time battery power capability, the power output of the auxiliary power unit and the energy storage battery system is dynamically adjusted to ensure that the system is always in the best working state. When the real-time discharging power exceeds the maximum discharging power, the real-time discharging power is adjusted to the maximum discharging power to avoid over-discharging of the battery and protect the battery health. When the real-time charging power exceeds the maximum charging power, the output power of the auxiliary power unit is reduced to ensure that the real-time charging power is less than the maximum charging power, avoiding over-charging of the battery and prolonging the battery life.

[0066] In a second aspect, the present application also provides an energy management system for executing the energy management method as described above, comprising an auxiliary power unit, a rectifier device, a power distribution device, an auxiliary electrical equipment, a motor, an energy storage battery system, a speed monitoring module, a logic gate control module, an energy management processing system;

[0067] The auxiliary power unit is used to output alternating current;

[0068] The rectifier is configured to convert the alternating current into direct current and send direct current power information to the energy management processing system;

[0069] The power distribution device is configured to receive the power distribution instruction sent by the energy management processing system, and adjust the power distribution of the energy storage battery system, the auxiliary electrical equipment and the motor based on the power distribution instruction;

[0070] The auxiliary electrical equipment is configured to realize the human-computer interaction between the vehicle and the user;

[0071] The motor is configured to drive the vehicle to travel;

[0072] The energy storage battery system is configured to store electric energy and supply energy to the motor; wherein the energy storage battery system comprises at least one energy storage battery;

[0073] The speed monitoring module is configured to monitor the traveling state of the vehicle in real time, generate the traveling information of the vehicle within a preset time length and send the traveling information to the logic gate control module;

[0074] The logic gate control module is configured to generate an SOC threshold update threshold according to the traveling information and send the SOC threshold update threshold to the energy management processing system;

[0075] The energy management processing system is configured to generate an energy management strategy according to the received SOC threshold update threshold, and generate a power distribution instruction to the power distribution device based on the energy management strategy;

[0076] The energy management processing system is further configured to adjust the real-time charge-discharge power of the energy storage battery according to the real-time battery power capability of the energy storage battery.

[0077] In the above scheme, the power distribution device dynamically adjusts the power distribution of the energy storage battery system, auxiliary electrical equipment and drive motor according to the power distribution instruction sent by the energy management processing system, ensuring efficient use of energy. The speed monitoring module monitors the driving state of the vehicle in real time and generates driving information sent to the logic gate control module. The logic gate control module generates an SOC threshold update threshold based on the driving information, and the energy management processing system dynamically adjusts the energy management strategy based on the threshold. The system comprehensively considers the vehicle performance, battery life and energy efficiency to generate the best energy management scheme, ensuring efficient work under different driving conditions. The auxiliary electrical equipment realizes the interaction between the vehicle and the user, provides rich information display and driving suggestions, and improves the driving experience. The speed monitoring module monitors the driving state of the vehicle in real time, and the user can obtain the driving information and energy management of the vehicle through the auxiliary electrical equipment, thereby enhancing the driving safety and comfort. The energy management processing system monitors the health status of the energy storage battery system in real time, avoids long-term work of the battery under high or low load, and prolongs the battery life. The logic gate control module generates an SOC threshold update threshold based on the driving information, and the energy management processing system adjusts the charging and discharging strategy of the battery based on the threshold, ensuring that the battery works in the appropriate SOC range and avoiding excessive charging and discharging. Through the cooperative work of each module, efficient energy utilization, flexible adaptability and good driving experience for users are realized.

[0078] In a third aspect, the present application also provides a terminal device, comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor implements the energy management method as described above when executing the computer program.

[0079] In a fourth aspect, the present application also provides a computer-readable storage medium, comprising a stored computer program, wherein the computer-readable storage medium controls the device where the computer-readable storage medium is located to execute the energy management method as described above when the computer program runs. BRIEF DESCRIPTION OF DRAWINGS

[0080] Figure 1 A flowchart of an energy management method provided in an embodiment of the present application;

[0081] Figure 2 A flowchart of a driving information generation process provided in an embodiment of the present application;

[0082] Figure 3 A power adjustment flowchart provided in an embodiment of the present application;

[0083] Figure 4 A system composition diagram of an energy management system provided in an embodiment of the present application. DETAILED DESCRIPTION

[0084] The specific embodiments of the present application will be further described in conjunction with the drawings and examples. The following examples are used to illustrate the present application but not to limit the scope of the present application.

[0085] The terms "first" and "second" and the like in the description and in the claims of the present application and the drawings are used to distinguish between similar objects, not to describe a particular sequential order. Moreover, the terms "include" and "have" and any variations thereof are intended to cover a non-exclusive inclusion. For example, a process, method, system, product, or device that comprises a list of steps or units is not limited to the listed steps or units but can optionally further include additional steps or units not listed or can optionally further include steps or units inherent to such process, method, product, or device.

[0086] Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the application. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are they necessarily mutually exclusive of one another. It is expressly understood that the embodiments described herein are merely examples from among a great variety of embodiments that can be made in accordance with the present application.

[0087] Embodiment 1

[0088] Reference is made to Figure 1 , Figure 1 A flowchart of an energy management method provided in an embodiment of the present application is shown. The embodiment of the present application provides steps 101 to 104, which are specifically as follows:

[0089] Step 101: Real-time monitoring of the driving state of a target vehicle to generate driving information of the target vehicle within a preset time length.

