New energy battery comprehensive test energy management system and method

Abstract

The invention relates to the technical field of new energy battery testing, and discloses a new energy battery comprehensive test energy management system and method, and the system comprises a battery testing unit, an energy conversion unit, an energy storage unit, an intelligent control unit, and a data monitoring and analysis unit. The data monitoring and analyzing unit is connected with the battery testing unit, the energy conversion unit, the energy storage unit and the intelligent control unit and used for monitoring operation data of all the units in real time, analyzing the data and providing a basis for scheduling decision making of the intelligent control unit. According to the invention, recycling of redundant electric energy in a battery testing process is realized, energy waste is greatly reduced, testing cost is reduced, energy utilization efficiency is improved, and effectiveness of energy recycling is verified.

Description

Technical Field

[0001] The present invention relates to the technical field of new energy battery testing, and in particular to a new energy battery comprehensive testing energy management system and method. Background Art

[0002] With the rapid development of the new energy vehicle industry, performance testing of new energy batteries has become particularly important. During the production and development of new energy batteries, various performance tests, such as battery capacity, cycle life, and charge and discharge performance, need to be conducted. However, existing new energy battery testing systems have many problems: on the one hand, a large amount of excess electricity is generated during the testing process, which is often directly wasted, resulting in significant energy loss; on the other hand, the testing system's energy scheduling is unreasonable. When the test power fluctuates greatly, it is easy to cause an impact on the external power grid, which also affects the stability and efficiency of the test; in addition, the existing system lacks accurate monitoring and analysis of energy usage, and cannot provide effective data support for optimizing the testing process.

[0003] In response to the above problems, the present invention proposes a new energy battery comprehensive testing energy management system and method. Summary of the Invention

[0004] The present invention provides a new energy battery comprehensive test energy management system and method, which are used to promote the solution of the problems mentioned in the above background technology.

[0005] The present invention provides the following technical solution: a new energy battery comprehensive test energy management system, including a battery testing unit, an energy conversion unit, an energy storage unit, an intelligent control unit and a data monitoring and analysis unit:

[0006] The battery testing unit is used to perform performance testing on new energy batteries;

[0007] The energy conversion unit is connected to the battery testing unit and the energy storage unit respectively, and is used to convert the excess electrical energy generated during the battery testing process and transmit it to the energy storage unit, and at the same time, it can convert the electrical energy in the energy storage unit into the form of electrical energy required for the test and supply it to the battery testing unit;

[0008] The intelligent control unit is connected to the battery test unit, the energy conversion unit, and the energy storage unit respectively, and is used to control the energy conversion unit through an intelligent energy scheduling algorithm according to the test requirements of the battery test unit and the energy storage status of the energy storage unit to achieve reasonable energy distribution;

[0009] The data monitoring and analysis unit is connected to the battery testing unit, energy conversion unit, energy storage unit and intelligent control unit respectively, and is used to monitor the operating data of each unit in real time and analyze the data to provide a basis for the scheduling decision of the intelligent control unit.

[0010] Furthermore, the performance test in the battery testing unit includes a battery capacity test, a cycle life test, and a charge and discharge performance test.

[0011] Furthermore, the specific strategy of the intelligent energy scheduling algorithm in the intelligent control unit is:

[0012] A. Obtain the real-time test power requirement of the battery test unit and the real-time energy storage capacity and current charge and discharge efficiency of the energy storage unit;

[0013] A. Calculate the optimal energy supply plan based on the test power demand and energy storage status;

[0014] The energy supply plan is: when the energy storage capacity of the energy storage unit is sufficient and the discharge efficiency meets the requirements, the electricity of the energy storage unit is used to power the battery testing unit first; when the energy storage unit has insufficient energy, the energy conversion unit is controlled to obtain electricity from the external power grid and store the excess electricity in the energy storage unit; when the battery testing unit generates excess electricity, the energy conversion unit is controlled to convert the excess electricity and store it in the energy storage unit.

