A new energy well repair machine energy storage battery management system and temperature control method thereof

Abstract

The invention discloses an energy storage battery management system and a temperature control method for a new energy well repair machine, which relate to the technical field of energy storage battery temperature control management, and include the following steps: performing data monitoring on the energy storage battery of the new energy well repair machine and collecting monitoring data; analyzing the monitoring data to obtain the influence relationship between the battery temperature and the operating power and the ambient temperature; analyzing the cooling efficiency and the heating efficiency of the temperature control equipment based on the monitoring data; performing intelligent temperature control on the energy storage battery based on the influence relationship, the cooling efficiency and the heating efficiency; the invention is used to solve the problem that the existing energy storage battery temperature control management technology still has the problem that the power regulation of the temperature control system is not accurate enough and the regulation is too frequent, resulting in the temperature regulation of the energy storage battery not being accurate enough and the failure rate of the temperature control system being greatly increased.

Description

Technical Field

[0001] The present invention relates to the technical field of temperature control management of energy storage batteries, and in particular to an energy storage battery management system for a new energy well repair machine and a temperature control method thereof. Background Art

[0002] Energy storage battery temperature control management technology refers to the technical means for monitoring, controlling and managing the temperature of battery packs in battery energy storage systems during the charging and discharging process. The main purpose of this technology is to ensure that the battery pack operates within an appropriate temperature range to improve battery performance and extend battery life, and ensure the safe and stable operation of the system. Energy storage batteries will generate heat during operation. Excessively high or low temperatures will have a negative impact on the performance and life of the battery, and may even cause safety hazards.

[0003] The existing temperature control management technology for energy storage batteries usually sets a power level and controls the power of the temperature control system in a graded manner, and the power of each level is usually fixed. After the temperature of the energy storage battery exceeds a certain range, the temperature control system is enabled to regulate the temperature of the energy storage battery through different power levels. This method cannot intelligently regulate the power of the temperature control system, so that the temperature control system may operate at overpower or underpower, resulting in excessive or insufficient regulation of the temperature of the energy storage battery. Under normal circumstances, the temperature of the energy storage battery during operation only needs to be maintained within the optimal range, without pursuing a certain optimal temperature, resulting in a waste of power resources and no improvement in the efficiency and life of the energy storage battery output. In addition, the existing temperature control management technology for energy storage batteries also regulates the temperature of the energy storage battery by adjusting the operating power of the temperature control system in real time, that is, after the temperature exceeds a certain range, the temperature control system is enabled system, if the temperature has not returned to the optimal range, the operating power of the temperature control system is continuously increased. This method adjusts the operating power of the temperature control system too frequently, resulting in a significant increase in the failure rate of the temperature control system, which is not conducive to the long-term use of the energy storage battery. For example, in the patent application with publication number CN118572255A, a temperature control structure and a temperature control method of an energy storage battery pack are disclosed. When adjusting the power of the heating element, this scheme only increases the power, and does not give a specific power adjustment value. Usually, the default is to continuously adjust the power until the power meets the requirements. This method adjusts the operating power of the temperature control system too frequently, resulting in a significant increase in the failure rate of the temperature control system. The existing energy storage battery temperature control management technology also has the problem that the power adjustment of the temperature control system is not accurate enough and the adjustment is too frequent, resulting in inaccurate temperature adjustment of the energy storage battery and a significant increase in the failure rate of the temperature control system. Summary of the invention

[0004] The present invention aims to solve one of the technical problems in the prior art by monitoring the energy storage battery of the new energy well repair machine, collecting the monitoring data, and then analyzing the monitoring data to obtain the influence relationship between the battery temperature and the operating power and the ambient temperature, and then analyzing the cooling efficiency of the temperature control equipment based on the monitoring data, and analyzing the heating efficiency of the temperature control equipment based on the monitoring data, and then obtaining the optimal working temperature range of the energy storage battery, and finally analyzing the target power of the temperature control equipment based on the optimal temperature range, the power heating influence equation and the power cooling influence equation, so as to solve the problem that the existing energy storage battery temperature control management technology still has the problem that the power regulation of the temperature control system is not accurate enough and the regulation is too frequent, resulting in inaccurate temperature regulation of the energy storage battery and a greatly increased failure rate of the temperature control system.

[0005] To achieve the above objectives, in a first aspect, the present application provides a method for managing temperature control of an energy storage battery of a new energy workover rig, comprising the following steps:

[0006] Monitor the energy storage batteries of new energy workover rigs and collect monitoring data;

[0007] Analyze the monitoring data to obtain the relationship between battery temperature, operating power and ambient temperature;

[0008] Analyze the cooling efficiency and heating efficiency of temperature control equipment based on monitoring data;

[0009] Based on the influence relationship, cooling efficiency and heating efficiency, the energy storage battery is intelligently temperature controlled.