[0090] In an embodiment, the driving state of the target vehicle is monitored in real time, and vehicle speed information of the target vehicle within a preset time length is calculated; wherein the vehicle speed information includes real-time driving speed, maximum driving speed, minimum driving speed, and average driving speed within the preset time length; a first speed difference value between the maximum driving speed and the average driving speed, and a second speed difference value between the minimum driving speed and the average driving speed are calculated, and the maximum value of the first speed difference value and the second speed difference value is selected as a maximum speed difference value; when the maximum speed difference value is less than or equal to a preset speed threshold, it is determined that the target vehicle is in a stable driving state, and the driving information is generated based on the vehicle speed information and the time at which the vehicle speed information is collected; when the maximum speed difference value is greater than the preset speed threshold, the vehicle speed information of the target vehicle is continuously updated.

[0091] In the embodiment of the present application, the driving state data of the vehicle is collected by using various sensors (such as a speed sensor, an acceleration sensor, a GPS, etc.), and the accuracy and reliability of the data are improved through sensor fusion technology. Preferably, the collected real-time driving speed data is filtered and processed by sliding average to reduce noise interference and obtain more stable speed information. See Figure 2 , Figure 2 The flowchart of a driving information generation process provided in an embodiment of the present application is shown. The maximum driving speed V max and the minimum driving speed V min of the vehicle in 5 seconds are recorded, and the average driving speed V in 5 seconds is calculated. Then, whether the vehicle is in a stable driving stage is determined according to the maximum speed difference in 5 seconds. If the vehicle is in a stable driving stage, the speed and time information of the vehicle in 5 seconds is output, otherwise the speed information, the maximum and minimum speed values are continuously updated and the average driving speed V is calculated.

[0092] Step 102: optimizing the driving information with the lowest equivalent fuel consumption as the optimization target to obtain an SOC threshold update threshold under the driving information; wherein the SOC threshold update threshold includes an SOC upper limit update threshold and an SOC lower limit update threshold.

[0093] In an embodiment, the optimizing the driving information with the lowest equivalent fuel consumption as the optimization target to obtain an SOC threshold update threshold under the driving information specifically includes:

[0094] constructing a target function with the lowest equivalent fuel consumption as the optimization target; wherein the expression of the target function is:

[0095]

[0096] wherein m eqv (t) is the equivalent fuel consumption at time t, i.e. the energy consumption of the battery is converted into equivalent fuel consumption; m fuel (t) is the actual fuel consumption at time t; s(t) is an equivalent fuel consumption factor, which represents the coefficient of converting the battery energy consumption into equivalent fuel consumption; m ess (t) is the battery energy consumption at time t, which is calculated by the power of the battery and the low heat value of fuel; P batt (t) is the power output by the battery at time t; Q lhv is the low heat value of fuel, which represents the heat released by each unit mass of fuel;

[0097] performing particle swarm optimization on the equivalent fuel consumption factor based on a preset constraint condition to obtain an equivalent fuel consumption factor optimization value, solving the target function based on the equivalent fuel consumption factor optimization value to obtain the lowest equivalent fuel consumption.

[0098] The SOC upper threshold value of the equivalent fuel consumption factor optimization value is used as the SOC upper update threshold value, and the SOC lower threshold value is used as the SOC lower update threshold value; wherein the expression of the equivalent fuel consumption factor is:

[0099]

[0100] wherein, respectively, the average efficiency of the motor, the motor controller, the battery, and the engine; SOC low is the SOC lower threshold value; SOC high is the SOC upper threshold value; SOC ref is the reference SOC value; and SOC(t) is the actual SOC value of the battery at time t.

[0101] The average efficiency of the motor is the efficiency of the motor in converting electrical energy into mechanical energy during operation; the average efficiency of the motor controller is the efficiency of the motor controller in converting direct current into alternating current during operation; the average efficiency of the battery is the efficiency of the battery during charging and discharging; and the average efficiency of the engine is the efficiency of the engine in converting fuel energy into mechanical energy during operation. In the embodiment of the present application, the indicators of fuel consumption and battery energy consumption are comprehensively considered, and a target function is constructed with the lowest equivalent fuel consumption as the optimization target. The particle swarm optimization algorithm is used to optimize the equivalent fuel consumption factor to minimize the equivalent fuel consumption. Specifically, the particle swarm optimization algorithm initializes a particle swarm, each particle in the particle swarm represents a possible s(t) value, calculates the equivalent fuel consumption corresponding to each particle, moves in the search space, updates the position and speed of the particle, and finds the global optimal solution to obtain the equivalent fuel consumption factor optimization value. The equivalent fuel consumption factor represents the difference in energy conversion efficiency between the battery system and the engine system. When the state of charge of the battery deviates from the reference state of charge, the equivalent fuel consumption factor will change accordingly, thereby adjusting the proportion of battery energy consumption converted into equivalent fuel consumption. This adjustment helps to optimize the energy management of the system and ensures efficient operation under different working conditions. When solving the target function based on the equivalent fuel consumption factor optimization value, the actual fuel consumption of the engine can be calculated through the engine torque and the engine angular velocity; the engine fuel consumption can be calculated through the motor torque, the motor angular velocity, the driving speed, and the driving time information; P batt (t) is obtained by real-time monitoring of the battery state, and based on the above parameters, the target function can be solved to obtain the lowest equivalent fuel consumption.