[0015] Furthermore, the energy conversion unit includes an AC / DC converter, a DC / DC converter and a bidirectional DC / AC converter; the AC / DC converter is used to convert the AC power of the external power grid into DC power; the DC / DC converter is used to regulate the voltage of the DC power; the bidirectional DC / AC converter is used to realize bidirectional conversion between DC power and AC power to meet the energy interaction between the battery testing unit and the energy storage unit.

[0016] Furthermore, the energy storage unit includes a lithium battery pack and a supercapacitor pack, wherein the lithium battery pack is used for long-term storage of electrical energy, and the supercapacitor pack is used for rapid charging and discharging to cope with instantaneous power fluctuations of the battery test unit.

[0017] Furthermore, the data monitoring and analysis unit includes a sensor module and a data analysis module; the sensor module is used to collect the voltage, current, and temperature of the battery test unit, the input and output power and conversion efficiency of the energy conversion unit, and the voltage, current, and SOC (State of Charge) value of the energy storage unit; the data analysis module is used to analyze the collected data, determine the operating status of each unit, and predict the power demand change trend of the battery test unit and the energy storage change trend of the energy storage unit.

[0018] Furthermore, a new energy battery comprehensive test energy management method is provided, the new energy battery comprehensive test energy management method comprising the following steps:

[0019] S1. Start the system, and the data monitoring and analysis unit begins to monitor the operating data of the battery test unit, energy conversion unit, and energy storage unit in real time;

[0020] S2. The intelligent control unit obtains the test requirements of the battery test unit and the energy storage status of the energy storage unit based on the data provided by the data monitoring and analysis unit;

[0021] S3. The intelligent control unit calculates the energy supply plan through the intelligent energy scheduling algorithm and sends control instructions to the energy conversion unit;

[0022] S4. The energy conversion unit performs energy conversion according to the control instructions to provide the required power for the battery test unit, while storing excess power in the energy storage unit or obtaining power from the external power grid to supplement the energy storage unit;

[0023] S5. During the test, the data monitoring and analysis unit continuously monitors the data of each unit and feeds it back to the intelligent control unit, which adjusts the energy supply plan in real time based on the feedback data.

[0024] S6. After the test is completed, the intelligent control unit controls the system to stop running, and the data monitoring and analysis unit performs statistical analysis on the energy usage of this test and generates a report.

[0025] Furthermore, the specific calculation process of the intelligent energy scheduling algorithm in S3 includes:

[0026] S31. Setting an objective function, the objective function is to minimize the total energy consumption during the test while satisfying the power requirements of the battery test unit and the charge and discharge constraints of the energy storage unit;

[0027] S32. Determine the constraints, including the upper and lower limits of the SOC value of the energy storage unit, the charge and discharge power limit, the conversion efficiency range of the energy conversion unit, and the power supply capacity limit of the external power grid;

[0028] S33. The particle swarm optimization algorithm is used to solve the objective function to obtain the optimal energy supply plan, including the charging and discharging power of the energy storage unit, the input power of the external power grid, and the excess energy recovery power of the battery test unit.

[0029] Furthermore, the specific method of adjusting the energy supply plan in real time in S5 is:

[0030] When the data monitoring and analysis unit detects a sudden increase in the power demand of the battery test unit, the intelligent control unit controls the supercapacitor bank to discharge quickly to fill the power gap, and at the same time controls the energy conversion unit to adjust the energy supply;

[0031] When the SOC value of the energy storage unit is detected to be lower than the lower limit, the intelligent control unit controls the energy conversion unit to obtain electric energy from the external power grid to charge it;

[0032] When it is monitored that the battery test unit generates excess electric energy and the SOC value of the energy storage unit has not reached the upper limit value, the energy conversion unit is controlled to store the excess electric energy in the energy storage unit.

