A parking air conditioning energy-saving control method

Abstract

This invention discloses a parking air conditioner energy-saving control method, which includes: recording a set target temperature; detecting and recording the outer ambient temperature and battery charge; inputting the target temperature, outer ambient temperature, and battery charge as feature parameters into a power consumption prediction model to obtain the power consumption and runtime of the parking air conditioner under these feature parameters; and prompting the user and / or automatically adjusting the temperature control strategy of the parking air conditioner based on the power consumption and runtime. This invention solves the problem of short operating time and poor user experience of parking air conditioners by estimating runtime based on target temperature and remaining battery charge, thereby reminding users to set a reasonable target temperature and improving the air conditioner's operating time and user experience.

Description

Technical Field

[0001] This invention relates to the field of intelligent control, and more specifically to a parking air conditioning energy-saving control method. Background Technology

[0002] The parking air conditioner is used in long-haul trucks, usually when the driver is resting at night. Therefore, if the air conditioner is not turned on at night, the temperature difference between the inside and outside of the truck is not significant. Thus, the operating load of the air conditioner largely depends on the outside temperature and the user's set temperature.

[0003] The parking air conditioner is mainly powered by the vehicle's battery. However, the battery has a limited capacity, and users cannot know in advance how much electricity the air conditioner will consume overnight. If the user sets an unreasonable temperature, the effective operating time of the parking air conditioner will be too short, resulting in a poor user experience. Summary of the Invention

[0004] This invention primarily relates to an energy-saving control method for parking air conditioners. By monitoring the power consumption of a large number of parking air conditioners within the same time period, the relationship between power consumption and outdoor ambient temperature and set temperature is determined. This allows for the prediction of future power consumption of the parking air conditioner within the same time period. Combined with the remaining battery power, the method reminds the user of the power consumption and the remaining operating time of the air conditioner, and suggests that the user adjust the set temperature in a timely manner to make the air conditioner usage time more reasonable.

[0005] According to a first aspect of the present invention, a parking air conditioner energy-saving control method is provided, wherein the parking air conditioner is equipped with a battery to provide power for its operation, characterized in that the parking air conditioner includes the following control steps:

[0006] Record the set target temperature;

[0007] Detect and record the outer ring temperature and battery charge;

[0008] The target temperature, the outer ring temperature, and the battery charge are used as feature parameters and input into the power consumption prediction model to obtain the power consumption and driving time of the parking air conditioner under the feature parameter conditions.

[0009] Based on the power consumption and the driving range, the user is prompted and / or the temperature control strategy of the parking air conditioner is automatically adjusted.

[0010] Optionally, before inputting the target temperature, the outer ring temperature, and the battery charge as feature parameters into the power consumption prediction model to obtain the power consumption and driving range of the parking air conditioner under the feature parameter conditions, the method further includes:

[0011] Construct sample data for parking air conditioners;

[0012] The electricity consumption prediction model is created using the sample data.

[0013] The sample data for constructing the parking air conditioner includes sample data accumulated during the use of this parking air conditioner and / or sample data accumulated from multiple similar parking air conditioners.

[0014] Optionally, creating the electricity consumption prediction model using the sample data includes:

[0015] The first sample data is formed using the aforementioned sample data;

[0016] The first sample data is reorganized into the second sample data;

[0017] The electricity consumption prediction model is created using the second sample data.

[0018] Optionally, the step of forming the first sample data using the sample data includes:

[0019] The sample data is selected as the first sample data and the temperature difference sequence is calculated or

[0020] The sample data is directly used as the first sample data to calculate the temperature difference sequence.

[0021] Optionally, the sample data includes:

[0022] For each parking air conditioner, the model, target temperature, initial outer ring temperature, location information, battery charge, operating time series, and outer ring temperature value series continuously recorded at fixed time intervals are specified.

[0023] Optionally, the step of selecting the sample data as the first sample data and calculating the temperature difference sequence includes:

[0024] The saved sample data is filtered, and the first air conditioner data with the same model and the same geographical location range is selected based on the model of the parking air conditioner and the location range of the parking air conditioner.

[0025] The second air conditioning data is filtered from the first air conditioning data using the target temperature and the set time period;

[0026] The target temperature, the initial outer ring temperature, the battery charge of the parking air conditioner, and the sequence of outer ring temperature values ​​continuously recorded at fixed time intervals are extracted from the second air conditioning data as the first sample data.

[0027] Based on the first sample data, the maximum and minimum values ​​of the outer ring temperature in the time period are extracted and subtracted to obtain the temperature difference sequence in the time period. The mode of the temperature difference sequence is then used as the temperature difference in the time period.

