Energy management method, energy management system, terminal equipment and storage medium

Abstract

The invention provides an energy management method, an energy management system, terminal equipment and a storage medium, and the method comprises the steps: monitoring the driving state of a target vehicle in real time, and generating the driving information of the target vehicle within a preset time duration; optimizing the driving information by taking the lowest equivalent oil consumption as an optimization target to obtain an SOC threshold update threshold value under the driving information; wherein the SOC threshold update threshold comprises an SOC upper limit update threshold and an SOC lower limit update threshold; and selecting an energy supply source based on a 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. The energy utilization efficiency of the tandem type hybrid power system can be improved, and energy waste is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of hybrid power system energy management, and in particular to an energy management method, an energy management system, a terminal device and a storage medium. Background Art

[0002] With the increase in global energy consumption, new energy vehicle technology has become a research hotspot. The hybrid system is not limited by battery life and charging issues, and can flexibly switch between the internal combustion engine and the electric motor or work simultaneously, thus providing greater driving flexibility and convenience. According to the power connection method, the hybrid system is divided into three types: series, parallel and hybrid. Among them, the series hybrid system is a power system that combines an auxiliary power unit consisting of an internal combustion engine and a generator with an energy storage battery system to drive the wheels alone through the drive 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 drive motor. The internal combustion engine is completely decoupled from the drive system, with a simple structure and easy design and maintenance. In addition, the internal combustion engine can work at a fixed speed and load, and always runs in the medium and high efficiency zone, thereby improving fuel economy and reducing emissions. The series hybrid system has a broader market prospect, especially for those users who need to take into account both cruising range and environmental performance.

[0003] The series hybrid system involves multiple power sources such as APU (Auxiliary Power Unit), energy storage battery system and drive motor. Through reasonable energy management, the hybrid system can flexibly switch power sources under different driving conditions and optimize energy utilization. In the prior art, energy management is often performed through a rule-based switching energy management strategy. Specifically, the start and 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 SOC lower limit threshold, the internal combustion engine starts to generate electricity to charge the energy storage battery; when the SOC is higher than the SOC upper limit threshold, the internal combustion engine is shut down and the vehicle enters pure electric mode. However, the above method does not take into account the impact of changes in different speeds and operating conditions during driving on energy management, which is likely to lead to energy waste. Summary of the invention

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

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

[0006] Monitor the driving status of the target vehicle in real time and generate driving information of the target vehicle within a preset time length;

[0007] The driving information is optimized with the minimum equivalent fuel consumption as the optimization target to obtain 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] An energy supply source is selected based on a comparison result between the real-time SOC value of the target vehicle and the SOC threshold update threshold, and a corresponding energy management strategy is called according to the energy supply source.

[0009] In the above scheme, driving information is generated by real-time monitoring of the driving status of the target vehicle, which enables the energy management strategy to be adjusted according to the current driving conditions. Taking the minimum equivalent fuel consumption as the optimization goal, the driving information is optimized to obtain the dynamic SOC threshold update threshold. 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, the appropriate energy supply source is selected. This reasonable energy allocation strategy can maximize the use of energy and reduce waste.

[0010] In one implementation, the real-time monitoring of the driving state of the target vehicle and the generation of driving information of the target vehicle within a preset time length specifically include:

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

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

[0013] When the maximum speed difference 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 above scheme, by real-time monitoring of vehicle speed information, the speed fluctuation of the vehicle is accurately calculated, a first speed difference and a second speed difference are generated, and the maximum value is selected as the maximum speed difference. When the maximum speed difference is less than or equal to the preset speed threshold, the system determines that the vehicle is in a stable driving state, ensuring accurate judgment of the vehicle state. When the vehicle is in a stable driving state, driving information is generated based on the vehicle speed information to optimize the energy allocation strategy. Energy management in a stable state is more predictable and efficient, reducing unnecessary energy waste.

[0015] In one implementation, optimizing the driving information with the minimum equivalent fuel consumption as the optimization target to obtain the SOC threshold update threshold under the driving information specifically includes:

[0016] The objective function is constructed with the minimum equivalent fuel consumption as the optimization target; wherein the expression of the objective function is:

[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 the equivalent fuel consumption factor; m ess (t) is the battery energy consumption at time t; P batt (t) is the power output of the battery at time t; Q lhv is the lower calorific value of the fuel;

[0019] Performing particle swarm optimization on the equivalent fuel consumption factor based on preset constraints to obtain an optimal value of the equivalent fuel consumption factor, and solving the objective function based on the optimal value of the equivalent fuel consumption factor to obtain the minimum equivalent fuel consumption;

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

[0021]

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

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

[0024] The objective function comprehensively considers the actual fuel consumption and battery energy consumption, and converts the battery energy consumption into equivalent fuel consumption through the equivalent fuel consumption factor to minimize the comprehensive fuel consumption. The particle swarm optimization algorithm is used to optimize the equivalent fuel consumption factor. The algorithm has the characteristics of global search capability and fast convergence speed, and can effectively find the optimal solution in the complex optimization space. Through the particle swarm algorithm, the optimal SOC threshold update threshold can be quickly determined to optimize 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 status and battery status of the vehicle to achieve real-time optimization of the energy management strategy. This dynamic adjustment capability enables the system to automatically adapt under different working conditions, improving the flexibility and adaptability of the entire system. Furthermore, the objective function considers the average efficiency of the motor, motor controller, battery and engine, as well as the upper and lower limits and reference values ​​of the SOC, to ensure 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 allows energy management to not only focus on fuel economy, but also take into account the service life and performance of the battery.

