WO2023272956A1 - 一种储充站发热功率的估算方法及终端 - Google Patents
一种储充站发热功率的估算方法及终端 Download PDFInfo
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- WO2023272956A1 WO2023272956A1 PCT/CN2021/118997 CN2021118997W WO2023272956A1 WO 2023272956 A1 WO2023272956 A1 WO 2023272956A1 CN 2021118997 W CN2021118997 W CN 2021118997W WO 2023272956 A1 WO2023272956 A1 WO 2023272956A1
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- 238000010438 heat treatment Methods 0.000 title claims abstract description 205
- 238000000034 method Methods 0.000 title claims abstract description 25
- 238000005315 distribution function Methods 0.000 claims abstract description 50
- 238000004590 computer program Methods 0.000 claims description 8
- 230000020169 heat generation Effects 0.000 description 11
- 238000004364 calculation method Methods 0.000 description 6
- 230000009286 beneficial effect Effects 0.000 description 3
- 238000009529 body temperature measurement Methods 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 230000017525 heat dissipation Effects 0.000 description 2
- 230000009466 transformation Effects 0.000 description 2
- 238000000844 transformation Methods 0.000 description 2
- 238000006243 chemical reaction Methods 0.000 description 1
- 238000001816 cooling Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000005265 energy consumption Methods 0.000 description 1
- 238000004146 energy storage Methods 0.000 description 1
- 238000005057 refrigeration Methods 0.000 description 1
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
Definitions
- the invention relates to the field of new energy technology, in particular to a method and terminal for estimating heating power of a storage and charging station.
- the storage and charging station needs to perform one or more energy transformations inside, so there is heat generation.
- the closed space includes but not limited to the container, it is necessary to accurately estimate the internal heating power, and Export the above heat to the outside of the system in a reasonable way, so as to avoid the continuous temperature rise of the system and affect the normal operation of the site.
- the technical problem to be solved by the present invention is to provide a method and terminal for estimating heating power of a storage and charging station, which can improve the accuracy of estimation.
- a method for estimating heating power of a storage and charging station comprising the steps of:
- a terminal for estimating heating power of a storage and charging station comprising a memory, a processor, and a computer program stored on the memory and operable on the processor, and the processor implements the following steps when executing the computer program:
- the beneficial effects of the present invention are: by counting the working condition data and temperature change value of the storage and charging station within the preset time, and calculating the heating power based on the temperature change value, the heating power under each working condition can be collected, and the heating power can be collected according to the working condition Record the heating power into the corresponding record set; weight the heating power in the record set according to the storage time of the heating power, judge whether the weighted record set is a credible record set, and calculate the probability distribution function of the credible record set, according to the probability
- the distribution function realizes the estimation of heating power; therefore, by combining the method of record set weighting and probability distribution function prediction, the distribution function of heating power under this working condition can be predicted, so as to estimate the heating power and improve the accuracy of estimation.
- Fig. 1 is a flowchart of a method for estimating heating power of a storage and charging station according to an embodiment of the present invention
- Fig. 2 is a schematic diagram of a terminal for estimating heating power of a storage and charging station according to an embodiment of the present invention
- Fig. 3 is a flow chart of specific steps of a method for estimating heating power of a storage and charging station according to an embodiment of the present invention
- Fig. 4 is a schematic diagram of the internal structure of the storage and charging station according to a method for estimating heating power of the storage and charging station according to an embodiment of the present invention.
- the embodiment of the present invention provides a method for estimating the heating power of a storage and charging station, including steps:
- the beneficial effect of the present invention is that: by counting the working condition data and temperature change value of the storage and charging station within the preset time, and calculating the heating power based on the temperature change value, the heating power under each working condition can be collected, And record the heating power into the corresponding record set according to the working conditions; weight the heating power in the record set according to the storage time of the heating power, judge whether the weighted record set is a credible record set, and calculate the probability of a credible record set Distribution function, the estimation of heating power is realized according to the probability distribution function; therefore, through the combination of record set weighting and probability distribution function prediction, the distribution function of heating power under this working condition can be predicted, so as to estimate the heating power and improve the accuracy of estimation Rate.
