Scene division-based adaptive coordination control method and system for new energy vehicle thermal management system, and device

Abstract

The application discloses a new energy automobile thermal management system adaptive coordination control method and system based on scene division and equipment. The new energy automobile thermal management system adaptive coordination control method comprises the following steps: identifying scenes affecting the energy consumption of the whole vehicle thermal management system, wherein the scenes include driving conditions, environment temperature, battery SOC and battery temperature; based on the identified different scenes, a pre-matched adaptive control strategy is adopted to adjust and optimize refrigerating capacity / heat capacity; a correction factor used for dynamic adjustment and optimization is continuously adjusted according to the temperature change rate of the passenger compartment and the temperature change rate of the battery, and is revalued, so that the energy consumption, comfort and safety are all optimized. The application realizes the matching of different strategies for different scenes and the efficient control of the new energy automobile thermal management system through adaptive judgment.

Description

Technical Field

[0001] This invention relates to the field of thermal management system technology for new energy vehicles, and in particular to an adaptive coordinated control method, system, and equipment for a thermal management system for new energy vehicles based on scenario segmentation. Background Technology

[0002] In recent years, new energy vehicles have become an important direction for the automotive industry due to their low pollutant emissions and high energy efficiency. With the support and guidance of national policies, the new energy vehicle industry has developed rapidly, but this has also led to a series of problems. The most concerning is the severe reduction in driving range under high and low temperature conditions, causing range anxiety among consumers. The main reason for this phenomenon is that new energy vehicles, especially pure electric vehicles, have no engine waste heat to utilize; the use of high-temperature air conditioning systems or low-temperature PTC systems leads to a significant increase in energy consumption. Heat pump air conditioning technology can achieve dual functions of high-temperature cooling and low-temperature heating through devices such as four-way reversing valves, multiple heat exchangers, or water-cooled condensers. It has significant advantages such as high efficiency, energy saving, environmental protection, and wide applicability, and has become a development trend for reducing vehicle air conditioning energy consumption. Furthermore, reasonable and effective control strategies are crucial for ensuring a coordinated match between passenger cabin comfort and safety and reducing energy consumption. However, most current control strategies are formulated for specific scenarios and do not consider the refined management of energy in a wider range of scenarios. Summary of the Invention

[0003] The purpose of this invention is to overcome the shortcomings and defects of the prior art and provide an adaptive coordinated control method, system, and device for a new energy vehicle thermal management system based on scenario segmentation. This invention achieves efficient control of the new energy vehicle thermal management system by matching different strategies to different scenarios and making adaptive judgments. It constructs a strategy matching mechanism for different driving environments and user needs, realizes dynamic optimization and precise control of the thermal management system, and aims to reduce the energy consumption of the air conditioning system, increase the vehicle's driving range, and alleviate user anxiety without affecting driving comfort and safety. It is applicable to the development of thermal management system control strategies for pure electric vehicles, hybrid electric vehicles, etc.

[0004] One objective of this invention is to provide an adaptive coordinated control method for a new energy vehicle thermal management system based on scenario segmentation, comprising the following steps:

[0005] Identify scenarios that affect the energy consumption of the vehicle's thermal management system, including driving conditions, ambient temperature, battery SOC, and battery temperature.

[0006] Based on the identified different scenarios, a pre-matched and adaptive control strategy is applied to the air conditioning system to optimize the cooling / heating capacity adjustment.

[0007] The correction factors used are continuously and dynamically adjusted and optimized based on the temperature change rate of the passenger compartment and the temperature change rate of the battery, and then re-assigned to achieve the best energy consumption, comfort and safety.

[0008] Preferably, the driving conditions are divided into three states: low speed, medium speed, and high speed; when the vehicle speed V≤V L When V is at its lowest speed, it is in a low-speed state; when V L <V<V H At this point, it is in a medium-speed state; when V≥V H At that time, it is in high-speed mode, V L V H The upper limit and the upper limit value are preset to determine the vehicle speed range for driving conditions.

[0009] Preferably, the ambient temperature is defined according to the dual functions of high-temperature cooling and low-temperature heating provided by the heat pump air conditioner:

[0010] During refrigeration, two states are distinguished: high temperature and ultra-high temperature; when T amb ≤T ambH At that time, it is in a high-temperature state; when T amb >T ambH At that time, it was in an ultra-high temperature state; T amb T represents the current ambient temperature. ambH The temperature threshold for determining high temperature or ultra-high temperature conditions;

[0011] When heating, it is divided into two states: low temperature and ultra-low temperature; when T ambL ≤T amb At that time, it is a low temperature state; when T amb <T ambL At that time, it was in an ultra-low temperature state; T ambL Temperature thresholds for determining low-temperature or ultra-low-temperature conditions.