[0102] In an embodiment, the particle swarm optimization of the equivalent fuel consumption factor based on the preset constraint condition specifically includes:

[0103] obtaining the constraint conditions, wherein constraint factors of the constraint conditions include motor angular velocity, engine angular velocity, battery state of charge and demanded torque;

[0104] constructing a working model of the target vehicle according to the constraint factors; wherein the working model includes a demanded torque model, an engine fuel consumption model, a motor model and a battery model;

[0105] performing particle swarm optimization on the preset constraint conditions according to an expression of each of the working models to obtain an optimized value of the equivalent fuel consumption factor; wherein the expression of the preset constraint conditions is:

[0106]

[0107] wherein w mot,max is the maximum allowable angular velocity of the motor; w eng,min is the minimum allowable angular velocity of the engine; w eng,max is the maximum allowable angular velocity of the engine; T eng,min , T eng,max are the minimum allowable torque and the maximum allowable engine torque at a given engine angular velocity; T mot,min , T mot,max are the minimum allowable motor torque and the maximum allowable motor torque at a given motor angular velocity.

[0108] In an embodiment, the performing particle swarm optimization on the preset constraint conditions according to an expression of each of the working models to obtain an optimized value of the equivalent fuel consumption factor specifically includes:

[0109] the expression of the demanded torque model is:

[0110]

[0111] wherein T 需求 is the demanded torque, representing the driving torque required by the vehicle under a specific working condition to overcome various resistances and push the vehicle forward; m is the mass of the vehicle; g is the acceleration of gravity; f r is the rolling resistance coefficient, representing the rolling friction resistance between the vehicle tires and the ground. It is usually a dimensionless parameter related to the road surface type and tire characteristics; cos a is the cosine value of the road inclination, representing the influence of the flatness or slope of the road on the rolling resistance of the vehicle; p α is the air density; C D is the air resistance coefficient, representing the influence of the shape of the vehicle on the air resistance, which depends on the shape and design of the vehicle and can be obtained from the data provided by the vehicle manufacturer; A fA is the frontal area of the vehicle, representing the area of the vehicle that is in contact with the air in front of the vehicle, typically calculated as the height of the vehicle multiplied by the width; v is the speed of the vehicle; sin a is the sine value of the road inclination, representing the slope resistance experienced by the vehicle when driving on a slope; s is the rotational mass conversion coefficient, typically dependent on the specific configuration of the drive system and can be obtained from data provided by the vehicle manufacturer. A is generally 1.05 to 1.1 for cars and 1.1 to 1.2 for trucks; R w R is the radius of the vehicle, representing the radius of the drive wheels of the vehicle, used to convert linear force to torque;

[0112] The expression of the engine fuel consumption model is:

[0113]

[0114] In the formula, C is the fuel consumption rate, representing the mass of fuel consumed by the engine per unit time. It is an important indicator of engine fuel economy; T eng T is the engine torque, representing the torque output by the engine. It is one of the core indicators of engine performance, reflecting the output capacity of the engine at a specific speed; w eng w is the angular velocity of the engine, representing the rotational speed of the engine crankshaft. It is another core indicator of engine performance, determining the working point (operating state) of the engine; f is the function law, describing the relationship between the engine fuel consumption rate and the engine torque and angular velocity. This function law can be a mathematical model, a mapping table or an equation fitted by experimental data, which quantifies the fuel consumption characteristics of the engine under different operating conditions. The specific value depends on the design and characteristics of the engine;

[0115] The expression of the motor model is:

[0116]

[0117] In the formula, P mot is the motor power; T mot is the motor torque; w mot is the angular velocity of the motor; sgn(T mot ) is the sign function, used to determine the direction of the motor torque; η mot is the motor efficiency;

[0118] The expression of the battery model is:

[0119]

[0120] In the formula, SOC is the state of charge of the battery; U oc is the open-circuit voltage of the battery; R int is the internal resistance of the battery; Pbatt Battery power; Q batt Battery capacity;

[0121] Solving the constraint factors according to the expression of each working model, obtaining a plurality of constraint factor values, and performing particle swarm optimization on the constraint condition based on the constraint factor values, to obtain an optimized equivalent fuel consumption factor value.

[0122] In the embodiment of the present application, a working model of the target vehicle is constructed, including a demand torque model, an engine fuel consumption model, a motor model and a battery model. Based on the expressions of these models, a particle swarm optimization algorithm is used to optimize the equivalent fuel consumption factor to meet the preset constraint condition, and finally the threshold update threshold of the optimized equivalent fuel consumption factor is obtained. By constructing the demand torque model, the engine fuel consumption model, the motor model and the battery model, the characteristics of each subsystem of the vehicle are comprehensively considered, and the accuracy and reliability of the optimization result are improved. Based on the real-time monitored driving state and the preset constraint condition, the optimization target and the constraint condition are dynamically adjusted, so that the optimization result is more suitable for the actual working condition. Through the preset multiple constraint factors, it is ensured that the optimization result is feasible in the actual working condition, and the problem of exceeding the physical and system limits is avoided, and the stability and safety of the system are improved.

[0123] Step 103: selecting an energy supply source based on the comparison result of the real-time SOC value of the target vehicle and the SOC threshold update threshold, and calling a corresponding energy management strategy according to the energy supply source.

[0124] In an embodiment, the selection of the energy supply source based on the comparison result of the real-time SOC value of the target vehicle and the SOC threshold update threshold specifically includes: obtaining the driving mode and the real-time SOC value of the target vehicle; when the driving mode is a pure electric driving mode and the real-time SOC value is greater than the SOC lower limit update threshold, switching the auxiliary power unit to an off state and selecting the energy storage battery system as the energy supply source; when the driving mode is the pure electric driving mode and the real-time SOC value is less than the SOC lower limit update threshold, or the driving mode is a hybrid driving mode and the real-time SOC value is less than the SOC upper limit update threshold, selecting the energy storage battery system and the auxiliary power unit as the energy supply source; wherein, when the driving mode is the hybrid driving mode, the auxiliary power unit is in an on state.

