Energy recovery type test equipment for photovoltaic battery and lithium battery

Abstract

The invention discloses an energy recovery type testing device for a photovoltaic battery and a lithium battery, and the device comprises an energy converter, a control switch, and a load switch controller, and the energy converter is connected in series between a to-be-tested battery pack and the control switch. The energy converter is used for converting received electric energy of the battery pack to be tested into light energy and radiating the light energy to a photovoltaic panel of the photovoltaic energy storage system, and the energy converter expands load power in a series connection and / or parallel connection mode; the load switch controller outputs a PWM control signal to the control switch based on the received PWM signal control instruction so as to adjust the switching period and the duty ratio of the control switch; the aging test equipment provided by the invention is high in reliability, wide in voltage range and high in expandability, and the aging test cost can be saved.

Description

Technical Field

[0001] The present application relates to the technical field of aging test equipment, and in particular to an energy recovery test equipment for photovoltaic cells and lithium batteries. Background Art

[0002] Existing photovoltaic energy storage systems include crystalline silicon photovoltaic modules (also known as photovoltaic panels) and batteries, which often require functional testing and aging testing before leaving the factory to verify product performance and improve product reliability.

[0003] The electronic loads used for battery aging tests in existing photovoltaic energy storage systems often use power semiconductors as energy dissipation devices. However, power semiconductors have the defects of high cost and poor reliability. For example, the cost of a TO-3 packaged power semiconductor dissipation device is about 10 yuan. If 1000W of power needs to be dissipated, 10-20 such devices need to be connected in series and parallel. Moreover, these power semiconductors connected in series and parallel must have quite good consistency, so the cost is high. If power semiconductors with poor consistency are combined together, they will be the biggest hidden danger to the reliability of the system and can easily cause the system to crash. In addition, the temperature characteristics of power semiconductors determine Its operation is unstable at high temperatures, and it must be used at a reduced rating and adopt a larger and more complex heat dissipation structure and a negative feedback temperature control system to solve the thermal stability problem. All of the above make it expensive to use power semiconductors as energy dissipation devices as electronic loads for battery aging testing. These stringent component requirements and complex system architectures greatly reduce the reliability of the system. In addition, the maximum current and maximum voltage that can be achieved by the existing power semiconductors as energy dissipation devices in electronic loads are fixed. Therefore, power semiconductors used as energy dissipation devices still have the defects of a small applicable voltage range and poor scalability, and there is room for improvement. Summary of the invention

[0004] In order to save aging test costs, improve reliability, widen voltage range, and improve scalability to solve the above-mentioned problems, the present application provides an energy recovery test equipment for photovoltaic cells and lithium batteries.

[0005] In the first aspect, the invention objective of the present application is achieved by adopting the following technical solutions: An energy recovery test device for photovoltaic cells and lithium batteries, comprising: an energy converter, a control switch and a load switch controller, wherein the energy converter is connected in series between a battery pack to be tested and the control switch, and is used to convert the received electrical energy of the battery pack to be tested into light energy and radiate it to a photovoltaic panel of a photovoltaic energy storage system, and the energy converter expands the load power in a series and / or parallel manner; the load switch controller outputs a PWM control signal to the control switch based on a received PWM signal control instruction to control the switching period and duty cycle of the control switch.

[0006] By adopting the above technical solution, the energy converter is also called an energy dissipation device. The energy converter of the present application is used to convert the electrical energy of the battery pack to be tested into light energy and heat radiation to the photovoltaic panel, and the photovoltaic panel combined with the photovoltaic cell can convert the light energy into electrical energy, and the energy conversion is: electricity->light->electricity->chemistry->electricity, forming a closed-loop energy cycle with high energy utilization rate; in practical application, the energy converter can adopt a low-cost energy converter such as a fluorescent lamp, which can not only convert electrical energy into light energy for easy recycling, but also eliminate the energy consumption of the power semiconductor device in the prior art, effectively solve its heating problem, and thus does not require a complex heat dissipation system, reducing costs; a 10 The cost of a 100W halogen lamp is about ten yuan, which is only equivalent to 5%~10% of the cost of using TO-3 packaged power semiconductors as energy dissipation devices; further, the energy converter of the present application is convenient for increasing the load voltage and load current by multi-series and multi-parallel methods, thereby expanding the load power. It can not only freely expand the load voltage range according to actual needs, but also has high scalability. By adjusting the switching cycle and duty cycle of the control switch through the PWM control signal, it is convenient to adjust the load current and power, thereby being compatible with more energy converters of different types and specifications, and having strong applicability; thus, the present application provides an aging test equipment with high reliability, wide voltage range and high scalability, which is convenient for saving aging test costs.