[0012] Preferably, the battery SOC is divided into two states: low SOC and medium-high SOC. When SOC ≤ SOC L When the SOC is high, it is considered a low SOC state; conversely, it is considered a medium to high SOC state. L The SOC threshold is used to determine whether the battery's SOC is in a low or medium-high SOC state.

[0013] Preferably, the battery temperature is related to the safety and performance of the entire vehicle. When the battery temperature T... bat Preset temperature threshold T batH At this time, the battery is in a high temperature state, and at the other hand, it is in a normal temperature state. The air conditioner will first meet the battery's cooling needs.

[0014] Preferably, based on the identified different scenarios, a pre-matched and adapted control strategy is applied to the air conditioning system to optimize the cooling / heating capacity adjustment, including:

[0015] When there is a need for cooling:

[0016] When traveling at low speeds, the cooling demand Q of the passenger compartment cab =α c1 *Q cab0 , where α c1 The correction factor is less than or equal to 1; Q cab0 Preset cooling requirements for the passenger cabin; battery cooling requirements Q bat =β c1 *Q bat0 , where β c1 The correction factor is less than or equal to 1; Q bat0 The preset battery cooling demand; when driving at medium speed, the passenger compartment cooling demand Q. cab =Q cab0 Battery cooling demand Q bat =Q bat0 When traveling at high speeds, the cooling demand Q of the passenger cabin is... cab =α c2 *Q cab0 , where α c2 The correction factor is greater than or equal to 1; Q cab0 This represents the original cooling demand; the battery cooling demand Q. bat =β c2 *Q bat0 , where β c2 The correction factor is greater than or equal to 1; Q bat0 This represents the original cooling demand.

[0017] When operating at high temperatures, the cooling demand Q of the passenger compartment cab =Q cab0 Battery cooling demand Q bat =Q bat0 When operating in extremely high temperatures, the cooling demand Q of the passenger compartment is... cab =α c3 *Q cab0 , where α c3 The correction factor is greater than or equal to 1; Q cab0 This represents the original cooling demand; the battery cooling demand Q. bat =β c3 *Q bat0 , where: β c3 The correction factor is greater than or equal to 1; Q bat0 This represents the original cooling demand.

[0018] When operating at a low SOC (State of Charge), the passenger cabin cooling requirement Q is... cab =α c4 *Q cab0 , where α c4 The correction factor is less than or equal to 1; Q cab0 Preset cooling requirements for the passenger cabin; battery cooling requirements Q bat =β c4 *Q bat0 , where: β c4 The correction factor is less than or equal to 1; Q bat0 The preset battery cooling demand; when driving at medium to high SOC, the passenger compartment cooling demand Q. cab =Q cab0 Battery cooling demand Q bat =Q bat0 .

[0019] When the battery is at a high temperature, battery cooling takes priority, and the cooling demand of the passenger cabin is Q. cab =Q cab0 Battery cooling demand Q bat =β c5 *Q bat0 , where β c5 The correction factor is greater than or equal to 1; Q bat0 This represents the original cooling demand; when the battery is at normal temperature, the crew cabin cooling demand is Q. cab =Q cab0 Battery cooling demand Q bat =Q bat0 ;

[0020] When there is a heating demand:

[0021] When traveling at low speeds, the passenger compartment heating demand Q cabh =α h1 *Q cabh0 , where α h1 The correction factor is less than or equal to 1; Q cabh0 Original heating demand; Battery heating demand Q bath =β h1 *Q bath0 , where β h1 The correction factor is greater than or equal to 1; Q bath0 This represents the original heating demand; when traveling at medium speed, the passenger compartment heating demand Q is... cabh =Q cabh0 Battery heating demand Q bath =Q bath0 When traveling at high speed, the passenger compartment's heating demand Q cabh =α h2 *Qcabh0 , where α h2 The correction factor is greater than or equal to 1; Q cabh0 Original heating demand; Battery heating demand Q bath =Q bath0 ;