- the weighting the heating power in the record set according to the storage time of the heating power includes:
- the heating power is weighted according to the time difference between the storage time of the heating power record and the current time.
- the average value and standard deviation of the set determine whether the record set is a credible record set.
- the absolute value of the difference between the heating power exceeding the preset ratio and the average value is less than two standard deviations, it is considered that the data under the same working condition
- This record set has reference significance.
- this record set shows that the fluctuation of heating power under the same working condition data is large, and this record set has no reference significance. Therefore, a record set with credible data can be obtained, which can Maximize the accuracy of estimation.
- estimating the heating power according to the probability distribution function includes:
- the probability distribution function the probability distribution of different heating powers under the corresponding working condition data is obtained
- the heating power of the storage and charging station is selected in combination with the probability distribution and the operation of the storage and charging station.
- calculating the heating power of the storage and charging station according to the temperature change value includes:
- the heat generation corresponding to the temperature change value is determined through the relationship table between temperature change and heat generation, so as to determine the heat generation power of the storage and charging station, and the heat generation power under various working conditions can be calculated in advance, which is convenient for subsequent calculation based on heat generation. Power is estimated.
- the temperature change value of the storage and charging station within the statistical preset time includes:
- the key points of the storage and charging station can be found through the field distribution, and the temperature sensor placed on the key point can measure the representative temperature value in the storage and charging station, so as to accurately obtain the temperature of the storage and charging station within the preset time change, improving the accuracy of the temperature measurement.
- FIG. 2 another embodiment of the present invention provides a terminal for estimating heating power of a storage and charging station, including a memory, a processor, and a computer program stored in the memory and operable on the processor.
- a terminal for estimating heating power of a storage and charging station including a memory, a processor, and a computer program stored in the memory and operable on the processor.
- the heating power under each working condition can be collected, and the heating power can be calculated according to the working condition.
- the weighting the heating power in the record set according to the storage time of the heating power includes:
- the heating power is weighted according to the time difference between the storage time of the heating power record and the current time.
- the average value and standard deviation of the set determine whether the record set is a credible record set.
- the absolute value of the difference between the heating power exceeding the preset ratio and the average value is less than two standard deviations, it is considered that the data under the same working condition
- This record set has reference significance.
- this record set shows that the fluctuation of heating power under the same working condition data is large, and this record set has no reference significance. Therefore, a record set with credible data can be obtained, which can Maximize the accuracy of estimation.
- estimating the heating power according to the probability distribution function includes:
- the probability distribution function the probability distribution of different heating powers under the corresponding working condition data is obtained
- the heating power of the storage and charging station is selected in combination with the probability distribution and the operation of the storage and charging station.
- calculating the heating power of the storage and charging station according to the temperature change value includes:
- the heat generation corresponding to the temperature change value is determined through the relationship table between temperature change and heat generation, so as to determine the heat generation power of the storage and charging station, and the heat generation power under various working conditions can be calculated in advance, which is convenient for subsequent calculation based on heat generation. Power is estimated.
- the temperature change value of the storage and charging station within the statistical preset time includes:
- the key points of the storage and charging station can be found through the field distribution, and the temperature sensor placed on the key point can measure the representative temperature value in the storage and charging station, so as to accurately obtain the temperature of the storage and charging station within the preset time change, improving the accuracy of the temperature measurement.
- the method and terminal for estimating heating power of storage and charging stations mentioned above in the present invention are suitable for estimating heating power of various closed storage and charging stations, improving the accuracy of heating power prediction, so that reasonable heat dissipation power can be selected, so that The internal temperature of the site is well controlled, and the energy consumption of heat dissipation is low.
- the temperature change value of the storage and charging station within the statistical preset time includes:
- Calculating the heating power of the storage and charging station according to the temperature change value includes:
- the statistical period of heating power is set as m minutes. During the operation of the equipment, it is judged whether a complete statistical period n has been experienced. If the complete period has been experienced, the PCS (energy storage conversion current Converter) output power P1, DCDC (direct current converter) output power P2, battery pack output power P3 and refrigeration system cooling power P4;
- PCS energy storage conversion current Converter
- DCDC direct current converter
- the probability distribution function the probability distribution of different heating powers under the corresponding working condition data is obtained
- the heating power of the storage and charging station is selected in combination with the probability distribution and the operation of the storage and charging station.
