A new energy vehicle-based energy storage battery control system and method

Abstract

This invention relates to the field of energy storage battery control technology, specifically to an energy storage battery control system and method based on new energy vehicles. The system includes a monitoring data acquisition module, a target analysis equipment determination module, a target adjustment data analysis module, a dynamic adjustment module, a main data comparison and analysis module, and a data application module. The monitoring data acquisition module identifies the distribution and usage of the data and first main data. The target analysis equipment determination module identifies the first and second target analysis equipment. The target adjustment data analysis module analyzes the first and second target analysis equipment and obtains adjustment data. The dynamic adjustment module adjusts the equipment that meets the first target adjustment data. The main data comparison and analysis module compares the adjusted second main data with the first main data. The data application module applies the analyzed first or second target adjustment data that meets the specified conditions.

Description

Technical Field

[0001] This invention relates to the field of energy storage battery control technology, specifically to an energy storage battery control system and method based on new energy vehicles. Background Technology

[0002] Currently, the application of new energy vehicle technology has brought new changes to people's lives. From the high cost of fuel in the past to the low cost of electricity for new energy vehicles, it not only saves car owners money in terms of consumption, but also makes great progress in energy conservation and environmental protection. The advent of new energy vehicles has greatly reduced the emissions pollution caused by fuel vehicles, making the concept of environmental protection more deeply rooted in people's hearts and closer to life.

[0003] However, the use of new energy vehicles also presents many problems. For example, since the main power source of new energy vehicles is electricity, how the onboard electricity is consumed has become a concern for many car owners. Moreover, the rich hardware equipment in current new energy vehicles has put considerable pressure on the power supply. For example, the air conditioning, seat heating equipment, and speakers in the car are all high-power-consuming devices in addition to the main power supply. So how can we effectively analyze the state and environment of the people in the car to analyze whether these high-power-consuming devices are necessary at present, and adjust them to solve the problem of high consumption of auxiliary equipment? Summary of the Invention

[0004] The purpose of this invention is to provide a control system and method for energy storage batteries based on new energy vehicles, so as to solve the problems mentioned in the background art.

[0005] To solve the above-mentioned technical problems, the present invention provides the following technical solution: a method for controlling energy storage batteries based on new energy vehicles, comprising the following steps:

[0006] Step S1: Obtain the distribution and usage of the energy storage battery of the new energy vehicle in the first monitoring period and the first main data. The distribution and usage includes the power consumption ratio of the temperature device and the power consumption ratio of the sound source device; the first main data includes the power consumption ratio of the first power device and the first total driving mileage. The first monitoring period is the interval between two consecutive charging times of the new energy vehicle.

[0007] Step S2: Compare the power consumption ratio of the temperature device and the power consumption ratio of the sound source device, extract the device corresponding to the maximum value as the first target analysis device, and the device corresponding to the minimum value as the second target analysis device; obtain the first target adjustment data based on the first target analysis device, and obtain the second target adjustment data based on the second target analysis device;

[0008] Step S3: Based on the first target adjustment data, adjust the equipment that meets the first target adjustment data in the second monitoring cycle, record the corresponding adjustment process as the first energy storage adjustment, and obtain the second main data in the second monitoring cycle after adjustment. The second main data includes the power consumption ratio of the second power equipment and the second total driving mileage; compare and analyze the first main data and the second main data to determine the effectiveness of the first target adjustment data.

[0009] Step S4: If the validity is greater than or equal to the validity threshold, the first energy storage adjustment is applied in the subsequent monitoring cycle; if the validity is less than the validity threshold, the data equipment is adjusted to meet the second target in the third monitoring cycle based on the first energy storage adjustment, and the corresponding adjustment process is recorded as the second energy storage adjustment, and the first energy storage adjustment and the second energy storage adjustment are applied in the subsequent monitoring cycle at the same time. The effectiveness measure indicates whether the first energy storage adjustment can increase the proportion of electricity consumption in the main power of new energy vehicles and improve the total range. If so, it means that the invention can produce beneficial effects on the main power of new energy vehicles by implementing a single adjustment, reducing unnecessary power supply, eliminating the need for further data analysis, and improving the convenience of data analysis. The effectiveness is based on the analysis of the first target adjustment data, because the first target adjustment data corresponds to the highest-consuming device among the other devices besides the main power supply equipment. If the highest-consuming device cannot solve the effective distribution of energy storage after adjustment, it means that other devices are consuming the saved electricity, so all potentially consuming devices are adjusted together. If the highest-consuming device can solve the effective distribution of energy storage after adjustment, it means that no further analysis of extra data is needed to achieve the desired effect, because additional analysis and adjustment may increase the electricity required by "other devices," thus failing to achieve the desired effect.