[0025] In one implementation, the performing particle swarm optimization on the equivalent fuel consumption factor based on preset constraints specifically includes:

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

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

[0028] According to the expression of each working model, the preset constraint condition is optimized by particle swarm optimization to obtain the optimization 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 permissible angular velocity of the motor; w eng,min is the minimum permissible angular velocity of the engine; w eng,max is the maximum permissible angular velocity of the engine; T eng,min 、T eng,maxis the minimum and maximum permissible engine torque at a given engine angular speed; T mot,min 、T mot,max is the minimum and maximum permissible motor torque for a given motor angular velocity.

[0031] In one implementation, performing particle swarm optimization on the preset constraint conditions according to the expression of each working model to obtain the optimal value of the equivalent fuel consumption factor specifically includes:

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

[0033]

[0034] Where, T 需求 is the required 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; ρ α is the air density; C D A is the air resistance coefficient; f is the frontal area of ​​the vehicle; v is the vehicle speed; sinα is the sine value of the road inclination; σ is the rotation mass conversion factor; R w is the vehicle radius;

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

[0036]

[0037] In the formula, is the fuel consumption rate; T eng is the engine torque; w eng is the engine angular velocity; is the function law;

[0038] The expression of the motor model is:

[0039]

[0040] 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 used to determine the direction of the motor torque; ηmo t is the motor efficiency;

[0041] The expression of the battery model is:

[0042]

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

[0044] The constraint factors are solved according to the expression of each working model to obtain a plurality of constraint factor values, and particle swarm optimization is performed on the constraint conditions based on the constraint factor values ​​to obtain the equivalent fuel consumption factor optimization value.

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

[0046] In one implementation, the selecting of the energy supply source based on the comparison result between the real-time SOC value of the target vehicle and the SOC threshold update threshold value specifically includes:

[0047] Obtaining the driving mode and 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, the auxiliary power unit is switched to a closed state, and the energy storage battery system is selected 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 power 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 energy supply sources; wherein, when the driving mode is the hybrid power mode, the auxiliary power unit is in an on state.

[0050] In the above scheme, when the vehicle is in pure electric drive mode and the SOC value is higher than the lower limit update threshold, the system shuts down the auxiliary power unit and only uses the energy storage battery system for power supply, achieving zero-emission driving and improving 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 hybrid mode, the system automatically turns on the auxiliary power unit, combining the battery and the auxiliary power unit to ensure continuous operation of the vehicle and maintain the battery SOC within a reasonable range. By monitoring the SOC value and adjusting the energy supply source in a timely manner, problems such as the vehicle being unable to drive due to low battery power can be prevented.

[0051] In one implementation, calling the corresponding energy management strategy according to the energy supply source specifically includes: obtaining the maximum supplyable power of the energy supply source, comparing the maximum supplyable power with the required 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 required power, controlling the energy storage battery system to adjust the discharge power to the required power;

[0053] When the energy supply source is the energy storage battery system and the auxiliary power unit, comparing the maximum power generation power of the auxiliary power unit with the required power;

[0054] If the maximum generated power is greater than or equal to the required power, controlling the auxiliary power unit to adjust the output power to the required power;

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

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

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

[0058] The average driving speed information used to generate the previous SCO threshold update threshold is obtained, and the average driving speed information is compared with the real-time vehicle speed information. 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.

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

[0060] In one implementation, the energy management method further includes adjusting the power of the auxiliary power unit and the energy storage battery system based on the real-time battery power capability, specifically:

[0061] Monitor the temperature information and SOC information of the energy storage battery system in real time, and generate the real-time battery power capability based on the temperature information and SOC information; wherein the real-time battery power capability includes the maximum charging power and the maximum discharging power;

[0062] 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, executing a first power adjustment strategy; wherein the first power adjustment strategy is to adjust the real-time discharge power to the maximum discharge 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 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.

[0065] In the above scheme, the power output of the auxiliary power unit and the energy storage battery system is dynamically adjusted according to the real-time battery power capacity to ensure that the system is always in the best working state. When the real-time discharge power exceeds the maximum discharge power, the real-time discharge power is adjusted to the maximum discharge power to avoid over-discharge 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 to avoid overcharging of the battery and extend the battery life.

[0066] In a second aspect, the present application further provides an energy management system for executing the energy management method as described above, including an auxiliary power unit, a rectifier, 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;

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

[0068] The rectifier device is used to convert the AC power into DC power and send the DC power information to the energy management processing system;

[0069] The power distribution device is used 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 power equipment and the motor based on the power distribution instruction;

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

[0071] The motor is used to drive the vehicle;

[0072] The energy storage battery system is used to store electrical 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 used to monitor the driving state of the vehicle in real time, generate the driving information of the vehicle within a preset time length and send the driving information to the logic gate control module;

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

[0075] The energy management processing system is used 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 also used 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.