[0022] When operating in low-temperature conditions, the passenger compartment heating demand Q cabh =Q cabh0 Battery heating demand Q bath =Q bath0 When operating in extremely low temperatures, the passenger compartment's heating demand Q cabh =α h3 *Q cabh0 , where α h3 The correction factor is greater than or equal to 1; Q cabh0 Original heating demand; Battery heating demand Q bath =β h2 *Q bath0 , where β h2 The correction factor is greater than or equal to 1; Q bath0 This is the original heating demand;

[0023] When operating at a low SOC (State of Charge), the passenger cabin heating demand Q cabh =α h4 *Q cabh0 , where α h4 The correction factor is less than or equal to 1; Q cabh0 Original heating demand; Battery heating demand Q bath =β h3 *Q bath0 , where: β h3 The correction factor is less than or equal to 1; Q bath0 This represents the original heating demand; when operating at medium to high SOC, the passenger compartment heating demand Q is... cabh =Q cabh0 Battery heating demand Q bath =Q bath0 .

[0024] Preferably, the correction factor used for continuously and dynamically adjusting and optimizing based on the temperature change rate of the passenger compartment and the temperature change rate of the battery includes comparing the temperature change rate of the passenger compartment and the temperature change rate of the battery with the corresponding preset target values, and comprehensively comparing the comparison results with the cooling or heating requirements to continuously and dynamically adjust and optimize the correction factor used.

[0025] When there is a cooling demand, if the temperature change rate of the passenger compartment is less than or equal to its preset target value, the correction factor will be increased; otherwise, the correction factor will be decreased. If the temperature change rate of the battery is less than or equal to its preset target value, the correction factor will be increased; otherwise, the correction factor will be decreased.

[0026] When there is a heating demand, if the temperature change rate of the passenger compartment is less than or equal to its preset target value, the correction factor will be increased; otherwise, the correction factor will be decreased. If the temperature change rate of the battery is less than or equal to its preset target value, the correction factor will be increased; otherwise, the correction factor will be decreased.

[0027] Preferably, the temperature change rate of the passenger compartment and the temperature change rate of the battery are calculated by the following formula; wherein, the temperature change rate of the passenger compartment is expressed as dT cab The rate of change of battery temperature is expressed as dT bat ;

[0028] dT cab =△T cab / △t;

[0029] dT bat =△T bat / △t;

[0030] Among them, △T cab ΔT represents the change in cabin temperature over time Δt; bat This represents the change in battery temperature over time Δt.

[0031] A second objective of this invention is to provide an adaptive coordination control system for a new energy vehicle thermal management system based on scenario partitioning, used to execute the adaptive coordination control method for the new energy vehicle thermal management system based on scenario partitioning, comprising:

[0032] The scene segmentation and recognition module is used to identify the scenes that affect the energy consumption of the vehicle thermal management system. The scenes include driving conditions, ambient temperature, battery SOC, and battery temperature.

[0033] The strategy response module is used to apply pre-matched and adaptive control strategies to the air conditioning system based on different identified scenarios, thereby optimizing the adjustment of cooling / heating capacity.

[0034] The adaptive adjustment module is used to continuously and dynamically adjust and optimize the correction factors based on the temperature change rate of the passenger compartment and the temperature change rate of the battery, and reassign them to achieve the best energy consumption, comfort and safety.

[0035] A third objective of this invention is to provide an apparatus comprising a memory, a processor, and a computer program stored in the memory, wherein the processor executes the program to implement the adaptive coordinated control method for a new energy vehicle thermal management system based on scenario partitioning.

[0036] The adaptive coordinated control method for the thermal management system of new energy vehicles based on scenario segmentation of the present invention can achieve refined control of the air conditioning system under different driving scenarios through driving scenario segmentation, strategy matching, and adaptive coordinated control, so as to achieve an efficient balance between air conditioning system energy consumption and cabin comfort.

[0037] The adaptive coordinated control method for the thermal management system of new energy vehicles based on scenario segmentation of the present invention can establish a strategy response mechanism under different sub-scenarios; can better balance the energy consumption, comfort and safety of the air conditioning system; and can be coupled with actual road scenarios to flexibly realize intelligent energy management and control. Attached Figure Description

[0038] Figure 1 This is a flowchart of the adaptive coordinated control method for the thermal management system of new energy vehicles based on scenario segmentation, as proposed in this invention.

[0039] Figure 2 This is a flowchart of the control strategy for the adaptive coordinated control method of the new energy vehicle thermal management system based on scenario segmentation provided in this embodiment of the invention, under cooling demand conditions.