[0033] The present invention has the following beneficial effects:

[0034] This new energy battery comprehensive testing energy management system, through the use of energy storage units and energy conversion units, recycles excess electrical energy during battery testing, significantly reducing energy waste, lowering testing costs, and improving energy efficiency. Specific formulas for calculating conversion efficiency and energy recovery clearly quantify energy utilization and verify the effectiveness of energy recycling.

[0035] The energy storage method adopts a combination of lithium battery packs and supercapacitor packs. The lithium battery pack meets the long-term energy storage needs, and the supercapacitor pack copes with instantaneous power fluctuations. Through their respective SOC value calculation formulas and charge and discharge power constraints, the system's adaptability to test power changes is improved, ensuring the stability of the test.

[0036] The intelligent control unit uses an intelligent energy scheduling algorithm. By setting the objective function of minimizing total energy consumption and various constraints, it applies a particle swarm optimization algorithm to solve the optimal energy supply plan. This achieves reasonable energy scheduling, reduces the impact on the external power grid, and improves testing efficiency. The particle swarm optimization algorithm's speed and position update formula ensure that the optimal solution can be found quickly.

[0037] The data monitoring and analysis unit can monitor the operating data of each unit in real time and conduct analysis and prediction. It uses the ARIMA model to predict the changing trend of the power demand of the battery test unit, providing a reliable basis for the decision-making of the intelligent control unit, facilitating the timely detection of system anomalies and adjustments, and improving the reliability and intelligence level of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 Schematic diagram of the system flow of the present invention;

[0039] Figure 2 Schematic diagram of the process of the present invention.

[0040] In the figure: 1. Battery testing unit; 2. Energy conversion unit; 3. Energy storage unit; 4. Intelligent control unit; 5. Data monitoring and analysis unit; 501. Sensor module; 502. Data analysis module. DETAILED DESCRIPTION

[0041] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. 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 making creative efforts are within the scope of protection of the present invention.

[0042] Example 1

[0043] Reference Figure 1 , a new energy battery comprehensive test energy management system, including a battery test unit 1, an energy conversion unit 2, an energy storage unit 3, an intelligent control unit 4 and a data monitoring and analysis unit 5:

[0044] The battery testing unit 1 is used to perform performance testing on new energy batteries;

[0045] The performance tests in the battery testing unit 1 include battery capacity test, cycle life test, and charge and discharge performance test;

[0046] The energy conversion unit 2 is connected to the battery testing unit 1 and the energy storage unit 3 respectively, and is used to convert the excess electrical energy generated during the battery testing process and transmit it to the energy storage unit. At the same time, it can convert the electrical energy in the energy storage unit 3 into the form of electrical energy required for the test and supply it to the battery testing unit 1;

[0047] The energy conversion unit 2 includes an AC / DC converter, a DC / DC converter and a bidirectional DC / AC converter; the AC / DC converter is used to convert the AC power of the external power grid into DC power. The conversion efficiency of the AC / DC converter is The calculation formula is: ,in, is the DC output efficiency of the AC / DC converter, is the AC input power of the AC / DC converter;

[0048] The DC / DC converter is used to adjust the voltage of direct current so that the electric energy can adapt to the voltage requirements of different units. The conversion efficiency of the DC / DC converter is The calculation formula is: ,in, is the output power of the DC / DC converter, is the input power of the DC / DC converter;

[0049] The bidirectional DC / AC converter is used to realize bidirectional conversion between direct current and alternating current to meet the energy interaction between the battery test unit 1 and the energy storage unit 3. It can convert the excess direct current generated by the battery test unit 1 into alternating current and store it in the energy storage unit 3 (if the energy storage unit 3 requires alternating current), and can also convert the electrical energy of the energy storage unit 3 into the alternating current required for testing and supply it to the battery test unit 1. The conversion efficiency of the bidirectional DC / AC converter is The formula is: ,in, is the output rate of the bidirectional DC / AC converter, is the input power of the bidirectional DC / AC converter;

[0050] The intelligent control unit 4 is connected to the battery test unit 1, the energy conversion unit 2, and the energy storage unit 3 respectively, and is used to control the energy conversion unit through the intelligent energy scheduling algorithm according to the test requirements of the battery test unit 1 and the energy storage status of the energy storage unit 3 to achieve reasonable energy distribution;

[0051] The intelligent energy scheduling algorithm can maximize the use of excess power generated during battery testing while meeting testing requirements, reducing dependence on the external power grid and lowering overall energy consumption.