- the probability distribution function of the S set needs to be calculated and saved; combined with the probability distribution of the probability distribution function and the operation of the storage and charging station, it is possible to judge whether to choose the probability distribution function based on the operation of the storage and charging station. Normal values with high probability or extreme values with low probability in the distribution are suitable for various scenarios of heating power estimation and realize accurate heating power estimation.
- Embodiment 1 The difference between this embodiment and Embodiment 1 is that it further defines how to judge whether the heating power set is credible, specifically:
- the weighting the heating power in the record set according to the storage time of heating power includes:
- the heating power in the record set is weighted sequentially, and the weighted value is gradually reduced.
- the weight of each record in the S set is updated according to the recording time, and the weight update method is: the smaller the time difference between the record and the current time, the greater the weight;
- Calculate the time difference between the storage time of each heating power record in the record set S and the current time sort each heating power record according to the time difference from small to large, weight the heating power in the record set S in turn, and gradually reduce the weight value. For example, in the record set, if one heating power is at 0:00 today, the weighted value is 10, and the other heating power is at 0:00 yesterday, then the weighted value is 9, and so on, and the specific limited values are for illustration only.
- the average avg and standard deviation ⁇ of the heating power in the S set are calculated;
- the S set is determined to be a credible set, otherwise it is an untrusted set. According to the above conclusions, mark the S set as a credible set or an untrusted set.
- a terminal for estimating heating power of a storage and charging station including a memory, a processor, and a computer program stored on the memory and operable on the processor, when the processor executes the computer program, it realizes Various steps of the method for estimating the heating power of the storage and charging station in the first or second embodiment.