[0010] Furthermore, data monitoring is performed on the energy storage battery of the new energy workover rig, and collecting monitoring data includes the following sub-steps:

[0011] The monitoring data includes battery temperature, battery power, ambient temperature, heating power and cooling power;

[0012] The heating power is the power when the temperature control device performs heating, and the cooling power is the power when the temperature control device performs cooling.

[0013] Further, analyzing the monitoring data to obtain the influence relationship between the battery temperature, the operating power and the ambient temperature includes the following sub-steps:

[0014] The symbol BT represents the battery temperature, AT represents the ambient temperature, and OP represents the battery power;

[0015] Get the mass and specific heat capacity of the energy storage battery, represented by symbols M and C respectively;

[0016] The heat dissipated by the energy storage battery is calculated by the formula Q=C×M×(BT-AT);

[0017] A plane rectangular coordinate system is established with OP as the X-axis and Q as the Y-axis, and is named the power heat impact diagram;

[0018] Obtain historical monitoring data of temperature control devices that are not enabled, mark them as natural monitoring historical data, calculate the Q of natural monitoring historical data, and enter the power heat impact diagram based on OP;

[0019] Perform regression analysis on the power-heat impact diagram, and select the regression equation with the smallest discrete degree as the result of the regression analysis, which is marked as the power-heat impact equation.

[0020] Furthermore, analyzing the cooling efficiency and heating efficiency of the temperature control device based on the monitoring data includes the following sub-steps:

[0021] Analyze the cooling efficiency of temperature control equipment based on monitoring data;

[0022] Analyze the heating efficiency of temperature control equipment based on monitoring data.

[0023] Further, analyzing the cooling efficiency of the temperature control device based on the monitoring data includes the following sub-steps:

[0024] Acquire historical monitoring data of the temperature control device when performing cooling, named cooling monitoring historical data, wherein the cooling monitoring historical data includes cooling battery historical temperature, cooling battery historical power, cooling environment historical temperature and cooling historical power;

[0025] The symbol CHAT represents the cooling environment historical temperature, CHBT represents the cooling battery historical temperature, CHOP represents the cooling battery historical power, and CHP represents the cooling historical power;

[0026] Substitute CHOP into the power heat effect equation and mark the result obtained as QC;

[0027] Substitute Q=QC and AT=CHAT into Q=C×M×(BT-AT), solve BT, and mark the result as H1;

[0028] The cooling efficiency of the temperature control equipment is calculated by the formula CE=C×M×(H1-CHBT), where CE is the cooling efficiency;

[0029] A plane rectangular coordinate system is established with CHP as the horizontal axis and CE as the vertical axis, named as the power cooling influence diagram, and CE is entered into the power cooling influence diagram according to CHP in the cooling monitoring historical data;

[0030] A regression analysis is performed on the power cooling influence diagram, and the regression equation with the smallest discrete degree is selected as the result of the regression analysis and marked as the power cooling influence equation.

[0031] Furthermore, analyzing the heating efficiency of the temperature control device based on the monitoring data includes the following sub-steps:

[0032] Acquire historical monitoring data of the temperature control device when performing heating, named heating monitoring historical data, wherein the heating monitoring historical data includes heating battery historical temperature, heating battery historical power, heating environment historical temperature and heating historical power;

[0033] The symbols HHAT represent the heating environment history temperature, HHBT represent the heating battery history temperature, HHOP represent the heating battery history power, and HHP represent the heating history power;

[0034] Substitute HHOP into the power heat effect equation and label the result obtained as QH;

[0035] Substitute Q=QH and AT=HHAT into Q=C×M×(BT-AT), solve BT, and mark the result as H2;

[0036] The cooling efficiency of the temperature control equipment is calculated by the formula HE=C×M×(HHBT-H2), where HE is the heating efficiency;

[0037] A plane rectangular coordinate system is established with HHP as the horizontal axis and HE as the vertical axis, named as the power heating influence diagram, and HE is entered into the power heating influence diagram according to HHP in the heating monitoring history data;

[0038] Perform regression analysis on the power heating influence diagram, and select the regression equation with the smallest discrete degree as the result of the regression analysis, which is marked as the power heating influence equation.

[0039] Furthermore, based on the influence relationship, cooling efficiency and heating efficiency, intelligent temperature control of the energy storage battery includes the following sub-steps:

[0040] Obtain the optimal temperature range for the operation of the energy storage battery;

[0041] The target power of the temperature control equipment is analyzed based on the optimal temperature range, the power heating influence equation, and the power cooling influence equation.

[0042] Furthermore, obtaining the optimal temperature range for the operation of the energy storage battery includes the following sub-steps:

[0043] Obtaining an optimal temperature range based on the specification information of the energy storage battery, wherein the optimal temperature range includes a minimum temperature and a maximum temperature, which are represented by symbols TD and TU respectively;

[0044] The minimum temperature and the maximum temperature are set or changed by the user.

