Charging station energy storage management system

Abstract

The invention provides a charging station energy storage management system, and the system comprises a data collection module which is used for collecting the operation data of a charging station; the energy conversion efficiency calculation module is used for calculating the energy conversion efficiency of the energy storage unit according to the data; the charging and discharging strategy determination module is used for determining a charging and discharging strategy of the energy storage unit according to the energy conversion efficiency; and the performance optimization module is used for judging whether the charging and discharging strategy of the energy storage unit meets the optimal energy efficiency and cost benefit standard or not, and adjusting the strategy as required. According to the invention, the energy conversion efficiency can be improved, the cooperative efficiency error is reduced, and the optimal energy efficiency and cost effectiveness are realized, so that the operation efficiency and economic benefits of the charging station are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of charging station energy storage, and more specifically, to a charging station energy storage management system. Background Art

[0002] Existing energy storage systems for charging stations face some challenges in energy management, especially in terms of improving energy conversion efficiency and reducing operating costs. The charging and discharging process of energy storage units usually relies on fixed strategies and lacks dynamic response and optimization to real-time data. In addition, the battery management system (BMS) of the energy storage unit can often only perform basic monitoring and control, but cannot achieve deep optimization and performance improvement. In practical applications, this limitation leads to low energy storage efficiency and large errors in synergistic efficiency, which makes it difficult to meet the growing charging demand and cost-effectiveness requirements. With the popularization of electric vehicles and the increase in charging demand, traditional energy storage management systems have been difficult to adapt to new market demands. These systems often lack intelligent data analysis and strategy adjustment capabilities, and cannot fully utilize the potential of energy storage units, resulting in energy waste and reduced economic benefits.

[0003] In the process of implementing the embodiments of the present invention, the inventors found that there are at least the following problems or defects in the prior art: the energy conversion efficiency of the energy storage unit is not high, the synergistic efficiency error is large, there is a lack of effective performance optimization mechanism, and the battery management system of the energy storage unit has limited functions and cannot achieve real-time monitoring and deep optimization management of the battery pack status. Summary of the invention

[0004] The invention provides an energy storage management system for a charging station.

[0005] In a first aspect of the present invention, a charging station energy storage management system is provided, comprising:

[0006] Data acquisition module, used to collect charging station operation data;

[0007] An energy conversion efficiency calculation module, used to calculate the energy conversion efficiency of the energy storage unit according to the data;

[0008] A charging and discharging strategy determination module is used to determine the charging and discharging strategy of the energy storage unit according to the energy conversion efficiency;

[0009] The performance optimization module is used to determine whether the charging and discharging strategy of the energy storage unit meets the optimal energy efficiency and cost-effectiveness standards, and adjust the strategy as needed.

[0010] In a second aspect of the present invention, a charging station energy storage management method is provided, comprising:

[0011] Including: collecting charging station operation data, including: charging status, discharging status of energy storage units, voltage and current of battery packs;

[0012] Calculating the energy conversion efficiency of the energy storage unit based on the data;

[0013] Determining a charging and discharging strategy of an energy storage unit according to the energy conversion efficiency;

[0014] Determine whether the charging and discharging strategy of the energy storage unit meets the optimal energy efficiency and cost-effectiveness standards. If so, execute the corresponding strategy; otherwise, adjust the strategy to optimize the energy storage performance.

[0015] Furthermore, the equivalent model of the energy storage unit in the energy storage system is:

[0016]

[0017] Among them, U b is the voltage of the energy storage unit, I b is the current flowing through the energy storage unit, R b is the equivalent resistance of the energy storage unit, L b is the equivalent inductance of the energy storage unit, ω is the angular frequency of the power supply, E chem is the chemical energy of the energy storage unit, C b is the equivalent capacitance of the energy storage unit. Further, based on the equivalent model, the energy conversion efficiency of the energy storage unit is calculated by the following formula:

[0018]

[0019] Where η is the energy conversion efficiency, E in is the input energy, E out For output energy.