- the method and terminal for estimating the heating power of a storage and charging station can calculate the heating power based on the temperature change value by counting the working condition data and temperature change value of the storage and charging station within a preset time, and can collect According to the heating power under each working condition, the heating power is recorded in the corresponding record set according to the working condition.
- the key points of the storage and charging station are found out based on the thermal field distribution, and the temperature is measured at the key points, and the key points can be used.
- the temperature change of the point represents the temperature change of the entire storage and charging station, which reduces the difficulty of calculation; according to the storage time of the heating power, the heating power in the record set is weighted to judge whether the weighted record set is a credible record set, and calculate the credible
- the probability distribution function of the record set is used to estimate the heating power according to the probability distribution function.
- the heating power can be estimated through the probability distribution function and the operation of the storage and charging station, so normal values with high probability or extreme values with low probability can be obtained , which is suitable for heating power estimation of various storage and charging stations, and improves the accuracy of estimation; therefore, by combining the method of record set weighting and probability distribution function prediction, the distribution function of heating power under this working condition can be predicted, so that the heating power Make estimates and improve the accuracy of estimates.
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Abstract
本发明公开了一种储充站发热功率的估算方法及终端，通过统计预设时间内储充站的工况数据和温度变化值，基于温度变化值计算发热功率，能够采集到每一工况下的发热功率，并按工况将发热功率记录到对应的记录集中；根据发热功率的存储时间对记录集中的发热功率进行加权，判断加权后的记录集是否是可信记录集，并计算可信记录集的概率分布函数，根据概率分布函数实现发热功率的估算；因此通过记录集加权和概率分布函数预测结合的方法，能够预测该工况下发热功率的分布函数，从而对发热功率进行估算，提高估算的准确率。
Description