[0045] Furthermore, analyzing the target power of the temperature control device based on the optimal temperature range, the power heating influence equation, and the power cooling influence equation includes the following sub-steps:

[0046] Acquire monitoring data, and mark the battery temperature in the monitoring data as the real-time battery temperature, represented by the symbol TS;

[0047] Compare the real-time battery temperature with the minimum temperature and the maximum temperature. If the real-time battery temperature is lower than the minimum temperature, a low temperature signal is output; if the real-time battery temperature is greater than or equal to the minimum temperature and less than or equal to the maximum temperature, a suitable temperature signal is output; if the real-time battery temperature is greater than the maximum temperature, a high temperature signal is output;

[0048] If a low temperature signal is output, TD-TS is calculated and the result is marked as ΔT. ΔT is substituted into C×M×ΔT, and the result is substituted into the power heating influence equation. The solved power is marked as the target power, and the heating power is adjusted to the target power.

[0049] If a high temperature signal is output, TS-TU is calculated and the result is marked as ΔT. ΔT is substituted into C×M×ΔT, and the result is substituted into the power cooling influence equation. The solved power is marked as the target power, and the cooling power is adjusted to the target power.

[0050] In a second aspect, the present application provides a new energy well repair machine energy storage battery management system, including a data monitoring module, a heating analysis module, a temperature control analysis module and an intelligent temperature control module; the data monitoring module, the heating analysis module and the temperature control analysis module are respectively connected to the intelligent temperature control module data;

[0051] The data monitoring module is used to monitor the energy storage battery of the new energy workover rig and collect monitoring data;

[0052] The heating analysis module is used to analyze the monitoring data to obtain the influence relationship between the battery temperature, the operating power and the ambient temperature;

[0053] The temperature control analysis module is used to analyze the cooling efficiency and heating efficiency of the temperature control equipment based on the monitoring data;

[0054] The intelligent temperature control module is used to perform intelligent temperature control on the energy storage battery based on the influence relationship, cooling efficiency and heating efficiency.

[0055] Beneficial effects of the present invention: The present invention monitors the data of the energy storage battery of the new energy well repair machine, collects the monitoring data, and then analyzes the monitoring data to obtain the influence relationship between the battery temperature and the operating power and the ambient temperature. The advantage is that the heat emitted by the energy storage battery when running under different power states can be analyzed, which provides a data basis for subsequent intelligent temperature control adjustment, and improves the accuracy and reliability of the energy storage battery temperature control technology;

[0056] The present invention analyzes the cooling efficiency of the temperature control device based on the monitoring data, analyzes the heating efficiency of the temperature control device based on the monitoring data, then obtains the optimal temperature range of the energy storage battery, and finally analyzes the target power of the temperature control device based on the optimal temperature range, the power heating influence equation and the power cooling influence equation. The advantage of the present invention is that the heat that can be taken away and the heat generated by the temperature control device at different operating powers during cooling and heating can be analyzed to provide accurate power parameters for the temperature regulation of the energy storage battery, thereby improving the accuracy and rationality of the energy storage battery temperature control technology. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 is a functional block diagram of the system of the present invention;

[0058] Figure 2 It is the power heat influence diagram of the present invention;

[0059] Figure 3 is a power cooling impact diagram of the present invention;

[0060] Figure 4 It is the power heating influence diagram of the present invention;

[0061] Figure 5 The figure is a flow chart of the steps of the method of the present invention. DETAILED DESCRIPTION

[0062] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0063] Example 1, please refer to Figure 1 As shown, the present application provides a new energy well repair machine energy storage battery management system, including a data monitoring module, a heating analysis module, a temperature control analysis module and an intelligent temperature control module; the data monitoring module, the heating analysis module and the temperature control analysis module are respectively connected to the intelligent temperature control module data;

[0064] The data monitoring module is used to monitor the energy storage battery of the new energy workover rig and collect monitoring data; the monitoring data includes battery temperature, battery power, ambient temperature, heating power and cooling power;

[0065] The heating power is the power of the temperature control device when performing heating, and the cooling power is the power of the temperature control device when performing cooling;

[0066] In actual applications, the monitoring data is saved through the monitoring database. Part of the data in the monitoring database is shown in Table 1 below:

[0067] Table 1 Partial data of monitoring database

[0068]

[0069] Among them, the symbol "——" represents that the heating function or cooling function of the temperature control device is not enabled. There are three monitoring data in Table 1, which are marked as the first data, the second data and the third data from top to bottom.