[0020] Furthermore, the synergistic efficiency error coefficient of the energy storage unit is calculated by the following formula:

[0021]

[0022] Among them, E eff is the collaborative efficiency error coefficient, E in，i and E out，i are the input and output energies of the i-th energy storage unit respectively, and N is the total number of energy storage units.

[0023] Further, the optimal energy efficiency and cost-effectiveness criteria include: wherein Eset is a collaborative efficiency error coefficient threshold;

[0024] E eff ≤Eset;

[0025] If E effIf the above formula is satisfied, the corresponding charging and discharging strategy is executed, otherwise, the strategy is adjusted.

[0026] Further, according to the optimal energy efficiency and cost-effectiveness criteria, the optimal charging and discharging strategy is identified, including: when E eff If the formula is satisfied, a high-efficiency and cost-effective charging and discharging strategy is implemented;

[0027] When E eff If the formula is not satisfied, other charging and discharging strategies are adjusted.

[0028] Furthermore, the executing a corresponding charging and discharging strategy includes: sending a control signal to the energy storage unit to adjust its charging and discharging state.

[0029] Furthermore, the energy storage unit adopts a battery management system BMS to achieve real-time monitoring and optimized management of the battery pack status.

[0030] Furthermore, the synergistic efficiency error coefficient of the energy storage unit is calculated by the following formula, and the performance of the energy storage system is optimized based on this error coefficient:

[0031]

[0032] Among them, E opt The optimized synergistic efficiency error coefficient is used to optimize the performance of the energy storage system by minimizing the error coefficient.

[0033] According to the above-mentioned embodiments of the present invention, at least the following beneficial effects are achieved: the charging station energy storage management system of the present invention can significantly improve the operating efficiency of the energy storage unit by integrating multiple modules such as data acquisition, energy conversion efficiency calculation, charge and discharge strategy determination and performance optimization. The system can collect the operating data of the charging station in real time, including key parameters such as the charging state, discharge state, voltage and current of the energy storage unit, and then calculate the energy conversion efficiency of the energy storage unit. Based on these data, the system can intelligently determine the optimal charge and discharge strategy to ensure that the energy storage unit operates under the premise of meeting the optimal energy efficiency and cost-effectiveness standards. In addition, the system also includes a performance optimization module, which can continuously evaluate and adjust the charge and discharge strategy of the energy storage unit to adapt to different operating conditions and requirements. By minimizing the synergistic efficiency error coefficient, the system can further optimize the energy storage performance and achieve higher energy utilization efficiency. This intelligent management method can not only reduce energy waste and reduce operating costs, but also improve the service capabilities and user satisfaction of charging stations, providing strong technical support for the popularization of electric vehicles and the development of charging infrastructure. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] The above and other objects, features and advantages of the exemplary embodiments of the present invention will become readily understood by reading the detailed description below with reference to the accompanying drawings. In the accompanying drawings, several embodiments of the present invention are shown in an exemplary and non-limiting manner, in which:

[0035] Figure 1 A schematic diagram of the structure of a charging station energy storage management system provided by an embodiment of the present invention;

[0036] Figure 2 A schematic diagram of a flow chart of a charging station energy storage management method provided by an embodiment of the present invention;

[0037] Figure 3 The schematic diagram schematically shows the structure of an electronic device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0038] The principles and spirit of the present invention will be described below with reference to several exemplary embodiments. It should be understood that these embodiments are provided only to enable those skilled in the art to better understand and implement the present invention, and are not intended to limit the scope of the present invention in any way. On the contrary, these embodiments are provided to make the present invention more thorough and complete, and to fully convey the scope of the present invention to those skilled in the art.

[0039] Those skilled in the art know that the embodiments of the present invention can be implemented as a system, device, apparatus, method or computer program product. Therefore, the present invention can be specifically implemented in the following forms, namely: complete hardware, complete software (including firmware, resident software, microcode, etc.), or a combination of hardware and software.