本发明涉及新能源技术领域，特别涉及一种储充站发热功率的估算方法及终端。
储充站由于在内部需要进行一次或多次能量变换，因此存在发热现象，当储充站各部件处于封闭的空间时，封闭空间包括但不限于集装箱，需要准确预估内部的发热功率，并通过合理的方式将上述发热量导出到系统外部，从而避免系统持续温升，影响站点的正常运行。
由于储充站内各种部件的运行工况变化组合很复杂、不同站点不同时段的部件效率不同、通过测量温度的方式计算发热功率具有滞后性等问题，难以准确预估系统发热功率。
本发明所要解决的技术问题是：提供一种储充站发热功率的估算方法及终端，能够提高估算的准确率。
为了解决上述技术问题，本发明采用的技术方案为：
一种储充站发热功率的估算方法，包括步骤：
统计预设时间内所述储充站的工况数据和温度变化值，根据所述温度变化值计算所述储充站的发热功率，将所述发热功率存储在所述工况数据对应的记录集中；
根据发热功率的存储时间对所述记录集中的发热功率进行加权，判断所述加权后的所述记录集是否为可信记录集，若是，则计算所述可信记录集的概率分布函数，根据所述概率分布函数进行发热功率的估算。
为了解决上述技术问题，本发明采用的另一种技术方案为：
一种储充站发热功率的估算终端，包括存储器、处理器以及存储在所述存储器上并可在处理器上运行的计算机程序，所述处理器执行所述计算机程序时实现以下步骤：
统计预设时间内所述储充站的工况数据和温度变化值，根据所述温度变化值计算所述储充站的发热功率，将所述发热功率存储在所述工况数据对应的记录集中；
根据发热功率的存储时间对所述记录集中的发热功率进行加权，判断所述加权后的所述记录集是否为可信记录集，若是，则计算所述可信记录集的概率分布函数，根据所述概率分布函数进行发热功率的估算。
本发明的有益效果在于：通过统计预设时间内储充站的工况数据和温度变化值，基于温度变化值计算发热功率，能够采集到每一工况下的发热功率，并按工况将发热功率记录到对应的记录集中；根据发热功率的存储时间对记录集中的发热功率进行加权，判断加权后的记录集是否是可信记录集，并计算可信记录集的概率分布函数，根据概率分布函数实现发热功率的估算；因此通过记录集加权和概率分布函数预测结合的方法，能够预测该工况下发热功率的分布函数，从而对发热功率进行估算，提高估算的准确率。
图1为本发明实施例的一种储充站发热功率的估算方法的流程图；
图2为本发明实施例的一种储充站发热功率的估算终端的示意图；
图3为本发明实施例的一种储充站发热功率的估算方法的具体步骤流程图；
图4为本发明实施例的一种储充站发热功率的估算方法的储充站内部结构示意图。
为详细说明本发明的技术内容、所实现目的及效果，以下结合实施方式并配合附图予以说明。
请参照图1和图3，本发明实施例提供了一种储充站发热功率的估算方法，包括步骤：
统计预设时间内所述储充站的工况数据和温度变化值，根据所述温度变化值计算所述储充站的发热功率，将所述发热功率存储在所述工况数据对应的记录集中；
根据发热功率的存储时间对所述记录集中的发热功率进行加权，判断所述加权后的所述记录集是否为可信记录集，若是，则计算所述可信记录集的概率分布函数，根据所述概率分布函数进行发热功率的估算。
从上述描述可知，本发明的有益效果在于：通过统计预设时间内储充站的工况数据和温度变化值，基于温度变化值计算发热功率，能够采集到每一工况下的发热功率，并按工况将发热功率记录到对应的记录集中；根据发热功率的存储时间对记录集中的发热功率进行加权，判断加权后的记录集是否是可信记录集，并计算可信记录集的概率分布函数，根据概率分布函数实现发热功率的估算；因此通过记录集加权和概率分布函数预测结合的方法，能够预测该工况下发热功率的分布函数，从而对发热功率进行估算，提高估算的准确率。
进一步地，所述根据发热功率的存储时间对所述记录集中的发热功率进行加权包括：
获取所述记录集中每一个发热功率记录的存储时间与当前时间的时间差，将每一个发热功率记录按照时间差从小到大排序；
对所述记录集中的发热功率依次进行加权，并且逐渐减小加权权值；
判断所述加权后的所述记录集是否为可信记录集包括：
计算加权后的所述记录集中所有所述发热功率的平均值和标准差；
判断所述记录集中是否超过预设比例的发热功率与所述平均值的差值的绝对值小于两个标准差，若是，则所述记录集为可信记录集，否则，所述记录集为不可信记录集。
由上述描述可知，按照发热功率记录的存储时间与当前时间的时间差对发热功率进行加权，时间差越小加权权值越大，因此能够重点根据最近的发热记录进行计算；同时，使用加权后的记录集的平均值和标准差确定记录集是否为可信记录集，当超过预设比例的发热功率与所述平均值的差值的绝对值小于两个标准差，则认为同一工况数据下的发热功率波动较小，因此，该记录集具有参考意义，反之则说明同一工况数据下的发热功率的波动较大，该记录集不具有参考意义，由此得到数据可信的记录集，能够最大化地提高估算的准确率。
进一步地，根据所述概率分布函数进行发热功率的估算包括：
根据所述概率分布函数得到对应工况数据下不同发热功率的概率分布情况；
结合所述概率分布情况和所述储充站的运行情况选择所述储充站的发热功率。
由上述描述可知，结合概率分布情况和储充站的运行情况选择储充站的发热功率，能够基于储充站的运行情况判断是否要选择概率分布中概率较大的正常值或者概率较小的极端值，从而适用于各种发热功率估算的场景。