[0077] In the above scheme, the power distribution device dynamically adjusts the power distribution of the energy storage battery system, auxiliary power equipment and drive motor according to the power distribution instruction sent by the energy management processing system to ensure efficient use of energy. The speed monitoring module monitors the driving status 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 the SOC threshold update threshold according to the driving information, and the energy management processing system dynamically adjusts the energy management strategy based on this threshold. The system comprehensively considers vehicle performance, battery life and energy efficiency, generates the best energy management solution, and ensures efficient operation under different driving conditions. Auxiliary power 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 status of the vehicle in real time, and the user can obtain the driving information and energy management status of the vehicle through the auxiliary power equipment to enhance driving safety and comfort. The energy management processing system monitors the health status of the energy storage battery system in real time to avoid the battery from working for a long time under high load or low load conditions and extend the battery life. The logic gate control module generates an SOC threshold update threshold based on driving information, and the energy management processing system adjusts the battery charge and discharge strategy based on this threshold to ensure that the battery operates within the appropriate SOC range and avoids over-charge and over-discharge. Through the coordinated work of various modules, efficient energy utilization is achieved, with flexible adaptability, which can bring a good driving experience to users.

[0078] In a third aspect, the present application also provides a terminal device, including 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 as described above when executing the computer program.

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

[0080] Figure 1 A schematic diagram of a flow chart of an energy management method provided in one embodiment of the present invention;

[0081] Figure 2 A schematic diagram of a process of generating driving information provided in one embodiment of the present invention;

[0082] Figure 3 A schematic diagram of a power adjustment process provided in an embodiment of the present invention;

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

[0084] The specific implementation of the present invention is further described in detail below in conjunction with the accompanying drawings and examples. The following examples are used to illustrate the present invention, but are not intended to limit the scope of the present invention.

[0085] The terms "first" and "second" and the like in the specification, claims and drawings of this application are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device comprising a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products or devices.

[0086] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0087] Example 1

[0088] See also Figure 1 , Figure 1 The flowchart of an energy management method provided in one embodiment of the present invention is shown in FIG. The embodiment of the present invention includes steps 101 to 104, and each step is specifically as follows:

[0089] Step 101: monitor the driving status of a target vehicle in real time, and generate driving information of the target vehicle within a preset time length.

[0090] In one embodiment, the real-time monitoring of the driving state of the target vehicle calculates the speed information of the target vehicle within a preset time length; wherein the speed information includes the real-time driving speed, the maximum driving speed, the minimum driving speed and the average driving speed within the preset time length; a first speed difference between the maximum driving speed and the average driving speed, and a second speed difference between the minimum driving speed and the average driving speed are calculated, and the maximum value of the first speed difference and the second speed difference is selected as the maximum speed difference; when the maximum speed difference 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 speed information and the time when the speed information is collected; when the maximum speed difference is greater than the preset speed threshold, the speed information of the target vehicle is continuously updated.

[0091] In the embodiment of the present invention, multiple sensors (such as speed sensors, acceleration sensors, GPS, etc.) are used to collect vehicle driving status data, and sensor fusion technology is used to improve the accuracy and reliability of the data. 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. Figure 2 , Figure 2 The figure is a flow chart of a driving information generation process provided in one embodiment of the present invention. Record the maximum driving speed V of the vehicle within 5 seconds max and minimum travel speed V min , and calculate the average driving speed V within 5 seconds. Then determine whether the vehicle is in a stable driving stage based on the maximum speed difference within 5 seconds. If the vehicle is in a stable driving stage, output the speed and time information of the vehicle within 5 seconds, otherwise continue to update the vehicle speed information, maximum and minimum speed values ​​and calculate the average driving speed V.

[0092] Step 102: Optimize the driving information with the minimum equivalent fuel consumption as the optimization target to obtain 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.

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

[0094] The objective function is constructed with the minimum equivalent fuel consumption as the optimization target; wherein the expression of the objective function is:

[0095]

[0096] In the formula, m eqv (t) is the equivalent fuel consumption at time t, which is the energy consumption of the battery converted into equivalent fuel consumption; m fuel (t) is the actual fuel consumption at time t; s(t) is the equivalent fuel consumption factor, which represents the coefficient for converting battery energy consumption into equivalent fuel consumption; m ess (t) is the battery energy consumption at time t, which is calculated by the battery power and the lower calorific value of the fuel; P batt (t) is the power output of the battery at time t; Q lhv It is the lower calorific value of fuel oil, which indicates the amount of heat that can be released per unit mass of fuel oil;

[0097] Performing particle swarm optimization on the equivalent fuel consumption factor based on preset constraints to obtain an optimal value of the equivalent fuel consumption factor, and solving the objective function based on the optimal value of the equivalent fuel consumption factor to obtain the minimum equivalent fuel consumption;

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

[0099]