[0040] It should be noted that any number of elements in the drawings is for illustration rather than limitation, and any naming is only for distinction and does not have any limiting meaning.

[0041] Reference below Figure 1 , Figure 1 This is a schematic diagram of the structure of a charging station energy storage management system provided by an embodiment of the present invention. Figure 1 As shown, a charging station energy storage management system 100 includes:

[0042] The data collection module 101 is used to collect the operation data of the charging station;

[0043] An energy conversion efficiency calculation module 102, used to calculate the energy conversion efficiency of the energy storage unit according to the data;

[0044] A charge and discharge strategy determination module 103, used to determine the charge and discharge strategy of the energy storage unit according to the energy conversion efficiency;

[0045] The performance optimization module 104 is used to determine whether the charging and discharging strategy of the energy storage unit meets the optimal energy efficiency and cost-effectiveness standards, and adjust the strategy as needed.

[0046] It should be noted that the data acquisition module is responsible for collecting various operating parameters of the charging station, including but not limited to the charging state, discharging state, voltage and current of the energy storage unit, etc. The energy conversion efficiency calculation module uses this data to calculate the energy conversion efficiency of the energy storage unit through a specific algorithm. The charging and discharging strategy determination module intelligently formulates the charging and discharging strategy based on the calculated energy conversion efficiency and combined with the preset parameters and conditions. The performance optimization module further analyzes these strategies to ensure that they meet the optimal energy efficiency and cost-effectiveness standards.

[0047] Preferably, the data acquisition module can use high-precision sensors and data acquisition cards to ensure the accuracy and real-time nature of the data. The calculation of energy conversion efficiency can use a preset algorithm, such as a calculation method based on the equivalent circuit model of the energy storage unit, to improve the accuracy of the calculation. The determination of the charging and discharging strategy can take into account factors such as peak and valley electricity prices and charging demand forecasts to achieve a more flexible and economical charging and discharging arrangement. The performance optimization module can use machine learning technology to continuously adjust and optimize the charging and discharging strategy based on historical data and real-time feedback to achieve higher energy efficiency and economic benefits. In addition, the system can also integrate a battery management system BMS to achieve real-time monitoring and in-depth optimization management of the battery pack status.

[0048] The above-mentioned embodiments of the present invention have the following beneficial effects: the energy storage management system of the charging station of the present invention can improve the energy conversion efficiency of the energy storage unit by intelligently collecting and analyzing the operation data of the charging station. The system can not only monitor the charging and discharging status of the energy storage unit in real time, but also accurately calculate the energy conversion efficiency, and then intelligently formulate the charging and discharging strategy based on these data. The formulation and execution of this strategy can ensure that the energy storage unit operates under the optimal energy efficiency and cost-effectiveness standards, thereby improving the energy utilization efficiency and economic benefits of the entire charging station. Furthermore, the management system continuously evaluates and adjusts the charging and discharging strategy through the performance optimization module, which can reduce unnecessary energy waste and reduce operating costs. This intelligent management and control can not only improve the service capacity of the charging station and meet the growing demand for electric vehicle charging, but also enhance the adaptability and flexibility of the system to cope with different operating conditions and market changes. In addition, by integrating the battery management system BMS, the system can more effectively monitor and manage the status of the battery pack, extend the service life of the battery, and further improve the reliability and stability of the charging station.

[0049] like Figure 2 As shown, in some embodiments, a charging station energy storage management method 200 includes:

[0050] Step 201, collecting charging station operation data, including: charging state, discharging state of energy storage unit, voltage and current of battery pack;

[0051] Step 202, calculating the energy conversion efficiency of the energy storage unit according to the data;

[0052] Step 203: Determine the charging and discharging strategy of the energy storage unit according to the energy conversion efficiency.

[0053] Step 204, determine whether the charging and discharging strategy of the energy storage unit meets the optimal energy efficiency and cost-effectiveness standards. If so, execute the corresponding strategy; otherwise, adjust the strategy to optimize the energy storage performance.