[0010] Furthermore, step S2 includes the following steps:

[0011] If the power consumption of the temperature equipment is at its maximum, the first target analysis equipment includes the vehicle air conditioner and seat heating device;

[0012] The first target analysis device is used as the start time of the device's activation within the first monitoring period. The first relevant data of the in-vehicle personnel recorded by the vehicle within a first duration threshold with the start time as the midpoint is obtained. The first relevant data includes image data and audio data.

[0013] A causal feature dataset was established, which includes surface clothing features, audio features, and elbow movement features of the people inside the vehicle. Clothing features refer to the initial unchanged state of the clothing of the people inside the vehicle captured in the image data. Audio features include keywords containing the word "hot" and keywords containing the word "cold". Elbow movement features include the distance between the hands and the angle between the elbows. The distance between the hands includes zero and the length of the first line segment. The angle between the elbows includes angle one when the distance between the hands is 0 and angle two when the distance between the hands is the length of the first line segment.

[0014] The average duration t1 of feature data that matches the cause dataset is extracted from the image data within the first duration threshold. The duration index a1 = t1 / T1 is calculated, where T1 refers to the duration of the first duration threshold. The duration threshold a0 is set, and the feature data corresponding to a1 greater than or equal to a0 is extracted. Based on the feature data, the result feature dataset of the corresponding type within the second duration threshold and with a first similarity less than the first similarity threshold is captured. The second duration threshold is the time period after the first duration threshold.

[0015] Establishing a causal relationship data pair W = {cause feature dataset, result feature dataset} will output the causal relationship data pair as the first target adjustment data. This causal relationship data pair can visualize the data from the initial cause of an in-vehicle person actively opening the first target analysis device to the final behavioral state that may differ from the initial intention of opening the device, thus facilitating the subsequent capture of behavioral data during the monitoring cycle.

[0016] Furthermore, step S2 also includes the following steps:

[0017] If the power consumption of the audio source device is at its minimum, the second target analysis device is the in-vehicle audio device.

[0018] The starting point is the moment when the second target analysis device is turned on during the first monitoring period. The second relevant data of the occupants in the vehicle is acquired within the first time period threshold starting from the starting point. The second relevant data includes audio data. The total duration of the first time period threshold is greater than or equal to the sum of the first duration threshold and the second duration threshold.

[0019] Extract the audio data of the people in the vehicle within the first time period threshold. When the second similarity between the keywords corresponding to the audio of the people in the vehicle and the keywords output by the second target analysis device after it is turned on is greater than or equal to the second similarity threshold, record the duration t2 of the audio of the people in the vehicle. When the second similarity between the keywords corresponding to the audio of the people in the vehicle and the keywords output by the second target analysis device after it is turned on is less than the second similarity threshold, record the duration t3 of the audio of the people in the vehicle.

[0020] Calculate the dynamic index a2 = |t2-t1| / (t1+t2), where t1 is not equal to 0. Extract the durations t2 and t3 corresponding to dynamic index a2 being less than the dynamic index threshold, forming the time period set Q = {t2, t3}, and let the time period set Q be the second target adjustment data.

[0021] Furthermore, when the power consumption ratio of the audio source device is at its maximum and the power consumption ratio of the temperature device is at its minimum, the corresponding first target analysis device is the in-vehicle audio equipment, and the second target analysis device is the in-vehicle air conditioning and seat heating device; and the analysis of the adjustment data of the first target is the same as the analysis of the data of the in-vehicle audio equipment, and the analysis of the adjustment data of the second target is the same as the analysis of the data of the in-vehicle air conditioning and seat heating device.