[0040] Figure 3 This is a schematic diagram of the control process interaction of the adaptive coordination control system for the thermal management system of new energy vehicles based on scenario division according to the present invention. Detailed Implementation

[0041] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0042] The exemplary embodiment of this application provides an adaptive coordinated control method for a new energy vehicle thermal management system based on scenario segmentation. By accurately classifying the driving environment and operating conditions, establishing a mapping relationship between multiple scenarios and control strategies, and introducing an adaptive mechanism, it achieves efficient control of the air conditioning system under different conditions.

[0043] See Figure 1 As shown in the exemplary embodiment of this application, the adaptive coordinated control method for a new energy vehicle thermal management system based on scenario partitioning includes the following steps:

[0044] S1. Identify the scenarios that affect the energy consumption of the vehicle's thermal management system. These scenarios include driving conditions, ambient temperature, battery SOC, and battery temperature.

[0045] S2. Based on the identified different scenarios, a pre-matched and adaptive control strategy is applied to the air conditioning system to optimize the cooling / heating capacity adjustment;

[0046] S3. The correction factors used are continuously and dynamically adjusted and optimized based on the temperature change rate of the passenger compartment and the temperature change rate of the battery, and revalued to achieve the best energy consumption, comfort and safety.

[0047] The energy consumption of the vehicle thermal management system is affected by many factors, such as driving conditions, ambient temperature, SOC, battery temperature, and passenger compartment temperature. This invention application focuses on the more important factors of driving conditions, ambient temperature, SOC, and battery temperature to subdivide the scenarios and realize the strategy response under different conditions.

[0048] Based on driving conditions, they are divided into three states: low speed, medium speed, and high speed. When the vehicle speed V≤V L When V is at its lowest speed, it is in a low-speed state; when V L <V<V H At this point, it is in a medium-speed state; when V≥V H At that time, it is in high-speed mode;

[0049] Depending on the ambient temperature, heat pump air conditioners can achieve dual functions of high-temperature cooling and low-temperature heating. During cooling, the system is divided into two states: high temperature and ultra-high temperature. amb ≤T ambH At that time, it is in a high-temperature state; when T amb >T ambH When T is at its highest temperature, it is in an ultra-high temperature state; when heating, it is divided into two states: low temperature and ultra-low temperature. ambL ≤T amb At that time, it is a low temperature state; when T amb <T ambL At that time, it was in an ultra-low temperature state.

[0050] Battery SOC is divided into two states: low SOC and medium-high SOC. When SOC ≤ SOC L When the SOC is high, it is a low SOC state; otherwise, it is a medium to high SOC state.

[0051] Regarding battery temperature, it is closely related to the safety and performance of the entire vehicle. When the battery needs to be cooled, when T... bat >T batH When the air conditioner is on high temperature, it must first meet the battery's cooling needs; otherwise, it will be at its normal temperature.

[0052] After identifying different scenarios, different strategies are applied to the air conditioning system based on these scenarios, adjusting and optimizing it according to the original cooling / heating capacity requirements. Specifically, these include:

[0053] When there is a need for cooling:

[0054] When traveling at low speeds, the cooling demand Q of the passenger compartment cab =α c1 *Qcab0 , where: α c1 The correction factor is less than or equal to 1; Q cab0 Preset cooling requirements for the passenger cabin; battery cooling requirements Q bat =β c1 *Q bat0 , where: β c1 The correction factor is less than or equal to 1; Q bat0 This is the preset battery cooling requirement. When traveling at medium speed, the passenger compartment cooling requirement Q is... cab =Q cab0 Battery cooling demand Q bat =Q bat0 When traveling at high speeds, the cooling demand Q of the passenger cabin is... cab =α c2 *Q cab0 , where: α c2 The correction factor is greater than or equal to 1; Q cab0 Preset cooling requirements for the passenger cabin; battery cooling requirements Q bat =β c2 *Q bat0 , where: β c2 The correction factor is greater than or equal to 1; Q bat0 This is the preset battery cooling requirement.

[0055] When the ambient temperature is high, the cooling demand Q of the crew cabin cab =Q cab0 Battery cooling demand Q bat =Q bat0 When operating in extremely high temperatures, the cooling demand Q of the passenger compartment is... cab =α c3 *Q cab0 , where: α c3 The correction factor is greater than or equal to 1; Q cab0 Preset cooling requirements for the passenger cabin; battery cooling requirements Q bat =β c3 *Q bat0 , where: β c3 The correction factor is greater than or equal to 1; Q bat0 This is the preset battery cooling requirement.