[0028] Optionally, the step of organizing the first sample data into second sample data includes:

[0029] Find the minimum value T of the outer ring temperature from the sequence of outer ring temperature values ​​continuously recorded at fixed time intervals from the first sample data. min and maximum value T max ;

[0030] Calculate the temperature difference ΔT = T over the specified time period. max -T min ;

[0031] Divide the temperature difference ΔT over the time period by the number n of the fixed time intervals included in the time period to obtain the average temperature difference for each time interval. Where n≧1, a natural number;

[0032] Using the initial power Q1 and the final power Q2 of the parking air conditioner during the time period, calculate the power consumption Q of the parking air conditioner during the time period: Q = Q1 - Q2;

[0033] The outer ring temperature recorded at the start time of the time period is marked as the initial outer ring temperature T. init Using the target temperature T obj and the initial outer ring temperature T init Combining the temperature difference ΔT and the average temperature difference pass A temperature difference matrix D is constructed from the first sample data, and the temperature difference matrix D and the electricity consumption Q are used as the second sample data, where n≧1, a natural number.

[0034] Optionally, the step of creating the electricity consumption prediction model using the second sample data includes:

[0035] The relationship between the temperature difference matrix D and the power consumption Q is represented by a parameter matrix K;

[0036] The electricity consumption prediction model is represented as KD = Q;

[0037] Here K = [k1 k2 k3 … k] n ],

[0038] Where n≧1, a natural number.

[0039] Based on the temperature difference matrix D and the electricity consumption Q in the second sample data, calculate the parameter matrix K of the electricity consumption prediction model.

[0040] Optionally, the step of inputting the target temperature, the outer ring temperature, and the battery charge as feature parameters into the power consumption prediction model to obtain the power consumption and driving range of the parking air conditioner within a preset time period under the condition of the feature parameters includes:

[0041] Using the outer ring temperature, the target temperature, the temperature difference matrix constructed from the statistical temperature difference data within the preset time period, and the parameter matrix of the power consumption prediction model, the power consumption Q of the parking air conditioner within the preset time period is predicted. p ;

[0042] Using the predicted electricity consumption Q p The average electricity consumption per unit time is obtained by combining the number of unit time intervals n contained in the preset time period. The remaining safe power Q of the parking air conditioner is calculated. c Battery life within the preset time period The remaining safe power of the parking air conditioner is the parking air conditioner battery power minus the minimum safe power required by the vehicle where the parking air conditioner is located.

[0043] Optionally, the strategy of prompting the user and / or automatically adjusting the temperature control of the parking air conditioner based on the power consumption and the driving range includes:

[0044] If the power consumption exceeds the remaining safe power of the parking air conditioner, the user is prompted to adjust the currently set target temperature, or the currently set target temperature is automatically corrected; otherwise, the current temperature control strategy is operated normally.

[0045] Optionally, the second sample data can be used to optimize the electricity consumption prediction model.

[0046] According to a second aspect of the present invention, an apparatus for energy-saving control of a parking air conditioner is provided, comprising one or more processors and a non-transitory computer-readable storage medium storing program instructions, wherein when the one or more processors execute the program instructions, the one or more processors are configured to implement the method according to any one of the first aspects.

[0047] Optionally, the parking air conditioner is equipped with a monitoring module for recording the air conditioning data and uploading it to a data platform. The monitoring module includes a data acquisition module and a network communication module.

[0048] The data acquisition module is used to record the air conditioner data, and its logical structure includes: a main control unit, a temperature detection unit, a power detection unit, and a storage unit;

[0049] The main control unit controls the operation of the air conditioner and records the air conditioner model and location information;

[0050] The temperature detection unit measures the outer ring temperature;

[0051] The power detection unit detects the battery power of the parking air conditioner;

[0052] The storage unit records the target temperature, the outer ring temperature, and the battery charge.

[0053] The logical structure of the network communication module includes a storage unit and a network unit;

[0054] The network unit is used to establish communication between the parking air conditioner and the data platform.

[0055] According to a third aspect of the invention, a non-transitory computer-readable storage medium stores program instructions thereon, which, when executed by one or more processors, are configured to implement the method according to any one aspect.

[0056] According to a fourth aspect of the invention, an air conditioner includes the method described in the first aspect and the apparatus described in the second aspect, or has a non-transitory computer-readable storage medium as described in the third aspect.