[0052] In the intelligent energy scheduling algorithm, the objective function is to minimize the total energy consumption, which is expressed as: ,in, is the total energy consumption of the test process, is the total test duration, For the moment Power drawn from the external grid;

[0053] The power balance constraint formula is: ,in, For the moment The power demand of the battery test unit 1 is positive when it consumes electricity, and negative when it generates excess electricity. For the moment Discharge power of lithium battery pack, is the discharge efficiency of the lithium battery pack, For the moment The discharge power of the supercapacitor bank, is the discharge efficiency of the supercapacitor bank, For the moment Recover excess electrical power;

[0054] The 3SOC constraint of the energy storage unit is: ,in, For energy storage unit 3 at time SOC value, 、 They are The lower and upper limits of

[0055] Charge and discharge power constraints: , ,

[0056] in, is the discharge power, and are the minimum discharge power and the maximum discharge power respectively, is the charging power, and are the minimum charging power and the maximum charging power respectively;

[0057] The intelligent energy scheduling algorithm uses a particle optimization algorithm to solve the objective function. The position of the particle represents the parameters of the energy supply plan, such as the external grid input power and the charge and discharge power of the energy storage unit 3. The fitness function of the particle is the objective function. The optimal solution is found by iteratively updating the particle position and velocity. The particle velocity update formula is: ,in, For the The particle in Wei Shangdi The speed of iterations, is the inertia weight, 、 is the acceleration factor, 、 for A random number between For the The particle in The optimal position of an individual in dimension, For the The particle in Wei Shangdi The position of the iteration, The global optimal position is The value on the dimension;

[0058] The particle position update formula is: .

[0059] The specific strategy of the intelligent energy scheduling algorithm in the intelligent control unit 4 is:

[0060] A1 obtains the real-time test power demand of the battery test unit 1 and the real-time energy storage capacity and current charge and discharge efficiency of the energy storage unit 3;

[0061] A2. Calculate the optimal energy supply solution based on the test power requirements and energy storage status;

[0062] The energy supply scheme is as follows: when the energy storage capacity of the energy storage unit 3 is sufficient and the discharge efficiency meets the requirements, the electric energy of the energy storage unit 3 is used first to power the battery test unit; when the energy storage unit 3 is insufficient, the energy conversion unit 2 is controlled to obtain electric energy from the external power grid and store the excess electric energy in the energy storage unit 3; when the battery test unit generates excess electric energy, the energy conversion unit 2 is controlled to convert the excess electric energy and store it in the energy storage unit 3;

[0063] The energy storage unit 3 includes a lithium battery pack and a supercapacitor pack. The lithium battery pack is used for long-term storage of electrical energy, and the supercapacitor pack is used for rapid charging and discharging to cope with the instantaneous power fluctuations of the battery test unit. The lithium battery pack has a high energy density and is suitable for long-term storage of electrical energy, and can provide stable energy support for the system; the supercapacitor pack has an extremely high power density and can achieve rapid charging and discharging, which can effectively cope with the instantaneous power fluctuations that occur in the battery test unit during the test process and ensure the stability of the test;

[0064] The SOC value calculation formula of the lithium battery pack is: ,in, For lithium batteries at all times SOC value, is the initial SOC value of the lithium battery pack, For lithium battery pack at all times The current (positive when charging, negative when discharging) is the rated capacity of the lithium battery pack;

[0065] The SOC value calculation formula of the supercapacitor group is: ,in, For the supercapacitor group at time SOC value, is the initial SOC value of the supercapacitor group, For the supercapacitor group at time The current (positive when charging, negative when discharging) is the rated capacity of the supercapacitor bank;

[0066] The data monitoring and analysis unit 5 is connected to the battery testing unit 1, the energy conversion unit 2, the energy storage unit 3 and the intelligent control unit 4 respectively, and is used to monitor the operating data of each unit in real time and analyze the data to provide a basis for the scheduling decision of the intelligent control unit 4.