[0125] In the embodiment of the present application, the SOC value of the energy storage battery system is monitored in real time to ensure its accuracy, and the data of multiple sensors can be fused to improve the reliability of measurement. The current driving mode of the vehicle (such as pure electric driving mode, hybrid mode, energy regeneration mode, etc.) is accurately identified, and corresponding energy management strategies are adopted according to different modes. When the vehicle is in pure electric driving mode and the SOC value is greater than the lower limit update threshold, the energy storage battery system continues to be used as the only energy supply source and the auxiliary power unit is turned off to save fuel and reduce emissions; when the vehicle is in pure electric driving mode and the SOC value is less than the lower limit update threshold, the auxiliary power unit is turned on and works together with the energy storage battery system as an energy supply source. By providing additional power support through the auxiliary power unit, the battery is prevented from being over-discharged; when the vehicle is in hybrid mode and the SOC value is less than the upper threshold, the auxiliary power unit is turned on and works together with the energy storage battery system as an energy supply source. The use of the battery is balanced to avoid too high or too low SOC value.

[0126] In an embodiment, the energy management strategy corresponding to the energy supply source is called according to the energy supply source, specifically including: obtaining the maximum available power of the energy supply source, comparing the maximum available power with the demand power, and calling the corresponding energy management strategy according to the power comparison result; wherein, when the energy supply source is the energy storage battery system, if the maximum discharge power of the energy storage battery system is greater than or equal to the demand power, the discharge power of the energy storage battery system is adjusted to the demand power; when the energy supply source is the energy storage battery system and the auxiliary power unit, the maximum power generation of the auxiliary power unit is compared with the demand power; if the maximum power generation is greater than or equal to the demand power, the output power of the auxiliary power unit is adjusted to the demand power; if the maximum power generation is less than the demand power, the output power of the auxiliary power unit is adjusted to the maximum power generation, and the discharge power of the energy storage battery system is adjusted to a first discharge power, which is the power difference between the demand power and the maximum power generation.

[0127] The demand power is composed of the power of auxiliary electrical equipment and driving motor, and can be calculated according to the stroke of the accelerator pedal and the current rotating speed of the motor. In the embodiment of the present application, according to the comparison result of the maximum supply power of the energy supply source and the demand power, the corresponding energy management strategy is called. Specifically, when the energy supply source is the energy storage battery system or the energy storage battery system and the auxiliary power unit, the system generates the corresponding initial power distribution result according to the maximum power and the demand power of each, to ensure the reasonable distribution and efficient use of energy. The first initial power distribution result is provided by the energy storage battery system independently; the second initial power distribution result is provided by the auxiliary power unit independently; and the third initial power distribution result is provided by the energy storage battery system and the auxiliary power unit together, wherein the auxiliary power unit provides the maximum power generation, and the energy storage battery system provides the remaining part. For example, when the auxiliary power unit APU is in the open state, i.e. the auxiliary power unit serves as the energy supply source, if the maximum power generation of the engine is equal to the demand power, the power demand of the driving motor and the auxiliary electrical equipment is preferentially met, and if there is excess power, it can be used to charge the energy storage battery. If the maximum power generation of the engine is less than the demand power, the power difference between the maximum power generation and the demand power needs to be determined, and the insufficient part is supplemented by the energy storage battery. When the auxiliary power unit is in the closed state, i.e. the energy storage battery system serves as the energy supply source, if the maximum discharge power of the energy storage battery is greater than or equal to the demand power, the power demand of the driving motor and the auxiliary electrical equipment is preferentially met; if the maximum discharge power is less than the demand power, the auxiliary power unit needs to be started, and the hybrid power mode is switched to, so that the energy storage battery system and the auxiliary power unit serve as the energy supply source together.

[0128] In an embodiment, before the corresponding energy management strategy is called according to the energy supply source, the method further comprises:

[0129] The average driving speed information used for generating the previous SCO threshold update threshold value is obtained, and the average driving speed information is compared with real-time vehicle speed information. If the deviation of the two is within a preset range, the energy management strategy is taken as the energy management result of the target vehicle and the energy management strategy is executed.

[0130] In the embodiment of the present application, the average driving speed data used for updating the previous SOC threshold value and the actual driving speed of the vehicle at the current time are obtained. The difference between the average driving speed and the real-time vehicle speed is calculated. If the deviation is within a preset range, it is considered that the current driving condition is similar to the condition when the previous threshold value is updated, and therefore the energy management strategy can be taken as the final energy management result. If the deviation exceeds the preset range, the SOC threshold update threshold value needs to be recalculated, and the power distribution result is regenerated based on the new SOC threshold update threshold value.

[0131] In an implementation, the energy management method further comprises adjusting power of the auxiliary power unit and the energy storage battery system based on real-time battery power capability, specifically: monitoring temperature information and SOC information of the energy storage battery system in real time, generating the real-time battery power capability based on the temperature information and the SOC information; wherein the real-time battery power capability comprises maximum charging power and maximum discharging power; when the target vehicle is in the hybrid power mode and real-time discharging power of the energy storage battery is greater than the maximum discharging power, a first power adjustment strategy is executed; wherein the first power adjustment strategy is adjusting the real-time discharging power to the maximum discharging power; when the target vehicle is in the hybrid power mode and real-time charging power of the energy storage battery is greater than the maximum charging power, a second power adjustment strategy is executed; wherein the second power adjustment strategy is reducing output power of the auxiliary power unit until the real-time charging power is less than the maximum charging power; when the target vehicle is in the pure electric driving mode and real-time discharging power of the energy storage battery is greater than the maximum discharging power, a third power adjustment strategy is executed; wherein the third power adjustment strategy is adjusting the real-time discharging power to the maximum discharging power.

