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

Abstract

An energy management method, an energy management system, a terminal device, and a storage medium. The method comprises: monitoring the traveling state of a target vehicle in real time, and generating traveling information of the target vehicle within a preset time length; performing optimization on the traveling information with the minimum equivalent fuel consumption as an optimization objective, so as to obtain an updated SOC threshold value under the traveling information, wherein the updated SOC threshold value comprises an updated upper SOC threshold value and an updated lower SOC threshold value; and selecting an energy supply source on the basis of a comparison result between a real-time SOC value of the target vehicle and the updated SOC threshold value, and invoking a corresponding energy management strategy on the basis of the energy supply source. Therefore, the energy utilization efficiency of a series hybrid power system can be improved, and energy waste can be reduced.

Description

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

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

[0002] With increasing global energy consumption and intensifying environmental pollution pressures, new energy vehicle technology has become a research hotspot. Hybrid systems, unaffected by battery range and charging issues, can flexibly switch between or operate simultaneously with internal combustion engines and electric motors, offering greater driving flexibility and convenience. Based on the power connection method, hybrid systems are classified into three types: series, parallel, and series-parallel. A series hybrid system combines an auxiliary power unit consisting of an internal combustion engine and a generator with an energy storage battery system, using a drive motor to independently drive the wheels. The internal combustion engine does not directly drive the wheels; instead, the generator converts mechanical energy into electrical energy, which can be stored in the battery or directly supplied to the drive motor. The internal combustion engine and drive system are completely decoupled, resulting in a simple structure that is easy to design and maintain. Furthermore, the internal combustion engine can operate at fixed speeds and loads, consistently running in the medium-to-high efficiency range, thereby improving fuel economy and reducing emissions. Series hybrid systems have a broader market prospect, especially suitable for users who need to balance driving range and environmental performance.

[0003] Series hybrid systems involve multiple power sources, including an APU (Auxiliary Power Unit), a battery storage system, and a drive motor. Through proper energy management, the hybrid system can flexibly switch power sources under different driving conditions, optimizing energy utilization. Existing technologies often employ rule-based on-off energy management strategies. Specifically, this involves setting logical thresholds to control the start-stop of the internal combustion engine and the charging and discharging of the battery. When the State of Charge (SOC) is below the lower threshold, the internal combustion engine starts generating electricity to charge the battery; when the SOC is above the upper threshold, the internal combustion engine shuts off, and the vehicle enters pure electric mode. However, this method does not consider the impact of changes in speed and operating conditions during driving on energy management, which can easily 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-mentioned technical problems, improve the energy utilization efficiency of series hybrid power systems, and reduce energy waste.

[0005] To address the aforementioned technical problems, this invention provides an energy management method, comprising:

[0006] Real-time monitoring of the driving status of the target vehicle generates driving information of the target vehicle within a preset time period;

[0007] The driving information is optimized with the goal of minimizing equivalent fuel consumption to obtain the SOC threshold update threshold under the driving information; wherein, the SOC threshold update threshold includes an upper SOC update threshold and a lower SOC update threshold.

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

[0009] In the above scheme, driving information is generated by real-time monitoring of the target vehicle's driving status, enabling the energy management strategy to adjust according to current driving conditions. With the goal of minimizing equivalent fuel consumption, the driving information is optimized to obtain a dynamic SOC threshold update. This dynamic adjustment better adapts to different driving conditions, improving the system's flexibility and adaptability. Based on the real-time SOC value and the dynamically updated SOC threshold, a suitable energy supply source is selected. This rational energy allocation strategy maximizes energy utilization and reduces waste.

[0010] In one implementation, the real-time monitoring of the target vehicle's driving status and the generation of the target vehicle's driving information within a preset time period specifically includes:

[0011] The driving status of the target vehicle is monitored in real time, and the vehicle speed information of the target vehicle within a preset time period is calculated; wherein, the vehicle speed information includes the real-time driving speed, maximum driving speed, minimum driving speed and average driving speed within the preset time period;

[0012] Calculate the first speed difference between the maximum driving speed and the average driving speed, and the second speed difference between the minimum driving speed and the average driving speed, and select 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, the target vehicle is determined to be 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 was collected.

[0014] In the above scheme, by monitoring vehicle speed information in real time, the system accurately calculates vehicle speed fluctuations, generates a first speed difference and a second speed difference, and selects the maximum value as the maximum speed difference. When the maximum speed difference is less than or equal to a preset speed threshold, the system determines that the vehicle is in a stable driving state, ensuring accurate judgment of the vehicle's state. When the vehicle is in a stable driving state, driving information is generated based on vehicle speed information to optimize energy allocation strategies. 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 goal of minimizing equivalent fuel consumption to obtain the SOC threshold update threshold under the driving information specifically includes:

[0016] An objective function is constructed with the goal of minimizing equivalent fuel consumption; the expression of the objective function is as follows:

[0017] In the formula, m eqv (t) represents the equivalent fuel consumption at time t; m fuel (t) represents the actual fuel consumption at time t; s(t) is the equivalent fuel consumption factor; m ess (t) represents the battery energy consumption at time t; P batt (t) represents the power output of the battery at time t; Q lhv It has a low calorific value;

[0018] Based on preset constraints, particle swarm optimization is performed on the equivalent fuel consumption factor to obtain the optimization value of the equivalent fuel consumption factor. Based on the optimization value of the equivalent fuel consumption factor, the objective function is solved to obtain the minimum equivalent fuel consumption.

[0019] The upper limit threshold of the SOC based on the optimized value of the equivalent fuel consumption factor is used as the upper limit update threshold of the SOC, and the lower limit threshold of the SOC based on the optimized value of the equivalent fuel consumption factor is used as the lower limit update threshold of the SOC; wherein, the expression for the equivalent fuel consumption factor is:

[0020] In the formula, These represent the average efficiency of the motor, motor controller, battery, and engine, respectively; SOC (State of Charge). low The lower limit threshold of SOC; SOC high The upper limit threshold of SOC; SOC ref The reference SOC value is used; SOC(t) is the actual SOC value of the battery at time t.