[0022] Furthermore, step S3 includes the following specific steps:

[0023] Let u1 be the proportion of the first power-consuming equipment in the first main data and v1 be the total driving mileage in the first main data, and let u2 be the proportion of the second power-consuming equipment in the second main data and v2 be the total driving mileage in the second main data;

[0024] Calculate the validity of the first target adjustment data Q=u2 / u1+v2 / v1, and set the validity threshold Q0 of the first target adjustment data.

[0025] Furthermore, the data that satisfies the first objective is that the relevant data obtained by real-time monitoring have a causal relationship with the data pair W or the time period set Q, and the data that satisfies the second objective is also that the data obtained by real-time monitoring satisfies the data pair W or the set Q, while the data that satisfies the first objective is different from the data that satisfies the second objective.

[0026] When the first target analysis device is the in-vehicle audio equipment, the first energy storage adjustment is to reduce the volume of the in-vehicle speakers or turn them off after a duration of t3. When the second target analysis device is the in-vehicle air conditioning and seat heating devices, the second energy storage adjustment is to adjust the in-vehicle air conditioning temperature to the temperature corresponding to the power saving mode or to stop the seat heating and turn off the air conditioning equipment after the result feature dataset appears. This is because the volume of the audio equipment in the same device has a large difference in power consumption, and the volume of the sound is directly proportional to the power consumption.

[0027] A battery control system for energy storage based on new energy vehicles includes a monitoring data acquisition module, a target analysis equipment determination module, a target adjustment data analysis module, a dynamic adjustment module, a main data comparison and analysis module, and a data application module.

[0028] The monitoring data acquisition module is used to acquire the distribution and usage of energy storage batteries in new energy vehicles and the primary data during the first monitoring period;

[0029] The target analysis device determination module is used to compare the power consumption ratio of temperature devices and the power consumption ratio of sound source devices in the distributed usage, and then determine the first target analysis device and the second target analysis device.

[0030] The target adjustment data analysis module is used to analyze the first target analysis device and the second target analysis device, and obtain the corresponding adjustment data;

[0031] The dynamic adjustment module is used to adjust devices that meet the first target adjustment data, and to adjust devices that do not meet the second target adjustment data based on the main data comparison and analysis module.

[0032] The main data comparison and analysis module is used to compare the size relationship between the adjusted second main data and the first main data.

[0033] The data application module is used to apply analysis to adjust data for either the first objective or the second objective, based on the conditions met.

[0034] Furthermore, the target adjustment data analysis module includes a first target adjustment data establishment unit and a second target adjustment data establishment unit;

[0035] The first target adjustment data establishment unit is used to establish the cause feature dataset and analyze the corresponding result feature dataset, and construct the first target adjustment data by the correspondence between the cause feature dataset and the result feature dataset.

[0036] The second target adjustment data establishment unit is based on the calculation of dynamic index, extracts the time period set that meets the dynamic index setting conditions, and constructs the time period set into the second target adjustment data;

[0037] The results in the first and second target adjustment data can be adjusted accordingly based on the results of the target analysis device's determination module, and the analysis method of the target adjustment data remains unchanged based on the device.

[0038] Furthermore, the main data comparison and analysis module includes a first main data acquisition unit, a second main data acquisition unit, and an effectiveness calculation unit;

[0039] The first main data acquisition unit is used to acquire the first power consumption equipment ratio and the first total driving mileage in the first main data, and the second main data acquisition unit is used to acquire the second power consumption equipment ratio and the second total driving mileage in the second main data.

[0040] The validity calculation unit is used to calculate the validity of the first target adjustment data and set the corresponding validity threshold for judgment.