[0067] The data monitoring and analysis unit 5 includes a sensor module 501 and a data analysis module 502; the sensor module 501 is used to collect the voltage, current, and temperature of the battery test unit 1, the input and output power and conversion efficiency of the energy conversion unit 2, and the voltage, current, and SOC (State of Charge) value of the energy storage unit 3; the data analysis module 502 is used to analyze the collected data, determine the operating status of each unit, and predict the power demand change trend of the battery test unit 1 and the energy storage change trend of the energy storage unit 3;

[0068] The data monitoring and analysis unit 5 uses the ARIMA model in the time series prediction model to predict the power demand change trend of the battery test unit. The ARIMA model expression is: ,in, For the moment The power demand, is the lag operator, is the difference order, is the autoregressive coefficient polynomial, is the moving average coefficient polynomial, is a white noise sequence;

[0069] The specific strategy for ARIMA model prediction is:

[0070] Data preprocessing: Collect the power demand data of the battery test unit over a period of time and form a time series . Check the stationarity of the data. If the data is not stationary, The order difference processing is used to make the series meet the requirements of stationarity. The stationarity test can be carried out by using methods such as unit root test (such as ADF test);

[0071] Parameter estimation: Determine the autoregressive order p, that is, The order of p and the moving average order q are determined by observing the truncation of the autocorrelation function (ACF) and the partial autocorrelation function (PACF). Then, the maximum likelihood estimation method is used to perform accurate parameter estimation to determine the value range of p and q. 、 the specific form of

[0072] Model test: Test the ARIMA model with estimated parameters to see if the residual sequence is a white noise sequence. The randomness of the residual sequence can be tested by methods such as the Ljung-Box test. If the residual sequence is not a white noise sequence, it means that the model fitting effect is not good and it is necessary to readjust p, q, until a suitable model is obtained;

[0073] Prediction: After confirming After the model is built, the power demand at future moments is predicted using the model. Combining the known historical power demand data and estimated parameters, the future power demand at each moment can be recursively calculated. Power demand forecast value ;

[0074] The prediction result is the power demand prediction value of the battery test unit at a specified time in the future These predicted values ​​are presented in the form of a time series, reflecting the changing trend of the battery test unit's power demand over time. For example, through prediction, we can know the approximate value of the battery test unit's power demand in the next 1 hour, 2 hours, or even longer. This provides data support for the new energy battery comprehensive test energy management system to rationally allocate energy and optimize resource scheduling, helping the system to plan energy supply in advance and avoid problems such as energy shortages or waste caused by sudden changes in power demand.

[0075] Example 2

[0076] See also Figure 2 A new energy battery comprehensive test energy management method, the new energy battery comprehensive test energy management method comprising the following steps:

[0077] S1 start the system, the data monitoring and analysis unit 5 starts working, through the sensor module 501 real-time acquisition of the battery test unit 1, the energy conversion unit 2 and the energy storage unit 3 of the operating data, and transmits the data to the data analysis module 502;

[0078] S2. The intelligent control unit 4 obtains the test requirements of the battery test unit 1 and the energy storage status of the energy storage unit 3 based on the data provided by the data monitoring and analysis unit 5. Specifically, the intelligent control unit 4 obtains information such as the current test power requirement and test phase of the battery test unit 1, as well as energy storage status information such as the real-time energy storage capacity, SOC value, and charge and discharge efficiency of the energy storage unit 3 from the data monitoring and analysis unit 5;

[0079] S3. Intelligent control unit 4 calculates an energy supply plan using an intelligent energy scheduling algorithm and sends control instructions to energy conversion unit 2. The intelligent energy scheduling algorithm first sets the objective function as minimizing total energy consumption during the test process, while also specifying various constraints, such as the upper and lower limits of the SOC value of energy storage unit 3, the charge and discharge power limits, the conversion efficiency range of energy conversion unit 2, and the power supply capacity limit of the external power grid. A particle swarm optimization algorithm is then used to solve the objective function to obtain the optimal energy supply plan, which includes the charge and discharge power of energy storage unit 3, the input power of the external power grid, and the excess power recovered from battery test unit 1.