[0056] When operating at a low SOC (State of Charge), the passenger cabin cooling requirement Q is... cab =α c4 *Q cab0 , where: α c4 The correction factor is less than or equal to 1; Q cab0 Preset cooling requirements for the passenger cabin; battery cooling requirements Q bat =β c4*Q bat0 , where: β c4 The correction factor is less than or equal to 1; Q bat0 This is a preset battery cooling requirement. When driving at a medium-to-high SOC (State of Charge), the passenger compartment cooling requirement Q is... cab =Q cab0 Battery cooling demand Q bat =Q bat0 .

[0057] When the battery is at a high temperature, the battery cooling demand takes priority, and the passenger cabin cooling demand Q... cab =Q cab0 Battery cooling demand Q bat =β c5 *Q bat0 , where: β c5 The correction factor is greater than or equal to 1; Q bat0 This is the preset battery cooling requirement. When the battery is at normal operating temperature, the crew cabin cooling requirement is Q. cab =Q cab0 Battery cooling demand Q bat =Q bat0 .

[0058] When there is a heating demand:

[0059] When traveling at low speeds, the passenger compartment heating demand Q cabh =α h1 *Q cabh0 , where: α h1 The correction factor is less than or equal to 1; Q cabh0 Preset passenger cabin heating requirements; battery heating requirements Q bath =β h1 *Q bath0 , where: β h1 The correction factor is greater than or equal to 1; Q bath0 This is a preset battery heating requirement. When driving at medium speed, the passenger compartment heating requirement Q is... cabh =Q cabh0 Battery heating demand Q bath =Q bath0 When traveling at high speed, the passenger compartment's heating demand Q cabh =α h2 *Q cabh0 , where: α h2 The correction factor is greater than or equal to 1; Q cabh0 Preset passenger cabin heating requirements; battery heating requirements Q bath =Q bath0 .

[0060] When operating in low-temperature conditions, the passenger compartment heating demand Q cabh =Q cabh0 Battery heating demand Q bath =Q bath0 When operating in extremely low temperatures, the passenger compartment's heating requirement, Q, is... cabh =α h3 *Q cabh0 , where: α h3 The correction factor is greater than or equal to 1; Q cabh0 Preset passenger cabin heating requirements; battery heating requirements Q bath =β h2 *Q bath0 , where: β h2 The correction factor is greater than or equal to 1; Q bath0 This is to preset the battery heating demand.

[0061] When operating at a low SOC (State of Charge), the passenger cabin heating demand Q cabh =α h4 *Q cabh0 , where: α h4 The correction factor is less than or equal to 1; Q cabh0 Preset passenger cabin heating requirements; battery heating requirements Q bath =β h3 *Q bath0 , where: β h3 The correction factor is less than or equal to 1; Q bath0 This is a preset battery heating requirement. When driving at a medium-to-high SOC (State of Charge), the passenger compartment heating requirement Q is... cabh =Q cabh0 Battery heating demand Q bath =Q bath0 .

[0062] The specific correction factors for cooling and heating are represented in the following scenarios:

[0063] In this embodiment of the invention, in step S3, an adaptive coordination control mechanism is set up to adaptively adjust the strategy response using two indicators: the rate of change of the crew cabin temperature and the rate of change of the battery temperature.

[0064] 1) Rate of change of temperature in the crew cabin: dT cab =△T cab / △t;

[0065] 2) Battery temperature change rate: dT bat =△T bat / △t;

[0066] Where: △Tcab ΔT represents the change in cabin temperature over time Δt; bat This represents the change in battery temperature over time Δt.

[0067] Specifically, during adaptive adjustment, when there is a cooling demand, dT cab A value less than or equal to the preset target value indicates a low rate of temperature drop in the passenger cabin, requiring an increase in the correction factor to meet comfort requirements. Conversely, a value greater than the target value requires a decrease in the correction factor to avoid energy waste. bat If the value is less than or equal to the preset target value, it indicates that the battery cooling effect is not good, and the correction factor needs to be increased to meet the battery cooling requirements; otherwise, it should be decreased.

[0068] When there is a heating demand: dT cab A value less than or equal to the preset target value indicates a low rate of temperature rise in the passenger cabin, requiring an increase in the correction factor to meet comfort requirements. Conversely, a value greater than or equal to the target value requires a decrease in the correction factor to avoid energy waste. bat If the value is less than or equal to the preset target value, it indicates that the battery heating effect is not good, and the correction factor needs to be increased to meet the battery cooling requirements; otherwise, it should be decreased.

