Battery PACK charging and discharging test system

Abstract

The invention provides a battery PACK charging and discharging test system, and relates to the technical field of battery testing, the battery PACK charging and discharging test system comprises a centralized controller used for determining a global adjustment coefficient by using a particle swarm optimization algorithm according to a test instruction, determining a micro adjustment coefficient according to the global adjustment coefficient by using a fuzzy algorithm, and determining a micro adjustment coefficient according to the micro adjustment coefficient; determining a real-time adjustment coefficient according to the microscopic adjustment coefficient and the initial adjustment coefficient; and the converter assembly is used for controlling the test voltage and current of the tested battery PACK according to the real-time adjustment coefficient. According to the invention, it can be ensured that the adjustment response is ultra-fast and has no overshoot, the core problem that the PI adjustment output speed in the prior art cannot meet the ultra-high speed and no-overshoot requirements is accurately solved, the dynamic response performance of the battery PACK test is ensured, and thus the requirements of the battery PACK test working conditions are met.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of battery test, in particular to a battery PACK charging and discharging test system. BACKGROUND

[0002] The battery PACK is a complex energy system which integrates the battery cell, the management system, the thermal system and the structural member and can work independently. The battery PACK charging and discharging test can check the safety hazards such as overcharging and overdischarging, verify the key performances such as the capacity and the cycle life, simulate the actual working conditions such as the vehicle-mounted conditions, ensure the adaptation to the use requirements and provide reliable data support for the subsequent application.

[0003] In the related art, the voltage and current in the charging and discharging process are mainly controlled by the bidirectional DC / DC controller, and the PID control method is used to accurately control the voltage and current, so that the output voltage and current precision control is within the battery PACK test requirement range. However, the PID control method is not only complicated in steps, but also has hysteresis and delay, and when the parameters are not appropriate, there will be an overshoot phenomenon, which cannot meet the response requirements of the battery PACK test working condition which needs to be fast and without overshoot. SUMMARY

[0004] The problem solved by the present application is how to meet the fast and non-overshoot requirements of the battery PACK test working condition.

[0005] To solve the above problems, the present application provides a battery PACK charging and discharging test system, comprising: a centralized controller configured to determine a global adjustment coefficient by using a particle swarm optimization algorithm according to a test instruction, determine a micro adjustment coefficient by using a fuzzy algorithm according to the global adjustment coefficient, and determine a real-time adjustment coefficient according to the micro adjustment coefficient and an initial adjustment coefficient; a converter assembly configured to control the test voltage and current of the battery PACK under test according to the real-time adjustment coefficient.

[0006] Optionally, the global adjustment coefficient includes a global proportional coefficient and global integral data, and the global adjustment coefficient is determined by using the particle swarm optimization algorithm according to the test instruction, comprising: determining a current given slope step, a step change rate, a current control error and an error change rate according to the test instruction, respectively; initializing the running parameters of the particle swarm algorithm; based on the running parameters, using the particle swarm algorithm to perform iterative operation according to the given slope step, the step change rate, the current control error and the error change rate, to generate the global proportional coefficient and the global integral data.

[0007] Optionally, the micro-adjustment coefficient comprises a micro-proportion coefficient and micro-integral data, and the micro-adjustment coefficient is determined by using a fuzzy algorithm according to the global-adjustment coefficient, comprising: The global-proportion coefficient and the global-integral data are converted into a global-proportion fuzzy quantity and a global-integral fuzzy quantity respectively by using the fuzzy algorithm, and the membership is defined by using the membership function of the fuzzy domain; The micro-proportion coefficient and the micro-integral data are generated by using the barycentric method to solve the fuzzification according to the global-proportion fuzzy quantity and the global-integral fuzzy quantity.

[0008] Optionally, the real-time adjustment coefficient is determined according to the micro-adjustment coefficient and the initial adjustment coefficient, comprising: The real-time adjustment coefficient is determined by adding and fusing the micro-adjustment coefficient and the initial adjustment coefficient.

[0009] Optionally, the converter assembly comprises a plurality of front-stage AC / DC modules and a plurality of rear-stage DC / DC modules connected in an electrical manner, and the plurality of front-stage AC / DC modules and the plurality of rear-stage DC / DC modules are in communication connection with the centralized controller, and the plurality of rear-stage DC / DC modules are further in electrical connection with the battery PACK to be tested; The plurality of front-stage AC / DC modules are used to convert the mains voltage into the DC bus voltage; The plurality of rear-stage DC / DC modules are used to convert the DC bus voltage into the test voltage and current corresponding to the real-time adjustment coefficient.