进一步地，根据所述温度变化值计算所述储充站的发热功率包括：
根据所述温度变化值查询温度变化和发热量的关系表，得到所述温度变化对应的发热量；
根据所述发热量计算所述储充站的发热功率。
由上述描述可知，通过温度变化和发热量的关系表确定温度变化值对应的发热量，从而确定储充站的发热功率，能够预先计算各种工况下的发热功率，便于后续基于计算得到发热功率进行估算。
进一步地，所述统计预设时间内所述储充站的温度变化值包括：
根据热场分布找出储充站的关键点，在所述关键点上放置温度传感器；
计算加权后的所述记录集中所有所述发热功率的平均值和标准差；
由上述描述可知，通过场分布找出储充站的关键点，在关键点上放置温度传感器能够测量出储充站中具有代表性的温度值，从而准确获取预设时间内储充站的温度变化，提高了温度测量的精度。
请参照图2，本发明另一实施例提供了一种储充站发热功率的估算终端，包括存储器、处理器以及存储在所述存储器上并可在处理器上运行的计算机程序，所述处理器执行所述计算机程序时实现以下步骤：
统计预设时间内所述储充站的工况数据和温度变化值，根据所述温度变化值计算所述储充站的发热功率，将所述发热功率存储在所述工况数据对应的记录集中；
根据发热功率的存储时间对所述记录集中的发热功率进行加权，判断所述加权后的所述记录集是否为可信记录集，若是，则计算所述可信记录集的概率分布函数，根据所述概率分布函数进行发热功率的估算。
由上述描述可知，通过统计预设时间内储充站的工况数据和温度变化值，基于温度变化值计算发热功率，能够采集到每一工况下的发热功率，并按工况将发热功率记录到对应的记录集中；根据发热功率的存储时间对记录集中的发热功率进行加权，判断加权后的记录集是否是可信记录集，并计算可信记录集的概率分布函数，根据概率分布函数实现发热功率的估算；因此通过记录集加权和概率分布函数预测结合的方法，能够预测该工况下发热功率的分布函数，从而对发热功率进行估算，提高估算的准确率。
进一步地，所述根据发热功率的存储时间对所述记录集中的发热功率进行加权包括：
获取所述记录集中每一个发热功率记录的存储时间与当前时间的时间差，将每一个发热功率记录按照时间差从小到大排序；
对所述记录集中的发热功率依次进行加权，并且逐渐减小加权权值；
判断所述加权后的所述记录集是否为可信记录集包括：
计算加权后的所述记录集中所有所述发热功率的平均值和标准差；
判断所述记录集中是否超过预设比例的发热功率与所述平均值的差值的绝对值小于两个标准差，若是，则所述记录集为可信记录集，否则，所述记录集为不可信记录集。
由上述描述可知，按照发热功率记录的存储时间与当前时间的时间差对发热功率进行加权，时间差越小加权权值越大，因此能够重点根据最近的发热记录进行计算；同时，使用加权后的记录集的平均值和标准差确定记录集是否为可信记录集，当超过预设比例的发热功率与所述平均值的差值的绝对值小于两个标准差，则认为同一工况数据下的发热功率波动较小，因此，该记录集具有参考意义，反之则说明同一工况数据下的发热功率的波动较大，该记录集不具有参考意义，由此得到数据可信的记录集，能够最大化地提高估算的准确率。
进一步地，根据所述概率分布函数进行发热功率的估算包括：
根据所述概率分布函数得到对应工况数据下不同发热功率的概率分布情况；
结合所述概率分布情况和所述储充站的运行情况选择所述储充站的发热功率。
由上述描述可知，结合概率分布情况和储充站的运行情况选择储充站的发热功率，能够基于储充站的运行情况判断是否要选择概率分布中概率较大的正常值或者概率较小的极端值，从而适用于各种发热功率估算的场景。
进一步地，根据所述温度变化值计算所述储充站的发热功率包括：
根据所述温度变化值查询温度变化和发热量的关系表，得到所述温度变化对应的发热量；
根据所述发热量计算所述储充站的发热功率。
由上述描述可知，通过温度变化和发热量的关系表确定温度变化值对应的发热量，从而确定储充站的发热功率，能够预先计算各种工况下的发热功率，便于后续基于计算得到发热功率进行估算。
进一步地，所述统计预设时间内所述储充站的温度变化值包括：
根据热场分布找出储充站的关键点，在所述关键点上放置温度传感器；
计算所述温度传感器在所述预设时间内的温度变化。
由上述描述可知，通过场分布找出储充站的关键点，在关键点上放置温度传感器能够测量出储充站中具有代表性的温度值，从而准确获取预设时间内储充站的温度变化，提高了温度测量的精度。
本发明上述一种储充站发热功率的估算方法及终端，适用于对各种封闭式的储充站进行发热功率的估算，提高发热功率预测的准确度，从而可以选择合理的散热功率，使得站点内部温度控制良好、散热能耗低，以下通过具体实施方式进行说明：
实施例一
请参照图1、图3和图4，一种储充站发热功率的估算方法，包括步骤：
S1、统计预设时间内所述储充站的工况数据和温度变化值，根据所述温度变化值计算所述储充站的发热功率，将所述发热功率存储在所述工况数据对应的记录集中。
S11、所述统计预设时间内所述储充站的温度变化值包括：
根据热场分布找出储充站的关键点，在所述关键点上放置温度传感器；
计算所述温度传感器在所述预设时间内的温度变化。