[0132] In the embodiment of the present application, the real-time battery power capability SOP containing the maximum charging power and the maximum discharging power is generated based on the real-time state (such as temperature and SOC information) of the energy storage battery, and the power distribution of the auxiliary power unit and the energy storage battery system is dynamically adjusted based on the real-time battery power capability SOP, so as to ensure that the working state of the energy storage battery is within a safe range in different driving modes, and overcharging, overdischarging or overheating is avoided, while the utilization of energy is optimized. Figure 3 , Figure 3 A power adjustment process schematic diagram is provided in an embodiment of the present application. When the discharging power P 放 of the current energy storage battery exceeds the maximum discharging power that can actually be provided, a first power adjustment strategy is executed. The real-time discharging power P 放 of the energy storage battery is adjusted to the maximum discharging power, so as to ensure that the battery is not damaged due to overdischarging; when the charging power P 充 of the current energy storage battery exceeds the maximum charging power that can actually be accepted, a second power adjustment strategy is executed. The output power of the auxiliary power unit is reduced until the real-time charging power P 充 of the energy storage battery is less than the maximum charging power. In this way, overcharging of the battery can be avoided, and the battery is protected; when the discharging power P 放 of the current energy storage battery exceeds the maximum discharging power that can actually be provided, a third power adjustment strategy is executed. The real-time discharging power P 放Adjusting to the maximum discharge power ensures that the battery will not be damaged by over-discharge. By monitoring the charging and discharging state of the battery in real time, overcharging or over-discharge of the battery can be avoided, ensuring that the battery operates within a safe temperature range and avoiding performance degradation or damage caused by overheating or overcooling. According to the real-time state of the battery, the power distribution of the auxiliary power unit and the energy storage battery is dynamically adjusted to further maximize energy utilization efficiency.

[0133] In the embodiments of the present application, an energy management device is also provided, which includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, and the processor implements the energy management method described above when executing the computer program.

[0134] In the embodiments of the present application, a computer readable storage medium is also provided, which includes a stored computer program, wherein the computer readable storage medium controls the device where the computer readable storage medium is located to execute the energy management method described above when the computer program is running.

[0135] For example, the computer program can be divided into one or more modules, which are stored in the memory and executed by the processor to complete the present application. One or more modules can be a series of computer program instruction segments that can complete a specific function, which are used to describe the execution process of the computer program in the energy management device.

[0136] The energy management device can be a desktop computer, a notebook computer, a palm computer, a cloud server, and other computing devices. The energy management device can include, but is not limited to, a processor, a memory, a display. Those skilled in the art can understand that the above components are only examples of the energy management device and do not constitute a limitation on the energy management device, and can include more or fewer components than the components, or combine certain components, or different components, for example, the energy management device can also include an input / output device, a network access device, a bus, etc.

[0137] The processor can be a central processing unit (CPU), and can also be other general-purpose processors, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, etc. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor. The processor is the control center of the energy management device, and connects various parts of the energy management device through various interfaces and lines.

[0138] The memory can be used to store computer programs and / or modules, and the processor realizes various functions of the energy management device by running or executing the computer programs and / or modules stored in the memory, and calling the data stored in the memory. The memory can mainly include a program storage area and a data storage area. The program storage area can store an operating system, at least one application program required by a function (such as a sound playing function, a text conversion function, etc.), etc.; and the data storage area can store data created according to the use of the mobile phone (such as audio data, text message data, etc.), etc. In addition, the memory can include a high-speed random access memory, and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, at least one disk storage device, a flash memory device, or other volatile solid-state memory device.

[0139] Wherein, based on the module for energy management if in the form of software function unit is realized and as independent product sells or uses, can be stored in a computer readable storage medium. Based on such understanding, the application realizes all or part of the processes in the above-mentioned embodiment method, also can be through the computer program to instruct the relevant hardware to complete, the computer program can be stored in a computer readable storage medium, the computer program is executed by the processor, can realize the steps of each method embodiment described above. Wherein, the computer program includes computer program code, the computer program code can be in the form of source code, object code form, executable file or some intermediate form etc. The computer readable medium can include: any entity or device that can carry computer program code, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium etc. Those skilled in the art can understand and implement without paying creative labor.

[0140] The embodiment of the application provides an energy management method, which generates driving information by monitoring the driving state of a target vehicle in real time, so that the energy management strategy can be adjusted according to the current driving condition. The driving information is optimized to obtain a dynamic SOC threshold update threshold value with the lowest equivalent fuel consumption as the optimization target. The dynamic adjustment can better adapt to different driving conditions and improve the flexibility and adaptability of the system. Based on the real-time SOC value and the dynamically updated SOC threshold value, a suitable energy supply source is selected, and the reasonable energy distribution strategy can maximize the use of energy and reduce waste.

[0141] Embodiment 2

[0142] Reference Figure 4 , Figure 4 The system composition schematic diagram of an energy management system provided in an embodiment of the application. The embodiment of the application provides an energy management system for executing the energy management method as described in embodiment 1, which comprises an auxiliary power unit, a rectifier device, a power distribution device, auxiliary electrical equipment, a motor, an energy storage battery system, a speed monitoring module, a logic gate control module and an energy management processing system.