[0070] The heating analysis module is used to analyze the monitoring data to obtain the influence relationship between battery temperature, operating power and ambient temperature;

[0071] The fever analysis module is configured with a fever analysis strategy, which includes:

[0072] The symbol BT represents the battery temperature, AT represents the ambient temperature, and OP represents the battery power;

[0073] Get the mass and specific heat capacity of the energy storage battery, represented by symbols M and C respectively;

[0074] The heat dissipated by the energy storage battery is calculated by the formula Q=C×M×(BT-AT);

[0075] See also Figure 2 As shown, a plane rectangular coordinate system is established with OP as the X-axis and Q as the Y-axis, and is named the power heat influence diagram;

[0076] Obtain historical monitoring data of temperature control devices that are not enabled, mark them as natural monitoring historical data, calculate the Q of natural monitoring historical data, and enter the power heat impact diagram based on OP;

[0077] Perform regression analysis on the power-heat influence diagram, select the regression equation with the smallest degree of dispersion as the result of the regression analysis, and mark it as the power-heat influence equation;

[0078] In practical applications, the mass M of the energy storage battery is obtained to be 16kg=16000g, and the specific heat capacity C is 0.9 J / (g·°C). The temperature control device is not enabled in the first data. Therefore, taking the first data as an example, Q=0.9J / (g·℃)×16000g×(30℃-16℃)=201600J is calculated, and the power heat influence diagram is constructed as follows: Figure 2 As shown in the figure, the power heat influence equation obtained by regression is Q=50673×e 0.028×OP .

[0079] The temperature control analysis module is used to analyze the cooling efficiency and heating efficiency of the temperature control equipment based on the monitoring data; the temperature control analysis module includes a cooling efficiency analysis unit and a heating efficiency analysis unit;

[0080] The cooling efficiency analysis unit is used to analyze the cooling efficiency of the temperature control equipment based on the monitoring data;

[0081] The cooling efficiency analysis unit is configured with a cooling efficiency analysis strategy, which includes:

[0082] Acquire historical monitoring data of the temperature control device when performing cooling, named cooling monitoring historical data, the cooling monitoring historical data includes cooling battery historical temperature, cooling battery historical power, cooling environment historical temperature and cooling historical power;

[0083] The symbol CHAT represents the cooling environment historical temperature, CHBT represents the cooling battery historical temperature, CHOP represents the cooling battery historical power, and CHP represents the cooling historical power;

[0084] Substitute CHOP into the power heat effect equation and mark the result obtained as QC;

[0085] Substitute Q=QC and AT=CHAT into Q=C×M×(BT-AT), solve BT, and mark the result as H1;

[0086] The cooling efficiency of the temperature control equipment is calculated by the formula CE=C×M×(H1-CHBT), where CE is the cooling efficiency;

[0087] See also Figure 3 As shown, a plane rectangular coordinate system is established with CHP as the horizontal axis and CE as the vertical axis, named as the power cooling influence diagram, and CE is entered into the power cooling influence diagram according to CHP in the cooling monitoring historical data;

[0088] Perform regression analysis on the power cooling influence diagram, select the regression equation with the smallest discrete degree as the result of the regression analysis, and mark it as the power cooling influence equation;

[0089] In practical applications, the second data in Table 1 enables the cooling function. Therefore, taking the second data as an example, CHAT is 17°C, CHBT is 35°C, CHOP is 69kW, and CHP is 4kW. Substitute OP=CHOP=69kW into Q=50673×e 0.028 ×OP , and solve to get QC=349811J. Substitute Q=QC=349811J and AT=CHAT=17℃ into Q=C×M×(BT-AT) and solve to get H1=41℃. All calculation results are integers. Substitute H1 into the formula and solve to get CE=86400J. The power cooling influence diagram is constructed as shown below: Figure 3 As shown, the power cooling influence equation obtained by regression is CE=109854×ln(CHP)-67600.

[0090] The heating efficiency analysis unit is used to analyze the heating efficiency of the temperature control device based on the monitoring data;

[0091] The heating efficiency analysis unit is configured with a heating efficiency analysis strategy, which includes:

[0092] Acquire historical monitoring data of the temperature control device when performing heating, named heating monitoring historical data, the heating monitoring historical data includes heating battery historical temperature, heating battery historical power, heating environment historical temperature and heating historical power;

[0093] The symbols HHAT represent the heating environment history temperature, HHBT represent the heating battery history temperature, HHOP represent the heating battery history power, and HHP represent the heating history power;

[0094] Substitute HHOP into the power heat effect equation and label the result obtained as QH;

[0095] Substitute Q=QH and AT=HHAT into Q=C×M×(BT-AT), solve BT, and mark the result as H2;

[0096] The cooling efficiency of the temperature control equipment is calculated by the formula HE=C×M×(HHBT-H2), where HE is the heating efficiency;