[0054] It should be noted that the steps described in the charging station energy storage management method 200 are similar to those described in the reference Figure 1 Therefore, the modules, features and beneficial effects described above for the charging station energy storage management system are also applicable to the charging station energy storage management method 200 and the operations contained therein.

[0055] In some embodiments, the equivalent model of the energy storage unit in the energy storage system is:

[0056]

[0057] Among them, U b is the voltage of the energy storage unit, U b is the current flowing through the energy storage unit, R b is the equivalent resistance of the energy storage unit, L b is the equivalent inductance of the energy storage unit, ω is the angular frequency of the power supply, E chem is the chemical energy of the energy storage unit, C b is the equivalent capacitance of the energy storage unit.

[0058] It should be noted that the equivalent model of the energy storage unit is the basis for calculating energy conversion efficiency and optimizing charging and discharging strategies. This model simulates the electrical characteristics of the energy storage unit, allowing the system to more accurately predict and control the behavior of the energy storage unit. The equivalent model usually includes the equivalent resistance, inductance and capacitance of the energy storage unit, which are crucial for optimizing the performance of the energy storage unit.

[0059] The voltage of the energy storage unit is equal to the sum of the current multiplied by the resistance and inductance, plus the ratio of chemical energy to capacitance. Specifically, when the current passes through the energy storage unit, it will first be affected by the resistance, resulting in a voltage drop proportional to the current and resistance. At the same time, the change in current will cause the inductance to react, generating a voltage proportional to the speed of current change and the inductance value. This voltage is related to the angular frequency of the power supply and reflects the response of the energy storage unit to the change in current. In addition, the chemical energy of the energy storage unit is related to the amount of charge it stores, and the ratio of chemical energy to capacitance represents the voltage level of the energy storage unit when there is no current flowing, that is, the open circuit voltage. This comprehensive model takes into account the resistance, inductance and capacitance of the energy storage unit during the charging and discharging process, as well as the influence of chemical energy, and provides a scientific basis for the energy storage management system of the charging station, helping to achieve more effective energy management and optimize control strategies.

[0060] In some embodiments, an equivalent model of an energy storage unit can be established by measuring the voltage, current, input energy and output energy of a battery pack under specific conditions. These parameters can be determined experimentally or set based on data provided by the battery manufacturer. For example, equivalent resistance and inductance can be determined by measuring the response of the battery under different loads, while capacitance can be evaluated by charge and discharge tests. Input energy and output energy can be calculated by monitoring the energy flow of the battery during charge and discharge.

[0061] Preferably, the equivalent model of the energy storage unit can be further refined to improve its accuracy and applicability. For example, the parameters of the equivalent model can be adjusted by introducing factors such as temperature, degree of aging, and charge and discharge rate. In addition, numerical methods and algorithms, such as finite element analysis or circuit simulation, can be used to more accurately simulate the behavior of the energy storage unit. In practical applications, it is also possible to consider using machine learning techniques to continuously optimize the equivalent model so that it can adapt to changes in different battery types and usage conditions. Alternatives may include using different types of battery models, such as RC circuit models or more complex dynamic models to adapt to different application scenarios and performance requirements.

[0062] In some embodiments, based on the equivalent model, the energy conversion efficiency of the energy storage unit is calculated by the following formula:

[0063]

[0064] Where η is the energy conversion efficiency, E in is the input energy, E out For output energy.

[0065] It should be noted that the equivalent model of the energy storage unit includes the equivalent resistance and inductance (Rb, Lb), capacitance (Cs), input energy (Ein) and output energy (Eout) of the energy storage unit. These parameters can be obtained through experimental measurement or from the data provided by the manufacturer. For example, resistance and inductance can be determined by measuring the voltage drop of the energy storage unit at different currents, and capacitance can be estimated through charge and discharge tests. Input energy and output energy represent the amount of electrical energy absorbed and released by the energy storage unit in a certain period of time.