[0143] The auxiliary power unit is used for outputting alternating current;

[0144] The rectifier device is used for converting the alternating current into direct current and sending direct current power information to the energy management processing system;

[0145] The power distribution device is configured to receive a power distribution instruction sent by the energy management processing system, and adjust the power distribution of the energy storage battery system, the auxiliary power equipment and the motor based on the power distribution instruction.

[0146] The auxiliary power equipment is configured to realize human-computer interaction between the vehicle and the user.

[0147] The motor is configured to drive the vehicle to travel.

[0148] The energy storage battery system is configured to store electric energy and supply energy to the motor, wherein the energy storage battery system comprises at least one energy storage battery.

[0149] The speed monitoring module is configured to monitor the traveling state of the vehicle in real time, generate traveling information of the vehicle within a preset time length, and send the traveling information to the logic gate control module.

[0150] The logic gate control module is configured to generate an SOC threshold update threshold according to the traveling information, and send the SOC threshold update threshold to the energy management processing system.

[0151] The energy management processing system is configured to generate an energy management strategy according to the received SOC threshold update threshold, and generate a power distribution instruction to the power distribution device based on the energy management strategy.

[0152] The energy management processing system is further configured to adjust the real-time charging and discharging power of the energy storage battery according to the real-time battery power capability of the energy storage battery.

[0153] In the embodiment of the present application, the auxiliary power unit includes an internal combustion engine and a generator, the internal combustion engine is mechanically connected with the generator, and the generator outputs alternating current through the internal combustion engine crankshaft. The rectifier device converts the alternating current output by the auxiliary power unit into direct current, and transmits the output direct current power information to the energy management processing system in real time through the data line. The rectifier device ensures the unified format (direct current) of the energy, facilitating subsequent power management and distribution. The power distribution device receives the power distribution instructions sent by the energy management processing system, and adjusts the power distribution of the energy storage battery system, auxiliary electrical equipment and driving motor according to the instructions. The power distribution device is the "energy scheduling center" of the whole system, ensuring that each electrical equipment and driving system obtains reasonable power support. The auxiliary electrical equipment refers to the power steering system, human-computer interaction equipment and the like, realizing the human-computer interaction between the vehicle and the user, and providing functions such as information display, navigation and entertainment. The motor, also called electric drive load, is used to drive the vehicle to run and receives the electric energy provided by the energy storage battery or the rectifier device. The driving motor is the power source of the vehicle, and its efficiency directly affects the running performance of the vehicle. The energy storage battery system is used to store electric energy and supply energy to the electric drive load (motor). The energy storage battery system can store the direct current rectified by the rectifier device, and can also supply electric energy to the electric drive load (motor), so as to drive the vehicle to run. Further, the energy storage battery system is provided with a battery management system (BMS), which can also monitor key parameters such as the state of charge of the battery, the state of health of the battery and the power capability of the battery in real time. The speed monitoring module is used to monitor the running state of the vehicle in real time, generate the running information of the vehicle within a preset time length, and send the information to the logic gate control module.

[0154] The speed monitoring module provides real-time running data for the energy management system, helping the system to make dynamic energy management decisions. The logic gate module generates an SOC threshold update threshold value according to the running information sent by the speed monitoring module, and sends the threshold value to the energy management processing system. The logic gate control module is the "intelligent decision unit" of the energy management system, dynamically adjusts the SOC threshold according to the running state, and ensures the flexibility and efficiency of the system. The energy management processing system calls the corresponding energy management strategy according to the received SOC threshold update threshold value. The power distribution device generates a power distribution instruction based on the called energy management strategy. The real-time charging and discharging power of the energy storage battery is adjusted according to the real-time battery power capability of the energy storage battery. The energy management processing system is the "brain" of the whole system, responsible for comprehensive analysis and processing of the input data of each module, and generation of the optimal energy management scheme.

[0155] After the APU is started, AC power is output to provide additional power support for the vehicle. The rectifier receives the AC power output by the APU and converts it into DC power, and at the same time, sends the power information of the DC power to the energy management processing system. The logic gate control module generates an SOC threshold update threshold value according to the driving information provided by the speed monitoring module, and sends it to the energy management processing system. The energy management processing system generates an energy management result according to the SOC threshold update threshold value and the real-time state of the energy storage battery. According to the energy management result, the energy management processing system generates a power distribution instruction and sends it to the power distribution device. The power distribution device adjusts the power distribution of the energy storage battery system, auxiliary electrical equipment and drive motor according to the received power distribution instruction. The energy storage battery system adjusts its charging and discharging power according to the received power distribution instruction to ensure the rational use of energy.

[0156] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the above-described device can refer to the corresponding process in the foregoing method embodiment, which will not be described here.

[0157] The energy management system provided by the embodiment of the present application can dynamically adjust the power distribution of the energy storage battery system, auxiliary electrical equipment and drive motor according to the power distribution instruction sent by the energy management processing system, and ensure efficient use of energy. The speed monitoring module monitors the driving state of the vehicle in real time and generates driving information and sends it to the logic gate control module. The logic gate control module generates an SOC threshold update threshold value according to the driving information, and the energy management processing system dynamically adjusts the energy management strategy based on this threshold value. The system comprehensively considers the vehicle performance, battery life and energy efficiency to generate the best energy management scheme, ensuring efficient work under different driving conditions. The auxiliary electrical equipment realizes the interaction between the vehicle and the user, provides rich information display and driving suggestions, and improves the driving experience. The speed monitoring module monitors the driving state of the vehicle in real time, and the user can obtain the driving information and energy management of the vehicle through the auxiliary electrical equipment, thereby enhancing the driving safety and comfort. The energy management processing system monitors the health state of the energy storage battery system in real time, avoids long-term work of the battery under high or low load, and prolongs the battery life. The logic gate control module generates an SOC threshold update threshold value according to the driving information, and the energy management processing system adjusts the charging and discharging strategy of the battery based on this threshold value, ensuring that the battery works in the appropriate SOC range and avoiding excessive charging and discharging. Through the cooperative work of each module, efficient energy utilization, flexible adaptability and good driving experience for users are realized.