[0057] This invention proposes a method to collect air conditioning data over a set time period and process it into standard sample data to construct a power consumption model for the parking air conditioner during that time period. By recording the target temperature of the parking air conditioner, monitoring the outer ambient temperature and the parking air conditioner's power consumption during the set time period, and estimating the air conditioner's power consumption and runtime based on the constructed power consumption prediction model, the invention provides guidance to users on setting the temperature appropriately or for the system to automatically adjust the set temperature. This avoids the problem of insufficient air conditioning operating time and poor user experience, thereby improving the air conditioner's runtime and overall user experience. Attached Figure Description

[0058] To more clearly illustrate the technical solution of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0059] Figure 1 This is a system flowchart of an embodiment of the present invention;

[0060] Figure 2 This is a logic diagram of the air conditioning monitoring device of the parking air conditioner according to an embodiment of the present invention;

[0061] Figure 3 This is a schematic diagram of the process for constructing the electricity consumption prediction model according to an embodiment of the present invention; Detailed Implementation

[0062] As used herein, the terms "first," "second," etc., can be used to describe elements in exemplary embodiments of the present invention. These terms are used only to distinguish one element from another, and the inherent features or order of the corresponding elements are not limited by the term. Unless otherwise defined, all terms used herein (including technical or scientific terms) have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. Terms defined in common dictionaries are to be interpreted as having the same meaning as in the context of the relevant technical field, and not as having an ideal or overly formal meaning, unless explicitly defined as having such a meaning in this invention.

[0063] Those skilled in the art will understand that the apparatus and methods of the present invention described herein and illustrated in the accompanying drawings are non-limiting exemplary embodiments, and the scope of the invention is defined only by the claims. Features illustrated or described in conjunction with an exemplary embodiment may be combined with features of other embodiments. Such modifications and variations are included within the scope of the invention.

[0064] In the following description, exemplary embodiments of the present invention will be described in detail with reference to the accompanying drawings. In the drawings, detailed descriptions of known functions or configurations are omitted to avoid unnecessarily obscuring the key technical aspects of the invention. Furthermore, throughout the description, the same reference numerals always refer to the same circuits, modules, or units, and for the sake of brevity, repeated descriptions of the same circuits, modules, or units are omitted.

[0065] Furthermore, it should be understood that one or more of the following methods or aspects can be performed by at least one control system, control unit, or controller. The terms "control unit," "controller," "control module," or "main control module" can refer to a hardware device including a memory and a processor, and the term "air conditioning" can refer to a device similar to a refrigeration unit. The memory or computer-readable storage medium is configured to store program instructions, and the processor is specifically configured to execute the program instructions to perform one or more processes, which will be further described below. Moreover, it should be understood that, as those skilled in the art will recognize, the following methods can be performed by including a processor in conjunction with one or more other components.

[0066] Parking air conditioners are primarily powered by the vehicle's battery. However, battery power is limited, and users cannot know in advance how much electricity the air conditioner will consume overnight. If the user sets an unreasonable temperature, the effective operating time of the parking air conditioner will be too short, resulting in a poor user experience. This invention proposes a method to collect air conditioner data over a set time period, process it into standard sample data, and construct a power consumption model for the parking air conditioner during that time period. By recording the target temperature of the parking air conditioner, monitoring the outer ambient temperature and the parking air conditioner's power consumption during the set time period, and analyzing the power consumption prediction model based on the constructed model, the power consumption and runtime of the air conditioner within the set time period can be obtained. This allows the system to remind the user to set the temperature appropriately or automatically adjust the set temperature, thus avoiding the problem of insufficient air conditioner operating time and a poor user experience.

[0067] The following examples illustrate the implementation methods in detail.

[0068] Example 1

[0069] This invention provides a parking air conditioning energy-saving control method, such as... Figure 1 As shown, the parking air conditioning energy-saving control method of this embodiment of the invention may include at least the following steps S1, S2, S3, and S4.

[0070] S1, the parking air conditioner records the set target temperature.

[0071] The control method in this embodiment can be triggered when the air conditioner is turned on or during its operation. The trigger can be a user-initiated button or automatic system startup. The set temperature can be set by the user after the parking air conditioner is turned on, or it can be a target temperature set by the control device's program. Regardless of who sets the target temperature, this program will automatically record and save it on the parking air conditioner.

[0072] Users can set the target temperature value via remote control, manually on the parking air conditioning panel, or through a mobile app.

[0073] S2 detects and records the outer ring temperature and battery charge.

[0074] When the parking air conditioner is used at night, its operating load largely depends on the ambient temperature and the user-set temperature. In this step, the parking air conditioner needs to detect the current ambient temperature and the battery level of the vehicle where the air conditioner is located, and save this detection information and data.

[0075] The battery charge level here refers to the current remaining charge of the battery, and the outer ring temperature refers to the ambient temperature of the parking air conditioner.

[0076] S3 predicts the power consumption and driving time of the parking air conditioner based on the power consumption prediction model.