[0010] Optionally, the plurality of front-stage AC / DC modules comprise a plurality of AC / DC converters connected in parallel; and the plurality of rear-stage DC / DC modules comprise a plurality of DC / DC converters connected in parallel.

[0011] Optionally, the battery PACK charging and discharging test system further comprises a host computer in communication connection with the centralized controller, and the host computer is used to acquire a user instruction and transmit the user instruction to the centralized controller, wherein the test instruction is confirmed by the user instruction.

[0012] Optionally, the battery PACK charging and discharging test system further comprises a middle computer in communication connection with the centralized controller and the host computer respectively, and the middle computer is used to store the user instruction and convert the user instruction into a test instruction recognizable by the centralized controller.

[0013] Optionally, the centralized controller comprises a communication device and an analog quantity collector; The communication device is used to be in communication connection with the converter assembly; The analog quantity collector is used to collect the voltage and the current of the converter assembly.

[0014] Optionally, the centralized controller comprises an MCU control chip connected with the communication device and the analog quantity collector.

[0015] The beneficial effects of the battery PACK charge / discharge testing system of the present invention are: The centralized controller first determines the global regulation coefficient using a particle swarm optimization algorithm. This algorithm requires minimal parameter pre-setting and iteratively searches for the optimal solution vector, avoiding the cumbersome trial-and-error process of traditional PID control. It also solves the problem of poor applicability across the entire range in traditional parameter tuning, providing a globally optimal regulation foundation for voltage and current control. Based on this, the centralized controller then uses a fuzzy algorithm to determine the micro-regulation coefficients according to the global regulation coefficients. The fuzzy algorithm can perform precise micro-adjustments based on the current system operating state, compensating for the lag and delay defects of traditional PID control. Then, based on the micro-regulation coefficients, the initial regulation coefficients are adjusted to determine the most suitable real-time regulation coefficients. Finally, the converter component controls the test voltage and current of the battery pack under test according to these real-time regulation coefficients, ensuring an ultra-fast and overshoot-free regulation response. This precisely solves the core problem in existing technologies where PI regulation output speed cannot meet the requirements for ultra-high speed and no overshoot, guaranteeing the dynamic response performance of the battery pack test and thus meeting the requirements of the battery pack test conditions. Attached Figure Description

[0016] Figure 1 This is one of the structural schematic diagrams of the battery PACK charge-discharge test system provided in an embodiment of the present invention; Figure 2 This is a second schematic diagram of the battery PACK charge and discharge test system provided in an embodiment of the present invention; Figure 3 This is a schematic diagram of the structure of a converter component provided in an embodiment of the present invention; Figure 4 The algorithm flowchart of the centralized controller provided in the embodiment of the present invention is shown. Detailed Implementation

[0017] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Although some embodiments of the present invention are shown in the drawings, it should be understood that the present invention can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of the present invention. It should be understood that the accompanying drawings and embodiments of the present invention are for illustrative purposes only and are not intended to limit the scope of protection of the present invention.

[0018] It should be understood that the various steps described in the method embodiments of the present invention may be performed in different orders and / or in parallel. Furthermore, the method embodiments may include additional steps and / or omit the steps shown. The scope of the present invention is not limited in this respect.

[0019] The term "include," and variations thereof, as used herein, is meant to be open-ended and permit the inclusion of items that are not listed or other items that are not expressly included. The term "based on" is meant to be open-ended, and allow for being based on implicitly on a stated basis as well as an explicitly stated basis. The term "one embodiment" is used herein to refer to at least one embodiment. The term "another embodiment" is used herein to refer to at least one additional embodiment. The term "some embodiments" is used herein to refer to at least one embodiment. The term "optional" is used herein to refer to an embodiment that is not required, but can be present. Relative terms such as "first" and "second" and the like can be used solely to distinguish one entity from another entity (for example, a first device and a second device), and do not necessarily indicate that the entities are present in a sequence or that the entities are dependent upon each other. Other definitions will be apparent from the context in which the terms are used.

[0020] It should be noted that the terms "one" and "a" or "an" as used herein are defined as "one or more" unless explicitly indicated to the contrary. It should be noted that the terms "first," "second," and the like as used herein are meant as labels, and are not intended to denote a particular order of importance or a particular order of occurrence.