[0158] The above is only the preferred embodiment of the present application, and it should be pointed out that for ordinary skilled in the art, without departing from the technical principles of the present application, several improvements and replacements can be made, which should also be considered as the protection scope of the present application.

Claims

1. An energy management method, characterized by, The method comprises the following steps: monitoring a driving state of a target vehicle in real time to generate driving information of the target vehicle within a preset time length; optimizing the driving information with a lowest equivalent fuel consumption as an optimization target to obtain an SOC threshold update threshold under the driving information; wherein the SOC threshold update threshold comprises an SOC upper limit update threshold and an SOC lower limit update threshold; the optimization of the driving information with the lowest equivalent fuel consumption as the optimization target to obtain the SOC threshold update threshold under the driving information specifically comprises: constructing a target function with the lowest equivalent fuel consumption as the optimization target; wherein an expression of the target function is: where m eqv (t) is the equivalent fuel consumption at time t; m fuel (t) is the actual fuel consumption at time t; s(t) is the equivalent fuel consumption factor; m ess (t) is the battery energy consumption at time t; P batt (t) is the power output by the battery at time t; Q lhv is the lower heating value of the fuel; performing particle swarm optimization on the equivalent fuel consumption factor based on a preset constraint condition to obtain an equivalent fuel consumption factor optimization value, solving the target function based on the equivalent fuel consumption factor optimization value to obtain the lowest equivalent fuel consumption; taking an SOC upper limit threshold of the equivalent fuel consumption factor optimization value as the SOC upper limit update threshold and taking an SOC lower limit threshold as the SOC lower limit update threshold; wherein an expression of the equivalent fuel consumption factor is: wherein respectively the average efficiency of the electric machine, the electric machine controller, the battery, the engine; SOC low is the lower threshold value for the SOC; SOC high is the upper threshold value for the SOC; SOC ref is the reference SOC value; SOC(t) is the actual SOC value of the battery at time t; selecting an energy supply source based on a comparison result of a real-time SOC value of the target vehicle and the SOC threshold update threshold and calling a corresponding energy management strategy according to the energy supply source.

2. The energy management method of claim 1, wherein, The monitoring of the driving state of the target vehicle in real time to generate the driving information of the target vehicle within the preset time length specifically comprises: monitoring the driving state of the target vehicle in real time to calculate vehicle speed information of the target vehicle within the preset time length; wherein the vehicle speed information comprises a real-time driving speed, a maximum driving speed, a minimum driving speed and an average driving speed within the preset time length; calculating a first speed difference value of the maximum driving speed and the average driving speed and a second speed difference value of the minimum driving speed and the average driving speed, selecting a maximum value of the first speed difference value and the second speed difference value as a maximum speed difference value; when the maximum speed difference value is less than or equal to a preset speed threshold, determining that the target vehicle is in a stable driving state and generating the driving information based on the vehicle speed information and a time at which the vehicle speed information is collected.

3. The energy management method of claim 1, wherein, The particle swarm optimization on the equivalent fuel consumption factor based on the preset constraint condition to obtain the equivalent fuel consumption factor optimization value specifically comprises: obtaining the constraint condition, wherein constraint factors of the constraint condition comprise a motor angular velocity, an engine angular velocity, a battery state of charge and a required torque; constructing a working model of the target vehicle according to the constraint factors; wherein the working model comprises a required torque model, an engine fuel consumption model, a motor model and a battery model; performing particle swarm optimization on the preset constraint condition according to an expression of each of the working models to obtain the equivalent fuel consumption factor optimization value; wherein an expression of the preset constraint condition is: where w mot,max is the maximum allowed angular velocity of the motor; w eng,min is the minimum allowed angular velocity of the motor; w eng,max is the maximum allowed angular velocity of the motor; T eng,min , T eng,max are the minimum and maximum allowed motor torques at a given motor angular velocity; T mot,min , T mot,max are the minimum and maximum allowed motor torques at a given motor angular velocity.

4. The energy management method of claim 3, wherein, The particle swarm optimization on the preset constraint condition according to the expression of each of the working models to obtain the equivalent fuel consumption factor optimization value specifically comprises: an expression of the required torque model is: In the formula, T 需求 m is the required torque; g is the vehicle mass; f is the acceleration due to gravity; m is the vehicle mass; g is the acceleration due to gravity; f is the acceleration due to gravity. r ρ is the rolling resistance coefficient; cosα is the cosine of the road inclination angle; α C is the density of air. D A is the air drag coefficient; f V is the vehicle's frontal area; v is the vehicle speed; sinα is the sine of the road inclination angle; σ is the rotational mass conversion factor; R w The radius of the vehicle; an expression of the engine fuel consumption model is: wherein is the fuel consumption rate; T eng is the engine torque; w eng is the engine angular velocity; is the function law; An expression of the motor model is: where P mot is the motor power; T mot is the motor torque; w mot is the motor angular velocity; sgn(T mot ) is a sign function for determining the direction of the motor torque; and η mot is the motor efficiency. An expression of the battery model is: where SOC is the battery state of charge; U oc is the battery open circuit voltage; R int is the battery internal resistance; P batt is the battery power; Q batt is the battery capacity; Solving the constraint factors according to the expression of each working model, obtaining a plurality of constraint factor values, and performing particle swarm optimization on the constraint condition based on the constraint factor values to obtain an optimized equivalent fuel consumption factor value.