[0080] S4. The energy conversion unit 2 performs energy conversion according to the control instructions to provide the required electrical energy for the battery test unit 1, while storing the excess electrical energy in the energy storage unit 3 or obtaining electrical energy from the external power grid to supplement the energy storage unit 3. The specific strategy is as follows: when the battery test unit 1 needs electrical energy, if the energy storage unit 3 has sufficient energy storage and the discharge efficiency meets the requirements, the electrical energy of the energy storage unit 3 is preferentially converted into the form of electrical energy required for the test and supplied to the battery test unit 1; if the energy storage unit 3 has insufficient energy storage, AC power is obtained from the external power grid through an AC / DC converter and converted into DC power, which is then regulated by the DC / DC converter and supplied to the battery test unit 1, while the excess electrical energy is stored in the energy storage unit 3; when the battery test unit 1 generates excess electrical energy during the test, the bidirectional DC / AC converter and DC / DC converter are controlled to convert the excess electrical energy and store it in the energy storage unit 3. During this process, the appropriate charging power must be determined based on the type of energy storage unit 3 (lithium battery pack or supercapacitor pack) and the current SOC value to ensure charging efficiency and safety;

[0081] S5. During the test, the data monitoring and analysis unit 5 continuously monitors the data of each unit and feeds the data back to the intelligent control unit 4. The intelligent control unit 4 adjusts the energy supply plan in real time according to the feedback data. Specifically, the intelligent control unit 4 continuously evaluates the rationality of the current energy supply plan according to the feedback data. When it detects a sudden increase in the power demand of the battery test unit 1, it immediately controls the supercapacitor group to discharge quickly to fill the power gap. At this time, the discharge power of the supercapacitor group It must be within its maximum discharge power range, that is , and adjust the energy supply of the energy conversion unit 2 at the same time; when it is found that the SOC value of the energy storage unit 3 is lower than the lower limit, the energy conversion unit 2 is controlled to obtain electric energy from the external power grid to charge it. During the charging process, the charging power needs to be controlled to avoid exceeding the maximum charging power of the energy storage unit 3; when it is monitored that the battery test unit 1 generates excess electric energy and the SOC value of the energy storage unit 3 has not reached the upper limit, the energy conversion unit 2 is promptly controlled to store the excess electric energy in the energy storage unit 3 to achieve energy recycling;

[0082] S6. After the test is completed, the intelligent control unit 4 stops the control system, and the data monitoring and analysis unit 5 performs statistical analysis on the energy usage of this test and generates a report. Specifically, the data monitoring and analysis unit 5 performs a comprehensive statistical analysis on the energy usage during this test, including total energy consumption, external power grid power consumption, energy recovery, energy conversion efficiency, etc. The calculation formula is: , the total conversion efficiency of energy conversion unit 2 The total conversion efficiency of the energy conversion path from the external grid to the battery test unit can be calculated using the conversion efficiency of each converter: , and generate detailed energy usage reports to provide reference for subsequent test optimization and system improvement.

[0083] The specific calculation process of the intelligent energy scheduling algorithm described in S3 includes:

[0084] S31 sets the objective function, the objective function is to minimize the total energy consumption during the test, while meeting the power requirements of the battery test unit 1 and the charge and discharge constraints of the energy storage unit 3;

[0085] S32 determines the constraints, including the upper and lower limits of the SOC value of the energy storage unit 3, the charge and discharge power limit, the conversion efficiency range of the energy conversion unit 2, and the power supply capacity limit of the external grid;

[0086] S33. Use the particle swarm optimization algorithm to solve the objective function and obtain the optimal energy supply plan, including the charging and discharging power of the energy storage unit 3, the input power of the external power grid, and the excess electric energy recovery power of the battery test unit.