[0021] In the above scheme, by constructing an objective function with the lowest equivalent fuel consumption as the optimization goal, and using particle swarm optimization algorithm for optimization, the system can effectively reduce the overall fuel consumption of the vehicle and improve fuel economy.

[0022] The objective function comprehensively considers both actual fuel consumption and battery energy consumption, converting battery energy consumption into equivalent fuel consumption through an equivalent fuel consumption factor to minimize overall fuel consumption. Particle swarm optimization (PSO) is used to optimize the equivalent fuel consumption factor. This algorithm features global search capability and fast convergence speed, effectively finding the optimal solution in a complex optimization space. Through PSO, 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 based on the vehicle's real-time driving status and battery status, achieving real-time optimization of the energy management strategy. This dynamic adjustment capability allows the system to automatically adapt to different operating conditions, improving the overall system's flexibility and adaptability. 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 SOC, ensuring that the energy management strategy comprehensively considers the efficiency of each part of the system and the battery's health status. This comprehensive consideration ensures that energy management not only focuses on fuel economy but also takes into account battery lifespan and performance.

[0023] In one implementation, the step of performing particle swarm optimization on the equivalent fuel consumption factor based on preset constraints specifically includes:

[0024] Obtain the constraints, wherein the constraint factors of the constraints include motor angular velocity, engine angular velocity, battery state of charge, and required torque;

[0025] A working model of the target vehicle is constructed based on the aforementioned constraints; wherein the working model includes a demand torque model, an engine fuel consumption model, a motor model, and a battery model;

[0026] Based on the expression of each working model, particle swarm optimization is performed on the preset constraints to obtain the optimized value of the equivalent fuel consumption factor; wherein, the expression of the preset constraints is:

[0027] In the formula, w mot (t) represents the angular velocity of the motor at time t; w mot,max w is the maximum permissible angular velocity of the motor. eng (t) represents the engine angular velocity at time t; w eng,min The minimum permissible angular velocity of the engine; w eng,max T represents the engine's maximum permissible angular velocity; Trequirement represents the required torque; Trequirement eng(t) Engine torque at a given engine angular velocity at time t; T eng,min T eng,max T represents the minimum and maximum permissible engine torque at a given engine angular velocity. mot (t) The motor torque at a given engine angular velocity at time t; T mot,min T mot,max These represent the minimum and maximum permissible motor torques at a given motor angular velocity.

[0028] In one implementation, the step of performing particle swarm optimization on the preset constraints based on the expression of each working model to obtain the optimized value of the equivalent fuel consumption factor specifically includes:

[0029] The expression for the demand torque model is:

[0030] In the formula, Tdemand is the required torque; m is the vehicle mass; g is the gravitational acceleration; fr is the rolling resistance coefficient; cosα is the cosine of the road inclination angle; ρ α C is the density of air. D α is the air drag coefficient; Af 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; Rw is the vehicle radius.

[0031] The expression for the engine fuel consumption model is:

[0032] In the formula, Fuel consumption rate; T eng For engine torque; w eng The engine angular velocity; For functional rules;

[0033] The expression for the motor model is:

[0034] In the formula, P mot T represents the motor power. mot w represents the motor torque. mot sgn(T) is the angular velocity of the motor. mot ) is the sign function used to determine the direction of motor torque; η mot For motor efficiency;

[0035] The expression for the battery model is:

[0036] In the formula, SOC represents the battery's state of charge; U oc R is the battery open-circuit voltage. int P is the battery's internal resistance.batt Battery power; Q batt Battery capacity;

[0037] The constraint factors are solved according to the expression of each working model to obtain several constraint factor values. Based on the constraint factor values, particle swarm optimization is performed on the constraint conditions to obtain the optimization value of the equivalent fuel consumption factor.

[0038] The above scheme achieves dynamic optimization of the equivalent fuel consumption factor through an accurate vehicle working model, comprehensive constraints, and an efficient particle swarm optimization algorithm.

[0039] In one implementation, the selection 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 specifically includes:

[0040] Obtain the driving mode and real-time SOC value of the target vehicle;

[0041] When the driving mode is pure electric drive mode and the real-time SOC value is greater than the SOC lower limit update threshold, the auxiliary power unit is switched to the off state and the energy storage battery system is selected as the energy supply source.

[0042] When the driving mode is the pure electric drive mode and the real-time SOC value is less than the lower limit update threshold of SOC, or when the driving mode is the hybrid mode and the real-time SOC value is less than the upper limit update threshold of SOC, the energy storage battery system and the auxiliary power unit are selected as the energy supply source together; wherein, when the driving mode is the hybrid mode, the auxiliary power unit is in the on state.

[0043] In the above scheme, when the vehicle is in pure electric drive mode and the SOC value is higher than the lower update threshold, the system shuts down the auxiliary power unit and uses only the energy storage battery system for power, achieving zero-emission driving and improving energy efficiency. When the SOC value is lower than the lower threshold or lower than the upper update threshold in hybrid mode, the system automatically activates the auxiliary power unit, combining battery and auxiliary power unit power to ensure continuous vehicle operation 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.

[0044] In one implementation, the step of invoking the corresponding energy management strategy based on the energy supply source specifically includes: obtaining the maximum supplyable power of the energy supply source, comparing the maximum supplyable power with the demanded power, and invoking the corresponding energy management strategy based on the power comparison result; wherein...

[0045] 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, the energy storage battery system is controlled to adjust the discharge power to the required power.

[0046] 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 required power.

[0047] If the maximum power generation of the auxiliary power unit is greater than or equal to the required power, the auxiliary power unit is controlled to adjust its output power to the required power.

[0048] If the maximum power generation of the auxiliary power unit is less than the required 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 the first discharge power, where the first discharge power is the power difference between the required power and the maximum power generation.

[0049] 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, direct, and ensures stable power supply. When using both the energy storage battery system and the auxiliary power unit, intelligent power allocation is performed based on the relationship between the maximum power output 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 demands, enabling the system to flexibly adapt to various driving conditions and load requirements. When the required power changes, the power allocation can be adjusted in real time to ensure that the system is always in an optimal operating state.