[0097] See also Figure 4 As shown, a plane rectangular coordinate system is established with HHP as the horizontal axis and HE as the vertical axis, named as the power heating influence diagram, and HE is entered into the power heating influence diagram according to HHP in the heating monitoring history data;

[0098] Perform regression analysis on the power heating influence diagram, select the regression equation with the smallest discrete degree as the result of the regression analysis, and mark it as the power heating influence equation;

[0099] In practical applications, the third data in Table 1 enables the heating function. Therefore, taking the third data as an example, HHAT is -3°C, HHBT is 12°C, HHOP is 45kW, and HHP is 5kW, OP=HHOP is substituted into Q=50673×e 0.028×OP , QH=178644J is obtained by solution, Q=QH=178644J and AT=HHAT=-3℃ are substituted into Q=C×M×(BT-AT), and H2=9℃ is obtained by solution. All calculation results are integers, and HE=43200J is obtained by further calculation. The power heating influence diagram is constructed as follows Figure 4 As shown in the figure, the power heating influence equation obtained through regression analysis is HE=214×HHP 2 +6862×HHP+4650.

[0100] The intelligent temperature control module is used to perform intelligent temperature control on the energy storage battery based on the influence relationship, cooling efficiency and heating efficiency; the intelligent temperature control module includes a temperature control limiting unit and a temperature control unit;

[0101] The temperature control limiting unit is used to obtain the optimal temperature range for the energy storage battery to work;

[0102] The temperature control limiting unit is configured with a temperature control limiting strategy, which includes:

[0103] The optimal temperature range is obtained based on the specification information of the energy storage battery. The optimal temperature range includes a minimum temperature and a maximum temperature, which are represented by symbols TD and TU respectively;

[0104] The minimum temperature and the maximum temperature are set or changed by the user;

[0105] In actual application, the best temperature range is [10℃, 35℃], where TD is 10℃ and TU is 35℃. Users can also change it by themselves. If the user does not change it manually, the existing best temperature range is used by default.

[0106] The temperature control unit is used to analyze the target power of the temperature control device based on the optimal temperature range, the power heating influence equation, and the power cooling influence equation;

[0107] The temperature control unit is configured with a temperature control strategy, which includes:

[0108] Acquire monitoring data, and mark the battery temperature in the monitoring data as the real-time battery temperature, represented by the symbol TS;

[0109] Compare the real-time battery temperature with the minimum temperature and the maximum temperature. If the real-time battery temperature is lower than the minimum temperature, a low temperature signal is output; if the real-time battery temperature is greater than or equal to the minimum temperature and less than or equal to the maximum temperature, a suitable temperature signal is output; if the real-time battery temperature is greater than the maximum temperature, a high temperature signal is output;

[0110] If a low temperature signal is output, TD-TS is calculated and the result is marked as ΔT. ΔT is substituted into C×M×ΔT, and the result is substituted into the power heating influence equation. The solved power is marked as the target power, and the heating power is adjusted to the target power.

[0111] If a high temperature signal is output, TS-TU is calculated and the result is marked as ΔT. ΔT is substituted into C×M×ΔT, and the result is substituted into the power cooling influence equation. The solved power is marked as the target power, and the cooling power is adjusted to the target power.

[0112] In actual applications, the real-time battery temperature TS is monitored to be 42°C. Through comparison, it is found that the real-time battery temperature is greater than the maximum temperature, and a high temperature signal is output. ΔT=TS-TU=7°C is calculated. Substituting C×M×ΔT, the result is 100800. Substitute CE=100800 into CE=109854×ln(CHP)-67600, solve CHP, and get the target power of 5kW. The calculation result retains the integer, enables the cooling function of the temperature control device, and adjusts the cooling power to 5kW.

[0113] Example 2, please refer to Figure 5 As shown, the present application provides a method for managing temperature control of an energy storage battery of a new energy well repair machine, comprising the following steps:

[0114] Step S1, monitoring the energy storage battery of the new energy workover rig and collecting monitoring data; Step S1 includes the following sub-steps:

[0115] Step S101, monitoring data includes battery temperature, battery power, ambient temperature, heating power and cooling power;

[0116] Step S102, the heating power is the power when the temperature control device performs heating, and the cooling power is the power when the temperature control device performs cooling;

[0117] Step S2, analyzing the monitoring data to obtain the influence relationship between the battery temperature, the operating power and the ambient temperature; Step S2 includes the following sub-steps:

[0118] Step S201, the battery temperature is represented by BT, the ambient temperature is represented by AT, and the battery power is represented by OP;

[0119] Step S202, obtaining the mass and specific heat capacity of the energy storage battery, represented by symbols M and C respectively;

[0120] Step S203, calculating the heat dissipated by the energy storage battery by the formula Q=C×M×(BT-AT);

[0121] Step S204, establishing a plane rectangular coordinate system with OP as the X-axis and Q as the Y-axis, and naming it a power heat impact diagram;