[0077] In this step, the power consumption prediction model can be a pre-built model. This model calculates the power consumption and driving range of the parking air conditioner based on the previously recorded current target temperature, ambient temperature, and battery level, as well as the temperature difference over a set time period. The power consumption here is based on the current battery level, the driving time (i.e., driving range) at the target temperature and ambient temperature within the set time period, and the required power consumption. The set time period is typically the evening rest period, such as 10 PM to 6 AM (8 hours). Of course, this time period is not absolute and can be set to a fixed time period based on geographical location and local latitude and longitude characteristics, with the program starting its detection every day during this time period.

[0078] S4 prompts the user and / or automatically adjusts the temperature control strategy of the parking air conditioner based on power consumption and driving time.

[0079] In this step, the predicted power consumption and driving range are presented to the user, and / or the parking air conditioner's temperature control strategy is automatically adjusted. This includes determining whether the set target temperature is reasonable based on the predicted power consumption during the set time period and the current remaining battery power. If it is not reasonable, the user is prompted to correct the target temperature, or the system automatically corrects the target temperature. If it is reasonable, the parking air conditioner continues to execute the current temperature control strategy.

[0080] In a further preferred embodiment, before predicting the power consumption and driving range of the parking air conditioner based on the target temperature, the outer ambient temperature, and the battery charge using a power consumption prediction model, the following two steps, S31 and S32, are included:

[0081] S31, Construct sample data for parking air conditioners;

[0082] S32, Use the sample data to create the electricity consumption prediction model.

[0083] Due to the different sources of sample data, it can be divided into air conditioning data collected from multiple parking air conditioners by a big data cloud platform, or air conditioning data recorded by the parking air conditioner during user use, or a combination of both.

[0084] When the sample data comes from the air conditioning data recorded by the parking air conditioner during user use, under a fixed set temperature, by monitoring the outer ambient temperature and air conditioning power consumption over a set time period, such as 10 PM to 6 AM (8 hours), the power consumption of the air conditioner under different outer ambient temperature ranges can be determined. Thus, based on the known time period and set temperature, the power consumption of the air conditioner under different outer ambient temperature ranges can be calculated and stored in the storage chip. Generally, the temperature difference in the same location is relatively stable, and the temperature difference can be determined. On a clear night, the outdoor ambient temperature changes roughly consistently, dropping first and then rising again around 5 or 6 AM. After accumulating a large amount of previous air conditioning power consumption data, when the user uses the air conditioner again, checking the current outer ambient temperature, combined with the set temperature and temperature difference, allows for a rough estimate of the total power consumption required by the air conditioner overnight. The method of recording air conditioning data during user use can be further optimized by using machine learning to form a power consumption prediction model.

[0085] Data samples can be collected from massive amounts of air conditioning data recorded by parking air conditioners through a big data cloud platform. This data can include: parking air conditioner model, target temperature, location information, battery level, operating time series, and a sequence of continuously recorded outer ambient temperature values ​​at fixed time intervals. After data filtering and processing, the resulting standard sample data provides massive data support for training the electricity consumption prediction model.

[0086] Regardless of the data source (either individually or in combination), the overall data processing procedure includes:

[0087] The first sample data is formed using the aforementioned sample data;

[0088] The first sample data is reorganized into the second sample data;

[0089] The electricity consumption prediction model is created using the second sample data.

[0090] in:

[0091] The first sample data can be formed using the aforementioned sample data in one of the following ways:

[0092] The sample data was selected as the first sample data and the temperature difference sequence was calculated;

[0093] The sample data is directly used as the first sample data to calculate the temperature difference sequence.

[0094] The method of selecting sample data as the first sample data and calculating the temperature difference sequence is preferably applied to sample data containing air conditioning data recorded by parking air conditioners collected through a big data cloud platform. The specific processing steps include two steps: S311 and S312.

[0095] S311, the process of selecting air conditioning data as the first sample data includes the following three steps.

[0096] S3111, the saved air conditioning data is filtered, and the first air conditioning data with the same model and the same geographical location range is filtered out according to the parking air conditioner model and the location range of the parking air conditioner.

[0097] S3112, Filter out the second air conditioning data from the first air conditioning data using the target temperature and the set time period;

[0098] S3113, extract the target temperature, outer ring temperature, battery power, and the sequence of outer ring temperature values ​​continuously recorded at fixed time intervals from the second air conditioning data as the first sample data.

[0099] Preferably, the parking air conditioners collected by the big data cloud platform are of the same model and geographical location, and ideally, they are the target temperature, outer ring temperature, battery charge, and outer ring temperature value sequence data continuously recorded at fixed time intervals within the same time period. This is equivalent to directly collecting the first sample data, which can greatly reduce the amount of data processing.