具体的，请参照图4，在热场分布找出储充站的关键点，在关键点上放置温度传感器，能够将关键点的温度变化坐标用来代表整个储充站的温度变化，以便于对发热功率的计算。
S12、根据所述温度变化值计算所述储充站的发热功率包括：
根据所述温度变化值查询温度变化和发热量的关系表，得到所述温度变化对应的发热量；
根据所述发热量计算所述储充站的发热功率。
具体的，设定发热功率的统计时段为m分钟，在设备运行过程中，判断是否经历了一个完整的统计时段n，如果已经历完整时段，则统计这一时段内的PCS（储能变流器）输出功率P1、DCDC（直流变换器）的输出功率P2、电池包的输出功率P3和制冷系统降温功率P4；
根据这一时段的温度变化，结合温度变化和发热量关系表，计算这一时段的温度变化对应的发热量Wm，发热功率Pm
= Wm / m，将P1、P2、P3、P4和Pm统称为一条记录Pn。
从历史记录中找寻与P1、P2、P3和P4即工况数据一致的发热功率记录集S，若找到S集，则将Pn记录加入S集；若没有找到S集，则新建S集后将Pn记录加入新S集中。
S2、根据发热功率的存储时间对所述记录集中的发热功率进行加权，判断所述加权后的所述记录集是否为可信记录集，若是，则计算所述可信记录集的概率分布函数，根据所述概率分布函数进行发热功率的估算。
S21、根据所述概率分布函数进行发热功率的估算包括：
根据所述概率分布函数得到对应工况数据下不同发热功率的概率分布情况；
结合所述概率分布情况和所述储充站的运行情况选择所述储充站的发热功率。
具体的，对于可信集合，需要计算出S集的概率分布函数，并保存；结合概率分布函数的概率分布情况和储充站的运行情况，能够基于储充站的运行情况判断是否要选择概率分布中概率较大的正常值或者概率较小的极端值，从而适用于各种发热功率估算的场景，实现准确的发热功率估算。
实施例二
本实施例与实施例一的区别在于，进一步限定了如何判断发热功率集是否可信，具体的：
所述根据发热功率的存储时间对所述记录集中的发热功率进行加权包括：
获取所述记录集中每一个发热功率记录的存储时间与当前时间的时间差，将每一个发热功率记录按照时间差从小到大排序；
对所述记录集中的发热功率依次进行加权，并且逐渐减小加权权值。
在本实施例中，根据记录时间更新S集合内各记录权值，权值更新方法为：记录与当前时间的时间差越小，权值越大；
计算记录集S中每一个发热功率记录的存储时间与当前时间的时间差，将每一个发热功率记录按照时间差从小到大排序，对记录集S中的发热功率依次进行加权，并且逐渐减小加权权值。比如，在记录集中有一发热功率是在今天零点，则加权权值为10，而另一发热功率是在昨天零点，则加权权值为9，以此类推，具体限定的数值仅做说明使用。
判断所述加权后的所述记录集是否为可信记录集包括：
计算加权后的所述记录集中所有所述发热功率的平均值和标准差；
判断所述记录集中是否超过预设比例的发热功率与所述平均值的差值的绝对值小于两个标准差，若是，则所述记录集为可信记录集，否则，所述记录集为不可信记录集。
具体的，在本实施例中，计算S集内发热功率的平均值avg和标准差σ；
若S集中超过95%的发热功率与平均值avg的差值的绝对值小于2σ，则判定S集合为可信集合，否则为不可信集合。按上述结论标记S集属于可信集合或不可信集合。
实施例三
请参照图2，一种储充站发热功率的估算终端，包括存储器、处理器以及存储在所述存储器上并可在处理器上运行的计算机程序，所述处理器执行所述计算机程序时实现实施例一或实施例二的储充站发热功率的估算方法的各个步骤。
综上所述，本发明提供的一种储充站发热功率的估算方法及终端，通过统计预设时间内储充站的工况数据和温度变化值，基于温度变化值计算发热功率，能够采集到每一工况下的发热功率，并按工况将发热功率记录到对应的记录集中，其中，基于热场分布找出储充站的关键点，在关键点上进行温度测量，能够使用关键点的温度变化代表整个储充站的温度变化，减少了计算难度；根据发热功率的存储时间对记录集中的发热功率进行加权，判断加权后的记录集是否是可信记录集，并计算可信记录集的概率分布函数，根据概率分布函数实现发热功率的估算，其中通过概率分布函数和储充站的运行情况能够进行发热功率的估算，因此可以得到概率大的正常值或者概率小的极端值，适用于各种储充站的发热功率估算，提高了估算的准确率；因此通过记录集加权和概率分布函数预测结合的方法，能够预测该工况下发热功率的分布函数，从而对发热功率进行估算，提高估算的准确率。
以上所述仅为本发明的实施例，并非因此限制本发明的专利范围，凡是利用本发明说明书及附图内容所作的等同变换，或直接或间接运用在相关的技术领域，均同理包括在本发明的专利保护范围内。
Claims (10)
- 一种储充站发热功率的估算方法，其特征在于，包括步骤：统计预设时间内所述储充站的工况数据和温度变化值，根据所述温度变化值计算所述储充站的发热功率，将所述发热功率存储在所述工况数据对应的记录集中；根据发热功率的存储时间对所述记录集中的发热功率进行加权，判断所述加权后的所述记录集是否为可信记录集，若是，则计算所述可信记录集的概率分布函数，根据所述概率分布函数进行发热功率的估算。