[0069] Through the above specific steps, the method of the present invention realizes the continuous dynamic adjustment and reassignment of correction factors based on the temperature change rate of the passenger cabin and the temperature change rate of the battery, so that energy consumption, comfort and safety are all optimized.

[0070] Figure 2 This is a schematic diagram of the control strategy flow of the adaptive coordinated control method provided in this embodiment of the invention under cooling demand. Specifically, during implementation, the following steps are taken: first, the vehicle speed range to which the input vehicle speed belongs is determined: low speed, medium speed, high speed; second, the ambient temperature range to which the input ambient temperature belongs is determined: high temperature, ultra-high temperature, low temperature, ultra-low temperature; third, the SOC range to which the input SOC belongs is determined: low SOC, medium-high SOC; and fourth, the battery temperature range to which the input SOC belongs is determined: high temperature, normal temperature. Based on the aforementioned strategy response mechanism, the passenger compartment cooling / heating demand correction factor (cab1-cab4) and the battery cooling / heating demand correction factor (bat1-) are obtained. The initial cooling / heating demand (bat4) is multiplied by the original cooling / heating demand to obtain the final passenger compartment and battery demand. This is applied to the vehicle's air conditioning system to calculate the passenger compartment temperature change rate and battery temperature change rate over a certain period. It is then determined whether these meet the target values. If they do, the current strategy response mechanism can be executed. If not, the cooling / heating correction factor needs to be adjusted to ensure that the passenger compartment temperature change rate and battery temperature change rate meet the targets. This process is continuously and dynamically adjusted, and the newly generated cooling / heating correction factor is reassigned to the strategy response module to optimize all aspects of the system's functions. It is particularly important to note that when the battery temperature is high, the battery cooling demand should be prioritized; in this case, the passenger compartment cooling demand correction factor is always 1.

[0071] The adaptive coordinated control method for the thermal management system of new energy vehicles based on scenario segmentation of the present invention can achieve refined control of the air conditioning system under different driving scenarios through driving scenario segmentation, strategy matching, and adaptive coordinated control, so as to achieve an efficient balance between air conditioning system energy consumption and cabin comfort.

[0072] See Figure 3 As shown, this embodiment of the invention also provides an adaptive coordination control system for a new energy vehicle thermal management system based on scenario partitioning, used to execute the adaptive coordination control method for the new energy vehicle thermal management system based on scenario partitioning, including:

[0073] The scene segmentation and recognition module is used to identify the scenes that affect the energy consumption of the vehicle thermal management system. The scenes include driving conditions, ambient temperature, battery SOC, and battery temperature.

[0074] The strategy response module is used to optimize the cooling / heating capacity adjustment by adopting a pre-matched and adaptive control strategy based on the identified different scenarios.

[0075] The adaptive adjustment module is used to continuously and dynamically adjust and optimize the correction factors based on the temperature change rate of the passenger compartment and the temperature change rate of the battery, and reassign them to achieve the best energy consumption, comfort and safety.

[0076] In this embodiment of the invention, the scene segmentation and recognition module can identify the scene based on the preset scene segmentation principle and the input driving conditions, ambient temperature, battery SOC, and battery temperature, and output the corresponding scene. The strategy response module identifies the corresponding adjustment and control strategy based on the input scene and applies it to the air conditioning system. The adaptive adjustment module judges whether the preset target is met based on the temperature change rate of the passenger compartment and the temperature change rate of the battery. If the preset target is not met, the strategy correction factor is adjusted to meet the comfort and safety requirements and balance energy consumption.

[0077] This invention also provides a device, including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the program to implement the adaptive coordinated control method for the new energy vehicle thermal management system based on scenario partitioning.

[0078] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. It will be apparent to those skilled in the art that the present invention is not limited to the details of the above exemplary embodiments, and that the present invention can be implemented in other specific forms without departing from the spirit or basic features of the present invention.

[0079] Therefore, the embodiments should be regarded as exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, it is intended that all variations falling within the meaning and scope of the equivalents of the claims be included within the invention.

[0080] Furthermore, it should be understood that although this specification describes embodiments, not every embodiment contains only one independent technical solution. This narrative style is merely for clarity. Those skilled in the art should consider the specification as a whole, and the technical solutions in each embodiment can also be appropriately combined to form other embodiments that can be understood by those skilled in the art.