[0100] In the formula, are the average efficiency of the motor, motor controller, battery, and engine; SOC low is the SOC lower limit threshold; SOC high is the upper limit threshold of SOC; SOC ref is the reference SOC value; 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 converting electrical energy into mechanical energy during operation; the average efficiency of the motor controller is the efficiency of the motor controller converting direct current into alternating current during operation; the average efficiency of the battery is the efficiency of the battery during charging and discharging; the average efficiency of the engine is the efficiency of the engine converting fuel energy into mechanical energy during operation. In the embodiment of the present invention, the indicators of fuel consumption and battery energy consumption are comprehensively considered, and the objective function is constructed with the minimum equivalent fuel consumption as the optimization target. The equivalent fuel consumption factor is optimized using a particle swarm algorithm to minimize the equivalent fuel consumption. Specifically, the particle swarm 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 ratio of battery energy consumption converted to equivalent fuel consumption. This adjustment helps optimize the system's energy management and ensures efficient operation under different operating conditions. When solving the objective function based on the optimal value of the equivalent fuel consumption factor, the actual fuel consumption of the engine can be calculated through the engine torque and engine angular velocity; the engine fuel consumption can be calculated through the information of motor torque, motor angular velocity, driving speed and driving time; batt (t) is obtained by real-time monitoring of the battery status. Based on the above parameters, the objective function can be solved to obtain the minimum equivalent fuel consumption.

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

[0103] Acquire the constraint condition, wherein the constraint factors of the constraint condition include motor angular velocity, engine angular velocity, battery state of charge and required torque;

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

[0105] According to the expression of each working model, the preset constraint condition is optimized by particle swarm optimization to obtain the optimization value of the equivalent fuel consumption factor; wherein the expression of the preset constraint condition is:

[0106]

[0107] In the formula, w mot,max is the maximum permissible angular velocity of the motor; w eng,min is the minimum permissible angular velocity of the engine; w eng,max is the maximum permissible angular velocity of the engine; T eng,min 、T eng,max is the minimum allowable torque and the maximum allowable engine torque at a given engine angular speed; T mot,min 、T mot,max is the minimum and maximum permissible motor torque for a given motor angular velocity.

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

[0109] The expression of the demand torque model is:

[0110]

[0111] Where, T 需求 is the required torque, which indicates the driving torque required by the vehicle under specific working conditions to overcome various resistances and push the vehicle forward; m is the vehicle mass; g is the gravitational acceleration; f r is the rolling resistance coefficient, which indicates the rolling friction resistance between the vehicle tire and the ground. It is usually a dimensionless parameter related to the road surface type and tire characteristics; cosα is the cosine value of the road inclination, which indicates the effect of the flatness or slope of the road on the vehicle rolling resistance; ρ α is the air density; C D A is the air resistance coefficient, which indicates the effect of the vehicle shape on air resistance. It depends on the shape and design of the vehicle and can be obtained from the data provided by the vehicle manufacturer. fis the frontal area of ​​the vehicle, which indicates the area in front of the vehicle in contact with the air, usually calculated as the vehicle's height multiplied by its width; v is the vehicle speed; sinα is the sine value of the road inclination, which indicates the slope resistance encountered by the vehicle when driving on a slope; σ is the rotational mass conversion factor, which usually depends on the specific configuration of the drive system and can be obtained from the data provided by the vehicle manufacturer. For sedans, it is generally 1.05 to 1.1, and for trucks, it is generally 1.1 to 1.2; R w is the vehicle radius, which represents the radius of the vehicle's driving wheels and is used to convert linear force into torque;

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

[0113]

[0114] In the formula, T is the fuel consumption rate, which indicates the fuel mass consumed by the engine per unit time. It is an important indicator to measure the fuel economy of the engine; eng The engine torque represents the torque output by the engine. It is one of the core indicators of engine performance and reflects the output capacity of the engine at a specific speed. eng The engine angular velocity indicates the rotation speed of the engine crankshaft. It is another core indicator of engine performance and determines the engine's operating point (operating state); It is a function law that describes 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 obtained by fitting experimental data. It 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] 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 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] Where SOC is the battery state of charge; U oc is the open circuit voltage of the battery; R int is the internal resistance of the battery; Pbatt is the battery power; Q batt is the battery capacity;

[0121] The constraint factors are solved according to the expression of each working model to obtain a plurality of constraint factor values, and particle swarm optimization is performed on the constraint conditions based on the constraint factor values ​​to obtain the equivalent fuel consumption factor optimization value.

[0122] In an embodiment of the present invention, a working model of a 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 constraints, 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 fully considered, and the accuracy and reliability of the optimization results are improved. Based on the real-time monitored driving status and preset constraints, the optimization target and constraints are dynamically adjusted to make the optimization results more suitable for actual working conditions. Through the preset multiple constraints, it is ensured that the optimization results are feasible in actual working conditions, avoiding the problem of exceeding physical and system limitations, and improving the stability and safety of the system.

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

[0124] In one embodiment, the energy supply source is selected based on the comparison result of the real-time SOC value of the target vehicle and the SOC threshold update threshold, specifically including: obtaining the driving mode and real-time SOC value of the target vehicle; 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; 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 power 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 together; wherein, when the driving mode is the hybrid power mode, the auxiliary power unit is in an on state.

