A method for an adaptive photovoltaic robotic bms for multiple cell types

Abstract

The application discloses a method for a photovoltaic robot BMS adaptive to multiple battery cells, comprising the following steps: S1, a special data entry process is designed, key data of different battery cells is entered, and a control strategy matched with the battery cells is generated by using a specific algorithm according to the entered data; S2, a corresponding macro definition switch is automatically generated by the system for each new battery cell data, and is registered in a program as a key element for switching different control strategies; and S3, a multi-dimensional data matrix is constructed, and key data and corresponding control instructions of different battery cells in various states are stored as dimensions of battery cell types and battery state parameters. The application can realize software version unification, reduce software maintenance troubles caused by battery cell replacement, improve the generality and flexibility of photovoltaic robot battery management, reduce costs, and improve product competitiveness.

Description

Technical Field

[0001] This invention relates to a battery management system for photovoltaic robots, and more specifically to a method for an adaptive photovoltaic robot BMS with multiple battery cells. Background Technology

[0002] In the application of photovoltaic robots, the significant differences in climate conditions across different regions lead to diverse demands for battery cells, including those for ambient temperature, cryogenic, and ultra-low temperature applications. Current technologies require additional software versions for each battery cell replacement due to variations in data and related controls. This makes project maintenance difficult, increases development costs and management complexity, hinders efficient product development and maintenance, and limits the flexible application of photovoltaic robots in various environments. Summary of the Invention

[0003] The purpose of this invention is to overcome the shortcomings of the prior art and provide a method for adaptive photovoltaic robot BMS for multiple battery cells. This method can achieve software version standardization, reduce the software maintenance trouble 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 an adaptive photovoltaic robot BMS for multiple battery cells includes the following steps:

[0006] S1. Cell Data Entry and Strategy Generation

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

[0008] S2, Macro Definition Switch Operation

[0009] Each time new battery cell data is entered, the system automatically generates a corresponding macro definition switch and registers it in the program. This serves as a key element for controlling 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 that battery cell by turning on the corresponding macro definition switch.

[0010] S3, Data Matrix Construction and Management

[0011] A multi-dimensional data matrix is ​​constructed, with cell type and battery state parameters as dimensions, to store key data and corresponding control commands for different cells under various states. When the system is running, it can quickly locate the corresponding unit in the data matrix based on the real-time monitored parameters of cell type, power, and temperature, and obtain and execute the corresponding control commands.

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

[0013] The temperature characteristic data includes changes in internal resistance and capacity decay at different temperatures.

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

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

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

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

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

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

[0020] (1) Achieve software version uniformity, 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) Adaptive to various battery cells, enabling photovoltaic robots to operate stably in different climatic environments, broadening the application range of products and enhancing market competitiveness;

[0022] (3) Precise 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) Provides a flexible cell switching mechanism to facilitate users to select the appropriate cell 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 the safe and reliable operation of the battery. Attached Figure Description

[0025] To more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0026] Figure 1 This 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 Implementation

[0028] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0029] To illustrate the technical solution described in this invention, specific embodiments are described below.

[0030] Please see Figure 1 This invention provides a method for an adaptive photovoltaic robot BMS for various battery cells, comprising the following steps:

[0031] S1. Cell Data Entry and Strategy Generation

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

[0033] Based on the entered data, a specific algorithm is used to generate a matching control strategy, which includes a charging control strategy (dynamically adjusting the charging current and voltage according to the cell status to ensure safe and efficient charging), a discharging control strategy (rationally allocating the discharge power according to the load demand and the remaining capacity of the cell), and a battery status monitoring strategy (real-time monitoring of cell voltage, temperature, current and other parameters to detect abnormalities and take protective measures in a timely manner).

[0034] S2, Macro Definition Switch Operation

[0035] Each time new battery cell data is entered, the system automatically generates a corresponding macro definition switch and registers it in the program. This serves as a key element for controlling 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 for that battery cell, achieving seamless switching and adaptive management between different battery cells.