[0021] The names of the messages or information exchanged between the devices in the embodiments of the present application are used only for illustrative purposes, and are not intended to limit the scope of the messages or information.

[0022] As shown in Figure 1 The battery PACK charging and discharging test system provided by the embodiment of the present application comprises: The centralized controller is configured to determine a global adjustment coefficient by using a particle swarm optimization algorithm according to a test instruction, determine a micro adjustment coefficient by using a fuzzy algorithm according to the global adjustment coefficient, and determine a real-time adjustment coefficient according to the micro adjustment coefficient and an initial adjustment coefficient.

[0023] Specifically, the centralized controller can employ a microprocessor (MCU) to determine a global adjustment coefficient according to the test instruction using a particle swarm optimization algorithm. The particle swarm optimization algorithm can learn a mapping relationship from a system dynamic state to an optimal control parameter: when the step value is large (i.e., fast acceleration) and the error change rate is positive, i.e., moving away from the target value, a larger proportional coefficient needs to be output to accelerate the response, but at the same time a smaller integral coefficient (or even a negative integral coefficient) is needed to suppress integral saturation, effectively preventing overshoot. The particle swarm optimization algorithm will constantly update the most reasonable output parameter according to the input and output, realizing a nonlinear mapping from the "system dynamic state" to the "optimal control parameter", and then realizing macro global optimization. Then a fuzzy algorithm is used to determine a micro adjustment coefficient according to the global adjustment coefficient. The fuzzy algorithm is responsible for micro adjustment, which uses the output of the particle swarm optimization algorithm to perform fuzzy reasoning according to the current real-time step and error, and calculates the proportional integral parameter fine tuning amount most suitable for the current instant. Through the particle swarm optimization algorithm and the fuzzy algorithm, real-time adaptive adjustment of the proportional integral parameters can be realized in the full voltage and full power range, and the best balance between high dynamic response and no overshoot control can be finally achieved. Finally, the real-time adjustment coefficient is determined according to the micro adjustment coefficient and the initial adjustment coefficient, so that the control amount increment is output based on the real-time adjustment coefficient through the PI controller, and then the test voltage and current of the battery PACK under test are controlled. Exemplarily, the PI controller can employ an incremental PI controller, and its control formula includes: ; wherein, when , the control formula can be expressed as: ; wherein, is the control amount increment of the PI controller at the kth moment, K p is the real-time proportional coefficient in the real-time adjustment coefficient, K i is the real-time integral coefficient in the real-time adjustment coefficient, e is the control error at the kth moment, k is the control error at the (k-1)th moment. e k

[0024] A converter assembly is configured to control the test voltage and current of the battery PACK under test according to the real-time adjustment coefficient.

[0025] Specifically, as shown in Figure 3 , the converter assembly converts the control amount increment output by the PI controller according to the real-time adjustment coefficient into a physical change of the actual output voltage, thereby performing charge and discharge detection on the battery PACK under test.

[0026] ​​In this embodiment, the centralized controller first determines the global adjustment coefficient by the particle swarm optimization algorithm. The particle swarm optimization algorithm does not need to pre-set too many parameters, can search for the optimal solution vector by iteration, avoids the cumbersome process of adjusting parameters by relying on the trial-and-error method in traditional PID control, solves the problem of poor applicability of traditional parameter adjustment in the whole range, and provides a globally optimal adjustment basis for voltage and current control. On this basis, the centralized controller determines the micro-adjustment coefficient according to the global adjustment coefficient by using the fuzzy algorithm. The fuzzy algorithm can accurately fine-tune the system according to the current operating state, and makes up for the defects of hysteresis and time delay in traditional PID control. Then, the micro-adjustment coefficient is used to adjust the initial adjustment coefficient to determine the most suitable real-time adjustment coefficient. Finally, the converter assembly controls the test voltage and current of the measured battery PACK according to the real-time adjustment coefficient, ensures that the adjustment response is both ultra-fast and without overshoot, accurately solves the core problem that the output speed of PI adjustment in the prior art cannot meet the ultra-high speed and no-overshoot demand, and guarantees the dynamic response performance of the battery PACK test, thereby meeting the demand of the battery PACK test working condition.