[0050] In one implementation, before invoking the corresponding energy management strategy based on the energy supply source, the method further includes:

[0051] Obtain the average driving speed information used in the previous SCO threshold update, compare the average driving speed information with the real-time vehicle speed information, and if the deviation between the two is within a preset range, use the energy management strategy as the energy management result of the target vehicle and execute the energy management strategy.

[0052] In the above scheme, by comparing the current real-time vehicle speed information with the information used when the SOC threshold update was generated previously, it is ensured that vehicle speed changes are within a preset range. This avoids frequent adjustments to the energy management strategy due to frequent vehicle speed changes, thus improving system stability.

[0053] 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 real-time battery power capability, specifically:

[0054] The temperature and 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 and SOC information; wherein, the real-time battery power capability includes the maximum charging power and the maximum discharging power;

[0055] 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.

[0056] 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;

[0057] 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, 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.

[0058] 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 capability to ensure that the system is always in the optimal operating 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 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 battery life.

[0059] Secondly, this application also provides an energy management system for executing the energy management method 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;

[0060] The auxiliary power unit is used to output alternating current;

[0061] The rectifier is used to convert the alternating current into direct current and send the direct current power information to the energy management processing system;

[0062] The power distribution device is used 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 equipment and the motor based on the power distribution instructions;

[0063] The auxiliary electrical equipment is used to enable human-machine interaction between the vehicle and the user;

[0064] The motor is used to drive the vehicle.

[0065] The energy storage battery system is used to store electrical energy and supply power to the motor; wherein, the energy storage battery system includes at least one energy storage battery;

[0066] The speed monitoring module is used to monitor the vehicle's driving status in real time, generate the vehicle's driving information within a preset time period, and send the driving information to the logic gate control module.

[0067] The logic gate control module is used to generate a SOC threshold update threshold based on the driving information and send the SOC threshold update threshold to the energy management processing system;

[0068] The energy management processing system is used to generate an energy management strategy based on the received SOC threshold update threshold, and to generate a power allocation instruction to the power allocation device based on the energy management strategy;

[0069] 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.

[0070] In the above scheme, the power distribution device dynamically adjusts the power distribution among the energy storage battery system, auxiliary electrical equipment, and drive motor according to the power distribution commands sent by the energy management processing system, ensuring efficient energy utilization. The speed monitoring module monitors the vehicle's driving status in real time and generates driving information, which is sent to the logic gate control module. The logic gate control module generates a SOC threshold update threshold based on 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 to generate the optimal energy management scheme, ensuring efficient operation under different driving conditions. Auxiliary electrical equipment enables interaction between the vehicle and the user, providing rich information displays and driving suggestions to improve the driving experience. The speed monitoring module monitors the vehicle's driving status in real time, and users can obtain vehicle driving information and energy management status through auxiliary electrical equipment, enhancing driving safety and comfort. The energy management processing system monitors the health status of the energy storage battery system in real time, preventing the battery from operating for extended periods under high or low load conditions, thus extending battery life. The logic gate control module generates a State of Charge (SOC) threshold update based on driving information. The energy management system then adjusts the battery's charging and discharging strategy based on this threshold to ensure the battery operates within a suitable SOC range and avoids overcharging and over-discharging. Through the coordinated work of these modules, efficient energy utilization, flexible adaptability, and a superior driving experience are achieved.

[0071] Thirdly, this 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, wherein the processor executes the computer program to implement the energy management method described above.

[0072] Fourthly, this application also provides a computer-readable storage medium, which includes a stored computer program, wherein the computer program, when running, controls the device where the computer-readable storage medium is located to perform the energy management method as described above. Attached Figure Description

[0073] Figure 1 is a flowchart illustrating an energy management method provided in one embodiment of the present invention;

[0074] Figure 2 is a flowchart illustrating a driving information generation process provided in one embodiment of the present invention;

[0075] Figure 3 is a schematic diagram of a power adjustment process provided in an embodiment of the present invention;

[0076] Figure 4 is a schematic diagram of the system composition of an energy management system provided in one embodiment of the present invention. Detailed Implementation

[0077] The specific embodiments of the present invention will be described in further detail below with reference to the accompanying drawings and examples. The following examples are for illustrative purposes only and are not intended to limit the scope of the invention.

[0078] The terms "first" and "second," etc., in the specification, claims, and drawings of this application are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes 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 such processes, methods, products, or apparatus.

[0079] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0080] Example 1

[0081] Referring to Figure 1, Figure 1 is a schematic flowchart of an energy management method provided in an embodiment of the present invention. The embodiment of the present invention includes steps 101 to 104, each step being as follows:

[0082] Step 101: Monitor the driving status of the target vehicle in real time and generate driving information of the target vehicle within a preset time period.

[0083] In one embodiment, the real-time monitoring of the target vehicle's driving status involves calculating the vehicle's speed information over a preset time period. The speed information includes the real-time driving speed, maximum driving speed, minimum driving speed, and average driving speed over the preset time period. 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. The maximum speed difference is selected as the maximum speed difference. When the maximum speed difference is less than or equal to a preset speed threshold, the target vehicle is determined to be in a stable driving state. Driving information is generated based on the vehicle speed information and the time it was collected. When the maximum speed difference is greater than the preset speed threshold, the vehicle speed information is continuously updated.

[0084] In this embodiment of the 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 subjected to moving average processing to reduce noise interference and obtain more stable speed information. Referring to Figure 2, Figure 2 is a flowchart illustrating a driving information generation process provided in one embodiment of the invention. The maximum driving speed V of the vehicle within 5 seconds is recorded. max and minimum driving speed V min The system calculates the average speed V over 5 seconds. Then, it determines whether the vehicle is in a stable driving phase based on the maximum speed difference over 5 seconds. If the vehicle is in a stable driving phase, it outputs the vehicle's speed and time information over 5 seconds; otherwise, it continues to update the vehicle speed information, maximum and minimum speed values, and calculate the average speed V.