[0100] S312, Organize the first sample data into the second sample data, the process of which includes the following five steps.

[0101] S3121, Find the minimum value T of the outer ring temperature from the sequence of outer ring temperature values ​​continuously recorded at fixed time intervals in the first sample data. min and maximum value T max ;

[0102] S3122, Calculate the temperature difference ΔT = T over a set time period. max -T min ;

[0103] S3123, Divide the temperature difference ΔT of this time period by the number of fixed time intervals n contained in the set time period to obtain the average temperature difference of each time interval. Where n≧1, a natural number;

[0104] S3124, using the initial power Q1 and the final power Q2 of the parking air conditioner during this time period, calculate the power consumption Q of the parking air conditioner during this time period: Q = Q1 - Q2.

[0105] S3125, mark the outer ring temperature at the start of this time period as the initial outer ring temperature T. init Using the target temperature T obj and the initial outer ring temperature T init Combining the temperature difference ΔT and the average temperature difference pass A temperature difference matrix D is constructed from the first sample data. The temperature difference matrix D and the electricity consumption Q are used as the second sample data, where n ≧ 1, a natural number. The second sample is used as the standard sample data.

[0106] The parking air conditioner records simple data with a small volume, so it can process the data itself. First, the air conditioning data collected by the parking air conditioner is filtered by a set time period. The data includes: target temperature, parking air conditioner battery level, parking air conditioner operating time series, and a series of outer ambient temperature values ​​continuously recorded at fixed time intervals. Then, the air conditioning data is processed using the processing method in S2 above. This yields standard sample data that can be directly used to train the power consumption prediction model.

[0107] Specifically, the preferred steps for creating an electricity consumption prediction model using standard sample data include the following:

[0108] S321, the relationship between the temperature difference matrix D and the power consumption Q is represented as a parameter matrix K;

[0109] The electricity consumption prediction model is represented as KD = Q;

[0110] Here K = [k1 k2 k3 … k] n ],

[0111] Where n≧1, a natural number.

[0112] S322, Calculate the parameter matrix K of the electricity consumption prediction model based on the temperature difference matrix D and electricity consumption Q in the sample data.

[0113] After the power consumption prediction model is constructed, the power consumption Q of the parking air conditioner during the preset time period can be predicted using the outer ring temperature, the target temperature, the temperature difference matrix constructed from the statistical temperature difference data within the preset time period, and the parameter matrix of the power consumption prediction model. p .

[0114] Using the predicted electricity consumption Q p The average electricity consumption per unit time is obtained by combining the number of unit time intervals n contained in the preset time period. The remaining safe power Q of the parking air conditioner is calculated. c Battery life within the preset time period

[0115] The preferred remaining safe power level here is the parking air conditioner battery level minus the minimum safe power level required by the vehicle where the parking air conditioner is located. This minimum safe power level ensures the vehicle can start normally, guaranteeing safe vehicle use. Since the parking air conditioner is an in-vehicle air conditioner used for waiting and resting while parked, it operates continuously using the vehicle's DC power battery. Therefore, for gasoline-powered vehicles, regardless of whether the engine is off, the remaining safe power level of the parking air conditioner is the vehicle's battery level minus the power required for normal engine starting. For electric vehicles, the remaining safe power level is the vehicle's battery level minus the minimum power level required for the vehicle's next normal start.

[0116] Therefore, the aforementioned strategy of prompting the user and / or automatically adjusting the temperature control of the parking air conditioner based on the predicted power consumption and the remaining driving time includes:

[0117] If the power consumption exceeds the remaining safe power of the parking air conditioner, the system will prompt the user to adjust the currently set target temperature or automatically correct the currently set target temperature. Otherwise, the system will operate the current temperature control strategy normally.

[0118] In this way, given the predicted power consumption of the air conditioner and the remaining safe power of the battery, if the predicted power consumption is less than the remaining safe power of the battery, the user is informed that they can use the air conditioner with confidence; if the predicted power consumption is greater than the remaining safe power of the battery, the user is reminded of the remaining battery life and advised to adjust the set temperature. If the mode is cooling, the set temperature should be increased; if the mode is heating, the set temperature should be decreased to extend the air conditioner's usage time.

[0119] Or it can be automated---

[0120] Example 2

[0121] This invention provides the construction process of the electricity consumption prediction model in Embodiment 1.

[0122] like Figure 3 This diagram illustrates the process of building a prediction model for the electricity consumption of a parking air conditioner. A concrete example of the entire process is as follows:

[0123] When the parking air conditioner is used at night, its operating load largely depends on the outer ambient temperature and the user-set temperature.