[0007] In a preferred example of the present application: the energy converter includes one or more of a halogen lamp, a fluorescent lamp, and an ultraviolet lamp; the energy converter is configured to be plug-and-play and hot-swappable.

[0008] By adopting the above technical solutions, halogen lamps simulate high-intensity light sources and are suitable for testing the performance of photovoltaic modules under high temperature and high light conditions; fluorescent lamps are closer to the natural spectrum and are used to evaluate the performance of photovoltaic modules under normal sunlight; UV lamps are used to accelerate the aging process of photovoltaic modules and simulate the impact of long-term ultraviolet radiation, thereby improving the accuracy of aging tests on photovoltaic energy storage systems; plug-and-play, hot-swappable energy converters are conducive to providing an aging test device with high scalability and a wide voltage test range, and are easy to replace and maintain the energy converter without complicated installation steps or downtime maintenance, thereby improving the efficiency of battery aging tests.

[0009] In a preferred example of the present application: the battery pack to be tested includes a discharge battery pack in a discharge cycle and a charge battery pack in a charge cycle; the discharge electrical energy of the discharge battery pack is transferred to the charge battery pack for charging.

[0010] By adopting the above-mentioned technical scheme, efficient recovery and reuse of energy is achieved. Compared with the traditional battery pack aging test process, the electric energy released by the discharged battery pack is wasted or only used for simple heat energy consumption. The present application directly converts the electric energy into the battery pack that needs to be charged, thereby realizing the internal circulation of energy and improving the energy utilization efficiency.

[0011] In a preferred example of the present application: the energy converter is also connected to a motor to be tested and a generator to be tested, the energy converter provides electrical energy to the motor to be tested, and the motor to be tested drives the generator to be tested to generate electricity through mechanical transmission.

[0012] By adopting the above technical scheme, the present application can not only perform aging tests on the battery packs of the photovoltaic energy storage system, the motor to be tested is an electric motor, but also test the performance of the motor and the generator at the same time, by using the electric energy output by the energy converter to drive the motor to be tested, and then the motor drives the generator to generate electricity, thereby realizing secondary conversion and recovery of energy; not only reducing the demand for external power supply, but also further improving energy utilization efficiency and reducing the energy consumption cost of testing; and in the photovoltaic energy storage system, the motor and the generator often work in coordination, which is conducive to imitating the actual coordinated working mode of the photovoltaic energy storage system, and the reliability of the aging test process and test data of the photovoltaic energy storage system is higher.

[0013] In the second aspect, the invention objective of the present application is achieved by adopting the following technical solutions: An energy recovery aging test method for a photovoltaic energy storage system is applied to an energy recovery test device for a photovoltaic cell and a lithium battery as described above, and the method comprises: The electric energy of the battery group to be tested is obtained through an energy converter, and the energy converter is adapted to the photovoltaic panel of the photovoltaic energy storage system; and the received electric energy of the battery group to be tested is converted into light energy in real time and radiated to the photovoltaic panel of the photovoltaic energy storage system, forming an energy recovery test system; The preset user terminal sends a PWM signal control instruction to the load switch controller, and the load switch controller outputs a PWM control signal to the control switch based on the received PWM signal control instruction to control the switching period and duty cycle of the control switch.

[0014] By adopting the above technical solution, the energy converter of the present application is used to convert the electrical energy of the battery pack to be tested into light energy and heat radiation to the photovoltaic panel, and the photovoltaic panel can convert the light energy into electrical energy, and the energy conversion is: electricity->light->electricity->chemistry->electricity, forming a closed-loop energy cycle with high energy utilization rate; in actual application, the energy converter can adopt low-cost energy converters such as fluorescent lamps, which can not only convert electrical energy into light energy for easy recycling, but also eliminate the energy consumption of power semiconductor devices in the prior art, effectively solve the heating problem, and thus do not need a complex heat dissipation system, reducing costs; a 1000W halogen lamp costs about ten yuan Right, it is only equivalent to 5%~10% of the cost of using TO-3 packaged power semiconductors as energy dissipation devices; further, the energy converter of the present application can easily adopt a multi-series and multi-parallel method to increase the load voltage and load current, thereby expanding the load power. It can not only freely expand the load voltage range according to actual needs, but also has high scalability. By adjusting the switching cycle and duty cycle of the control switch through the PWM control signal, it is convenient to adjust the load current and power, thereby being compatible with more energy converters of different types and specifications, and having strong applicability; thus, the present application provides an aging test equipment with high reliability, wide voltage range, and high scalability, which is convenient for saving aging test costs.