[0122] Step S205, obtaining historical monitoring data of temperature control devices that are not enabled, marking them as natural monitoring historical data, calculating Q of the natural monitoring historical data and entering the power heat impact diagram based on OP;

[0123] Step S206, performing regression analysis on the power-heat influence diagram, selecting the regression equation with the smallest degree of dispersion as the result of the regression analysis, and marking it as the power-heat influence equation;

[0124] Step S3, analyzing the cooling efficiency and heating efficiency of the temperature control device based on the monitoring data; Step S3 includes the following sub-steps:

[0125] Step S301, analyzing the cooling efficiency of the temperature control device based on the monitoring data;

[0126] Step S301 includes the following sub-steps:

[0127] Step S301.1, obtaining historical monitoring data of the temperature control device when performing cooling, named cooling monitoring historical data, the cooling monitoring historical data includes cooling battery historical temperature, cooling battery historical power, cooling environment historical temperature and cooling historical power;

[0128] Step S301.2, using the symbol CHAT to represent the cooling environment historical temperature, CHBT to represent the cooling battery historical temperature, CHOP to represent the cooling battery historical power, and CHP to represent the cooling historical power;

[0129] Step S301.3, substituting CHOP into the power heat influence equation, and marking the result obtained as QC;

[0130] Step S301.4, substitute Q=QC and AT=CHAT into Q=C×M×(BT-AT), solve BT, and mark the result as H1;

[0131] Step S301.5, calculating the cooling efficiency of the temperature control device by the formula CE=C×M×(H1-CHBT), where CE is the cooling efficiency;

[0132] Step S301.6, establish a plane rectangular coordinate system with CHP as the horizontal axis and CE as the vertical axis, named as the power cooling influence diagram, and enter CE into the power cooling influence diagram according to CHP in the cooling monitoring historical data;

[0133] Step S301.7, performing regression analysis on the power cooling influence diagram, selecting the regression equation with the smallest degree of dispersion as the result of the regression analysis, and marking it as the power cooling influence equation;

[0134] Step S302, analyzing the heating efficiency of the temperature control device based on the monitoring data;

[0135] Step S302 includes the following sub-steps:

[0136] Step S302.1, obtaining historical monitoring data of the temperature control device when performing heating, named heating monitoring historical data, the heating monitoring historical data including heating battery historical temperature, heating battery historical power, heating environment historical temperature and heating historical power;

[0137] Step S302.2, using the symbol HHAT to represent the historical temperature of the heating environment, HHBT to represent the historical temperature of the heating battery, HHOP to represent the historical power of the heating battery, and HHP to represent the historical power of the heating battery;

[0138] Step S302.3, substitute HHOP into the power heat influence equation, and mark the result obtained as QH;

[0139] Step S302.4, substitute Q=QH and AT=HHAT into Q=C×M×(BT-AT), solve BT, and mark the result as H2;

[0140] Step S302.5, calculating the cooling efficiency of the temperature control device by the formula HE=C×M×(HHBT-H2), where HE is the heating efficiency;

[0141] Step S302.6, establish a plane rectangular coordinate system with HHP as the horizontal axis and HE as the vertical axis, named as the power heating influence diagram, and enter HE into the power heating influence diagram according to HHP in the heating monitoring history data;

[0142] Step S302.7, performing regression analysis on the power heating influence diagram, selecting the regression equation with the smallest degree of dispersion as the result of the regression analysis, and marking it as the power heating influence equation;

[0143] Step S4, based on the influence relationship, cooling efficiency and heating efficiency, intelligent temperature control is performed on the energy storage battery; step S4 includes the following sub-steps:

[0144] Step S401, obtaining the optimal operating temperature range of the energy storage battery;

[0145] Step S401 includes the following sub-steps:

[0146] Step S401.1, obtaining an optimal temperature range based on the specification information of the energy storage battery, where the optimal temperature range includes a minimum temperature and a maximum temperature, which are represented by symbols TD and TU respectively;

[0147] Step S401.2, the user sets or changes the minimum temperature and the maximum temperature;

[0148] Step S402, analyzing the target power of the temperature control device based on the optimal temperature range, the power heating influence equation, and the power cooling influence equation;

[0149] Step S402 includes the following sub-steps:

[0150] Step S402.1, acquiring monitoring data, marking the battery temperature in the monitoring data as the real-time battery temperature, represented by symbol TS;

[0151] Step S402.2, compare the real-time battery temperature with the minimum temperature and the maximum temperature. If the real-time battery temperature is lower than the minimum temperature, a low temperature signal is output; if the real-time battery temperature is greater than or equal to the minimum temperature and less than or equal to the maximum temperature, a suitable temperature signal is output; if the real-time battery temperature is greater than the maximum temperature, a high temperature signal is output;

[0152] Step S402.3, if a low temperature signal is output, TD-TS is calculated and the result is marked as ΔT, ΔT is substituted into C×M×ΔT, and the result is substituted into the power heating influence equation, the solved power is marked as the target power, and the heating power is adjusted to the target power;

[0153] Step S402.4, if a high temperature signal is output, calculate TS-TU and mark the result as ΔT, substitute ΔT into C×M×ΔT, and substitute the result into the power cooling influence equation, mark the solved power as the target power, and adjust the cooling power to the target power.