5. The energy management method of claim 1, wherein, The comparison result of the real-time SOC value of the target vehicle and the SOC threshold update threshold is used to select an energy supply source, specifically including: Obtaining the driving mode and real-time SOC value of the target vehicle; When the driving mode is a pure electric driving mode and the real-time SOC value is greater than the SOC lower limit update threshold, the auxiliary power unit is switched to an off state, and the energy storage battery system is selected as the energy supply source; When the driving mode is the pure electric driving mode and the real-time SOC value is less than the SOC lower limit update threshold, or the driving mode is a hybrid driving mode and the real-time SOC value is less than the SOC upper limit update threshold, the energy storage battery system and the auxiliary power unit are selected as the energy supply source; wherein, when the driving mode is the hybrid driving mode, the auxiliary power unit is in an on state.

6. A method of energy management as claimed in claim 5, wherein, The corresponding energy management strategy is called according to the energy supply source, specifically including: obtaining the maximum supply power of the energy supply source, comparing the maximum supply power with the demand power, and calling the corresponding energy management strategy according to the power comparison result; wherein, When the energy supply source is the energy storage battery system, if the maximum discharge power of the energy storage battery system is greater than or equal to the demand power, the discharge power of the energy storage battery system is adjusted to the demand power; When the energy supply source is the energy storage battery system and the auxiliary power unit, the maximum power generation of the auxiliary power unit is compared with the demand power; If the maximum power generation is greater than or equal to the demand power, the output power of the auxiliary power unit is adjusted to the demand power; If the maximum power generation is less than the demand power, the output power of the auxiliary power unit is adjusted to the maximum power generation, and the discharge power of the energy storage battery system is adjusted to a first discharge power, which is the power difference between the demand power and the maximum power generation.

7. The energy management method of claim 1, wherein, Before calling the corresponding energy management strategy according to the energy supply source, it further includes: Obtaining the average driving speed information used to generate the previous SCO threshold update threshold, comparing the average driving speed information with real-time vehicle speed information, and if the deviation between the two is within a preset range, the energy management strategy is used as the energy management result of the target vehicle and the energy management strategy is executed.

8. The energy management method of claim 5, wherein, The energy management method further includes adjusting the power of the auxiliary power unit and the energy storage battery system based on real-time battery power capability, specifically: Real-time monitoring of temperature information and SOC information of the energy storage battery system, and generating the real-time battery power capability based on the temperature information and SOC information; wherein, the real-time battery power capability includes maximum charging power and maximum discharging power; when the target vehicle is in the hybrid mode and the real-time discharge power of the energy storage battery is greater than the maximum discharge power, a first power adjustment strategy is executed; wherein the first power adjustment strategy is to adjust the real-time discharge power to the maximum discharge power; when the target vehicle is in the hybrid mode and the real-time charge power of the energy storage battery is greater than the maximum charge power, a second power adjustment strategy is executed; wherein the second power adjustment strategy is to reduce the output power of the auxiliary power unit until the real-time charge power is less than the maximum charge power; when the target vehicle is in the pure electric driving mode and the real-time discharge power of the energy storage battery is greater than the maximum discharge power, a third power adjustment strategy is executed; wherein the third power adjustment strategy is to adjust the real-time discharge power to the maximum discharge power.

9. An energy management system, characterized by An energy management system for executing the energy management method as claimed in any one of claims 1-8, comprising an auxiliary power unit, a rectifier device, a power distribution device, an auxiliary electrical device, an electric motor, an energy storage battery system, a speed monitoring module, a logic gate control module, an energy management processing system; the auxiliary power unit is configured to output alternating current; the rectifier device is configured to convert the alternating current into direct current and send direct current power information to the energy management processing system; the power distribution device is configured to receive power distribution instructions sent by the energy management processing system and adjust the power distribution of the energy storage battery system, the auxiliary electrical device and the electric motor based on the power distribution instructions; the auxiliary electrical device is configured to realize human-computer interaction between the vehicle and the user; the electric motor is configured to drive the vehicle to travel; the energy storage battery system is configured to store electrical energy and supply energy to the electric motor; wherein the energy storage battery system comprises at least one energy storage battery; the speed monitoring module is configured to monitor the driving state of the vehicle in real time, generate driving information of the vehicle within a preset time length and send the driving information to the logic gate control module; the logic gate control module is configured to generate an SOC threshold update threshold value according to the driving information and send the SOC threshold update threshold value to the energy management processing system; the energy management processing system is configured to generate an energy management strategy according to the received SOC threshold update threshold value and generate power distribution instructions to the power distribution device based on the energy management strategy; the energy management processing system is further configured to adjust the real-time charge and discharge power of the energy storage battery according to the real-time battery power capability of the energy storage battery.

10. A terminal device, comprising: A computer readable storage medium comprising a stored computer program, wherein the computer program, when executed, controls a device in which the computer readable storage medium is located to perform the energy management method as claimed in any one of claims 1-8.

11. A computer readable storage medium, characterized in that, A computer readable storage medium comprising a stored computer program, wherein the computer program, when executed, controls a device in which the computer readable storage medium is located to perform the energy management method as claimed in any one of claims 1-8.

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