[0066] Preferably, the calculation of energy conversion efficiency can be refined by the following formula: Where η represents the energy conversion efficiency. In order to improve the accuracy of the calculation, a numerical integration method can be used to deal with the changes in input and output energy over time. In addition, the efficiency changes of the energy storage unit under different working conditions can also be considered, and factors such as temperature and aging can be introduced as correction parameters to more accurately reflect the actual energy conversion efficiency. As an alternative, a machine learning algorithm can be used to train a model based on historical data to predict the energy conversion efficiency under different operating conditions.

[0067] In some embodiments, the synergistic efficiency error coefficient of the energy storage unit is calculated by the following formula:

[0068]

[0069] Among them, E eff is the collaborative efficiency error coefficient, E in,i and E out,i are the input and output energies of the i-th energy storage unit respectively, and N is the total number of energy storage units.

[0070] It should be noted that the synergistic efficiency error coefficient is calculated by comparing the input energy and output energy of the energy storage unit. In this process, it is necessary to set the equivalent model parameters of the energy storage unit, such as equivalent resistance, inductance and capacitance, as well as the measured values ​​of input and output energy. The setting of these parameters will directly affect the accuracy of the synergistic efficiency error coefficient. For example, the input energy E in，i and output energy E out，i They represent the amount of energy exchange of the i-th energy storage unit during the charging and discharging process, and the total number N represents the total number of energy storage units in the system.

[0071] Preferably, the calculation of the synergistic efficiency error coefficient can be further refined into the following steps: first, accurately measure the input and output energy of each energy storage unit to ensure the accuracy of the data; second, adjust the equivalent model parameters according to the characteristics and operating conditions of the energy storage unit to be closer to the actual operating state; then, use mathematical methods to calculate the synergistic efficiency error coefficient of each energy storage unit, such as using weighted average or median methods to reduce the impact of outliers. Finally, based on the calculation results, adjust the charging and discharging strategy of the energy storage unit to optimize the overall performance. In addition, machine learning algorithms can be introduced to automatically adjust parameter settings based on historical data and real-time feedback to achieve more accurate and dynamic performance optimization.

[0072] In some embodiments, the optimal energy efficiency and cost-effectiveness criteria include: wherein Eset is a synergistic efficiency error coefficient threshold;

[0073] E eff ≤Eset

[0074] If E eff If the above formula is satisfied, the corresponding charging and discharging strategy is executed, otherwise, the strategy is adjusted.

[0075] It should be noted that this embodiment mainly involves the use of the collaborative efficiency error coefficient E eff To determine whether to implement the corresponding charging and discharging strategy. This process requires precise monitoring and evaluation of the charging and discharging efficiency of the energy storage unit to ensure that it meets the established optimal energy efficiency and cost-effectiveness standards.

[0076] Specifically, the collaborative efficiency error coefficient E eff By comparing the actual input energy E of the energy storage unit jn，j and output energy E out，j The calculation of this coefficient involves the consideration of the total number of energy storage units N and the real-time monitoring of the input and output energy of each energy storage unit. In terms of parameter setting, Eset, as the threshold of the synergistic efficiency error coefficient, needs to be set according to the specific requirements and operating conditions of the energy storage system to ensure that the system can achieve cost-effectiveness while meeting efficiency requirements.

[0077] Preferably, in order to further refine the operation steps, real-time data analysis technology can be used to dynamically adjust the value of Eset to adapt to different operating environments and market changes. In addition, a prediction algorithm can be introduced to predict the charging and discharging efficiency of the energy storage unit, so as to adjust the charging and discharging strategy in advance and reduce efficiency errors. In terms of alternatives, different optimization algorithms, such as genetic algorithms or simulated annealing algorithms, can be considered to find the optimal charging and discharging strategy to minimize Eset. effAnd improve the overall performance of the energy storage system. At the same time, the system can also integrate an adaptive control mechanism to automatically adjust the charging and discharging strategy based on real-time feedback to cope with unpredictable external factors and internal changes.