[0041] Compared with existing technologies, the beneficial effects achieved by this invention are as follows: Based on the analysis of the usage scenarios of auxiliary equipment other than the main power supply equipment in new energy vehicles, this invention effectively captures relevant data of the occupants and establishes target feature data corresponding to different auxiliary equipment. In subsequent monitoring, energy storage adjustments are made accordingly for scenarios that meet these target feature data. By reducing unnecessary activation of auxiliary equipment in such scenarios, this invention adjusts the power supply of the energy storage battery to be primarily used by the main power supply equipment, thereby reducing the power consumption of auxiliary equipment in new energy vehicles, increasing the proportion of main power supply in new energy vehicles, and increasing the total driving range of the vehicle. This provides a better willingness to use new energy vehicles for application development. Attached Figure Description

[0042] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0043] Figure 1 This is a schematic diagram of the structure of an energy storage battery control system based on new energy vehicles according to the present invention. Detailed Implementation

[0044] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0045] Please see Figure 1 The present invention provides a technical solution: a method for controlling energy storage batteries based on new energy vehicles, comprising the following steps:

[0046] Step S1: Obtain the distribution and usage of the energy storage battery of the new energy vehicle in the first monitoring period and the first main data. The distribution and usage includes the power consumption ratio of the temperature device and the power consumption ratio of the sound source device; the first main data includes the power consumption ratio of the first power device and the first total driving mileage. The first monitoring period is the interval between two consecutive charging times of the new energy vehicle.

[0047] Step S2: Compare the power consumption ratio of the temperature device and the power consumption ratio of the sound source device, extract the device corresponding to the maximum value as the first target analysis device, and the device corresponding to the minimum value as the second target analysis device; obtain the first target adjustment data based on the first target analysis device, and obtain the second target adjustment data based on the second target analysis device;

[0048] Step S3: Based on the first target adjustment data, adjust the equipment that meets the first target adjustment data in the second monitoring cycle, record the corresponding adjustment process as the first energy storage adjustment, and obtain the second main data in the second monitoring cycle after adjustment. The second main data includes the power consumption ratio of the second power equipment and the second total driving mileage; compare and analyze the first main data and the second main data to determine the effectiveness of the first target adjustment data.

[0049] Step S4: If the validity is greater than or equal to the validity threshold, the first energy storage adjustment is applied in the subsequent monitoring cycle; if the validity is less than the validity threshold, the data equipment is adjusted to meet the second target in the third monitoring cycle based on the first energy storage adjustment, and the corresponding adjustment process is recorded as the second energy storage adjustment, and the first energy storage adjustment and the second energy storage adjustment are applied in the subsequent monitoring cycle at the same time. The effectiveness measure indicates whether the first energy storage adjustment can increase the proportion of electricity consumption in the main power of new energy vehicles and improve the total range. If so, it means that the invention can produce beneficial effects on the main power of new energy vehicles by implementing a single adjustment, reducing unnecessary power supply, eliminating the need for further data analysis, and improving the convenience of data analysis. The effectiveness is based on the analysis of the first target adjustment data, because the first target adjustment data corresponds to the highest-consuming device among the other devices besides the main power supply equipment. If the highest-consuming device cannot solve the effective distribution of energy storage after adjustment, it means that other devices are consuming the saved electricity, so all potentially consuming devices are adjusted together. If the highest-consuming device can solve the effective distribution of energy storage after adjustment, it means that no further analysis of extra data is needed to achieve the desired effect, because additional analysis and adjustment may increase the electricity required by "other devices," thus failing to achieve the desired effect.

[0050] Step S2 includes the following steps:

[0051] If the power consumption of the temperature equipment is at its maximum, the first target analysis equipment includes the vehicle air conditioner and seat heating device;

[0052] The first target analysis device is used as the start time of the device's activation within the first monitoring period. The first relevant data of the in-vehicle personnel recorded by the vehicle within a first duration threshold with the start time as the midpoint is obtained. The first relevant data includes image data and audio data.

[0053] A causal feature dataset was established, comprising surface clothing features, audio features, and elbow movement features of the occupants. Clothing features refer to the initial, unchanged state of the occupants' clothing captured in the image data. Audio features include keywords containing the word "hot" and "cold." Elbow movement features include the distance between the hands and the angle between the elbows. The distance between the hands includes zero and the length of the first line segment. The angle between the elbows includes angle one when the distance between the hands is 0 and angle two when the distance between the hands is the length of the first line segment. These features represent possible behaviors that could lead to the occupants activating the first target analysis device. Furthermore, the first target analysis device is divided into two scenarios: air conditioning and heating. The state of no change in clothing characteristics is to capture whether the user's behavior is affected by temperature changes when the device is first turned on. For example, if the air conditioning in the car is too cold and requires adding clothes, or if the heating in the car is too hot and causes the occupants to take off their coats, these changes alter the initial state of no change in clothing characteristics. Occupants may turn on the air conditioning to cool down when they feel hot, or to heat up when they feel cold. They may also fan themselves when they feel hot, or rub their palms together when they feel cold. These actions can reflect the reasons why occupants choose to turn on the first target analysis device within the first duration threshold.