[0015] In a preferred example, the present application includes: According to the historical monitoring data set of the battery group of the photovoltaic energy storage system during the entire aging process, the key battery parameters of the battery group of the photovoltaic energy storage system based on the time series are collected; and the key battery parameters are preprocessed; In the preset battery aging prediction model, the key characteristic parameters and change trends of the battery pack during the aging process are identified based on the preprocessed battery key parameters, and the aging operation mode information of the battery pack and the battery performance prediction result are obtained, wherein the aging operation mode information includes a plurality of performance parameter intervals corresponding to different battery usage efficiencies, and the battery pack is associated with a battery pack identifier; Obtaining current status information of the battery pack, and formulating a battery energy-saving maintenance strategy based on aging operation mode information of the battery pack and battery performance prediction results; During the aging test of the battery energy-saving maintenance strategy, a battery performance detection value of each performance parameter interval is obtained, the battery performance detection value is compared with the corresponding battery performance prediction value in the battery energy-saving maintenance strategy to obtain a battery performance difference value, and the battery performance difference value is compared with a preset change threshold value. If the battery performance difference value is outside the preset change threshold range, a battery aging change information collection instruction is triggered; According to the battery aging change information collection instruction, the adjustment parameter information of the battery energy-saving maintenance strategy is obtained.

[0016] By adopting the above technical solution and performing data preprocessing on key battery parameters, it is possible to more accurately identify parameter changes of the battery pack during the aging process, thereby improving the accuracy of battery performance prediction. The aging operation mode information includes multiple performance parameter intervals corresponding to different battery usage efficiencies, so that the formulated battery energy-saving and maintenance strategy can be personalized according to the actual situation of the battery pack, which is more in line with the actual needs of the battery pack. In addition, during the aging test, by comparing the battery performance detection value with the corresponding battery performance prediction value in the battery energy-saving and maintenance strategy, the change of the battery performance of the battery pack can be timely known, and when the battery performance difference value is outside the preset change threshold range, it is timely discovered and quickly responded to, and then the adjustment parameter information of the battery energy-saving and maintenance strategy is obtained; the battery energy-saving and maintenance strategy is dynamically optimized and adjusted to ensure that the battery energy-saving and maintenance strategy is maintained at a high maintenance level.

[0017] In a preferred example of the present application, the adjusting parameter information of the battery energy-saving maintenance strategy is obtained according to the battery aging change information collection instruction, including: Based on the battery aging change information collection instruction, collect the current state information of the battery pack, wherein the current state information of the battery pack includes the current state of the battery pack, environmental changes, and operating conditions; Based on the collected information on the current status of the battery pack and combined with the historical execution effect data of the battery energy-saving maintenance strategy, the adjustment parameters of the battery energy-saving maintenance strategy are optimized according to the preset intelligent optimization algorithm to generate a new battery energy-saving maintenance strategy.

[0018] By adopting the above technical scheme, the actual environment and working conditions of the battery pack can be more comprehensively understood based on the current status information of the battery pack, so as to accurately simulate and test the actual working conditions of the battery pack; based on the collected current status information of the battery pack, combined with the historical execution effect data of the battery energy-saving maintenance strategy, the adjustment parameters of the battery energy-saving maintenance strategy are optimized according to the preset intelligent optimization algorithm, effectively avoiding the subjectivity and uncertainty of human judgment, ensuring that the new battery energy-saving maintenance strategy achieves the best effect in theory, and improving the scientificity and reliability of the battery energy-saving maintenance strategy by incorporating feedback from the historical execution effect data.

[0019] In a preferred example of the present application, in the preset battery aging prediction model, the key characteristic parameters and change trends of the battery pack during the aging process are identified based on the preprocessed key battery parameters, and the aging operation mode information of the battery pack and the battery performance prediction results are obtained, including: Obtaining preprocessed time series data corresponding to key battery parameters, and performing K-means cluster analysis on the time series data in a preset battery aging prediction model, wherein the K value is pre-set according to specific characteristics of the energy storage battery pack and aging test requirements; Using a K-means algorithm to divide the time series data into K clusters, each cluster represents the performance characteristics of a battery pack at different aging stages, so as to obtain a clustering result; According to the clustering results, the performance differences of battery packs between clusters are analyzed, the key characteristic parameters and change trends of the performance degradation of each battery pack during the aging process are identified and extracted, and the aging operation mode information of the battery pack and the battery performance prediction results are obtained.