[0125] In the embodiment of the present invention, the SOC value of the energy storage battery system is monitored in real time to ensure its accuracy. It can be considered to use the data of multiple sensors for fusion to improve the reliability of the measurement. Accurately identify the current driving mode of the vehicle (such as pure electric drive mode, hybrid mode, energy regeneration mode, etc.), and adopt corresponding energy management strategies according to different modes. When the vehicle is in pure electric drive 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 drive mode and the SOC value is less than the lower limit update threshold, the auxiliary power unit is turned on and used together with the energy storage battery system as an energy supply source. Additional power support is provided by the auxiliary power unit to prevent excessive discharge of the battery; when the vehicle is in hybrid mode and the SOC value is less than the upper limit threshold, the auxiliary power unit is turned on and used together with the energy storage battery system as an energy supply source. To balance the use of the battery and avoid too high or too low SOC values.

[0126] In one embodiment, calling the corresponding energy management strategy according to the energy supply source specifically includes: obtaining the maximum supplyable power of the energy supply source, comparing the maximum supplyable power with the required 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 required power, controlling the energy storage battery system to adjust the discharge power to the required power; when the energy supply source is the energy storage battery system and the auxiliary power unit, comparing the maximum power generation power of the auxiliary power unit with the required power; if the maximum power generation power is greater than or equal to the required power, controlling the auxiliary power unit to adjust the output power to the required power; if the maximum power generation power is less than the required power, adjusting the output power of the auxiliary power unit to the maximum power generation power, and adjusting the discharge power of the energy storage battery system to a first discharge power, wherein the first discharge power is the power difference between the required power and the maximum power generation power.

[0127] The required power is composed of the power of the auxiliary electrical equipment and the drive motor, and can be calculated by the stroke of the accelerator pedal and the current speed of the motor. In the embodiment of the present invention, the corresponding energy management strategy is called according to the comparison result of the maximum supplyable power of the energy supply source and the required power. Specifically, when the energy supply source is an energy storage battery system or an energy storage battery system and an auxiliary power unit, the system will generate corresponding initial power allocation results according to their respective maximum power and required power to ensure the reasonable allocation and efficient use of energy. The first initial power allocation result is that the energy storage battery system independently provides the required power; the second initial power allocation result is that the auxiliary power unit independently provides the required power; the third initial power allocation result is that the energy storage battery system and the auxiliary power unit jointly provide the required power, wherein the auxiliary power unit provides the maximum power generation and the energy storage battery system provides the rest. Exemplarily, when the auxiliary power unit APU is in the on state, that is, the auxiliary power unit is used as the energy supply source, if the maximum power generation of the engine is or is equal to the required power, the power requirements of the drive motor and the auxiliary electrical equipment are 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 generator is less than the required power, the power difference between the maximum power generation and the required power needs to be determined, and the shortfall will be supplemented by the energy storage battery. When the auxiliary power unit is in the off state, that is, the energy storage battery system is used as the energy supply source, if the maximum discharge power of the energy storage battery is greater than or equal to the required power, the power requirements of the drive motor and auxiliary electrical equipment will be met first; if the maximum discharge power is less than the required power, the auxiliary power unit needs to be turned on and switched to hybrid power mode, with the energy storage battery system and the auxiliary power unit acting as the energy supply source.

[0128] In one embodiment, before calling the corresponding energy management strategy according to the energy supply source, the method further includes:

[0129] The average driving speed information used to generate the previous SCO threshold update threshold is obtained, and the average driving speed information is compared with the real-time vehicle speed information. 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.

[0130] In the embodiment of the present invention, the average driving speed data used in the last update of the SOC threshold value and the actual driving speed of the vehicle at the current moment are obtained. The difference between the average driving speed and the real-time vehicle speed is calculated. If the deviation is within the preset range, it means that the current driving conditions are similar to the conditions at the last threshold update, so the energy management strategy can be used as the final energy management result. If the deviation exceeds the preset range, the SOC threshold update threshold needs to be recalculated, and the power allocation result is regenerated based on the new SOC threshold update threshold.

[0131] In one implementation, the energy management method further includes adjusting the power of the auxiliary power unit and the energy storage battery system based on the real-time battery power capability, specifically: monitoring the temperature information and SOC information of the energy storage battery system in real time, and generating the real-time battery power capability based on the temperature information and SOC information; wherein the real-time battery power capability includes a maximum charging power and a 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, executing a first power adjustment strategy; 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 charging power of the energy storage battery is greater than the maximum charging power, executing a second power adjustment strategy; 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; when the target vehicle is in the pure electric drive mode and the real-time discharge power of the energy storage battery is greater than the maximum discharge power, executing a third power adjustment strategy; wherein the third power adjustment strategy is to adjust the real-time discharge power to the maximum discharge power.

[0132] In the embodiment of the present invention, a real-time battery power capability SOP including the maximum charging power and the maximum discharging power is generated based on the real-time state of the energy storage battery (such as temperature and SOC information), 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 to ensure that the working state of the energy storage battery is within a safe range under different driving modes, avoiding overcharging, overdischarging or overheating, and optimizing energy utilization. Figure 3 , Figure 3 Schematic diagram of a power adjustment process provided in an embodiment of the present invention. The current discharge power P of the energy storage battery 放 If the actual discharge power of the energy storage battery exceeds the maximum discharge power it can actually provide, the first power adjustment strategy is executed. 放 Adjust to the maximum discharge power to ensure that the battery will not be damaged due to over-discharge; the current charging power P of the energy storage battery 充 If the maximum charging power that the battery can actually accept is exceeded, the second power adjustment strategy is executed. The output power of the auxiliary power unit is reduced until the real-time charging power P of the energy storage battery reaches 充 Less than the maximum charging power. This can prevent overcharging of the battery and protect battery safety; the current discharge power P of the energy storage battery 放 If the actual discharge power of the energy storage battery exceeds the maximum discharge power it can actually provide, the third power adjustment strategy is executed. 放Adjust to the maximum discharge power to ensure that the battery will not be damaged by over-discharge. By real-time monitoring of the battery's charge and discharge status, it is possible to avoid overcharging or over-discharging of the battery, ensure that the battery operates within a safe temperature range, and avoid battery performance degradation or damage caused by overheating or overcooling. According to the real-time status of the battery, dynamically adjust the power distribution of the auxiliary power unit and the energy storage battery to further maximize energy utilization efficiency.