[0085] Step 102: Optimize the driving information with the lowest equivalent fuel consumption as the optimization objective to obtain the SOC threshold update threshold under the driving information; wherein, the SOC threshold update threshold includes the upper limit update threshold of SOC and the lower limit update threshold of SOC.

[0086] In one embodiment, optimizing the driving information with the goal of minimizing equivalent fuel consumption to obtain the SOC threshold update threshold under the driving information specifically includes:

[0087] An objective function is constructed with the goal of minimizing equivalent fuel consumption; the expression of the objective function is as follows:

[0088] In the formula, m eqv (t) represents the equivalent fuel consumption at time t, which is the conversion of battery energy consumption into equivalent fuel consumption; m fuel (t) represents the actual fuel consumption at time t; s(t) is the equivalent fuel consumption factor, which represents the coefficient that converts battery energy consumption into equivalent fuel consumption; m ess (t) represents the battery energy consumption at time t, calculated using the battery power and the lower calorific value of the fuel; P batt (t) represents the power output of the battery at time t; Q lhv The lower heating value of fuel oil indicates the amount of heat that can be released per unit mass of fuel oil.

[0089] Based on preset constraints, particle swarm optimization is performed on the equivalent fuel consumption factor to obtain the optimization value of the equivalent fuel consumption factor. Based on the optimization value of the equivalent fuel consumption factor, the objective function is solved to obtain the minimum equivalent fuel consumption.

[0090] The upper limit threshold of the SOC based on the optimized value of the equivalent fuel consumption factor is used as the upper limit update threshold of the SOC, and the lower limit threshold of the SOC based on the optimized value of the equivalent fuel consumption factor is used as the lower limit update threshold of the SOC; wherein, the expression for the equivalent fuel consumption factor is:

[0091] In the formula, These represent the average efficiency of the motor, motor controller, battery, and engine, respectively; SOC (State of Charge). low The lower limit threshold of SOC; SOC high The upper limit threshold of SOC; SOC ref The reference SOC value is used; SOC(t) is the actual SOC value of the battery at time t.

[0092] The average efficiency of the motor is the efficiency with which it converts electrical energy into mechanical energy during operation; the average efficiency of the motor controller is the efficiency with which it converts direct current into alternating current during operation; the average efficiency of the battery is the efficiency with which it charges and discharges; and the average efficiency of the engine is the efficiency with which it converts fuel energy into mechanical energy during operation. In this embodiment of the invention, considering both fuel consumption and battery energy consumption, an objective function is constructed with the minimum equivalent fuel consumption as the optimization goal. A particle swarm optimization (PSO) algorithm is used to optimize the equivalent fuel consumption factor to minimize the equivalent fuel consumption. Specifically, the PSO algorithm initializes a swarm of particles, where each particle represents a possible s(t) value. It calculates the equivalent fuel consumption for each particle, moves it in the search space, and updates the particle's position and velocity to find the global optimum, thus obtaining the optimized value of the equivalent fuel consumption factor. The equivalent fuel consumption factor represents the difference in energy conversion efficiency between the battery system and the engine system. When the battery's state of charge deviates from the reference state of charge, the equivalent fuel consumption factor changes accordingly, thereby adjusting the ratio of battery energy consumption to equivalent fuel consumption. This adjustment helps optimize the system's energy management, ensuring efficient operation under various conditions. When solving the objective function based on the equivalent fuel consumption factor optimization value, the actual fuel consumption of the engine can be calculated using engine torque and engine angular velocity; engine fuel consumption can be calculated using information on motor torque, motor angular velocity, driving speed, and driving time; P batt (t) is obtained by real-time monitoring of battery status. Based on the above parameters, the objective function can be solved to obtain the minimum equivalent fuel consumption.

[0093] In one embodiment, the step of performing particle swarm optimization on the equivalent fuel consumption factor based on preset constraints specifically includes:

[0094] Obtain the constraints, wherein the constraint factors of the constraints include motor angular velocity, engine angular velocity, battery state of charge, and required torque;

[0095] A working model of the target vehicle is constructed based on the aforementioned constraints; wherein the working model includes a demand torque model, an engine fuel consumption model, a motor model, and a battery model;

[0096] Based on the expression of each working model, particle swarm optimization is performed on the preset constraints to obtain the optimized value of the equivalent fuel consumption factor; wherein, the expression of the preset constraints is:

[0097] In the formula, w mot (t) represents the angular velocity of the motor at time t; w mot,max w is the maximum permissible angular velocity of the motor. eng (t) represents the engine angular velocity at time t; weng,min The minimum permissible angular velocity of the engine; w eng,max T represents the engine's maximum permissible angular velocity; Trequirement represents the required torque; Trequirement eng (t) Engine torque at a given engine angular velocity at time t; T eng,min T eng,max T represents the minimum and maximum permissible engine torque at a given engine angular velocity. mot (t) The motor torque at a given engine angular velocity at time t; T mot,min T mot,max These represent the minimum and maximum permissible motor torques at a given motor angular velocity.

[0098] In one embodiment, the step of performing particle swarm optimization on the preset constraints based on the expression of each working model to obtain the optimized value of the equivalent fuel consumption factor specifically includes:

[0099] The expression for the demand torque model is:

[0100] In the formula, T 需求 The required torque represents the driving torque needed by the vehicle under specific operating conditions to overcome various resistances and propel the vehicle forward; m is the vehicle mass; g is the acceleration due to gravity; f r ρ is the rolling resistance coefficient, representing the rolling friction resistance between the vehicle tire and the ground. It is usually a dimensionless parameter, related to road surface type and tire characteristics; cosα is the cosine of the road inclination angle, representing the effect of road smoothness or slope on vehicle rolling resistance; ρ α C is the density of air. D The drag coefficient represents the effect of vehicle shape on air resistance. It depends on the vehicle's shape and design and can be obtained from data provided by the vehicle manufacturer; A f The vehicle's frontal area represents the area in front of the vehicle that comes into contact with the air; it is usually calculated as the vehicle's height multiplied by its width. v is the vehicle's speed. sinα is the sine of the road inclination angle, representing the slope resistance experienced by the vehicle when driving on an incline. σ is the rotational mass conversion factor, which typically depends on the specific configuration of the drive system and can be obtained from data provided by the vehicle manufacturer. For cars, it is generally 1.05 to 1.1, and for trucks, it is generally 1.1 to 1.2. R w The radius of the vehicle is the radius of the drive wheels, used to convert linear force into torque.