[0020] By adopting the above technical solution, by performing K-means cluster analysis on the time series data corresponding to the pre-processed battery key parameters, the performance changes of the battery pack during the aging process can be divided into different stages or modes; the data-driven clustering analysis method of the present application can more accurately capture the subtle changes in the battery pack performance over time, improve the accuracy of aging prediction, and through the cluster division method of the clustering results, it is possible to more clearly understand the performance of the battery pack at different aging stages, and the analysis of key characteristic parameters and change trends provides data support for the remaining battery life, battery maintenance and targeted formulation of battery energy-saving maintenance strategies, and is conducive to predicting problems that may arise in the battery pack during the aging process, thereby improving the accuracy and reliability of aging prediction.

[0021] In summary, the present application includes at least one of the following beneficial technical effects: 1. The energy converter of the present application is used to convert the electrical energy of the battery pack to be tested into light energy and heat radiation to the photovoltaic panel, and the photovoltaic panel can convert the light energy into electrical energy, so that the energy conversion is: electricity->light->electricity->chemistry->electricity, forming a closed-loop energy cycle with high energy utilization rate; in practical application, the energy converter can adopt low-cost energy converters such as fluorescent lamps, which can not only convert electrical energy into light energy for easy recycling, but also eliminate the energy consumption of power semiconductor devices in the prior art, effectively solve the heating problem, and thus do not need a complex heat dissipation system, reducing costs; a 1000W halogen lamp costs about ten yuan, which is only equivalent to The cost of using TO-3 packaged power semiconductors as energy dissipation devices is 5% to 10%; further, the energy converter of the present application can easily increase the load voltage and load current by multi-series and multi-parallel methods, thereby expanding the load power. It can not only freely expand the load voltage range according to actual needs, but also has high scalability. By adjusting the switching cycle and duty cycle of the control switch through the PWM control signal, it is convenient to adjust the load current and power, thereby being compatible with more energy converters of different types and specifications, and having strong applicability; thus, the present application provides an aging test device with high reliability, wide voltage range, and high scalability, which is convenient for saving aging test costs; 2. Based on the current status information of the battery pack, the actual environment and working conditions of the battery pack can be more fully understood, so as to accurately simulate and test the actual working conditions of the battery pack; based on the collected current status information of the battery pack, combined with the historical execution effect data of the battery energy-saving maintenance strategy, the adjustment parameters of the battery energy-saving maintenance strategy are optimized according to the preset intelligent optimization algorithm, effectively avoiding the subjectivity and uncertainty of human judgment, ensuring that the new battery energy-saving maintenance strategy achieves the best effect in theory, and improving the scientificity and reliability of the battery energy-saving maintenance strategy by integrating the feedback of historical execution effect data; 3. By performing K-means cluster analysis on the time series data corresponding to the preprocessed key battery parameters, the performance changes of the battery pack during the aging process can be divided into different stages or modes; the data-driven clustering method of this application can more accurately capture the subtle changes in the battery pack performance over time and improve the accuracy of aging prediction. The clustering method of the clustering results can provide a clearer understanding of the performance of the battery pack at different aging stages, and the analysis of key characteristic parameters and change trends provides data support for the remaining battery life, battery maintenance and targeted formulation of battery energy-saving maintenance strategies, and is conducive to predicting problems that may arise in the battery pack during the aging process, thereby improving the accuracy and reliability of aging prediction. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 This is a schematic diagram of a battery aging test using a power semiconductor as an energy dissipation device in the prior art; Figure 2 This is a schematic diagram of an application of an energy recovery testing device for photovoltaic cells and lithium batteries in one embodiment of the present application; Figure 3 This is an application schematic diagram of another embodiment of an energy recovery testing device for photovoltaic cells and lithium batteries in one embodiment of the present application; Figure 4 This is a flow chart of an energy recovery testing method for photovoltaic cells and lithium batteries in one embodiment of the present application; Figure 5 This is a flow chart of step S20 in an energy recovery testing method for photovoltaic cells and lithium batteries in one embodiment of the present application. DETAILED DESCRIPTION

[0023] The present application is further described in detail below in conjunction with the accompanying drawings.

[0024] like Figure 1 As shown, Figure 1This is a schematic diagram of the prior art using power semiconductors as energy dissipation devices as electronic loads or battery discharge test equipment for battery aging testing. By way of example, a high-reliability TO-3 packaged power semiconductor dissipation device costs about 10 yuan. If 1000W of power needs to be dissipated, 10-20 such devices need to be connected in series or parallel to complete the task.