[0087] The specific method of adjusting the energy supply plan in real time in S5 is:

[0088] When the data monitoring and analysis unit 5 detects a sudden increase in the power demand of the battery test unit 1, the intelligent control unit 4 controls the supercapacitor bank to discharge quickly to supplement the power gap, and at the same time controls the energy conversion unit 2 to adjust the energy supply;

[0089] When it is detected that the SOC value of the energy storage unit 3 is lower than the lower limit, the intelligent control unit 4 controls the energy conversion unit 2 to obtain electric energy from the external power grid to charge it;

[0090] When it is monitored that the battery testing unit 1 generates excess electric energy and the SOC value of the energy storage unit 3 does not reach the upper limit, the energy conversion unit 2 is controlled to store the excess electric energy in the energy storage unit 3 .

[0091] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0092] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the technical principles of the present invention. These improvements and modifications should also be regarded as within the scope of protection of the present invention.

Claims

1. A new energy battery comprehensive test energy management system, comprising a battery test unit (1), an energy conversion unit (2), an energy storage unit (3), an intelligent control unit (4) and a data monitoring and analysis unit (5), characterized in that: The battery testing unit (1) is used to perform performance testing on new energy batteries; The energy conversion unit (2) is connected to the battery test unit (1) and the energy storage unit (3) respectively, and is used to convert the excess electric energy generated during the battery test and transmit it to the energy storage unit, and at the same time, it can convert the electric energy in the energy storage unit (3) into the form of electric energy required for the test and supply it to the battery test unit (1); The intelligent control unit (4) is connected to the battery test unit (1), the energy conversion unit (2), and the energy storage unit (3) respectively, and is used to control the energy conversion unit through an intelligent energy scheduling algorithm according to the test requirements of the battery test unit (1) and the energy storage state of the energy storage unit (3), so as to achieve reasonable distribution of energy; The data monitoring and analysis unit (5) is connected to the battery test unit (1), the energy conversion unit (2), the energy storage unit (3) and the intelligent control unit (4) respectively, and is used to monitor the operating data of each unit in real time and analyze the data to provide a basis for the scheduling decision of the intelligent control unit (4).

2. The new energy battery comprehensive test energy management system according to claim 1 is characterized in that: The performance test in the battery testing unit (1) includes a battery capacity test, a cycle life test, and a charge and discharge performance test.

3. The new energy battery comprehensive test energy management system according to claim 1 is characterized in that: The specific strategy of the intelligent energy scheduling algorithm in the intelligent control unit (4) is: A1. Obtaining the real-time test power requirement of the battery test unit (1) and the real-time energy storage capacity and current charge and discharge efficiency of the energy storage unit (3); A2. Calculate the optimal energy supply solution based on the test power requirements and energy storage status; The energy supply scheme is as follows: when the energy storage capacity of the energy storage unit (3) is sufficient and the discharge efficiency meets the requirements, the electric energy of the energy storage unit (3) is preferentially used to power the battery test unit; when the energy storage unit (3) is insufficient, the energy conversion unit (2) is controlled to obtain electric energy from the external power grid and store the excess electric energy in the energy storage unit (3); when the battery test unit generates excess electric energy, the energy conversion unit (2) is controlled to convert the excess electric energy and store it in the energy storage unit (3).

4. The new energy battery comprehensive test energy management system according to claim 1 is characterized in that: The energy conversion unit (2) includes an AC / DC converter, a DC / DC converter, and a bidirectional DC / AC converter; the AC / DC converter is used to convert alternating current from an external power grid into direct current; the DC / DC converter is used to adjust the voltage of the direct current; and the bidirectional DC / AC converter is used to achieve bidirectional conversion between direct current and alternating current to meet energy interaction between the battery test unit (1) and the energy storage unit (3).