[0054] The average duration t1 of feature data that matches the cause dataset is extracted from image data within the first duration threshold. The duration index a1 = t1 / T1 is calculated, where T1 refers to the duration of the first duration threshold. A duration threshold a0 is set, and feature data corresponding to a1 greater than or equal to a0 are extracted. Based on the feature data, a result feature dataset of the corresponding type within the second duration threshold and with a first similarity less than the first similarity threshold is captured. The second duration threshold is the time period after the first duration threshold. The result feature dataset includes changes in the initial clothing state corresponding to surface clothing features. For example, if the air conditioner makes the people in the car feel cold instead of cool after a period of cooling, the people in the car may add clothes, thus changing the initial clothing state. The audio features show a shift in similarity between keywords containing the word "hot" and keywords containing the word "cold." For example, after running the air conditioner for a while, one might feel cold and say "cold," even though the reason for running the air conditioner is heat. The similarity between keywords containing "hot" and "cold" is extremely low. Similarly, elbow movement features, including the distance between the hands and the angle between the elbows, show opposite changes. For instance, after cooling for a period, people in the car might change from fanning themselves to rubbing their arms, resulting in changes in hand distance and angle. The similarity between these changes is also extremely low. This process illustrates the result of feature datasets with similarity values ​​below the similarity threshold.

[0055] Establishing a causal relationship data pair W = {cause feature dataset, result feature dataset} will output the causal relationship data pair as the first target adjustment data. This causal relationship data pair can visualize the data from the initial cause of an in-vehicle person actively opening the first target analysis device to the final behavioral state that may differ from the initial intention of opening the device, thus facilitating the subsequent capture of behavioral data during the monitoring cycle.

[0056] Step S2 also includes the following steps:

[0057] If the power consumption of the audio source device is at its minimum, the second target analysis device is the in-vehicle audio device.

[0058] The starting point is the moment when the second target analysis device is turned on during the first monitoring period. The second relevant data of the occupants in the vehicle is acquired within the first time period threshold starting from the starting point. The second relevant data includes audio data. The total duration of the first time period threshold is greater than or equal to the sum of the first duration threshold and the second duration threshold.

[0059] Extract the audio data of the people in the vehicle within the first time period threshold. When the second similarity between the keywords corresponding to the audio of the people in the vehicle and the keywords output by the second target analysis device after it is turned on is greater than or equal to the second similarity threshold, record the duration t2 of the audio of the people in the vehicle. When the second similarity between the keywords corresponding to the audio of the people in the vehicle and the keywords output by the second target analysis device after it is turned on is less than the second similarity threshold, record the duration t3 of the audio of the people in the vehicle.

[0060] Calculate the dynamic index a2 = |t2-t1| / (t1+t2), where t1 is not equal to 0. Extract the durations t2 and t3 corresponding to dynamic index a2 being less than the dynamic index threshold, forming the time period set Q = {t2, t3}, and let the time period set Q be the second target adjustment data. The smaller the dynamic index, the smaller the difference in the duration of the state maintained by the people in the car before and after the audio equipment output source. t1 not being equal to 0 is to ensure that there are two state changes of the people in the car within this time period threshold. The existence of t2 indicates that after the speaker is turned on, the people in the car may hum along with the audio output from the speaker. The existence of t3 indicates that the humming stops or something else interrupts the people in the car. The dynamic index threshold is set to ensure that the duration of t3 is not too short, because if it is too short, it may be a short pause of the people in the car that cannot fully explain that the people in the car actively changed their state.

[0061] When the power consumption ratio of the audio source device is at its maximum and the power consumption ratio of the temperature device is at its minimum, the corresponding first target analysis device is the in-vehicle audio equipment, and the second target analysis device is the in-vehicle air conditioning and seat heating device. The analysis of the adjustment data of the first target is the same as the analysis of the data of the in-vehicle audio equipment, and the analysis of the adjustment data of the second target is the same as the analysis of the data of the in-vehicle air conditioning and seat heating device.