[0078] In some embodiments, according to the optimal energy efficiency and cost-effectiveness criteria, identifying the optimal charging and discharging strategy includes: when E eff If the formula is satisfied, a high-efficiency and cost-effective charging and discharging strategy is implemented;

[0079] When E eff If the formula is not satisfied, other charging and discharging strategies are adjusted.

[0080] It should be noted that the identification process of the optimal charging and discharging strategy involves the energy storage unit collaborative efficiency error coefficient E eff The evaluation of the energy storage unit and the comparison with the preset threshold Eset ensure that the energy storage unit performs the most optimized charging and discharging operations while meeting the energy efficiency and cost-effectiveness standards.

[0081] Specifically, the identification of the optimal charging and discharging strategy first requires the definition of the specific value of Eset, which is usually based on the design objectives and operating costs of the energy storage system. eff The calculation involves inputting energy E into each energy storage unit. in，i and output energy E out，i Real-time monitoring and accumulation of E, and its association with the total number of energy storage units N. eff By comparing the result with Eset, the system can determine whether to execute the current charging and discharging strategy or whether adjustments need to be made.

[0082] Preferably, the system can use algorithms and models to predict and evaluate E under different charging and discharging strategies. eff For example, historical data and machine learning techniques can be used to predict load changes at charging stations and optimize E jn and E out In addition, the system can also set multiple Eset thresholds to flexibly adjust the charging and discharging strategies according to different operating scenarios and needs. In practical applications, it is also possible to consider introducing user feedback and market dynamics to further optimize the formulation and implementation of charging and discharging strategies.

[0083] In some embodiments, the executing the corresponding charging and discharging strategy includes: sending a control signal to the energy storage unit to adjust its charging and discharging state.

[0084] It should be noted that the execution of the corresponding charging and discharging strategy involves sending control signals to the energy storage unit to adjust its charging and discharging state, which is a key step to ensure that the energy storage unit operates according to the optimal strategy. This process requires precise control logic and communication mechanisms to achieve real-time adjustment of the energy storage unit state.

[0085] Specifically, when executing the charge and discharge strategy, the generation of control signals needs to be based on the current state of the energy storage unit and the preset charge and discharge strategy. These control signals may include, but are not limited to, parameters such as the magnitude of the charging current, the threshold of the discharge voltage, and the duration of the charge and discharge. Conceptually, these parameters can be dynamically adjusted based on the battery type and health status of the energy storage unit, as well as the needs of the external power grid.

[0086] Preferably, the transmission of the control signal can be achieved through an integrated communication interface that supports data exchange with the battery management system (BMS) inside the energy storage unit. More specifically, wireless communication technology or wired Ethernet connection can be used to ensure the reliability and real-time performance of signal transmission. In addition, the generation of control signals can be combined with machine learning algorithms to automatically adjust the charging and discharging parameters based on historical data and prediction models to optimize the performance of the energy storage unit and extend the battery life. A fault detection and response mechanism can also be designed so that when the system detects an abnormal situation, the charging and discharging strategy can be automatically adjusted to protect the energy storage unit from damage.

[0087] In some embodiments, the energy storage unit uses a battery management system BMS to achieve real-time monitoring and optimized management of the battery pack status.

[0088] It should be noted that the energy storage unit adopts a battery management system BMS, which means that the system can monitor and optimize the status of the battery pack in real time. BMS is a key component to ensure battery safety, extend battery life and improve battery performance. It ensures that the battery operates in the best condition by monitoring battery voltage, current, temperature and other parameters.

[0089] Specifically, the battery management system BMS can include multiple sub-modules, such as voltage monitoring module, current detection module, temperature sensing module and state estimation module. These modules work together to collect battery operation data in real time, and process and analyze the data through built-in algorithms. For example, the voltage monitoring module can set a threshold. When the battery voltage exceeds the safe range, the system will automatically adjust the charge and discharge strategy to prevent the battery from overcharging or over-discharging. The current detection module can monitor the battery's charge and discharge current to ensure that the current is within a safe range and avoid battery overheating.