[0154] Embodiment 3, the present application provides an electronic device, which may include: a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other through the communication bus. The memory stores computer-readable instructions, and the processor can call the instructions in the memory. When the computer-readable instructions are executed by the processor, the steps in a method for managing temperature control of an energy storage battery of a new energy well repair machine are executed to achieve the following functions: data monitoring of the energy storage battery of the new energy well repair machine is performed, and monitoring data is collected; the monitoring data is analyzed to obtain the influence relationship between the battery temperature and the operating power and the ambient temperature; the cooling efficiency and heating efficiency of the temperature control device are analyzed based on the monitoring data; based on the influence relationship, cooling efficiency and heating efficiency, the energy storage battery is intelligently temperature controlled.

[0155] In addition, the logic instructions in the above-mentioned memory can be implemented in the form of software functional units and can be stored in a computer-readable storage medium when sold or used as an independent product. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium, including several instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0156] Embodiment 4, the present application also provides a computer-readable storage medium, the present application provides a storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above-mentioned temperature control method for managing a storage battery of a new energy well servicing machine are executed to achieve the following functions: data monitoring of the energy storage battery of the new energy well servicing machine is performed, and monitoring data is collected; the monitoring data is analyzed to obtain the influence relationship between the battery temperature and the operating power and the ambient temperature; the cooling efficiency and heating efficiency of the temperature control equipment are analyzed based on the monitoring data; based on the influence relationship, cooling efficiency and heating efficiency, the energy storage battery is intelligently temperature controlled.

[0157] Through the description of the above implementation methods, the embodiments of the present invention can be provided as methods, systems or computer program products. Based on such an understanding, the above technical solutions can be essentially or partly contributed to the prior art in the form of software products, which can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, an optical disk, etc., and include several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0158] In the embodiments provided in the present application, it should be understood that the disclosed system or method can be implemented in other ways. The embodiments described above are merely schematic. For example, the division of modules or units is only a logical function division. There may be other division methods in actual implementation. For example, multiple modules or units can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some communication interfaces, and the indirect coupling or communication connection of systems, modules and units can be electrical, mechanical or other forms.

