Photovoltaic robot BMS method self-adaptive to various battery cells

Abstract

The invention discloses a photovoltaic robot BMS method self-adaptive to various battery cells, and the method comprises the following steps: S1, designing a special data input process, inputting key data of different battery cells according to the different battery cells, and generating a control strategy matched with the input data by using a specific algorithm according to the input data; s2, a system automatically generates a corresponding macro definition switch every time new cell data is input, and the macro definition switch is registered in a program to serve as a key element for controlling switching of different control strategies; and S3, constructing a multi-dimensional data matrix, and storing key data and corresponding control instructions of different battery cells in various states by taking the battery cell types and the battery state parameters as dimensions. According to the method, software version unification can be realized, the software maintenance trouble caused by battery cell replacement is reduced, the universality and flexibility of photovoltaic robot battery management are improved, the cost is reduced, and the product competitiveness is improved.

Description

Technical Field

[0001] The present invention relates to a battery management system for a photovoltaic robot, and in particular to a method for adaptively managing a photovoltaic robot BMS with multiple battery cells. Background Art

[0002] In the application of photovoltaic robots, the climate conditions in different regions vary greatly, resulting in diverse demands for battery cells, including normal temperature battery cells, low temperature battery cells, ultra-low temperature battery cells, etc. In the prior art, due to the different data and related controls of different battery cells, each time a battery cell is replaced, the product software version needs to be increased, which makes project maintenance difficult, increases development costs and management complexity, is not conducive to efficient product development and maintenance, and limits the flexible application of photovoltaic robots in different environments. Summary of the invention

[0003] The purpose of the present invention is to overcome the shortcomings of the prior art and provide a method for a photovoltaic robot BMS that is adaptive to multiple battery cells, which can achieve unified software versions, reduce the software maintenance troubles caused by battery cell replacement, improve the versatility and flexibility of photovoltaic robot battery management, reduce costs, and enhance product competitiveness.

[0004] The technical solution of the present invention is as follows:

[0005] A method for adaptively controlling a photovoltaic robot BMS of multiple battery cells comprises the following steps:

[0006] S1. Cell data entry and strategy generation

[0007] Design a special data entry process to enter key data for different cells, and use specific algorithms to generate matching control strategies based on the entered data;

[0008] S2, macro definition switch operation

[0009] Every time a new battery cell data is entered, the system will automatically generate a corresponding macro definition switch and register it in the program as a key element to control the switching of different control strategies. When a specific battery cell needs to be used, the system can quickly call the pre-set control strategy for the battery cell by turning on the corresponding macro definition switch.

[0010] S3. Data matrix construction and management

[0011] A multidimensional data matrix is ​​constructed with battery cell type and battery status parameters as dimensions to store key data and corresponding control instructions of different battery cells in various states. When the system is running, the corresponding unit in the data matrix can be quickly located according to the real-time monitored parameters of battery cell type, power and temperature, and the corresponding control instructions can be obtained and executed.

[0012] In step S1, the key data includes charge and discharge curve data, temperature characteristic data, voltage platform data, and safety threshold data.

[0013] The temperature characteristic data includes internal resistance change and capacity attenuation at different temperatures.

[0014] The safety threshold data includes an overcharge protection voltage, an over-discharge protection voltage, and an over-current protection threshold.

[0015] In step S1, the control strategy includes a charging control strategy, a discharging control strategy and a battery status monitoring strategy.

[0016] The charging control strategy can dynamically adjust the charging current and voltage according to the battery cell status.

[0017] The discharge control strategy can reasonably allocate the discharge power according to the load demand and the remaining power of the battery cell.

[0018] The battery status monitoring strategy can monitor the parameters of battery cell voltage, temperature, and current in real time, detect abnormalities in time and take protective measures.

[0019] Compared with the prior art, the beneficial effects of the present invention are:

[0020] (1) Unify software versions to avoid frequent development of new software versions due to battery cell replacement, greatly reduce software development and maintenance costs, and improve development efficiency and quality stability;

[0021] (2) Adapt to a variety of battery cells to enable photovoltaic robots to operate stably in different climate environments, broaden the scope of product application, and enhance market competitiveness;

[0022] (3) Accurate battery management strategies extend battery life, improve charging and discharging efficiency, reduce energy consumption, and enhance the overall performance and working endurance of photovoltaic robots;

[0023] (4) Provide a flexible battery switching mechanism to facilitate users to select appropriate batteries according to actual needs, thereby improving the adaptability and operability of the system;

[0024] (5) The construction of the data matrix facilitates data management and strategy invocation, improves the accuracy and timeliness of battery management, and ensures safe and reliable operation of the battery. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0026] Figure 1 It is a schematic diagram of the system principle of the present invention;

[0027] Figure 2 This is an example diagram of the data matrix of the present invention. DETAILED DESCRIPTION

[0028] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0029] In order to illustrate the technical solution of the present invention, a specific embodiment is provided below for illustration.