[0101] The expression for the engine fuel consumption model is:

[0102] In the formula, Fuel consumption rate, or T, represents the mass of fuel consumed by an engine per unit of time. It is an important indicator for measuring engine fuel economy; eng Engine torque represents the torque output by the engine. It is one of the core indicators of engine performance, reflecting the engine's output capability at a specific engine speed; w eng Angular velocity is the engine's rotational speed, representing the speed at which the engine crankshaft rotates. It is another core indicator of engine performance, determining the engine's operating point (operating state). This is a functional rule that describes the relationship between engine fuel consumption rate and engine torque and angular velocity. This functional rule 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, and its specific value depends on the engine's design and characteristics.

[0103] The expression for the motor model is:

[0104] In the formula, P mot T represents the motor power. mot w represents the motor torque. mot sgn(T) is the angular velocity of the motor. mot ) is the sign function used to determine the direction of motor torque; η mot For motor efficiency;

[0105] The expression for the battery model is:

[0106] In the formula, SOC represents the battery's state of charge; U oc R is the battery open-circuit voltage. int P is the battery's internal resistance. batt Battery power; Q batt Battery capacity;

[0107] The constraint factors are solved according to the expression of each working model to obtain several constraint factor values. Based on the constraint factor values, particle swarm optimization is performed on the constraint conditions to obtain the optimization value of the equivalent fuel consumption factor.

[0108] In this embodiment of the invention, 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 preset constraints, ultimately obtaining the threshold update threshold of the optimized equivalent fuel consumption factor. By constructing the demand torque model, engine fuel consumption model, motor model, and battery model, the characteristics of each subsystem of the vehicle are comprehensively considered, improving the accuracy and reliability of the optimization results. Based on real-time monitoring of the driving status and preset constraints, the optimization objective and constraints are dynamically adjusted to make the optimization results more adaptable to actual operating conditions. Through multiple preset constraints, the feasibility of the optimization results in actual operating conditions is ensured, avoiding problems exceeding physical and system limitations, and improving the stability and safety of the system.

[0109] 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 invoke the corresponding energy management strategy according to the energy supply source.

[0110] In one embodiment, the selection of 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 specifically includes: obtaining the driving mode and real-time SOC value of the target vehicle; when the driving mode is pure electric drive mode and the real-time SOC value is greater than the lower SOC threshold update threshold, switching the auxiliary power unit to the off state and selecting the energy storage battery system as the energy supply source; when the driving mode is pure electric drive mode and the real-time SOC value is less than the lower SOC threshold update threshold, or the driving mode is hybrid mode and the real-time SOC value is less than the upper SOC threshold update threshold, selecting the energy storage battery system and the auxiliary power unit together as the energy supply source; wherein, when the driving mode is hybrid mode, the auxiliary power unit is in the on state.

[0111] In this embodiment of the invention, the SOC value of the energy storage battery system is monitored in real time to ensure its accuracy. Data fusion from multiple sensors can be considered to improve measurement reliability. The system accurately identifies the vehicle's current driving mode (e.g., pure electric drive mode, hybrid mode, regenerative braking mode, etc.) and adopts corresponding energy management strategies based on 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 sole energy 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, working together with the energy storage battery system as an energy source. The auxiliary power unit provides additional power support to prevent excessive battery discharge. 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, working together with the energy storage battery system as an energy source. This balances battery usage and avoids excessively high or low SOC values.

[0112] In one embodiment, the step of invoking the corresponding energy management strategy based on the energy supply source specifically includes: obtaining the maximum supplyable power of the energy supply source, comparing the maximum supplyable power with the demand power, and invoking the corresponding energy management strategy based on 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, controlling the energy storage battery system to adjust its discharge power to the demand power; when the energy supply source is the energy storage battery system and the auxiliary power unit, comparing the maximum power generation of the auxiliary power unit with the demand power; if the maximum power generation of the auxiliary power unit is greater than or equal to the demand power, controlling the auxiliary power unit to adjust its output power to the demand power; if the maximum power generation of the auxiliary power unit is less than the demand power, adjusting the output power of the auxiliary power unit to the maximum power generation, 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 demand power and the maximum power generation.

[0113] The required power consists of the power of auxiliary electrical equipment and the drive motor, and can be calculated from the accelerator pedal travel and the current motor speed. In this embodiment of the invention, the corresponding energy management strategy is invoked based on the comparison between the maximum available 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 generates corresponding initial power allocation results based on their respective maximum power and required power to ensure reasonable energy allocation and efficient utilization. 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 remaining power. For example, when the auxiliary power unit (APU) is in the on state, that is, when the auxiliary power unit acts as an energy supply source, if the maximum power generation of the engine is equal to or equal to the required power, the power requirements of the drive motor and auxiliary electrical equipment are prioritized, and any excess power can be used to charge the energy storage battery. If the generator's maximum power output is less than the required power, the power difference between the maximum power output and the required power needs to be determined, and the shortfall is supplemented by the energy storage battery. When the auxiliary power unit is off, i.e., when the energy storage battery system is 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 are prioritized; if the maximum discharge power is less than the required power, the auxiliary power unit needs to be turned on, switching to hybrid mode, where the energy storage battery system and the auxiliary power unit jointly serve as the energy supply source.

[0114] In one embodiment, before invoking the corresponding energy management strategy based on the energy supply source, the method further includes:

[0115] Obtain the average driving speed information used in the previous SCO threshold update, compare the average driving speed information with the real-time vehicle speed information, and if the deviation between the two is within a preset range, use the energy management strategy as the energy management result of the target vehicle and execute the energy management strategy.