[0090] Preferably, the BMS can further integrate fault diagnosis and predictive maintenance functions. The fault diagnosis module can analyze battery operation data, identify potential faults and provide early warnings, thereby reducing unexpected downtime. The predictive maintenance module can predict the maintenance needs of the battery and optimize the maintenance plan based on the battery usage and historical data. In addition, the BMS can also interact with other modules of the energy storage management system, such as the data acquisition module and the performance optimization module, to achieve more comprehensive system optimization. For example, the BMS can provide real-time feedback on the battery status for the performance optimization module to adjust the charging and discharging strategy to achieve higher energy efficiency and economic benefits.

[0091] In some embodiments, the synergistic efficiency error coefficient of the energy storage unit is calculated by the following formula, and the performance of the energy storage system is optimized based on the error coefficient:

[0092]

[0093] Among them, E opt The optimized synergistic efficiency error coefficient is used to optimize the performance of the energy storage system by minimizing the error coefficient.

[0094] It should be noted that the energy storage unit adopts a battery management system BMS, whose core function is to monitor and optimize the status of the battery pack in real time. As an important part of the energy storage system, BMS can ensure that the battery operates in a safe and efficient state while extending the battery life.

[0095] Specifically, BMS usually includes multiple subsystems such as battery status monitoring, charge and discharge control, thermal management, and balancing control. Battery status monitoring can track key parameters such as battery voltage, current, and temperature in real time. Charge and discharge control intelligently adjusts the charge and discharge process according to the battery status and external demand to avoid overcharging or over-discharging. Thermal management ensures that the battery operates within a suitable temperature range to avoid performance degradation or safety issues due to overheating. Balancing control ensures the consistency and stability of the battery pack by adjusting the voltage between different battery cells.

[0096] Preferably, the BMS can further integrate intelligent diagnosis and predictive maintenance functions, and predict the health status and potential problems of the battery by analyzing the historical data and real-time status of the battery, so as to perform maintenance or replacement in advance. In addition, the BMS can also work with other modules of the charging station energy storage management system, for example, dynamically adjust the battery charging and discharging parameters according to the energy conversion efficiency and charging and discharging strategy to achieve the best energy efficiency and cost-effectiveness. In some cases, the BMS can also use wireless communication technology to achieve remote monitoring and control, and improve the flexibility and response speed of the system.

[0097] Reference below Figure 3, which shows a schematic diagram of a structure 300 of an electronic device suitable for implementing some embodiments of the present invention. The electronic devices in some embodiments of the present invention may include, but are not limited to, mobile terminals such as mobile phones, notebook computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 3 The terminal device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0098] like Figure 3 As shown, the electronic device 300 may include a processing device (e.g., a central processing unit, a graphics processing unit, etc.) 301, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 302 or a program loaded from a storage device 308 into a random access memory (RAM) 303. In the RAM 303, various programs and data required for the operation of the electronic device 300 are also stored. The processing device 301, the ROM 302, and the RAM 303 are connected to each other via a bus 304. An input / output (I / O) interface 305 is also connected to the bus 304.

[0099] Typically, the following devices may be connected to the I / O interface 305: input devices 306 including, for example, a touch screen, a touch pad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; output devices 307 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; storage devices 308 including, for example, a magnetic tape, a hard disk, etc.; and communication devices 309. The communication devices 309 may allow the electronic device 300 to communicate with other devices wirelessly or by wire to exchange data. Although Figure 3 The electronic device 300 is shown with various devices, but it should be understood that it is not required to implement or possess all the devices shown. More or fewer devices may be implemented or possessed instead. Figure 3 Each block shown in the figure may represent one device, or may represent multiple devices as required.