- 根据权利要求1所述的一种储充站发热功率的估算方法，其特征在于，所述根据发热功率的存储时间对所述记录集中的发热功率进行加权包括：获取所述记录集中每一个发热功率记录的存储时间与当前时间的时间差，将每一个发热功率记录按照时间差从小到大排序；对所述记录集中的发热功率依次进行加权，并且逐渐减小加权权值；判断所述加权后的所述记录集是否为可信记录集包括：计算加权后的所述记录集中所有所述发热功率的平均值和标准差；判断所述记录集中是否超过预设比例的发热功率与所述平均值的差值的绝对值小于两个标准差，若是，则所述记录集为可信记录集，否则，所述记录集为不可信记录集。
- 根据权利要求1所述的一种储充站发热功率的估算方法，其特征在于，根据所述概率分布函数进行发热功率的估算包括：根据所述概率分布函数得到对应工况数据下不同发热功率的概率分布情况；结合所述概率分布情况和所述储充站的运行情况选择所述储充站的发热功率。
- 根据权利要求1所述的一种储充站发热功率的估算方法，其特征在于，根据所述温度变化值计算所述储充站的发热功率包括：根据所述温度变化值查询温度变化和发热量的关系表，得到所述温度变化对应的发热量；根据所述发热量计算所述储充站的发热功率。
- 根据权利要求1至4中任一项所述的一种储充站发热功率的估算方法，其特征在于，所述统计预设时间内所述储充站的温度变化值包括：根据热场分布找出储充站的关键点，在所述关键点上放置温度传感器；计算所述温度传感器在所述预设时间内的温度变化。
- 一种储充站发热功率的估算终端，包括存储器、处理器以及存储在所述存储器上并可在处理器上运行的计算机程序，其特征在于，所述处理器执行所述计算机程序时实现以下步骤：统计预设时间内所述储充站的工况数据和温度变化值，根据所述温度变化值计算所述储充站的发热功率，将所述发热功率存储在所述工况数据对应的记录集中；根据发热功率的存储时间对所述记录集中的发热功率进行加权，判断所述加权后的所述记录集是否为可信记录集，若是，则计算所述可信记录集的概率分布函数，根据所述概率分布函数进行发热功率的估算。
- 根据权利要求6所述的一种储充站发热功率的估算终端，其特征在于，所述根据发热功率的存储时间对所述记录集中的发热功率进行加权包括：获取所述记录集中每一个发热功率记录的存储时间与当前时间的时间差，将每一个发热功率记录按照时间差从小到大排序；对所述记录集中的发热功率依次进行加权，并且逐渐减小加权权值；判断所述加权后的所述记录集是否为可信记录集包括：计算加权后的所述记录集中所有所述发热功率的平均值和标准差；判断所述记录集中是否超过预设比例的发热功率与所述平均值的差值的绝对值小于两个标准差，若是，则所述记录集为可信记录集，否则，所述记录集为不可信记录集。
- 根据权利要求6所述的一种储充站发热功率的估算终端，其特征在于，根据所述概率分布函数进行发热功率的估算包括：根据所述概率分布函数得到对应工况下不同发热功率的概率分布情况；结合所述概率分布情况和所述储充站的运行情况选择储充站的发热功率。
- 根据权利要求6所述的一种储充站发热功率的估算终端，其特征在于，根据所述概率分布函数进行发热功率的估算包括：根据所述概率分布函数得到对应工况数据下不同发热功率的概率分布情况；结合所述概率分布情况和所述储充站的运行情况选择所述储充站的发热功率。
- 根据权利要求6至9中任一项所述的一种储充站发热功率的估算终端，其特征在于，所述统计预设时间内所述储充站的温度变化值包括：根据热场分布找出储充站的关键点，在所述关键点上放置温度传感器；计算所述温度传感器在所述预设时间内的温度变化。
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| US20050041370A1 (en) * | 2001-10-04 | 2005-02-24 | Wilk Michael D. | High-power ultracapacitor energy storage pack and method of use |
| CN109614662A (zh) * | 2018-11-20 | 2019-04-12 | 中国电力科学研究院有限公司 | 一种确定锂电池组在热仿真实验中的散热方式的方法和系统 |
| CN110174191A (zh) * | 2019-05-15 | 2019-08-27 | 北京长城华冠汽车科技股份有限公司 | 确定电池模组的发热功率的方法和装置、介质、电子设备 |
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| US20050041370A1 (en) * | 2001-10-04 | 2005-02-24 | Wilk Michael D. | High-power ultracapacitor energy storage pack and method of use |
| CN109614662A (zh) * | 2018-11-20 | 2019-04-12 | 中国电力科学研究院有限公司 | 一种确定锂电池组在热仿真实验中的散热方式的方法和系统 |
| CN110174191A (zh) * | 2019-05-15 | 2019-08-27 | 北京长城华冠汽车科技股份有限公司 | 确定电池模组的发热功率的方法和装置、介质、电子设备 |
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