[0133] In an embodiment of the present invention, an energy management device is also provided, including 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 above-mentioned energy management method when executing the computer program.

[0134] In an embodiment of the present invention, a computer-readable storage medium is further provided. The computer-readable storage medium includes a stored computer program. When the computer program is running, the device where the computer-readable storage medium is located is controlled to execute the above-mentioned energy management method.

[0135] Exemplarily, the computer program may be divided into one or more modules, one or more modules are stored in a memory and executed by a processor to implement the present invention. One or more modules may be a series of computer program instruction segments capable of completing specific functions, and the instruction segments are used to describe the execution process of the computer program in the energy management device.

[0136] The energy management device may be a computing device such as a desktop computer, a notebook, a PDA, and a cloud server. The energy management device may include, but is not limited to, a processor, a memory, and a display. Those skilled in the art will appreciate that the above components are merely examples of energy management devices and do not constitute a limitation on the energy management device. The energy management device may include more or fewer components than the components, or a combination of certain components, or different components. For example, the energy management device may also include input and output devices, network access devices, buses, etc.

[0137] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of the energy management device, and uses various interfaces and lines to connect the various parts of the entire energy management device.

[0138] The memory can be used to store computer programs and / or modules. 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, wherein the program storage area can store an operating system, an application required for at least one function (such as a sound playback function, a text conversion function, etc.), etc.; 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 memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0139] Among them, if the module based on energy management is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. Computer-readable media may include: any entity or device capable of carrying computer program code, recording medium, U disk, mobile hard disk, 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. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0140] An embodiment of the present invention provides an energy management method, which generates driving information by real-time monitoring of the driving status of the target vehicle, so that the energy management strategy can be adjusted according to the current driving conditions. Taking the minimum equivalent fuel consumption as the optimization goal, the driving information is optimized to obtain a dynamic SOC threshold update threshold. 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. This reasonable energy allocation strategy can maximize the use of energy and reduce waste.

[0141] Example 2

[0142] See also Figure 4 , Figure 4 The present invention provides a schematic diagram of the system composition of an energy management system provided in an embodiment of the present invention. The present invention provides an energy management system for executing the energy management method described in Example 1, including an auxiliary power unit, a rectifier, a power distribution device, auxiliary power 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 to output alternating current;

[0144] The rectifier device is used to convert the AC power into DC power and send the DC power information to the energy management processing system;

[0145] The power distribution device is used 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 power equipment and the motor based on the power distribution instruction;

[0146] The auxiliary electrical equipment is used to realize human-computer interaction between the vehicle and the user;

[0147] The motor is used to drive the vehicle;

[0148] The energy storage battery system is used to store electrical 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 used to monitor the driving state of the vehicle in real time, generate the driving information of the vehicle within a preset time length and send the driving information to the logic gate control module;

[0150] The logic gate control module is used to generate an SOC threshold update threshold according to the driving information and send the SOC threshold update threshold to the energy management processing system;

[0151] The energy management processing system is used 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 also used 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 invention, the auxiliary power unit includes an internal combustion engine and a generator, the internal combustion engine and the generator are mechanically connected, and the generator is driven by the crankshaft of the internal combustion engine to output AC power. The rectifier converts the AC power output by the auxiliary power unit into DC power, and transmits the output DC power information to the energy management processing system in real time through the data line. The rectifier ensures the uniform format of energy (DC power), which is convenient for subsequent power management and distribution. The power distribution device receives the power distribution instruction sent by the energy management processing system, and adjusts the power distribution of the energy storage battery system, auxiliary power equipment and drive motor according to the instruction. The power distribution device is the "energy dispatching center" of the entire system, ensuring that each power equipment and drive system obtains reasonable power support. Auxiliary power equipment refers to power steering system, human-computer interaction equipment, etc., which realizes human-computer interaction between the vehicle and the user, and provides information display, navigation, entertainment and other functions. The motor, also known as the electric drive load, is used to drive the vehicle to drive and receives the power provided by the energy storage battery or the rectifier. The drive motor is the power source of the vehicle, and its efficiency directly affects the driving performance of the vehicle. The energy storage battery system is used to store electrical energy and supply energy to the electric drive load (motor). The energy storage battery system can store direct current rectified by the rectifier, and can also supply electrical energy to the electric drive load (motor), thereby driving the vehicle. Furthermore, the energy storage battery system has a built-in battery management system (BMS), which can also monitor key parameters such as battery charge status, battery health status, and battery power capacity in real time. The speed monitoring module is used to monitor the driving status of the vehicle in real time, generate the driving 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 driving data for the energy management system, helping the system make dynamic energy management decisions. The logic gate module generates the SOC threshold update threshold based on the driving information sent by the speed monitoring module, and sends the threshold to the energy management processing system. The logic gate control module is the "intelligent decision-making unit" of the energy management system, which dynamically adjusts the SOC threshold according to the driving status to ensure the flexibility and efficiency of the system. The energy management processing system calls the corresponding energy management strategy based on the received SOC threshold update threshold. Based on the called energy management strategy, a power distribution instruction is generated to the power distribution device. The real-time charging and discharging power of the energy storage battery is adjusted according to the real-time battery power capacity of the energy storage battery. The energy management processing system is the "brain" of the entire system, responsible for comprehensive analysis and processing of the input data of each module to generate the optimal energy management solution.