[0027] Optionally, the global adjustment coefficient includes a global proportional coefficient and global integral data, and the global adjustment coefficient is determined by using the particle swarm optimization algorithm according to the test instruction, including: The current given slope step, the step change rate, the current control error and the error change rate are determined respectively according to the test instruction. The running parameters of the particle swarm optimization algorithm are initialized. Based on the running parameters, the particle swarm optimization algorithm is used to perform iterative operation according to the given slope step, the step change rate, the current control error and the error change rate, to generate the global proportional coefficient and the global integral data.

[0028] Specifically, the current given slope step, the step change rate, the current control error and the error change rate are determined respectively according to the test instruction. The current given slope step reflects the dynamic change rate of the system and is input by the user. The step change rate is used to predict the acceleration or deceleration trend of the dynamic process. The current control error refers to the instantaneous deviation of the given value and the feedback value. The error change rate reflects the speed and direction of the system response. The running parameters of the particle swarm optimization algorithm are initialized, including the dimension, the particle swarm size, the maximum number of iterations, the inertia factor, the acceleration constant, the position and speed of each particle. Based on the running parameters, the particle swarm optimization algorithm performs iterative operation according to the given slope step, the step change rate, the current control error and the error change rate until the termination condition is met, to generate the global proportional coefficient and the global integral data. Exemplarily, the dimension in the running parameters can be set to 4, the particle swarm size can be set to 40, the maximum number of iterations can be set to 10 times, the inertia factor can be set to 0.8, and the acceleration constant can be set to 0.2.

[0029] Optionally, the micro-adjustment coefficient comprises a micro-proportion coefficient and micro-integral data, and the micro-adjustment coefficient is determined by using a fuzzy algorithm according to the global-adjustment coefficient, comprising: The global-proportion coefficient and the global-integral data are converted into a global-proportion fuzzy quantity and a global-integral fuzzy quantity respectively by using the fuzzy algorithm, and the membership degree is defined by using the membership function of the fuzzy domain. The fuzzy reasoning is performed according to the global-proportion fuzzy quantity and the global-integral fuzzy quantity, the defuzzification is performed by using the gravity method, and the micro-proportion coefficient and the micro-integral data are generated.

[0030] Specifically, the global-proportion coefficient and the global-integral data are processed by using the fuzzy algorithm, and are converted into a global-proportion fuzzy quantity and a global-integral fuzzy quantity respectively, and the membership degree is defined by using the membership function of the fuzzy domain; the fuzzy reasoning is performed by using a fuzzy formula group according to the global-proportion fuzzy quantity and the global-integral fuzzy quantity, the defuzzification is performed by using the gravity method, and the final generated micro-proportion coefficient and micro-integral data are determined by using the control rule table. The fuzzy formula group comprises: ; wherein, is the micro-proportion coefficient, E is the global-proportion fuzzy quantity, EC is the global-integral fuzzy quantity, o is the synthetic calculation, is the fuzzy relationship of the proportion coefficient, is the micro-integral data, is the fuzzy relationship of the integral coefficient.

[0031] Optionally, the real-time adjustment coefficient is determined according to the micro-adjustment coefficient and the initial adjustment coefficient, comprising: The real-time adjustment coefficient is determined by adding the micro-adjustment coefficient and the initial adjustment coefficient.

[0032] Specifically, the micro-adjustment coefficient comprises a micro-proportion coefficient and a micro-integral coefficient, the initial adjustment coefficient comprises an initial proportion coefficient and an initial integral coefficient, and the real-time adjustment coefficient comprises a real-time proportion coefficient and a real-time integral coefficient. The real-time proportion coefficient is obtained by adding the micro-proportion coefficient and the initial proportion coefficient, and the real-time integral coefficient is obtained by adding the micro-integral coefficient and the initial integral coefficient, so that the initial integral coefficient is dynamically adjusted, and the performance of the PI controller completely meets the core requirements of the battery PACK charging and discharging test.