[0036] S3, Data Matrix Construction and Management

[0037] Constructing a multidimensional data matrix, such as Figure 2As shown, the key data and corresponding control commands of different cells under various states are stored in the matrix unit of "low temperature cell - 50% charge - temperature -10℃". For example, the appropriate charging current, discharge power limit and trigger protection threshold data under this state are stored in the matrix unit of "low temperature cell - 50% charge - temperature -10℃".

[0038] During system operation, based on real-time monitoring of cell type, charge level, and temperature parameters, the system quickly locates the corresponding unit in the data matrix, obtains and executes the corresponding control commands, and achieves precise battery management decisions to ensure the safe and stable operation of the battery under different conditions.

[0039] Example

[0040] S1. Cell Data Entry and Strategy Generation

[0041] During initial system setup or cell replacement, detailed cell data is accurately entered into the system via a specific data entry interface. For example, for a new ultra-low temperature cell, its charge-discharge curve data at low temperatures such as -40°C, -30°C, and -20°C, as well as the corresponding internal resistance changes, capacity decay data, and its standard voltage platform (e.g., 3.2V-4.0V) and safety thresholds (e.g., overcharge protection voltage 4.1V, over-discharge protection voltage 2.8V, overcurrent protection threshold 3A, etc.) are entered.

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

[0043] S2, Macro Definition Switch Operation

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

[0045] When switching to cryogenic cell operating mode is required, set this macro definition switch to the "on" state in the system configuration file or operation interface. Upon system startup, the system detects the switch state change and automatically loads the cryogenic cell control strategy module to achieve cell switching.

[0046] S3, Data Matrix Construction and Management

[0047] Based on the monitored parameters, the corresponding unit is quickly located in the data matrix to obtain the appropriate control commands. For example, when the battery cell is detected to have 30% charge and a temperature of -5°C, the recommended charging current (e.g., 0.6C) and discharge power limit (e.g., 80W) for this state are found in the data matrix, and battery management operations are performed according to these commands to ensure the battery operates safely and efficiently under the current conditions.

[0048] The above are merely 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 within the protection scope of the present invention.

Claims

1. A method for an adaptive photovoltaic robot BMS for multiple battery cells, characterized in that, Includes the following steps: S1. Cell Data Entry and Strategy Generation Design a dedicated data entry process to enter key data for different battery cells, and use specific algorithms to generate matching control strategies based on the entered data. The key data includes charge / discharge curve data, temperature characteristic data, voltage platform data, and safety threshold data. The temperature characteristic data includes changes in internal resistance and capacity decay at different temperatures. The safety threshold data includes overcharge protection voltage, over-discharge protection voltage, and overcurrent protection threshold. The control strategy includes a charging control strategy, a discharging control strategy, and a battery state monitoring strategy. S2, Macro Definition Switch Operation Each time new battery cell data is entered, the system automatically generates a corresponding macro definition switch and registers it in the program. This serves as a key element for controlling 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 that battery cell by turning on the corresponding macro definition switch. S3, Data Matrix Construction and Management A multi-dimensional data matrix is ​​constructed, with cell type and battery state parameters as dimensions, to store key data and corresponding control commands for different cells under various states. When the system is running, it can quickly locate the corresponding unit in the data matrix based on the real-time monitored cell type, power, and temperature, and obtain and execute the corresponding control commands.

2. The method for an adaptive photovoltaic robot BMS with multiple battery cells according to claim 1, characterized in that: The charging control strategy can dynamically adjust the charging current and voltage according to the cell status.

3. The method for an adaptive photovoltaic robot BMS with multiple battery cells according to claim 1, characterized in that: The discharge control strategy can reasonably allocate discharge power based on load demand and the remaining power of the battery cell.

4. The method for an adaptive photovoltaic robot BMS with multiple battery cells according to claim 1, characterized in that: The battery status monitoring strategy can monitor cell voltage, temperature, and current in real time, promptly detect abnormalities, and take protective measures.

Images