[0124] With a fixed set temperature, by monitoring the outer ring temperature over a set time period, such as 10 PM to 6 AM (8 hours), as well as the initial monitoring power consumption Q1 and the final monitoring power consumption Q2 of the air conditioner, the power consumption of the air conditioner under different outer ring temperature ranges can be calculated as Q = Q1 - Q2.

[0125] Given the time period and set temperature, the power consumption of the parking air conditioner under different ambient temperature ranges was calculated and stored in a memory chip. On clear nights, the outdoor ambient temperature changes relatively consistently, dropping initially and then rising again around 5 or 6 AM. Temperature differences in the same location are also relatively stable. By recording temperature difference data and accumulating a large amount of previous air conditioner power consumption data, when the user uses the air conditioner next time, checking the current ambient temperature, combined with the set temperature and temperature difference range, allows for a rough estimate of the total power consumption of the air conditioner overnight.

[0126] The remaining safe battery level is the amount of electricity collected by the air conditioner when the target temperature is set, minus the minimum amount of electricity required to restart the vehicle.

[0127] The method for estimating electricity consumption is as follows:

[0128] The outer ring temperature during the monitoring period is known to be (T) min ,T max Between ) and ), the temperature is set to T. obj The initial outer ring temperature is T. init The electricity consumption during the 8-hour testing period (this value can be set according to the actual geographical location) is Q;

[0129] The temperature difference is then ΔT = T max -T min Average hourly temperature difference

[0130] Therefore, the temperature difference matrix can be constructed.

[0131] Assume the relationship between the temperature difference matrix and the electricity consumption is represented by the parameter matrix K, K = [k1 k2 k3 … k8].

[0132] The mathematical model of electricity consumption and target temperature is expressed as KD = Q;

[0133] Calculate the parameter matrix K of the model based on the temperature difference matrix D and the electricity consumption Q in the sample data.

[0134] The calculation method can obtain K = QD through matrix operations. T (DD T ) -1 It can also be solved through linear regression, least squares method, etc.

[0135] By combining a large amount of actual usage data, the parameter matrix K of the model is continuously revised. Then, using the above formula, combined with the detected initial outer ring temperature and the set temperature, the power consumption in the same time period is estimated.

[0136] The method for estimating battery life is as follows:

[0137] The outer ring temperature is (T) min ,T max Between ) and ), the temperature is set to T. obj If the electricity consumption is measured as W1 within an 8-hour period, then the electricity consumption per unit time is... Given the remaining battery capacity Q res Then the battery life

[0138] By continuously correcting the coefficients, the estimated power consumption and available time become more accurate, thereby guiding users to set a suitable set temperature or automatically adjusting to a more suitable temperature control strategy.

[0139] Example 3

[0140] The data used to construct the electricity consumption prediction model in this embodiment is the same as that in Embodiment 2. Similar to the method used in Embodiment 2, the electricity consumption prediction model can also be constructed using the following method:

[0141] Collect the outer ring temperature to obtain the minimum and maximum temperatures. Assume the outer ring temperature is within (T). min ,T max Between ) and ), the temperature is set to T. obj Let the detected power consumption be W1, the number of time intervals be n, and the time factor of the nth time interval be kn. The power consumption in one hour = power (kW) * time (1h). After simplification, the power consumption in one hour = power (kW). Then, the average temperature difference per hour...

[0142] W1=k1(T min -T obj )+k2(T min +Δt-T obj )+k3(T min +2*Δt-T obj )+…+k n (T min +(n-1)*Δt-T obj );

[0143] Those skilled in the art will know that air conditioning power mainly refers to the operating power of the compressor and fan; and the operating power of the compressor and fan depends on the current ambient temperature and the set temperature. Taking refrigeration as an example, when the ambient temperature minus the set temperature is a negative value, it has a negative impact on electricity consumption, and when the ambient temperature minus the set temperature is a positive value, it has a positive impact on electricity consumption.

[0144] The electricity consumption W for a certain hour is the weighted sum of the rated compressor power and the fan power. The actual power is affected by the ambient temperature and the set temperature, so the temperature difference factor is introduced. Combined with the deviation between the actual electricity consumption and the theoretical analysis, the value of k can be obtained. Therefore, W = k n (T min +(n-1)*Δt-T obj ).

[0145] Taking an 8-hour period as an example, the time interval is 1 hour;

[0146] The average temperature difference per hour is

[0147] W1=k1(T min -T obj )+k2(T min +Δt-T obj )+k3(T min +2*Δt-T obj )+…+k8(T min +7*Δt-T obj );

[0148] By combining a large amount of actual usage data of air conditioners and continuously refining k1 to k8, the power consumption in the same time period can be estimated by using the above formula and combining the initial outer ring temperature and the set temperature.