[0155] After the APU is started, it outputs AC power 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 sends the power information of the DC power to the energy management processing system. The logic gate control module generates the SOC threshold update threshold based on the driving information provided by the speed monitoring module and sends it to the energy management processing system. The energy management processing system generates energy management results based on the SOC threshold update threshold and the real-time status of the energy storage battery. Based on the energy management results, the energy management processing system generates a power allocation instruction and sends it to the power distribution device. The power distribution device adjusts the power distribution of the energy storage battery system, auxiliary power equipment and drive motor according to the received power allocation instruction. The energy storage battery system adjusts its charging and discharging power according to the received power allocation 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 device described above can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0157] The embodiment of the present invention provides an energy management system, in which the power distribution device dynamically adjusts the power distribution of the energy storage battery system, the auxiliary power equipment and the drive motor according to the power distribution instruction sent by the energy management processing system to 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 the SOC threshold update threshold according to the driving information, and the energy management processing system dynamically adjusts the energy management strategy based on this threshold. The system comprehensively considers the vehicle performance, battery life and energy efficiency, generates the best energy management solution, and ensures efficient operation under different driving conditions. The auxiliary power 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 status of the vehicle through the auxiliary power equipment to enhance driving safety and comfort. The energy management processing system monitors the health status of the energy storage battery system in real time to avoid the battery from working for a long time under high load or low load conditions and prolong the battery life. The logic gate control module generates an SOC threshold update threshold based on driving information, and the energy management processing system adjusts the battery charge and discharge strategy based on this threshold to ensure that the battery operates within the appropriate SOC range and avoids over-charge and over-discharge. Through the coordinated work of various modules, efficient energy utilization is achieved, with flexible adaptability, which can bring a good driving experience to users.

[0158] The above are only preferred embodiments of the present invention. It should be pointed out that, for ordinary technicians in this technical field, several improvements and substitutions can be made without departing from the technical principles of the present invention. These improvements and substitutions should also be regarded as the scope of protection of the present invention.

Claims

1. An energy management method, characterized in that: include: Monitor the driving status of the target vehicle in real time and generate driving information of the target vehicle within a preset time length; The driving information is optimized with the minimum equivalent fuel consumption as the optimization target to obtain 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; An energy supply source is selected based on a comparison result between the real-time SOC value of the target vehicle and the SOC threshold update threshold, and a corresponding energy management strategy is called according to the energy supply source.

2. An energy management method according to claim 1, characterized in that: The real-time monitoring of the driving state of the target vehicle and the generation of driving information of the target vehicle within a preset time length specifically include: Monitor the driving state of the target vehicle in real time and calculate the speed information of the target vehicle within a preset time length; wherein the speed information includes the real-time driving speed, maximum driving speed, minimum driving speed and average driving speed within the preset time length; Calculating a first speed difference between the maximum driving speed and the average driving speed, and a second speed difference between the minimum driving speed and the average driving speed, and selecting the maximum value of the first speed difference and the second speed difference as the maximum speed difference; When the maximum speed difference 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.

3. An energy management method according to claim 1, characterized in that: The optimizing the driving information with the minimum equivalent fuel consumption as the optimization target to obtain the SOC threshold update threshold under the driving information specifically includes: The objective function is constructed with the minimum equivalent fuel consumption as the optimization target; wherein the expression of the objective function is: 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 the equivalent fuel consumption factor; m ess (t) is the battery energy consumption at time t; P batt (t) is the power output of the battery at time t; Q lhv is the lower calorific value of the fuel; Performing particle swarm optimization on the equivalent fuel consumption factor based on preset constraints to obtain an optimal value of the equivalent fuel consumption factor, and solving the objective function based on the optimal value of the equivalent fuel consumption factor to obtain the minimum equivalent fuel consumption; The SOC upper limit threshold of the equivalent fuel consumption factor optimization value is used as the SOC upper limit update threshold, and the SOC lower limit threshold is used as the SOC lower limit update threshold; wherein the expression of the equivalent fuel consumption factor is: In the formula, are the average efficiency of the motor, motor controller, battery, and engine; SOC low is the SOC lower limit threshold; SOC high is the upper limit threshold of SOC; SOC ref is the reference SOC value; SOC(t) is the actual SOC value of the battery at time t.