[0116] In this embodiment of the invention, the average driving speed data used in the previous update of the SOC threshold 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 a preset range, it indicates that the current driving conditions are similar to those at the time of the previous threshold update, and therefore 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.

[0117] 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 real-time battery power capability. Specifically: real-time monitoring of the temperature and SOC information of the energy storage battery system, and generating the real-time battery power capability based on the temperature 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 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; 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 drive 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.

[0118] In this embodiment of the invention, a real-time battery power capability SOP, including maximum charging power and maximum discharging power, is generated based on the real-time status of the energy storage battery (such as temperature and SOC information). The power allocation between 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 energy storage battery operates within a safe range under different driving modes, avoiding overcharging, over-discharging, or overheating, while optimizing energy utilization. See Figure 3, which is a schematic diagram of a power adjustment process provided in one embodiment of the invention. The current discharge power P of the energy storage battery... 放 If the actual maximum discharge power is exceeded, the first power adjustment strategy is executed. The real-time discharge power P of the energy storage battery is adjusted. 放 Adjust to maximum discharge power to ensure the battery is not damaged by over-discharge; the current charging power P of the energy storage battery. 充 If the charging power exceeds its actual maximum acceptable level, a second power adjustment strategy is implemented. The output power of the auxiliary power unit is reduced until the real-time charging power P of the energy storage battery is reached. 充 The discharge power is less than the maximum charging power. This prevents overcharging and protects battery safety; the current discharge power P of the energy storage battery... 放 If the actual maximum discharge power is exceeded, the third power adjustment strategy is executed. The real-time discharge power P of the energy storage battery is adjusted. 放Adjusting to maximum discharge power ensures the battery is not damaged by over-discharge. Real-time monitoring of the battery's charging and discharging status prevents overcharging or over-discharging, ensuring the battery operates within a safe temperature range and avoiding performance degradation or damage caused by overheating or overcooling. Based on the battery's real-time status, the power allocation between the auxiliary power unit and the energy storage battery is dynamically adjusted to further maximize energy utilization efficiency.

[0119] In this embodiment of the 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. When the processor executes the computer program, it implements the energy management method described above.

[0120] In this embodiment of the invention, a computer-readable storage medium is also provided, which includes a stored computer program, wherein the computer program controls the device where the computer-readable storage medium is located to execute the above-described energy management method when it is running.

[0121] For example, a computer program may be divided into one or more modules, one or more of which are stored in memory and executed by a processor to carry out the present invention. The one or more modules may be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of the computer program in an energy management device. Optionally, the computer-readable storage medium is a non-transitory computer-readable storage medium.

[0122] The energy management device can be a computing device such as a desktop computer, laptop, handheld computer, or cloud server. The energy management device may include, but is not limited to, a processor, memory, and a display. Those skilled in the art will understand that the above components are merely examples of energy management devices and do not constitute a limitation on the energy management device. It may include more or fewer components, or combinations of certain components, or different components. For example, the energy management device may also include input / output devices, network access devices, buses, etc.

[0123] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor. The processor is the control center of the energy management device, connecting all parts of the device through various interfaces and lines.

[0124] The memory can be used to store computer programs and / or modules. The processor implements various functions of the energy management device by running or executing the computer programs and / or modules stored in the memory, and by accessing 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 the operating system, at least one application program required for a function (such as sound playback function, text conversion function, etc.), etc.; the data storage area can store data created based on the use of the mobile phone (such as audio data, text message data, etc.). In addition, the memory can include high-speed random access memory, and can also include non-volatile memory, such as hard disk, RAM, plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, at least one disk storage device, flash memory device, or other volatile solid-state storage device.

[0125] In this invention, if the energy management module is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the above embodiments can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. Those skilled in the art can understand and implement this invention without any inventive effort.

[0126] This invention provides an energy management method that generates driving information by real-time monitoring of the target vehicle's driving status, enabling the energy management strategy to adjust according to current driving conditions. With the goal of minimizing equivalent fuel consumption, the driving information is optimized to obtain a dynamic SOC threshold update. This dynamic adjustment better adapts to different driving conditions, improving the system's flexibility and adaptability. Based on the real-time SOC value and the dynamically updated SOC threshold, a suitable energy supply source is selected. This rational energy allocation strategy maximizes energy utilization and reduces waste.

[0127] Example 2

[0128] Referring to Figure 4, Figure 4 is a schematic diagram of the system composition of an energy management system provided in one embodiment of the present invention. The present invention provides an energy management system for executing the energy management method as described in Embodiment 1, 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;

[0129] The auxiliary power unit is used to output alternating current;

[0130] The rectifier is used to convert the alternating current into direct current and send the direct current power information to the energy management processing system;

[0131] The power distribution device is used 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 equipment and the motor based on the power distribution instructions;

[0132] The auxiliary electrical equipment is used to enable human-machine interaction between the vehicle and the user;

[0133] The motor is used to drive the vehicle.

[0134] The energy storage battery system is used to store electrical energy and supply power to the motor; wherein, the energy storage battery system includes at least one energy storage battery;

[0135] The speed monitoring module is used to monitor the vehicle's driving status in real time, generate the vehicle's driving information within a preset time period, and send the driving information to the logic gate control module.

[0136] The logic gate control module is used to generate a SOC threshold update threshold based on the driving information and send the SOC threshold update threshold to the energy management processing system;

[0137] The energy management processing system is used to generate an energy management strategy based on the received SOC threshold update threshold, and to generate a power allocation instruction to the power allocation device based on the energy management strategy;

[0138] 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.