[0062] Step S3 includes the following specific steps:

[0063] Let u1 be the proportion of the first power-consuming equipment in the first main data and v1 be the total driving mileage in the first main data, and let u2 be the proportion of the second power-consuming equipment in the second main data and v2 be the total driving mileage in the second main data;

[0064] Calculate the validity of the first target adjustment data Q=u2 / u1+v2 / v1, and set the validity threshold Q0 of the first target adjustment data.

[0065] The adjustment data that satisfies the first objective refers to the relevant data obtained by real-time monitoring that have a causal relationship with the data pair W or the time period set Q, and the adjustment data that satisfies the data pair W or the set Q. At the same time, the adjustment data for the first objective and the adjustment data for the second objective are different.

[0066] When the first target analysis device is the in-vehicle audio system, the first energy storage adjustment involves reducing the volume of the in-vehicle speakers or turning them off after a duration of t3. When the second target analysis device is the in-vehicle air conditioning and seat heating system, the second energy storage adjustment involves adjusting the in-vehicle air conditioning temperature to the temperature corresponding to the power-saving mode or stopping the seat heating and turning off the air conditioning system after the result feature dataset is generated. This is because the volume of the audio equipment within the same device has a significant impact on power consumption, and the volume of the sound is directly proportional to the power consumption. Furthermore, both of the above adjustment methods can capture image and audio data based on DMS system technology, achieving accuracy and effectiveness in data acquisition. These adjustment methods also transmit signals to the energy storage battery control system.

[0067] A battery control system for energy storage based on new energy vehicles includes a monitoring data acquisition module, a target analysis equipment determination module, a target adjustment data analysis module, a dynamic adjustment module, a main data comparison and analysis module, and a data application module.

[0068] The monitoring data acquisition module is used to acquire the distribution and usage of energy storage batteries in new energy vehicles and the primary data during the first monitoring period;

[0069] The target analysis device determination module is used to compare the power consumption ratio of temperature devices and the power consumption ratio of sound source devices in the distributed usage, and then determine the first target analysis device and the second target analysis device.

[0070] The target adjustment data analysis module is used to analyze the first target analysis device and the second target analysis device, and obtain the corresponding adjustment data;

[0071] The dynamic adjustment module is used to adjust devices that meet the first target adjustment data, and to adjust devices that do not meet the second target adjustment data based on the main data comparison and analysis module.

[0072] The main data comparison and analysis module is used to compare the size relationship between the adjusted second main data and the first main data.

[0073] The data application module is used to apply analysis to adjust data for either the first objective or the second objective, based on the conditions met.

[0074] The target adjustment data analysis module includes a first target adjustment data establishment unit and a second target adjustment data establishment unit;

[0075] The first target adjustment data establishment unit is used to establish the cause feature dataset and analyze the corresponding result feature dataset, and construct the first target adjustment data by the correspondence between the cause feature dataset and the result feature dataset.

[0076] The second target adjustment data establishment unit is based on the calculation of dynamic index, extracts the time period set that meets the dynamic index setting conditions, and constructs the time period set into the second target adjustment data;

[0077] The results in the first and second target adjustment data can be adjusted accordingly based on the results of the target analysis device's determination module, and the analysis method of the target adjustment data remains unchanged based on the device.

[0078] The main data comparison and analysis module includes a first main data acquisition unit, a second main data acquisition unit, and an effectiveness calculation unit;

[0079] The first main data acquisition unit is used to acquire the first power consumption equipment ratio and the first total driving mileage in the first main data, and the second main data acquisition unit is used to acquire the second power consumption equipment ratio and the second total driving mileage in the second main data.

[0080] The validity calculation unit is used to calculate the validity of the first target adjustment data and set the corresponding validity threshold for judgment.