4. An energy management method according to claim 3, characterized in that: Based on the preset constraints, the equivalent fuel consumption factor is optimized by particle swarm optimization to obtain the optimal value of the equivalent fuel consumption factor, specifically including: Acquire the constraint condition, wherein the constraint factors of the constraint condition include motor angular velocity, engine angular velocity, battery state of charge and required torque; Constructing a working model of the target vehicle according to the constraint factors; wherein the working model includes a demand torque model, an engine fuel consumption model, a motor model and a battery model; According to the expression of each working model, the preset constraint condition is optimized by particle swarm optimization to obtain the optimization value of the equivalent fuel consumption factor; wherein the expression of the preset constraint condition is: In the formula, w mot,max is the maximum permissible angular velocity of the motor; w eng,min is the minimum permissible angular velocity of the engine; w eng,max is the maximum permissible angular velocity of the engine; T eng,min , T eng,max is the minimum and maximum permissible engine torque at a given engine angular speed; T mot,min , T mot,max is the minimum and maximum permissible motor torque for a given motor angular velocity.

5. An energy management method according to claim 4, characterized in that: The performing particle swarm optimization on the preset constraint conditions according to the expression of each working model to obtain the optimization value of the equivalent fuel consumption factor specifically includes: The expression of the demand torque model is: Where, T 需求 is the required 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; ρ α is the air density; C D A is the air resistance coefficient; f is the frontal area of ​​the vehicle; v is the vehicle speed; sinα is the sine value of the road inclination; σ is the rotation mass conversion factor; R w is the vehicle radius; The expression of the engine fuel consumption model is: In the formula, is the fuel consumption rate; T eng is the engine torque; w eng is the engine angular velocity; is the function law; The 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 used to determine the direction of the motor torque; η mot is the motor efficiency; The expression of the battery model is: Where SOC is the battery state of charge; U oc is the open circuit voltage of the battery; R int is the internal resistance of the battery; P batt is the battery power; Q batt is the battery capacity; The constraint factors are solved according to the expression of each working model to obtain a plurality of constraint factor values, and particle swarm optimization is performed on the constraint conditions based on the constraint factor values ​​to obtain the equivalent fuel consumption factor optimization value.

6. An energy management method according to claim 1, characterized in that: The selecting of the energy supply source based on the comparison result between the real-time SOC value of the target vehicle and the SOC threshold update threshold value specifically includes: Obtaining the driving mode and real-time SOC value of the target vehicle; 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, the auxiliary power unit is switched to a closed 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 the hybrid power 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 energy supply sources; wherein, when the driving mode is the hybrid power mode, the auxiliary power unit is in an on state.

7. An energy management method according to claim 6, characterized in that: The calling of the corresponding energy management strategy according to the energy supply source specifically includes: obtaining the maximum supplyable power of the energy supply source, comparing the maximum supplyable power with the required 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 required power, controlling the energy storage battery system to adjust the discharge power to the required power; When the energy supply source is the energy storage battery system and the auxiliary power unit, comparing the maximum power generation power of the auxiliary power unit with the required power; If the maximum power generation is greater than or equal to the required power, controlling the auxiliary power unit to adjust the output power to the required power; If the maximum power generation power is less than the required power, the output power of the auxiliary power unit is adjusted to the maximum power generation power, and the discharge power of the energy storage battery system is adjusted to the first discharge power, which is the power difference between the required power and the maximum power generation power.

8. An energy management method according to claim 1, characterized in that: Before calling the corresponding energy management strategy according to the energy supply source, the method further includes: The average driving speed information used to generate the previous SCO threshold update threshold is obtained, and the average driving speed information is compared with the real-time vehicle speed information. 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.

9. An energy management method according to claim 6, characterized in that: The energy management method further includes adjusting the power of the auxiliary power unit and the energy storage battery system based on the real-time battery power capability, specifically: Monitor the temperature information and SOC information of the energy storage battery system in real time, and generate the real-time battery power capability based on the temperature information and SOC information; wherein the real-time battery power capability includes the maximum charging power and the 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, executing a first power adjustment strategy; 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 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; 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.

10. An energy management system, characterized in that: Used to execute the energy management method according to any one of claims 1 to 9, comprising an auxiliary power unit, a rectifier, 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; The auxiliary power unit is used to output alternating current; The rectifying device is used to convert the alternating current into direct current, and send the direct current power information to the energy management processing system; The power distribution device is used 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 power equipment and the motor based on the power distribution instruction; The auxiliary electrical equipment is used to realize human-computer interaction between the vehicle and the user; The motor is used to drive the vehicle; The energy storage battery system is used to store electrical energy and supply energy to the motor; wherein the energy storage battery system comprises at least one energy storage battery; The speed monitoring module is used to monitor the driving state of the vehicle in real time, generate the 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 used to generate an SOC threshold update threshold according to the driving information and send the SOC threshold update threshold to the energy management processing system; The energy management processing system is used 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; The energy management processing system is also used 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.

11. A terminal device, characterized in that: The invention comprises a processor, a memory and a computer program stored in the memory and configured to be executed by the processor, and the energy management method according to any one of claims 1 to 9 is implemented when the processor executes the computer program.

12. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored computer program, wherein when the computer program is executed, the device where the computer-readable storage medium is located is controlled to execute the energy management method according to any one of claims 1 to 9.

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