[0149] For example, if the outer ring temperature remains constant, and the set temperature is t2, then

[0150] W2=k1(T min -t2)+k2(T min +Δt-t2)+k3(T min +2*Δt-t2)+…+k8(T max -t2);

[0151] If the set temperature t1 remains constant, and the initial temperature of the outer ring is T3, the difference in the outer ring temperature at night is consistent, then ΔT = T. max -T min Then the outer ring range is (T3-ΔT,T3).

[0152] Electricity consumption W2=k1(T3-ΔT-t1)+k2(T3-ΔT+Δt-t1)+k3(T3-ΔT+2*Δt-t1)+…+k8(T3-t1);

[0153] The time estimation method is as follows:

[0154] The outer ring temperature is (T) min ,T max Between ), the temperature is set to t1, and the power consumption is measured as W1 within an 8-hour period. Then the power consumption per unit time is w1 = W1 / 8.

[0155] The W2 obtained using the power estimation method is also the total power consumed over 8 hours. Therefore, the power consumption per unit time is w2 = W2 / 8. Given the remaining battery power Q... res Then the battery life t = Q res / w2.

[0156] By continuously adjusting the coefficients, the estimated power consumption and battery life become more accurate, thereby guiding users to set a suitable temperature.

[0157] Example 4

[0158] This invention provides a control device for a parking air conditioner, whose logic unit mainly includes: a main control unit, a temperature detection unit, a power detection unit, a storage unit, a calculation unit, a network unit, and a display unit. The main control unit records the model and location information of the current parking air conditioner and is used to complete the operation control of the parking air conditioner; the temperature detection unit is used to detect the outer ambient temperature; the power detection unit is used to detect the battery power of the parking air conditioner; the storage unit is used to store data from other functional units; the calculation unit is used for processing sample data and building and predicting models; the network unit is used for data communication between the big data cloud platform and the parking air conditioner; and the display unit is used to display the various parameter values ​​of the parking air conditioner.

[0159] The parking air conditioner is equipped with a monitoring module to record air conditioning data and upload it to a big data cloud platform, such as... Figure 2 As shown, the monitoring module consists of a data acquisition module and a network communication module. The data acquisition module records air conditioning data; the network communication module uploads the air conditioning data to the big data cloud platform. The data acquisition module includes: a main control unit, a temperature detection unit, a power detection unit, and a storage unit. The network communication module includes a storage unit and a network unit.

[0160] In summary, it will be readily understood by those skilled in the art that the aforementioned advantageous methods can be freely combined and superimposed without conflict. The above descriptions are merely embodiments of the present invention and are not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of the claims of the present invention.

Claims

1. A method for energy-saving control of a parking air conditioner, wherein the parking air conditioner is equipped with a battery to provide power for its operation, characterized in that, The parking air conditioner has the following control steps: Record the set target temperature; Detect and record the outer ring temperature and battery charge; The target temperature, the outer ambient temperature, and the battery charge are used as feature parameters and input into the power consumption prediction model to obtain the power consumption and driving time of the parking air conditioner under the feature parameter conditions; wherein, the driving time and power consumption are based on the driving time and power consumption required by the current battery charge, target temperature, and outer ambient temperature within a preset time period. Based on the power consumption and the driving time, prompt the user and / or automatically adjust the temperature control strategy of the parking air conditioner; The method further includes: The first sample data is organized into the second sample data. The first sample data includes the target temperature, the initial outer ring temperature, the battery charge of the parking air conditioner, and the sequence of outer ring temperature values ​​continuously recorded at fixed time intervals. The electricity consumption prediction model is created using the second sample data; The process of organizing the first sample data into the second sample data includes: The minimum value of the outer ring temperature is found from the sequence of outer ring temperature values ​​continuously recorded at fixed time intervals from the first sample data. and maximum value ; Calculate the temperature difference over the preset time period. ; The temperature difference over the preset time period Divide the preset time period by the number n of the fixed time intervals to obtain the average temperature difference for each time interval. , where n≧1, is a natural number; The initial power of the parking air conditioner during the preset time period. and termination power Calculate the power consumption of the parking air conditioner during the preset time period. ; The outer ring temperature recorded at the start time of the preset time period is designated as the initial outer ring temperature. Using the target temperature and the initial outer ring temperature Combined with the aforementioned temperature difference and average temperature difference ,pass A temperature difference matrix D is constructed from the first sample data, and the temperature difference matrix D is correlated with the electricity consumption. As the second sample data, where n≧1, a natural number; The step of creating the electricity consumption prediction model using the second sample data includes: Set the temperature difference matrix D and the power consumption. The relationship between them can be represented by a parameter matrix K; The electricity consumption prediction model is expressed as follows: ; Here K= , D= , where n≧1, is a natural number; Based on the temperature difference matrix D and electricity consumption in the second sample data Calculate the parameter matrix K of the electricity consumption prediction model.