[0159] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit it. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for managing temperature control of energy storage batteries for a new energy well repair machine, characterized in that: The steps include: Monitor the energy storage batteries of new energy workover rigs and collect monitoring data; Analyze the monitoring data to obtain the relationship between battery temperature, operating power and ambient temperature; Analyze the cooling efficiency and heating efficiency of temperature control equipment based on monitoring data; Intelligent temperature control of energy storage batteries based on influence relationship, cooling efficiency and heating efficiency; Analyzing the monitoring data to obtain the relationship between battery temperature, operating power and ambient temperature includes the following sub-steps: The symbol BT represents the battery temperature, AT represents the ambient temperature, and OP represents the battery power; Get the mass and specific heat capacity of the energy storage battery, represented by symbols M and C respectively; The heat dissipated by the energy storage battery is calculated by the formula Q=C×M×(BT-AT); A plane rectangular coordinate system is established with OP as the X-axis and Q as the Y-axis, and is named the power heat impact diagram; Obtain historical monitoring data of temperature control devices that are not enabled, mark them as natural monitoring historical data, calculate the Q of natural monitoring historical data, and enter the power heat impact diagram based on OP; Perform regression analysis on the power-heat influence diagram, select the regression equation with the smallest degree of dispersion as the result of the regression analysis, and mark it as the power-heat influence equation; Analyzing the cooling efficiency and heating efficiency of the temperature control equipment based on the monitoring data includes the following sub-steps: Analyze the cooling efficiency of temperature control equipment based on monitoring data; Analyze the heating efficiency of temperature control equipment based on monitoring data; Analyzing the cooling efficiency of temperature control equipment based on monitoring data includes the following sub-steps: Acquire historical monitoring data of the temperature control device when performing cooling, named cooling monitoring historical data, wherein the cooling monitoring historical data includes cooling battery historical temperature, cooling battery historical power, cooling environment historical temperature and cooling historical power; The symbol CHAT represents the cooling environment historical temperature, CHBT represents the cooling battery historical temperature, CHOP represents the cooling battery historical power, and CHP represents the cooling historical power; Substitute CHOP into the power heat effect equation and mark the result obtained as QC; Substitute Q=QC and AT=CHAT into Q=C×M×(BT-AT), solve BT, and mark the result as H1; The cooling efficiency of the temperature control equipment is calculated by the formula CE=C×M×(H1-CHBT), where CE is the cooling efficiency; A plane rectangular coordinate system is established with CHP as the horizontal axis and CE as the vertical axis, named as the power cooling influence diagram, and CE is entered into the power cooling influence diagram according to CHP in the cooling monitoring historical data; Perform regression analysis on the power cooling influence diagram, select the regression equation with the smallest discrete degree as the result of the regression analysis, and mark it as the power cooling influence equation; Analyzing the heating efficiency of temperature control equipment based on monitoring data includes the following sub-steps: Acquire historical monitoring data of the temperature control device when performing heating, named heating monitoring historical data, wherein the heating monitoring historical data includes heating battery historical temperature, heating battery historical power, heating environment historical temperature and heating historical power; The symbols HHAT represent the heating environment history temperature, HHBT represent the heating battery history temperature, HHOP represent the heating battery history power, and HHP represent the heating history power; Substitute HHOP into the power heat effect equation and label the result obtained as QH; Substitute Q=QH and AT=HHAT into Q=C×M×(BT-AT), solve BT, and mark the result as H2; The cooling efficiency of the temperature control equipment is calculated by the formula HE=C×M×(HHBT-H2), where HE is the heating efficiency; A plane rectangular coordinate system is established with HHP as the horizontal axis and HE as the vertical axis, named as the power heating influence diagram, and HE is entered into the power heating influence diagram according to HHP in the heating monitoring history data; Perform regression analysis on the power heating influence diagram, select the regression equation with the smallest discrete degree as the result of the regression analysis, and mark it as the power heating influence equation; Based on the influence relationship, cooling efficiency and heating efficiency, intelligent temperature control of energy storage batteries includes the following sub-steps: Obtain the optimal temperature range for the operation of the energy storage battery; Analyze the target power of the temperature control equipment based on the optimal temperature range, power heating influence equation, and power cooling influence equation; Obtaining the optimal temperature range for the energy storage battery includes the following sub-steps: Obtaining an optimal temperature range based on the specification information of the energy storage battery, wherein the optimal temperature range includes a minimum temperature and a maximum temperature, which are represented by symbols TD and TU respectively; The minimum temperature and the maximum temperature are set or changed by the user; Analyzing the target power of the temperature control device based on the optimal temperature range, the power heating influence equation, and the power cooling influence equation includes the following sub-steps: Acquire monitoring data, and mark the battery temperature in the monitoring data as the real-time battery temperature, represented by the symbol TS; Compare the real-time battery temperature with the minimum temperature and the maximum temperature. If the real-time battery temperature is lower than the minimum temperature, a low temperature signal is output; if the real-time battery temperature is greater than or equal to the minimum temperature and less than or equal to the maximum temperature, a suitable temperature signal is output; if the real-time battery temperature is greater than the maximum temperature, a high temperature signal is output; If a low temperature signal is output, TD-TS is calculated and the result is marked as ΔT. ΔT is substituted into C×M×ΔT, and the result is substituted into the power heating influence equation. The solved power is marked as the target power, and the heating power is adjusted to the target power. If a high temperature signal is output, TS-TU is calculated and the result is marked as ΔT. ΔT is substituted into C×M×ΔT, and the result is substituted into the power cooling influence equation. The solved power is marked as the target power, and the cooling power is adjusted to the target power.

2. The method for managing temperature control of energy storage batteries of a new energy well repairing machine according to claim 1, characterized in that: Data monitoring of the energy storage battery of the new energy workover rig and collection of monitoring data include the following sub-steps: The monitoring data includes battery temperature, battery power, ambient temperature, heating power and cooling power; The heating power is the power when the temperature control device performs heating, and the cooling power is the power when the temperature control device performs cooling.

3. A new energy well workover rig energy storage battery management system, used to implement a new energy well workover rig energy storage battery management temperature control method as claimed in claim 1 or 2, characterized in that: It includes a data monitoring module, a fever analysis module, a temperature control analysis module and an intelligent temperature control module; the data monitoring module, the fever analysis module and the temperature control analysis module are respectively connected to the intelligent temperature control module by data; The data monitoring module is used to monitor the energy storage battery of the new energy workover rig and collect monitoring data; The heating analysis module is used to analyze the monitoring data to obtain the influence relationship between the battery temperature, the operating power and the ambient temperature; The temperature control analysis module is used to analyze the cooling efficiency and heating efficiency of the temperature control equipment based on the monitoring data; The intelligent temperature control module is used to perform intelligent temperature control on the energy storage battery based on the influence relationship, cooling efficiency and heating efficiency.

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