Claims

1. An adaptive coordinated control method for a new energy vehicle thermal management system based on scenario partitioning, characterized in that, Includes the following steps: Identify scenarios that affect the energy consumption of the vehicle's thermal management system, including driving conditions, ambient temperature, battery SOC, and battery temperature. Based on the identified different scenarios, a pre-matched and adaptive control strategy is applied to the air conditioning system to optimize the cooling / heating capacity adjustment. The correction factors used are continuously and dynamically adjusted and optimized based on the temperature change rate of the passenger compartment and the temperature change rate of the battery, and then re-assigned to achieve the best energy consumption, comfort and safety. Based on the identified different scenarios, a pre-matched and adaptive control strategy is applied to the air conditioning system to optimize the cooling / heating capacity adjustment, including: When there is a need for cooling: When traveling at low speeds, the cooling demand Q of the passenger compartment cab =α c1 *Q cab0 , where α c1 Q is a correction factor, less than or equal to 1. cab0 Preset cooling requirements for the passenger cabin; battery cooling requirements Q bat =β c1 *Q bat0 , where β c1 Q is a correction factor, less than or equal to 1. bat0 The preset battery cooling demand; when driving at medium speed, the passenger compartment cooling demand Q. cab =Q cab0 Battery cooling demand Q bat =Q bat0 When traveling at high speeds, the cooling demand Q in the passenger cabin is... cab =α c2 *Q cab0 , where α c2 The correction factor is greater than or equal to 1; battery cooling demand Q bat =β c2 *Q bat0 , where β c2 The correction factor is greater than or equal to 1. When operating at high temperatures, the cooling demand Q of the passenger compartment cab =Q cab0 Battery cooling demand Q bat =Q bat0 When operating in extremely high temperatures, the cooling demand Q of the passenger compartment is... cab =α c3 *Q cab0 , where α c3 The correction factor is greater than or equal to 1; battery cooling demand Q bat =β c3 *Q bat0 , where: β c3 The correction factor is greater than or equal to 1. When operating at a low SOC (State of Charge), the passenger cabin cooling requirement Q is... cab =α c4 *Q cab0 , where α c4 The correction factor is less than or equal to 1; battery cooling demand Q bat =β c4 *Q bat0 , where: β c4 The correction factor is less than or equal to 1; when operating at medium to high SOC, the passenger cabin cooling demand Q cab =Q cab0 Battery cooling demand Q bat =Q bat0 ; When the battery is at a high temperature, battery cooling takes priority, and the cooling demand of the passenger cabin is Q. cab =Q cab0 Battery cooling demand Q bat =β c5 *Q bat0 , where β c5 The correction factor is greater than or equal to 1; when the battery is at normal temperature, the cooling demand Q of the crew cabin is... cab =Q cab0 Battery cooling demand Q bat =Q bat0 ; When there is a heating demand: When traveling at low speeds, the passenger compartment heating demand Q cabh =α h1 *Q cabh0 , where α h1 The correction factor is less than or equal to 1; Q cabh0 Preset passenger cabin heating requirements; battery heating requirements Q bath =β h1 *Q bath0 , where β h1 The correction factor is greater than or equal to 1; Q bath0 The preset battery heating demand; when driving at medium speed, the passenger compartment heating demand Q. cabh =Q cabh0 Battery heating demand Q bath =Q bath0 When traveling at high speed, the passenger compartment heating demand Q cabh =α h2 *Q cabh0 , where α h2 The correction factor is greater than or equal to 1; the battery heating requirement Q bath =Q bath0 ; When operating in low-temperature conditions, the passenger compartment heating demand Q cabh =Q cabh0 Battery heating demand Q bath =Q bath0 When operating in extremely low temperatures, the passenger compartment's heating demand Q cabh =α h3 *Q cabh0 , where α h3 The correction factor is greater than or equal to 1; the battery heating requirement Q bath =β h2 *Q bath0 , where β h2 The correction factor is greater than or equal to 1. When operating at a low SOC (State of Charge), the passenger cabin heating demand Q cabh =α h4 *Q cabh0 , where α h4 The correction factor is less than or equal to 1; the battery heating requirement Q bath =β h3 *Q bath0 , where β h3 The correction factor is less than or equal to 1; when operating at medium to high SOC, the passenger compartment heating demand Q cabh =Q cabh0 Battery heating demand Q bath =Q bath0； The correction factor used for continuous dynamic adjustment and optimization based on the temperature change rate of the passenger compartment and the temperature change rate of the battery includes comparing the temperature change rate of the passenger compartment and the temperature change rate of the battery with the corresponding preset target values, and comprehensively comparing the comparison results with the cooling or heating requirements to continuously and dynamically adjust and optimize the correction factor used for optimization. When there is a cooling demand, if the temperature change rate of the passenger compartment is less than or equal to its preset target value, the correction factor will be increased; otherwise, the correction factor will be decreased. If the temperature change rate of the battery is less than or equal to its preset target value, the correction factor will be increased; otherwise, the correction factor will be decreased. When there is a heating demand, if the temperature change rate of the passenger compartment is less than or equal to its preset target value, the correction factor will be increased; otherwise, the correction factor will be decreased. If the temperature change rate of the battery is less than or equal to its preset target value, the correction factor will be increased; otherwise, the correction factor will be decreased.