[0081] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0082] Finally, it should be noted that the above descriptions are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for controlling energy storage batteries based on new energy vehicles, characterized in that, Includes the following steps: Step S1: Obtain the distribution and usage of the energy storage battery of the new energy vehicle in the first monitoring period and the first main data. The distribution and usage includes the power consumption ratio of the temperature device and the power consumption ratio of the sound source device. The first main data includes the power consumption ratio of the first power device and the first total driving mileage. The first monitoring period is the interval between two consecutive charging times of the new energy vehicle. Step S2: Compare the power consumption ratio of the temperature device and the power consumption ratio of the sound source device, extract the device corresponding to the maximum value as the first target analysis device, and the device corresponding to the minimum value as the second target analysis device; First target adjustment data is obtained based on the analysis of the first target analysis device, and second target adjustment data is obtained based on the analysis of the second target analysis device. Step S2 includes the following steps: If the power consumption ratio of the temperature device is at its maximum, the first target analysis device includes the vehicle air conditioner and the seat heating device; The starting time is the moment when the first target analysis device is turned on within the first monitoring period. The first relevant data of the in-vehicle personnel recorded by the vehicle within a first duration threshold with the starting time as the midpoint is obtained. The first relevant data includes image data and audio data. A causal feature dataset is established, which includes surface clothing features, audio features, and elbow movement features of the people inside the vehicle. The clothing features refer to the initial unchanged state of the clothing of the people inside the vehicle captured in the image data. The audio features include keywords containing the word "hot" and keywords containing the word "cold". The elbow movement features include the distance between the hands and the angle between the elbows. The distance between the hands includes zero and the length of the first line segment. The angle between the elbows includes angle one when the distance between the hands is 0 and angle two when the distance between the hands is the length of the first line segment. The average duration t1 of feature data that matches the cause dataset is extracted from the image data within the first duration threshold. The duration index a1 = t1 / T1 is calculated, where T1 refers to the duration of the first duration threshold. The duration threshold a0 is set, and the feature data corresponding to a1 greater than or equal to a0 is extracted. Based on the feature data, the result feature dataset of the corresponding type within the second duration threshold and with a first similarity less than the first similarity threshold is captured. The second duration threshold is the time period after the first duration threshold. If a causal relationship data pair W = {cause feature dataset, result feature dataset} is established, then the output causal relationship data pair is the first target adjustment data; Step S3: Based on the first target adjustment data, adjust the equipment that meets the first target adjustment data in the second monitoring cycle, record the corresponding adjustment process as the first energy storage adjustment, and obtain the second main data in the second monitoring cycle after adjustment. The second main data includes the power consumption ratio of the second power equipment and the second total driving mileage; compare and analyze the first main data and the second main data to determine the effectiveness of the first target adjustment data. Step S4: If the validity is greater than or equal to the validity threshold, the first energy storage adjustment is applied in the subsequent monitoring cycle; if the validity is less than the validity threshold, the data equipment is adjusted to meet the second target in the third monitoring cycle based on the first energy storage adjustment, and the corresponding adjustment process is recorded as the second energy storage adjustment, and the first energy storage adjustment and the second energy storage adjustment are applied in the subsequent monitoring cycle at the same time.

2. The energy storage battery control method based on new energy vehicles according to claim 1, characterized in that: Step S2 further includes the following steps: If the power consumption of the audio source device is at its minimum, the second target analysis device is the in-vehicle audio device. The starting point is the moment when the second target analysis device is turned on during the first monitoring period. The second relevant data of the occupants in the vehicle is obtained within the first time period threshold starting from the starting point. The second relevant data includes audio data. The total duration of the first time period threshold is greater than or equal to the sum of the first duration threshold and the second duration threshold. Extract the audio data of the people in the vehicle within the first time period threshold. When the second similarity between the keywords corresponding to the audio of the people in the vehicle and the keywords output by the second target analysis device after it is turned on is greater than or equal to the second similarity threshold, record the duration t2 of the audio of the people in the vehicle. When the second similarity between the keywords corresponding to the audio of the people in the vehicle and the keywords output by the second target analysis device after it is turned on is less than the second similarity threshold, record the duration t3 of the audio of the people in the vehicle. Calculate the dynamic index a2 = |t2-t1| / (t1+t2), where t1 is not equal to 0. Extract the durations t2 and t3 corresponding to dynamic index a2 being less than the dynamic index threshold, forming a time period set Q = {t2, t3}, and let the time period set Q be the second target adjustment data.