[0033] Exemplarily, as Figure 4As shown, the input of the particle swarm algorithm is the current given slope step (Slope), the step change rate, the current control error and the error change rate, wherein the current given slope step (Slope) and the voltage / current given value (Vref*) in the figure are obtained from the user input instruction, the output voltage (Vout) is obtained by negative feedback, the current given slope step (Slope) can be directly used as the input parameter of the particle swarm algorithm, the derivative of the current given slope step (Slope) with respect to time is obtained, the current given slope step (Slope) is added to the output voltage (Vout), and then the voltage / current given value (Vref*) is limited to obtain the limiting parameter, and the limiting parameter is subtracted from the output voltage (Vout) to obtain the current control error, and the derivative of the current control error with respect to time is obtained to obtain the error change rate. After the particle swarm algorithm optimization of the current given slope step, the step change rate, the current control error and the error change rate, the global proportional coefficient and the global integral data , the global proportional coefficient and the global integral data are input into the fuzzy algorithm to obtain the micro proportional coefficient and the micro integral coefficient , the micro proportional coefficient is added to the initial proportional coefficient obtained by the feedforward network to obtain the real-time proportional coefficient, the micro integral coefficient is added to the initial integral coefficient obtained by the feedforward network to obtain the real-time integral coefficient, and the real-time proportional coefficient and the real-time integral coefficient are subjected to PI calculation to obtain the final output voltage (Vout).

[0034] Optionally, as shown in Figure 2 and Figure 3 , the converter assembly includes a plurality of front-stage AC / DC modules and a plurality of rear-stage DC / DC modules connected in an electrical manner, and the plurality of front-stage AC / DC modules and the plurality of rear-stage DC / DC modules are in communication connection with the centralized controller, and the plurality of rear-stage DC / DC modules are further in electrical connection with the measured battery PACK; The plurality of front-stage AC / DC modules are used to convert the commercial power voltage into a DC bus voltage; The plurality of rear-stage DC / DC modules are used to convert the DC bus voltage into a test voltage and current corresponding to the real-time adjustment coefficient.

[0035] Specifically, the multi-stage AC / DC module can convert 380V AC mains voltage to any level of DC bus voltage, such as 1600V. The multi-stage DC / DC module is used to convert the DC bus voltage to a test voltage corresponding to the real-time adjustment coefficient, and to achieve a wide range of voltage and current regulation, such as 20V DC to 1500V DC. Moreover, the current in the multi-stage DC / DC module can flow bidirectionally, which is the charging and discharging process, thereby enabling charge and discharge testing.

[0036] Optionally, such as Figure 2 As shown, the multi-front-stage AC / DC module includes multiple AC / DC converters connected in parallel; the multi-back-stage DC / DC module includes multiple DC / DC converters connected in parallel.

[0037] Specifically, the multi-stage AC / DC module includes multiple AC / DC converters connected in parallel, which can realize multiple levels of power output and current output; the multi-stage DC / DC module includes multiple DC / DC converters connected in parallel, which can realize multiple levels of power output and current output.

[0038] Optionally, such as Figure 2 As shown, the battery PACK charge and discharge test system also includes a host computer that is connected to the central controller. The host computer is used to obtain user instructions and transmit them to the central controller. The test instructions are confirmed by the user instructions.

[0039] Specifically, the battery PACK charge and discharge test system also includes a host computer that communicates with the central controller. Users can edit test steps and test condition requirements, i.e., user instructions, through the host computer. The host computer obtains the user instructions and transmits them to the central controller.

[0040] Optionally, such as Figure 2 As shown, the battery PACK charge and discharge test system also includes a mid-level computer that is connected to both the central controller and the host computer. The mid-level computer is used to store user instructions and convert the user instructions into test instructions that the central controller can recognize.

[0041] Specifically, the battery PACK charge and discharge test system also includes a mid-level computer that is connected to both the central controller and the host computer. The mid-level computer stores user instructions and converts the user instructions into test instructions that the central controller can recognize.

[0042] Optionally, such as Figure 2 As shown, the centralized controller includes a communicator and an analog signal acquisition unit; The communicator is used for communication connections with the converter components; Analog acquisition units are used to acquire the voltage and current of converter components.

[0043] Specifically, the centralized controller comprises a communication device and an analog quantity collector; the communication device is used for being communicatively connected with AC / DC converters of the plurality of front-stage AC / DC modules and DC / DC converters of the plurality of rear-stage DC / DC modules of the converter assembly, so as to facilitate issuing control instructions to the AC / DC converters and the DC / DC converters. The analog quantity collector comprises a voltage collector and a current collector, and is used for collecting voltages and currents of the converter assembly, respectively. The analog quantity collector further comprises a temperature collector, and is used for collecting temperatures in real time. Through the analog quantity collector, the operating state of the battery PACK under test can be conveniently collected.

[0044] Optionally, the centralized controller comprises an MCU control chip connected with the communication device and the analog quantity collector.