[0030] See also Figure 1 The present invention provides a method for a photovoltaic robot BMS that is adaptive to multiple battery cells, comprising the following steps:

[0031] S1. Cell data entry and strategy generation

[0032] Design a special data entry process to enter key data for different battery cells, such as charge and discharge curve data, temperature characteristic data (including internal resistance change and capacity attenuation at different temperatures), voltage platform data, and safety threshold data (such as overcharge protection voltage, over-discharge protection voltage, overcurrent protection threshold, etc.);

[0033] Based on the input data, a specific algorithm is used to generate a matching control strategy, including charging control strategy (dynamically adjusting the charging current and voltage according to the battery cell status to ensure safe and efficient charging), discharging control strategy (rationally allocating the discharge power according to the load demand and the remaining battery cell power) and battery status monitoring strategy (real-time monitoring of battery cell voltage, temperature, current and other parameters, timely detection of abnormalities and taking protective measures);

[0034] S2, macro definition switch operation

[0035] Every time a new battery cell data is entered, the system will automatically generate a corresponding macro definition switch and register it in the program as a key element to control the switching of different control strategies. When a specific battery cell needs to be used, by turning on the corresponding macro definition switch, the system can quickly call the pre-set control strategy of the battery cell to achieve seamless switching and adaptive management between different battery cells.

[0036] S3. Data matrix construction and management

[0037] Construct a multidimensional data matrix such as Figure 2As shown, with the cell type and battery status parameters as dimensions, key data and corresponding control instructions of different cells in various states are stored. For example, in the matrix unit of "low temperature cell - 50% power - temperature -10°C", data such as the appropriate charging current, discharge power limit and threshold for triggering protection in this state are stored;

[0038] When the system is running, it quickly locates the corresponding unit in the data matrix based on the real-time monitoring parameters of battery cell type, power and temperature, obtains and executes the corresponding control instructions, realizes accurate battery management decisions, and ensures the safe and stable operation of the battery in different states.

[0039] Example

[0040] S1. Cell data entry and strategy generation

[0041] During the initial system setup or when replacing a battery cell, the detailed data of the battery cell is accurately input into the system through a specific data entry interface. For example, for a new ultra-low temperature battery cell, enter its charge and discharge curve data in low temperature environments such as -40°C, -30°C, and -20°C, as well as the corresponding internal resistance change and capacity attenuation data, as well as its standard voltage platform (such as 3.2V-4.0V) and safety threshold (such as overcharge protection voltage 4.1V, over-discharge protection voltage 2.8V, overcurrent protection threshold 3A, etc.) data;

[0042] The entered data is verified and stored as the basis for subsequent generation of control strategies and management of batteries;

[0043] S2, macro definition switch operation

[0044] When adding new cell data, the system automatically generates the corresponding macro switch and registers it in the program. For example, the macro switch corresponding to the newly added ultra-low temperature cell is named "ULTRA_LOW_TEMP_CELL_SWITCH";

[0045] When you need to switch to the ultra-low temperature battery cell working mode, set the macro definition switch to "on" in the system configuration file or operation interface. When the system starts, it detects the switch state change and automatically loads the ultra-low temperature battery cell control strategy module to realize the battery cell switching;

[0046] S3. Data matrix construction and management

[0047] According to the monitored parameters, the corresponding unit is quickly found in the data matrix to obtain the corresponding control instructions. For example, when the low-temperature battery cell is monitored to have a power of 30% and a temperature of -5°C, the recommended charging current (such as 0.6C) and discharge power limit (such as 80W) under this state are found from the data matrix, and battery management operations are performed according to these instructions to ensure the safe and efficient operation of the battery in the current state.

[0048] The above are only preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for adaptive photovoltaic robot BMS of multiple battery cells, characterized in that: The following steps are involved: S1. Cell data entry and strategy generation Design a special data entry process to enter key data for different cells, and use specific algorithms to generate matching control strategies based on the entered data; S2, macro definition switch operation Every time a new battery cell data is entered, the system will automatically generate a corresponding macro definition switch and register it in the program as a key element to control the switching of different control strategies. When a specific battery cell needs to be used, the system can quickly call the pre-set control strategy for the battery cell by turning on the corresponding macro definition switch. S3. Data matrix construction and management A multidimensional data matrix is ​​constructed with battery cell type and battery status parameters as dimensions to store key data and corresponding control instructions of different battery cells in various states. When the system is running, the corresponding unit in the data matrix can be quickly located according to the real-time monitored parameters of battery cell type, power and temperature, and the corresponding control instructions can be obtained and executed.

2. The method of a photovoltaic robot BMS with adaptive multiple battery cells according to claim 1, characterized in that: In step S1, the key data includes charge and discharge curve data, temperature characteristic data, voltage platform data, and safety threshold data.

3. The method of a photovoltaic robot BMS with adaptive multiple battery cells according to claim 2, characterized in that: The temperature characteristic data includes internal resistance change and capacity attenuation at different temperatures.

4. The method of a photovoltaic robot BMS with adaptive multiple battery cells according to claim 2, characterized in that: The safety threshold data includes an overcharge protection voltage, an over-discharge protection voltage, and an over-current protection threshold.

5. The method of a photovoltaic robot BMS with adaptive multiple battery cells according to claim 1, characterized in that: In step S1, the control strategy includes a charging control strategy, a discharging control strategy and a battery status monitoring strategy.

6. The method of a photovoltaic robot BMS with adaptive multiple battery cells according to claim 5, characterized in that: The charging control strategy can dynamically adjust the charging current and voltage according to the battery cell status.

7. The method of the photovoltaic robot BMS with adaptive multiple battery cells according to claim 5, characterized in that: The discharge control strategy can reasonably allocate the discharge power according to the load demand and the remaining power of the battery cell.

8. The method of the photovoltaic robot BMS with adaptive multiple battery cells according to claim 5, characterized in that: The battery status monitoring strategy can monitor the parameters of battery cell voltage, temperature, and current in real time, detect abnormalities in time and take protective measures.

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