[0025] In one embodiment, if Figure 2 As shown, the present application discloses an energy recovery test equipment for photovoltaic cells and lithium batteries. The energy recovery aging test equipment includes an energy converter, a control switch and a load switch controller. The energy converter includes one or more of a halogen lamp, a fluorescent lamp, and an ultraviolet lamp. The storage battery of the photovoltaic energy storage system adopts a lithium battery. The energy-consuming element of the present application is not a switch, but an energy converter. The control switch can be a MOS switch or other type of switch. In actual application, the control switch is a switching device that does not have thermal stress. The energy converter is set to a plug-and-play, hot-swappable state. The energy converter is connected in series between the battery pack to be tested and the control switch. The energy converter is used to convert the received electrical energy of the battery pack to be tested into light energy and radiate it to the photovoltaic panel of the photovoltaic energy storage system. The energy converter can expand the load power in series and / or parallel. The load switch controller outputs a PWM control signal to the control switch based on the received PWM signal control instruction to adjust the switching cycle and duty cycle of the control switch.

[0026] The energy converter of the present application is used to convert the electrical energy of the battery pack to be tested into light energy and heat radiation to the photovoltaic panel, and the photovoltaic panel can convert the light energy into electrical energy, so that the energy conversion is: electricity->light->electricity->chemistry->electricity, forming a closed-loop energy cycle with high energy utilization rate; in actual application, the energy converter can adopt low-cost energy converters such as fluorescent lamps, which can not only convert electrical energy into light energy for easy recycling, but also eliminate the energy consumption of power semiconductor devices in the prior art, effectively solve the heating problem, and thus do not require a complex heat dissipation system, reducing costs; a 1000W halogen lamp costs about ten yuan, which is only equivalent to 5% to 10% of the cost of using TO-3 packaged power semiconductors as energy dissipation devices.

[0027] In actual application, the battery packs to be tested include discharge battery packs in a discharge cycle and rechargeable battery packs in a charge cycle; the discharge electric energy of the discharge battery pack is transferred to the rechargeable battery pack for charging; halogen lamps simulate high-intensity light sources and are suitable for testing the performance of photovoltaic modules under high temperature and high light conditions; fluorescent lamps are closer to the natural spectrum and are used to evaluate the performance of photovoltaic modules under normal sunlight; ultraviolet lamps are used to accelerate the aging process of photovoltaic modules, simulate the impact of long-term ultraviolet radiation, and improve the accuracy of aging tests on photovoltaic energy storage systems.

[0028] like Figure 3 As shown, in one embodiment, the energy converter is also connected to the motor to be tested and the generator to be tested, the energy converter provides electrical energy to the motor to be tested, and the motor to be tested drives the generator to be tested to generate electricity through mechanical transmission; the discharged electrical energy of the discharge battery pack is transferred to the charging battery pack for charging; the present application can not only perform aging test on the battery pack of the photovoltaic energy storage system, but also test the performance of the motor and the generator to be tested at the same time, by using the electrical energy output by the energy converter to drive the motor to be tested, and then the motor drives the generator to generate electricity, thereby realizing secondary conversion and recovery of energy; not only reducing the demand for external power supply, but also further improving energy utilization efficiency and reducing the energy consumption cost of testing; and in the photovoltaic energy storage system, the motor and the generator often work in coordination, which is conducive to imitating the actual coordinated working mode of the photovoltaic energy storage system, and the aging test process and test data of the photovoltaic energy storage system are more reliable.

[0029] In one embodiment, an energy recovery testing method for photovoltaic cells and lithium batteries is provided. The energy recovery testing method for photovoltaic cells and lithium batteries is applied to an energy recovery testing device for photovoltaic cells and lithium batteries as described above.

[0030] An energy recovery testing method for photovoltaic cells and lithium batteries specifically comprises the following steps: S1: Obtain the electric energy of the battery group to be tested through the energy converter, and adapt the energy converter to the photovoltaic panel of the photovoltaic energy storage system; and convert the received electric energy of the battery group to be tested into light energy in real time and radiate it to the photovoltaic panel of the photovoltaic energy storage system, so as to form an energy recovery test system.

[0031] S2: The preset user terminal sends a PWM signal control instruction to the load switch controller. Based on the received PWM signal control instruction, the load switch controller outputs a PWM control signal to the control switch to adjust the switching period and duty cycle of the control switch.