3. The energy storage battery control method based on new energy vehicles according to claim 2, characterized in that: When the power consumption ratio of the audio source device is at its maximum and the power consumption ratio of the temperature device is at its minimum, the corresponding first target analysis device is the in-vehicle audio equipment, and the second target analysis device is the in-vehicle air conditioning and seat heating device. The analysis method for the adjustment data of the first target is the same as the analysis method for the data of the in-vehicle audio equipment, and the analysis method for the adjustment data of the second target is the same as the analysis method for the data of the in-vehicle air conditioning and seat heating device.

4. The energy storage battery control method based on new energy vehicles according to claim 1, characterized in that: Step S3 includes the following specific steps: Let u1 be the proportion of the first power-consuming equipment in the first main data and v1 be the first total driving mileage; let u2 be the proportion of the second power-consuming equipment in the second main data and v2 be the second total driving mileage. Calculate the validity of the first target adjustment data Q=u2 / u1+v2 / v1, and set the validity threshold Q0 of the first target adjustment data.

5. The energy storage battery control method based on new energy vehicles according to claim 2, characterized in that: The first target adjustment data refers to the relevant data obtained by real-time monitoring that have a causal relationship data pair W or time period set Q, and the second target adjustment data also refers to the data obtained by real-time monitoring that satisfy the data pair W or set Q, while the first target adjustment data and the second target adjustment data are different; When the first target analysis device is an in-vehicle audio device, the first energy storage adjustment is to reduce the volume of the in-vehicle speaker or turn off the in-vehicle speaker after a duration t3. When the second target analysis device is the vehicle air conditioner and seat heating device, the second energy storage adjustment will adjust the vehicle air conditioner temperature to the temperature corresponding to the power saving mode or stop the seat heating and turn off the air conditioning device after the result feature dataset appears.

6. A control system for an energy storage battery based on a new energy vehicle, applying the energy storage battery control method for any one of claims 1-5, characterized in that, It includes a monitoring data acquisition module, a target analysis equipment determination module, a target adjustment data analysis module, a dynamic adjustment module, a key data comparison and analysis module, and a data application module; The monitoring data acquisition module is used to acquire the distribution and usage of energy storage batteries of new energy vehicles and the first main data during the first monitoring period; The target analysis device determination module is used to compare the power consumption ratio of temperature devices and the power consumption ratio of sound source devices in the distributed usage situation, and then determine the first target analysis device and the second target analysis device. The target adjustment data analysis module is used to analyze the first target analysis device and the second target analysis device, and obtain the corresponding adjustment data; The dynamic adjustment module is used to adjust devices that meet the first target adjustment data; and to adjust devices that do not meet the second target adjustment data based on the main data comparison and analysis module. The main data comparison and analysis module is used to compare the size relationship between the adjusted second main data and the first main data. The data application module is used to apply analysis to adjust the first target data or the second target data that meets the conditions.

7. A battery control system for energy storage based on new energy vehicles according to claim 6, characterized in that: The target adjustment data analysis module includes a first target adjustment data establishment unit and a second target adjustment data establishment unit; The first target adjustment data establishment unit is used to establish a cause feature dataset and analyze it to obtain the corresponding result feature dataset, and construct the first target adjustment data by the correspondence between the cause feature dataset and the result feature dataset; The second target adjustment data establishment unit extracts a set of time periods that meet the dynamic index setting conditions based on the calculation of the dynamic index, and constructs the set of time periods into the second target adjustment data; The results in the first target adjustment data and the second target adjustment data can be adjusted accordingly based on the results of the target analysis device determination module, and the analysis method of the target adjustment data remains unchanged based on the device.

8. A battery control system for energy storage based on new energy vehicles according to claim 7, characterized in that: The main data comparison and analysis module includes a first main data acquisition unit, a second main data acquisition unit, and an effectiveness calculation unit; The first main data acquisition unit is used to acquire the first power consumption equipment ratio and the first total driving mileage in the first main data, and the second main data acquisition unit is used to acquire the second power consumption equipment ratio and the second total driving mileage in the second main data; The validity calculation unit is used to calculate the validity of the first target adjustment data and set the corresponding validity threshold for judgment.

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