5. The new energy battery comprehensive test energy management system according to claim 1 is characterized in that: The energy storage unit (3) comprises a lithium battery pack and a supercapacitor pack, wherein the lithium battery pack is used for long-term storage of electrical energy, and the supercapacitor pack is used for rapid charging and discharging to cope with instantaneous power fluctuations of the battery test unit.

6. The new energy battery comprehensive test energy management system according to claim 1 is characterized in that: The data monitoring and analysis unit (5) comprises a sensor module (501) and a data analysis module (502); the sensor module (501) is used to collect the voltage, current, and temperature of the battery test unit (1), the input and output power and conversion efficiency of the energy conversion unit (2), and the voltage, current, and SOC (State of Charge) value of the energy storage unit (3); the data analysis module (502) is used to analyze the collected data, determine the operating status of each unit, and predict the power demand change trend of the battery test unit (1) and the energy storage change trend of the energy storage unit (3).

7. A new energy battery comprehensive test energy management method, applied to the new energy battery comprehensive test energy management system according to any one of claims 1 to 6, characterized in that: The new energy battery comprehensive test energy management method comprises the following steps: S1. Start the system, the data monitoring and analysis unit (5) begins to monitor the operating data of the battery test unit (1), the energy conversion unit (2) and the energy storage unit (3) in real time; S2. The intelligent control unit (4) obtains the test requirements of the battery test unit (1) and the energy storage status of the energy storage unit (3) based on the data provided by the data monitoring and analysis unit (5); S3. The intelligent control unit (4) calculates the energy supply plan through the intelligent energy scheduling algorithm and sends a control instruction to the energy conversion unit (2); S4. The energy conversion unit (2) performs energy conversion according to the control instruction, provides the required electric energy to the battery test unit (1), and stores the excess electric energy in the energy storage unit (3) or obtains electric energy from the external power grid to supplement the energy storage unit (3); S5. During the test, the data monitoring and analysis unit (5) continuously monitors the data of each unit and feeds the data back to the intelligent control unit (4). The intelligent control unit (4) adjusts the energy supply plan in real time according to the feedback data; S6. After the test is completed, the intelligent control unit (4) controls the system to stop running, and the data monitoring and analysis unit (5) performs statistical analysis on the energy usage of this test and generates a report.

8. The new energy battery comprehensive test energy management method according to claim 7, characterized in that: The specific calculation process of the intelligent energy scheduling algorithm in S3 includes: S31. Setting an objective function, wherein the objective function is to minimize the total energy consumption during the test while satisfying the power requirements of the battery test unit (1) and the charge and discharge constraints of the energy storage unit (3); S32. Determine the constraints, including the upper and lower limits of the SOC value of the energy storage unit (3), the charge and discharge power limit, the conversion efficiency range of the energy conversion unit (2), and the power supply capacity limit of the external power grid; S33. The objective function is solved by using a particle swarm optimization algorithm to obtain the optimal energy supply solution, including the charging and discharging power of the energy storage unit (3), the input power of the external power grid, and the excess power recovery power of the battery test unit.

9. The new energy battery comprehensive test energy management method according to claim 7, characterized in that: The specific method of adjusting the energy supply plan in real time in S5 is: When the data monitoring and analysis unit (5) detects that the power demand of the battery test unit (1) suddenly increases, the intelligent control unit (4) controls the supercapacitor group to discharge quickly to supplement the power gap, and at the same time controls the energy conversion unit (2) to adjust the energy supply; When it is detected that the SOC value of the energy storage unit (3) is lower than the lower limit value, the intelligent control unit (4) controls the energy conversion unit (2) to obtain electric energy from the external power grid to charge the energy storage unit (3); When it is monitored that the battery test unit (1) generates excess electric energy and the SOC value of the energy storage unit (3) does not reach an upper limit value, the energy conversion unit (2) is controlled to store the excess electric energy in the energy storage unit (3).

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