[0032] In this embodiment, the energy converter of the present application is used to convert the electric energy of the battery pack to be tested into light energy and heat radiation to the photovoltaic panel, and the photovoltaic panel combined with the photovoltaic cell can convert the light energy into electric energy, and the energy conversion is: electricity->light->electricity->chemistry->electricity, forming a closed-loop cycle of energy, and the energy utilization rate is high; in practical application, the energy converter can adopt a low-cost energy converter such as a fluorescent lamp, which can not only convert electric energy into light energy for easy recycling, but also eliminate the energy consumption of the power semiconductor device in the prior art, effectively solve its heating problem, and thus do not require a complex heat dissipation system, reducing the cost; a 1000W halogen lamp costs about ten yuan, which is only equivalent to 5% to 10% of the cost of using TO-3 packaged power semiconductors as energy dissipation devices; further, the energy converter of the present application can easily increase the load voltage and load current by a multi-series and multi-parallel method, thereby expanding the load power, and can not only freely expand the load voltage range according to actual needs, but also has high scalability, and adjusts the switching cycle and duty cycle of the control switch through the PWM control signal, so as to facilitate the adjustment of the load current and power, thereby being compatible with more energy converters of different types and specifications, and having strong applicability.

[0033] In one embodiment, if Figure 4 As shown, a photovoltaic cell and a lithium battery energy recovery test method also includes: S10: Based on the historical monitoring data set of the battery group of the photovoltaic energy storage system during the entire aging process, the key battery parameters of the battery group of the photovoltaic energy storage system based on the time series are collected; and the key battery parameters are preprocessed.

[0034] In this embodiment, in order to formulate a maintenance strategy for the battery pack (lithium battery in this embodiment) of the photovoltaic energy storage system, a comprehensive consideration is made based on the actual environment in which the battery pack of the photovoltaic energy storage system is located, the actual operating status of the battery pack and the actual aging of the battery pack of the photovoltaic energy storage system; the key battery parameters such as voltage, current, temperature, capacity, internal resistance, etc. of the battery pack are collected regularly (such as every hour or every day) to form a time series data set. The data preprocessing includes data cleaning, data interpolation, data normalization and feature extraction. The feature extraction includes extracting characteristic parameters that have a significant impact on the battery aging process from the preprocessed key battery parameters, such as capacity decay rate, internal resistance growth rate, etc.

[0035] S20: In the preset battery aging prediction model, the key characteristic parameters and change trends of the battery pack during the aging process are identified based on the preprocessed battery key parameters, and the aging operation mode information of the battery pack and the battery performance prediction results are obtained. The aging operation mode information includes multiple performance parameter intervals corresponding to different battery usage efficiencies, and the battery pack is associated with a battery pack identification.

[0036] In this embodiment, the model construction process of the battery aging prediction model includes: selecting a suitable machine learning or deep learning model, such as an LSTM network, according to the characteristics of battery aging and data characteristics; using a historical monitoring data set as a training set, and during the training process, using cross-validation, hyperparameter tuning and other techniques to train the model, and using an independent validation set to verify the trained model to ensure that the model can accurately predict the aging process and performance changes of the battery pack; the performance parameter interval corresponds to different charging stage intervals and different discharge stage intervals of the battery pack; the aging operation mode information includes different aging operation modes divided based on the changes in battery health, capacity attenuation interval and internal resistance change, including aging stage division and performance degradation law; the battery performance prediction results include battery voltage, current, temperature, capacity, internal resistance and other prediction data.

[0037] S30: Obtain current status information of the battery pack, and formulate a battery energy-saving maintenance strategy based on the aging operation mode information of the battery pack and the battery performance prediction result.

[0038] In this embodiment, the current status information of the battery pack includes the current voltage, current, temperature, and remaining capacity.

[0039] Specifically, a battery energy-saving maintenance strategy is formulated based on the battery pack's aging operating mode information and battery performance prediction results; the strategy content includes adjusting the charge and discharge strategy (such as limiting the charge and discharge depth, optimizing the charge and discharge rate), optimizing temperature management (such as adjusting the working environment temperature), and implementing balanced maintenance (such as regular battery balancing).

[0040] S40: During the aging test of the battery energy-saving and maintenance strategy, the battery performance detection value of each performance parameter interval is obtained, the battery performance detection value is compared with the corresponding battery performance prediction value in the battery energy-saving and maintenance strategy to obtain the battery performance difference value, and the battery performance difference value is compared with the preset change threshold value. If the battery performance difference value is outside the preset change threshold range, the battery aging change information collection instruction is triggered.

[0041] Specifically, when the performance difference value exceeds a threshold, a battery aging change information collection instruction is triggered; detailed information including the current state of the battery pack, environmental changes, operating conditions, etc. is collected.

[0042] S50: Acquire adjustment parameter information of the battery energy-saving maintenance strategy according to the battery aging change information collection instruction.

[0043] Specifically, the adjustment parameters in the battery energy-saving maintenance strategy are optimized and adjusted according to the adjustment parameter information.