[0100] Furthermore, the storage medium of the embodiment of the present application stores program instructions that can implement all the above methods, wherein the program instructions can be stored in the above storage medium in the form of a software product, including several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) or a processor to execute all or part of the steps of the methods described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, or terminal devices such as a computer, a server, a mobile phone, and a tablet.

[0101] The above descriptions are only some preferred embodiments of the present invention and an explanation of the technical principles used. Those skilled in the art should understand that the scope of the invention involved in the embodiments of the present invention is not limited to the technical solutions formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above inventive concept. For example, the above features are replaced with (but not limited to) technical features with similar functions disclosed in the embodiments of the present invention to form a technical solution.

Claims

1. A charging station energy storage management system, characterized in that: include: Data acquisition module, used to collect charging station operation data; An energy conversion efficiency calculation module, used to calculate the energy conversion efficiency of the energy storage unit according to the data; A charging and discharging strategy determination module is used to determine the charging and discharging strategy of the energy storage unit according to the energy conversion efficiency; The performance optimization module is used to determine whether the charging and discharging strategy of the energy storage unit meets the optimal energy efficiency and cost-effectiveness standards, and adjust the strategy as needed.

2. A charging station energy storage management method, characterized in that: include: Collect charging station operation data, including: charging status, discharging status of energy storage units, voltage and current of battery packs; Calculating the energy conversion efficiency of the energy storage unit based on the data; Determining a charging and discharging strategy of an energy storage unit according to the energy conversion efficiency; Determine whether the charging and discharging strategy of the energy storage unit meets the optimal energy efficiency and cost-effectiveness standards. If so, execute the corresponding strategy; otherwise, adjust the strategy to optimize the energy storage performance.

3. The charging station energy storage management method according to claim 2, characterized in that: The equivalent model of the energy storage unit in the energy storage system is: Among them, U b is the voltage of the energy storage unit, I b is the current flowing through the energy storage unit, R b is the equivalent resistance of the energy storage unit, L b is the equivalent inductance of the energy storage unit, ω is the angular frequency of the power supply, E chem is the chemical energy of the energy storage unit, C b is the equivalent capacitance of the energy storage unit.

4. The charging station energy storage management method according to claim 3, characterized in that: Based on the equivalent model, the energy conversion efficiency of the energy storage unit is calculated by the following formula: Where η is the energy conversion efficiency, E in is the input energy, E out For output energy.

5. The charging station energy storage management method according to claim 4, characterized in that: The synergistic efficiency error coefficient of the energy storage unit is calculated by the following formula: Among them, E eff is the collaborative efficiency error coefficient, E in,i and E out,i are the input and output energies of the i-th energy storage unit respectively, and N is the total number of energy storage units.

6. The charging station energy storage management method according to claim 5, characterized in that: The optimal energy efficiency and cost-effectiveness criteria include: wherein Eset is the collaborative efficiency error coefficient threshold; AND efff ≤Eset If E eff If the above formula is satisfied, the corresponding charging and discharging strategy is executed, otherwise, the strategy is adjusted.

7. The charging station energy storage management method according to claim 6, characterized in that: According to the optimal energy efficiency and cost-effectiveness criteria, the optimal charging and discharging strategy is identified, including: when E eff If the formula is satisfied, a high-efficiency and cost-effective charging and discharging strategy is implemented; When E eff If the formula is not satisfied, other charging and discharging strategies are adjusted.

8. The charging station energy storage management method according to claim 2, characterized in that: The corresponding charging and discharging strategy is executed by sending a control signal to the energy storage unit to adjust its charging and discharging state.

9. The charging station energy storage management method according to claim 3, characterized in that: The energy storage unit adopts a battery management system BMS to achieve real-time monitoring and optimized management of the battery pack status.

10. The charging station energy storage management method according to claim 9, characterized in that: The synergistic efficiency error coefficient of the energy storage unit is calculated by the following formula, and the performance of the energy storage system is optimized based on this error coefficient: Among them, E opt The optimized synergistic efficiency error coefficient is used to optimize the performance of the energy storage system by minimizing the error coefficient.

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