[0139] In this embodiment of the invention, the auxiliary power unit includes an internal combustion engine and a generator. The internal combustion engine and generator are mechanically connected, and the generator outputs alternating current (AC) through the crankshaft of the internal combustion engine. A rectifier converts the AC output from the auxiliary power unit into direct current (DC) and transmits the DC power information to the energy management and processing system in real time via a data line. The rectifier ensures a uniform energy format (DC), facilitating subsequent power management and distribution. The power distribution device receives power distribution commands from the energy management and processing system and adjusts the power distribution among the energy storage battery system, auxiliary electrical equipment, and drive motor according to the commands. The power distribution device is the "energy dispatch center" of the entire system, ensuring that each electrical device and drive system receives appropriate power support. Auxiliary electrical equipment refers to power steering systems, human-machine interface devices, etc., enabling human-machine interaction between the vehicle and the user, providing functions such as information display, navigation, and entertainment. The motor, also known as an electric drive load, is used to drive the vehicle and receives electrical energy from the energy storage battery or rectifier. The drive motor is the power source of the vehicle, and its efficiency directly affects the vehicle's driving performance. The energy storage battery system is used to store electrical energy and supply power to the electric drive load (motor). It can store rectified DC power and supply power to the electric drive load (motor) to drive the vehicle. Furthermore, the energy storage battery system incorporates a battery management system (BMS) that can monitor key parameters such as battery state of charge (SOC), battery health, and battery power capacity in real time. The speed monitoring module monitors the vehicle's driving status in real time, generates driving information for a preset time period, and sends this information to the logic gate control module. The speed monitoring module provides real-time driving data to the energy management system, helping it make dynamic energy management decisions. The logic gate control module generates a 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, dynamically adjusting the SOC threshold according to the driving status to ensure system flexibility and efficiency. The energy management processing system calls the corresponding energy management strategy based on the received SOC threshold update. Based on the called energy management strategy, it generates a power allocation command and sends it to the power allocation device. The real-time charging and discharging power of the energy storage battery is adjusted based on its real-time power capacity. The energy management processing system is the "brain" of the entire system, responsible for comprehensively analyzing and processing the input data from various modules to generate the optimal energy management plan.

[0140] After the APU starts, it outputs AC power to provide additional power support to the vehicle. The rectifier receives the AC power output from the APU and converts it to DC power, while simultaneously sending the DC power information to the energy management processing system. The logic gate control module generates a State of Charge (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 an energy management result based on the SOC threshold update threshold and the real-time status of the energy storage battery. Based on the energy management result, the energy management processing system generates a power allocation command and sends it to the power allocation device. The power allocation device adjusts the power allocation among the energy storage battery system, auxiliary electrical equipment, and drive motor according to the received power allocation command. The energy storage battery system adjusts its charging and discharging power according to the received power allocation command to ensure the rational utilization of energy.

[0141] In the embodiments of this application, the energy management system includes a power distribution device, a speed monitoring module, a logic gate control module, and an energy management processing system, each of which can be one or more processors, controllers, or chips with communication interfaces capable of implementing communication protocols. If necessary, it may also include a memory and related interfaces, system transmission buses, etc. The processor, controller, or chip executes program-related code to implement corresponding functions. Alternatively, an alternative approach is that the power distribution device, speed monitoring module, logic gate control module, and energy management processing system included in the energy management system share a single integrated chip or share processors, controllers, memory, and other devices. The shared processor, controller, or chip executes program-related code to implement corresponding functions.

[0142] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working process of the device described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0143] This invention provides an energy management system. A power distribution device dynamically adjusts the power allocation among the energy storage battery system, auxiliary electrical equipment, and drive motor based on power distribution commands sent by the energy management processing system, ensuring efficient energy utilization. A speed monitoring module monitors the vehicle's driving status in real time and generates driving information, which is sent to a logic gate control module. The logic gate control module generates a State of Charge (SOC) threshold update threshold based on 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 to generate the optimal energy management scheme, ensuring efficient operation under various driving conditions. Auxiliary electrical equipment enables interaction between the vehicle and the user, providing rich information displays and driving suggestions to improve the driving experience. The speed monitoring module monitors the vehicle's driving status in real time, and users can obtain vehicle driving information and energy management status through auxiliary electrical equipment, enhancing driving safety and comfort. The energy management processing system monitors the health status of the energy storage battery system in real time, preventing the battery from operating for extended periods under high or low load conditions, thus extending battery life. The logic gate control module generates a State of Charge (SOC) threshold update based on driving information. The energy management system then adjusts the battery's charging and discharging strategy based on this threshold to ensure the battery operates within a suitable SOC range and avoids overcharging and over-discharging. Through the coordinated work of these modules, efficient energy utilization, flexible adaptability, and a superior driving experience are achieved.

[0144] The above are merely preferred embodiments of the present invention. It should be noted that those skilled in the art can make several improvements and substitutions without departing from the technical principles of the present invention, and these improvements and substitutions should also be considered within the scope of protection of the present invention.

Claims

1. An energy management method, characterized in that, include: Real-time monitoring of the driving status of the target vehicle generates driving information of the target vehicle within a preset time period; The driving information is optimized with the goal of minimizing equivalent fuel consumption to obtain the SOC threshold update threshold under the driving information; wherein, the SOC threshold update threshold includes an upper SOC update threshold and a lower SOC update threshold. Based on the comparison result between the real-time SOC value of the target vehicle and the SOC threshold update threshold, an energy supply source is selected, and the corresponding energy management strategy is invoked according to the energy supply source.

2. The energy management method as described in claim 1, characterized in that, The real-time monitoring of the target vehicle's driving status and the generation of the target vehicle's driving information within a preset time period specifically include: The driving status of the target vehicle is monitored in real time, and the vehicle speed information of the target vehicle within a preset time period is calculated; wherein, the vehicle speed information includes the real-time driving speed, maximum driving speed, minimum driving speed and average driving speed within the preset time period; Calculate the first speed difference between the maximum driving speed and the average driving speed, and the second speed difference between the minimum driving speed and the average driving speed, and select 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, the target vehicle is determined to be 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 was collected.