2. The method according to claim 1, characterized in that, Before inputting the target temperature, the outer ring temperature, and the battery charge as feature parameters into the power consumption prediction model to obtain the power consumption and driving range of the parking air conditioner under the feature parameter conditions, the process further includes: Construct sample data for a parking air conditioner, the sample data being used to form the first sample data.

3. The method according to claim 2, characterized in that, The sample data for constructing the parking air conditioner includes sample data accumulated during the use of this parking air conditioner and / or sample data accumulated from multiple similar parking air conditioners.

4. The method according to claim 3, characterized in that, The method further includes: The first sample data is formed using the sample data.

5. The method according to claim 4, characterized in that, The process of forming the first sample data using the sample data includes: The sample data is selected as the first sample data and the temperature difference sequence is calculated or The sample data is directly used as the first sample data to calculate the temperature difference sequence.

6. The method according to claim 5, characterized in that, The sample data includes: For each parking air conditioner, the model, target temperature, initial outer ring temperature, location information, battery charge, operating time series, and outer ring temperature value series continuously recorded at fixed time intervals are specified.

7. The method according to claim 6, characterized in that, The step of selecting the sample data as the first sample data and calculating the temperature difference sequence includes: The saved sample data is filtered, and the first air conditioner data with the same model and the same geographical location range is selected based on the model of the parking air conditioner and the location range of the parking air conditioner. The second air conditioning data is filtered from the first air conditioning data using the target temperature and the set time period; The target temperature, the initial outer ring temperature, the battery charge of the parking air conditioner, and the sequence of outer ring temperature values ​​continuously recorded at fixed time intervals are extracted from the second air conditioning data as the first sample data. Based on the first sample data, the maximum and minimum values ​​of the outer ring temperature within the preset time period are extracted and subtracted to obtain the temperature difference sequence within the preset time period.

8. The method according to claim 4, characterized in that, The second sample data can be used to optimize the electricity consumption prediction model.

9. The method according to claim 1, characterized in that, The step of inputting the target temperature, the outer ring temperature, and the battery charge as feature parameters into the power consumption prediction model to obtain the power consumption and driving range of the parking air conditioner under the feature parameter conditions includes: Using the outer ring temperature, the target temperature, the temperature difference matrix D constructed from the statistical temperature difference data within the preset time period, and the parameter matrix K of the power consumption prediction model, the power consumption of the parking air conditioner within the preset time period is predicted. ; Using the predicted electricity consumption The average electricity consumption over the fixed time period is obtained by combining the number of fixed time intervals n included in the preset time period. The remaining safe power of the parking air conditioner was calculated. Battery life within the preset time period The remaining safe power of the parking air conditioner. The minimum safe power required by the vehicle where the parking air conditioner is located is subtracted from the battery power of the parking air conditioner.

10. The method according to claim 9, characterized in that, According to the electricity consumption The battery life reminder to the user and / or the automatic adjustment of the parking air conditioner's temperature control strategy include: Determine the power consumption Is it greater than the remaining safe power of the parking air conditioner? If so, the user will be prompted to adjust the currently set target temperature, or the currently set target temperature will be automatically corrected; otherwise, the current temperature control strategy will operate normally.

11. A parking air conditioning energy-saving control device, comprising one or more processors and a non-transitory computer-readable storage medium storing program instructions, wherein when the one or more processors execute the program instructions, the one or more processors are configured to implement the method according to any one of claims 1-10.

12. The control device according to claim 11, characterized in that, The parking air conditioner is equipped with a monitoring module to record air conditioning data and upload it to the data platform. The monitoring module includes a data acquisition module and a network communication module. The data acquisition module is used to record the air conditioner data, and its logical structure includes: a main control unit, a temperature detection unit, a power detection unit, and a storage unit; The main control unit controls the operation of the air conditioner and records the air conditioner model and location information; The temperature detection unit measures the outer ring temperature; The power detection unit detects the battery power of the parking air conditioner; The storage unit records the target temperature, the outer ring temperature, and the battery charge. The logical structure of the network communication module includes a storage unit and a network unit; The network unit is used to establish communication between the parking air conditioner and the data platform.

13. A non-transitory computer-readable storage medium having stored program instructions thereon, which, when executed by one or more processors, are configured to implement the method according to any one of claims 1-10.

14. An air conditioner that employs the method of any one of claims 1-10, or includes the apparatus of any one of claims 11-12, or has a non-transitory computer-readable storage medium as described in claim 13.

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