[0044] In one embodiment, in step S50, according to the battery aging change information collection instruction, the adjustment parameter information of the battery energy saving maintenance strategy is obtained, including: S501: Based on the battery aging change information collection instruction, collect the current state information of the battery pack, where the current state information of the battery pack includes the current state of the battery pack, environmental changes, and operating conditions.

[0045] In this embodiment, aging testing and energy-saving maintenance are performed based on the actual aging state and battery parameters of the battery pack in the actual application environment and in the actual operating conditions; the current state information of the battery pack includes real-time data such as the voltage, current, temperature, SOC (remaining power), SOH (health status) of the battery pack; environmental changes include parameters such as temperature, humidity, and light intensity; and operating conditions include operating data such as the charging / discharging power, number of cycles, and load changes of the photovoltaic energy storage system.

[0046] S502: Based on the collected current status information of the battery pack and the historical execution effect data of the battery energy-saving maintenance strategy, the adjustment parameters of the battery energy-saving maintenance strategy are optimized according to a preset intelligent optimization algorithm to generate a new battery energy-saving maintenance strategy.

[0047] Specifically, intelligent optimization algorithms include genetic algorithms and particle swarm optimization algorithms; parameter optimization uses algorithm iterative calculations to adjust key parameters in the battery energy-saving maintenance strategy, such as charging strategy (charging current, voltage), discharging strategy (discharge depth, rate), temperature control strategy, etc., to achieve the best energy-saving effect and battery life extension effect.

[0048] In one embodiment, if Figure 5 As shown, in step S20, in the preset battery aging prediction model, the key characteristic parameters and change trends of the battery pack during the aging process are identified based on the preprocessed battery key parameters, and the aging operation mode information of the battery pack and the battery performance prediction results are obtained, including: S201: Obtain preprocessed time series data corresponding to key battery parameters, and perform K-means cluster analysis on the time series data in a preset battery aging prediction model, wherein the K value is pre-set according to specific characteristics of the energy storage battery pack and aging test requirements.

[0049] In this embodiment, the key battery parameters are sensitive battery parameters during the battery aging process; the K value is set in advance according to the specific characteristics of the energy storage battery pack (such as battery type, capacity, use environment, etc.) and aging test requirements (such as test cycle, accuracy requirements, etc.); the selection of the K value should be able to reasonably reflect the performance changes of the battery pack at different aging stages.

[0050] S202: Using a K-means algorithm, the time series data is divided into K clusters, each cluster representing performance characteristics of a battery pack at different aging stages, to obtain a clustering result.

[0051] Specifically, the K-means algorithm clustering partitioning includes randomly selecting K data points as the initial cluster centers, assigning each data point to the cluster where the nearest cluster center is located, recalculating the cluster center of each cluster (usually the mean of the data points in the cluster), and repeating the above steps until the cluster center no longer changes significantly or reaches the preset number of iterations, thereby obtaining K clusters and their corresponding cluster centers.

[0052] S203: Analyze the performance differences of the battery packs between the clusters according to the clustering results, identify and extract the key characteristic parameters and change trends of the performance degradation of each battery pack during the aging process, and obtain the aging operation mode information of the battery pack and the battery performance prediction results.

[0053] In this embodiment, the performance difference analysis includes comparing the differences in battery pack performance parameters (such as voltage, current, temperature, SOC, SOH, etc.) between different clusters, and analyzing the relationship between these differences and the battery aging stage; the key characteristic parameter identification includes identifying the key characteristic parameters that are most sensitive to the battery aging process based on the results of the performance difference analysis; the change trend extraction is to observe the change trend of the key characteristic parameters in different clusters (i.e., different aging stages) and extract the typical characteristics of the battery pack aging process.

[0054] Specifically, the aging operation mode information and the current battery pack status information are used to predict the future performance of the battery and obtain the corresponding battery performance prediction result.

[0055] It should be understood that the serial numbers of the steps in the above embodiments do not imply a sequence of execution. The execution sequence of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0056] The embodiments described above are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, it should be understood by those skilled in the art that the technical solutions described in the aforementioned embodiments may still be modified, or some of the features thereof may be replaced by equivalents. Such 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, and should all be included in the protection scope of the present application.

Claims

1. An energy recovery testing device for photovoltaic cells and lithium batteries, characterized in that: include: An energy converter, a control switch and a load switch controller, wherein the energy converter is connected in series between the battery pack to be tested and the control switch, and is used to convert the received electrical energy of the battery pack to be tested into light energy and radiate it to the photovoltaic panel of the photovoltaic energy storage system, and the energy converter expands the load power in series and / or parallel manner; the load switch controller outputs a PWM control signal to the control switch based on the received PWM signal control instruction to control the switching period and duty cycle of the control switch.