3. The energy management method as described in claim 1, characterized in that, The optimization of the driving information with the goal of minimizing equivalent fuel consumption to obtain the SOC threshold update threshold under the driving information specifically includes: An objective function is constructed with the goal of minimizing equivalent fuel consumption; the expression of the objective function is as follows: In the formula, m eqv (t) represents the equivalent fuel consumption at time t; m fuel (t) represents the actual fuel consumption at time t; s(t) is the equivalent fuel consumption factor; m ess (t) represents the battery energy consumption at time t; P batt (t) represents the power output of the battery at time t; Q lhv It has a low calorific value; Based on preset constraints, particle swarm optimization is performed on the equivalent fuel consumption factor to obtain the optimization value of the equivalent fuel consumption factor. Based on the optimization value of the equivalent fuel consumption factor, the objective function is solved to obtain the minimum equivalent fuel consumption. The upper limit threshold of the SOC based on the optimized value of the equivalent fuel consumption factor is used as the upper limit update threshold of the SOC, and the lower limit threshold of the SOC based on the optimized value of the equivalent fuel consumption factor is used as the lower limit update threshold of the SOC; wherein, the expression for the equivalent fuel consumption factor is: In the formula, These represent the average efficiency of the motor, motor controller, battery, and engine, respectively; SOC (State of Charge). low The lower limit threshold of SOC; SOC high The upper limit threshold of SOC; SOC ref The reference SOC value is used; SOC(t) is the actual SOC value of the battery at time t.

4. The energy management method as described in claim 3, characterized in that, Based on preset constraints, particle swarm optimization is performed on the equivalent fuel consumption factor to obtain the optimized value of the equivalent fuel consumption factor, specifically including: Obtain the constraints, wherein the constraint factors of the constraints include motor angular velocity, engine angular velocity, battery state of charge, and required torque; A working model of the target vehicle is constructed based on the aforementioned constraints; wherein the working model includes a demand torque model, an engine fuel consumption model, a motor model, and a battery model; Based on the expression of each working model, particle swarm optimization is performed on the preset constraints to obtain the optimized value of the equivalent fuel consumption factor; wherein, the expression of the preset constraints is: In the formula, w mot (t) represents the angular velocity of the motor at time t; w mot,max w is the maximum permissible angular velocity of the motor. eng (t) represents the engine angular velocity at time t; w eng,min The minimum permissible angular velocity of the engine; w eng,max T is the engine's maximum permissible angular velocity. 需求 For the required torque; T eng (t) Engine torque at a given engine angular velocity at time t; T eng,min T eng,max T represents the minimum and maximum permissible engine torque at a given engine angular velocity. mot (t) The motor torque at a given engine angular velocity at time t; T mot,min T mot,max These represent the minimum and maximum permissible motor torques at a given motor angular velocity.

5. The energy management method as described in claim 4, characterized in that, The step of performing particle swarm optimization on the preset constraints based on the expression of each working model to obtain the optimized value of the equivalent fuel consumption factor specifically includes: The expression for the demand 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; ρ 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; The expression for the engine fuel consumption model is: In the formula, Fuel consumption rate; T eng For engine torque; w eng The engine angular velocity; For functional rules; The expression for the motor model is: In the formula, P mot T represents the motor power. mot w represents the motor torque. mot sgn(T) is the angular velocity of the motor. mot ) is the sign function used to determine the direction of motor torque; η mot For motor efficiency; The expression for the battery model is: In the formula, SOC represents the battery's state of charge; U oc R is the battery open-circuit voltage. int P is the battery's internal resistance. batt Battery power; Q batt Battery capacity; The constraint factors are solved according to the expression of each working model to obtain several constraint factor values. Based on the constraint factor values, particle swarm optimization is performed on the constraint conditions to obtain the optimization value of the equivalent fuel consumption factor.

6. The energy management method as described in claim 1, characterized in that, The selection 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 specifically includes: Obtain the driving mode and real-time SOC value of the target vehicle; When the driving mode is pure electric drive mode and the real-time SOC value is greater than the SOC lower limit update threshold, the auxiliary power unit is switched to the off state and the energy storage battery system is selected as the energy supply source. When the driving mode is the pure electric drive mode and the real-time SOC value is less than the lower limit update threshold of SOC, or when the driving mode is the hybrid mode and the real-time SOC value is less than the upper limit update threshold of SOC, the energy storage battery system and the auxiliary power unit are selected as the energy supply source together; wherein, when the driving mode is the hybrid mode, the auxiliary power unit is in the on state.

7. The energy management method as described in claim 6, characterized in that, The step of invoking the corresponding energy management strategy based on the energy supply source specifically includes: obtaining the maximum supplyable power of the energy supply source, comparing the maximum supplyable power with the demanded power, and invoking the corresponding energy management strategy based on 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, the energy storage battery system is controlled 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, the maximum power generation of the auxiliary power unit is compared with the required power. If the maximum power generation of the auxiliary power unit is greater than or equal to the required power, the auxiliary power unit is controlled to adjust its output power to the required power. If the maximum power generation of the auxiliary power unit is less than the required 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 the first discharge power, where the first discharge power is the power difference between the required power and the maximum power generation.

8. The energy management method as described in claim 1, characterized in that, Before invoking the corresponding energy management strategy based on the energy supply source, the process also includes: Obtain the average driving speed information used in the previous SCO threshold update, compare the average driving speed information with the real-time vehicle speed information, and if the deviation between the two is within a preset range, use the energy management strategy as the energy management result of the target vehicle and execute the energy management strategy.

9. The energy management method as described in 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 real-time battery power capabilities, specifically: The temperature and 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 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, 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 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 drive 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, The device is used to perform the energy management method as described in any one of claims 1-9, and includes 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 rectifier 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 power distribution instructions 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 instructions; The auxiliary electrical equipment is used to enable human-machine 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 power to the motor; wherein, the energy storage battery system includes at least one energy storage battery; The speed monitoring module is used to monitor the vehicle's driving status in real time, generate the vehicle's driving information within a preset time period, and send the driving information to the logic gate control module. The logic gate control module is used to generate a SOC threshold update threshold based on 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 based on the received SOC threshold update threshold, and to generate a power allocation instruction to the power allocation 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, It includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor executes the computer program to implement the energy management method as described in any one of claims 1 to 9.

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, it controls the device on which the computer-readable storage medium is located to perform the energy management method as described in any one of claims 1 to 9.

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