[0045] Specifically, the centralized controller comprises an MCU control chip connected with the communication device and the analog quantity collector, and the MCU control chip is loaded with a particle swarm algorithm and a fuzzy algorithm.

[0046] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by a computer program instructing related hardware. The program can be stored in a computer readable storage medium, and when the program is executed, the processes of the above-mentioned embodiments of the methods can be included. The storage medium can be a magnetic disc, an optical disc, a read-only memory (ROM) or a random access memory (RAM), etc. In the present application, the units described as separate components can be or can not be physically separated, and the components shown as units can be or can not be physical units, i.e., they can be located in one place or distributed on multiple network units. Part or all of the units can be selected according to actual needs to achieve the purpose of the embodiment of the present application. In addition, the functional units in each embodiment of the present application can be integrated in one processing unit, or each unit can exist physically, or two or more units can be integrated in one unit. The integrated unit can be realized in the form of hardware or in the form of a software functional unit.

[0047] Although the present application is disclosed as above, the protection scope of the present application is not limited to this. Those skilled in the art can make various changes and modifications without departing from the spirit and scope of the present application, and these changes and modifications will fall within the protection scope of the present application.

Claims

1. A battery PACK charge-discharge test system, characterized by, The application relates to a battery test system, which comprises a centralized controller, a transformer assembly and a host computer. The centralized controller is used for determining a global adjustment coefficient by using a particle swarm optimization algorithm according to a test instruction, determining a microcosmic adjustment coefficient by using a fuzzy algorithm according to the global adjustment coefficient, and determining a real-time adjustment coefficient according to the microcosmic adjustment coefficient and an initial adjustment coefficient. The transformer assembly is used for controlling test voltage and current of a battery PACK to be tested according to the real-time adjustment coefficient.

2. The battery PACK charge-discharge test system according to claim 1, wherein, The global adjustment coefficient comprises a global proportional coefficient and global integral data. The particle swarm optimization algorithm is used for determining a current given slope step, a step change rate, a current control error and an error change rate according to the test instruction. The particle swarm optimization algorithm is initialized. The global proportional coefficient and the global integral data are generated by using the particle swarm optimization algorithm according to the given slope step, the step change rate, the current control error and the error change rate.

3. The battery PACK charge-discharge test system according to claim 2, characterized in that, The microcosmic adjustment coefficient comprises a microcosmic proportional coefficient and microcosmic integral data. The global proportional coefficient and the global integral data are converted into global proportional fuzzy quantities and global integral fuzzy quantities by using the fuzzy algorithm, and membership degrees are defined by using a membership function of a fuzzy domain. The microcosmic proportional coefficient and the microcosmic integral data are generated by using a fuzzy inference and a barycentric method.

4. The battery PACK charge-discharge test system according to claim 1, wherein The real-time adjustment coefficient is determined by adding and fusing the microcosmic adjustment coefficient and the initial adjustment coefficient. The transformer assembly comprises a plurality of front-stage AC / DC modules and a plurality of rear-stage DC / DC modules which are electrically connected and communicated with the centralized controller.

5. The battery PACK charge-discharge test system according to claim 1, wherein, The plurality of front-stage AC / DC modules are used for converting commercial voltage into DC bus voltage. The plurality of rear-stage DC / DC modules are used for converting the DC bus voltage into the test voltage and current corresponding to the real-time adjustment coefficient. The plurality of front-stage AC / DC modules comprise a plurality of parallel AC / DC converters.

6. The battery PACK charge-discharge test system according to claim 5, wherein The host computer is communicated with the centralized controller and used for obtaining a user instruction and transmitting the user instruction to the centralized controller.

7. The battery PACK charge-discharge test system according to claim 1, wherein The user instruction is confirmed by the test instruction.

8. The battery PACK charge-discharge test system according to claim 7, characterized in that, The host computer is communicated with the centralized controller and used for obtaining a user instruction and transmitting the user instruction to the centralized controller.

9. The battery PACK charge-discharge test system according to claim 1, wherein, The centralized controller comprises a communication device and an analog quantity collector. The communication device is used for being communicated with the transformer assembly. The analog quantity collector is used for collecting voltage and current of the transformer assembly.

10. The battery PACK charge-discharge test system according to claim 9, characterized in that, The centralized controller comprises an MCU control chip connected with the communicator and the analog quantity collector.

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