2. The adaptive coordinated control method for a new energy vehicle thermal management system based on scenario partitioning as described in claim 1, characterized in that, The driving conditions are divided into three states: low speed, medium speed, and high speed; when the vehicle speed V≤V L When V is at its lowest speed, it is in a low-speed state; when V L <V<V H When V ≥ V, it is in the medium speed state; when V ≥ V H At that time, it is in high-speed mode, V L V H The upper limit and the upper limit value are preset to determine the vehicle speed range for driving conditions.

3. The adaptive coordinated control method for a new energy vehicle thermal management system based on scenario partitioning as described in claim 2, characterized in that, The ambient temperature is defined based on the dual functions of high-temperature cooling and low-temperature heating provided by heat pump air conditioners: During refrigeration, two states are distinguished: high temperature and ultra-high temperature; when T amb ≤T ambH At that time, it is in a high-temperature state; when T amb >T ambH At that time, it was in an ultra-high temperature state; T amb T represents the current ambient temperature. ambH The temperature threshold for determining high temperature or ultra-high temperature conditions; When heating, it is divided into two states: low temperature and ultra-low temperature; when T ambL ≤T amb At that time, it is a low temperature state; when T amb <T ambL At that time, it was in an ultra-low temperature state; T ambL Temperature thresholds for determining low-temperature or ultra-low-temperature conditions.

4. The adaptive coordinated control method for a new energy vehicle thermal management system based on scenario partitioning as described in claim 3, characterized in that, The battery SOC is divided into two states: low SOC and medium-high SOC. When SOC ≤ SOC L At this time, it is in a low SOC state; Conversely, it indicates a medium to high SOC state; SOC L The SOC threshold is used to determine whether the battery's SOC is in a low or medium-high SOC state.

5. The adaptive coordinated control method for a new energy vehicle thermal management system based on scenario partitioning according to claim 4, characterized in that, The battery temperature is related to the safety and performance of the entire vehicle. When the battery temperature T... bat Preset temperature threshold T batH At this time, the battery is in a high temperature state, and at the other hand, it is in a normal temperature state. The air conditioner will first meet the battery's cooling needs.

6. The adaptive coordinated control method for a new energy vehicle thermal management system based on scenario partitioning as described in claim 1, characterized in that, The crew cabin temperature change rate and the battery temperature change rate are calculated using the following formula; where the crew cabin temperature change rate is expressed as dT. cab The rate of change of battery temperature is expressed as dT bat ; dT cab =△T cab / △t; dT bat =△T bat / △t; Among them, △T cab ΔT represents the change in cabin temperature over time Δt; bat This represents the change in battery temperature over time Δt.

7. An adaptive coordination control system for a new energy vehicle thermal management system based on scenario segmentation, characterized in that, The method for implementing the adaptive coordinated control of a new energy vehicle thermal management system based on scenario partitioning as described in any one of claims 1-6 includes: The scene segmentation and recognition module is used to identify the scenes that affect the energy consumption of the vehicle thermal management system. The scenes include driving conditions, ambient temperature, battery SOC, and battery temperature. The strategy response module is used to apply pre-matched and adaptive control strategies to the air conditioning system based on different identified scenarios, thereby optimizing the adjustment of cooling / heating capacity. The adaptive adjustment module is used to continuously and dynamically adjust and optimize the correction factors based on the temperature change rate of the passenger compartment and the temperature change rate of the battery, and reassign them to achieve the best energy consumption, comfort and safety.

8. An apparatus, comprising a memory, a processor, and a computer program stored in the memory, characterized in that, When the processor executes the program, it implements the adaptive coordinated control method for the new energy vehicle thermal management system based on scenario partitioning as described in any one of claims 1-6.

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