2. The energy recovery testing equipment for photovoltaic cells and lithium batteries according to claim 1, characterized in that: The energy converter includes one or more of a halogen lamp, a fluorescent lamp, and an ultraviolet lamp; the energy converter is configured to be plug-and-play and hot-swappable.

3. The energy recovery testing equipment for photovoltaic cells and lithium batteries according to claim 1, characterized in that: The battery pack to be tested includes a discharge battery pack in a discharge cycle and a charge battery pack in a charge cycle; the discharge electric energy of the discharge battery pack is transferred to the charge battery pack for charging.

4. The energy recovery testing equipment for photovoltaic cells and lithium batteries according to claim 1, characterized in that: The energy converter is also connected to the motor to be tested and the generator to be tested. The energy converter provides electrical energy to the motor to be tested, and the motor to be tested drives the generator to be tested to generate electricity through mechanical transmission.

5. An energy recovery testing method for photovoltaic cells and lithium batteries, characterized in that: An energy recovery testing device for a photovoltaic cell and a lithium battery as claimed in any one of claims 1 to 4, the method comprising: The electric energy of the battery group to be tested is obtained through an energy converter, and the energy converter is adapted to the photovoltaic panel of the photovoltaic energy storage system; and the received electric energy of the battery group to be tested is converted into light energy in real time and radiated to the photovoltaic panel of the photovoltaic energy storage system, forming an energy recovery test system; The preset user terminal sends a PWM signal control instruction to the load switch controller, and the load switch controller outputs a PWM control signal to the control switch based on the received PWM signal control instruction to control the switching period and duty cycle of the control switch.

6. The energy recovery testing method for photovoltaic cells and lithium batteries according to claim 5, characterized in that: include: According to the historical monitoring data set of the battery pack of the photovoltaic energy storage system during the entire aging process, the key battery parameters of the battery pack of the photovoltaic energy storage system based on the time series are collected; Performing data preprocessing on the key parameters of the battery; In the preset battery aging prediction model, the key characteristic parameters and change trends of the battery pack during the aging process are identified based on the preprocessed battery key parameters, and the aging operation mode information of the battery pack and the battery performance prediction result are obtained, wherein the aging operation mode information includes a plurality of performance parameter intervals corresponding to different battery usage efficiencies, and the battery pack is associated with a battery pack identifier; Obtaining current status information of the battery pack, and formulating a battery energy-saving maintenance strategy based on aging operation mode information of the battery pack and battery performance prediction results; During the aging test of the battery energy-saving maintenance strategy, a battery performance detection value of each performance parameter interval is obtained, the battery performance detection value is compared with the corresponding battery performance prediction value in the battery energy-saving maintenance strategy to obtain a battery performance difference value, and the battery performance difference value is compared with a preset change threshold value. If the battery performance difference value is outside the preset change threshold range, a battery aging change information collection instruction is triggered; According to the battery aging change information collection instruction, the adjustment parameter information of the battery energy-saving maintenance strategy is obtained.

7. The energy recovery testing method for photovoltaic cells and lithium batteries according to claim 6, characterized in that: The step of obtaining the adjustment parameter information of the battery energy-saving maintenance strategy according to the battery aging change information collection instruction includes: Based on the battery aging change information collection instruction, collect the current state information of the battery pack, wherein the current state information of the battery pack includes the current state of the battery pack, environmental changes, and operating conditions; Based on the collected information on the current status of the battery pack and combined with the historical execution effect data of the battery energy-saving maintenance strategy, the adjustment parameters of the battery energy-saving maintenance strategy are optimized according to the preset intelligent optimization algorithm to generate a new battery energy-saving maintenance strategy.

8. The energy recovery testing method for photovoltaic cells and lithium batteries according to claim 6, characterized in that: In the preset battery aging prediction model, the key characteristic parameters and change trends of the battery pack during the aging process are identified based on the preprocessed key battery parameters, and the aging operation mode information of the battery pack and the battery performance prediction results are obtained, including: Obtaining preprocessed time series data corresponding to key battery parameters, and performing K-means cluster analysis on the time series data in a preset battery aging prediction model, wherein the K value is pre-set according to specific characteristics of the energy storage battery pack and aging test requirements; Using a K-means algorithm to divide the time series data into K clusters, each cluster represents the performance characteristics of a battery pack at different aging stages, so as to obtain a clustering result; According to the clustering results, the performance differences of battery packs between clusters are analyzed, the key characteristic parameters and change trends of the performance degradation of each battery pack during the aging process are identified and extracted, and the aging operation mode information of the battery pack and the battery performance prediction results are obtained.

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