A mobile energy storage vehicle battery management system and controller for car charging

Abstract

The present invention discloses a mobile energy storage vehicle battery management system and controller for automobile charging, which relates to the technical field of management controllers. The management controller body has excellent hardware configuration, and the 4-core Linux operating system gives it powerful computing and multi-tasking capabilities, ensuring stable operation of the system without lag. By assembling a local controller to manage the mobile energy storage vehicle battery system, the built-in IOT module can quickly and easily implement the OTA function, streamline and efficiently control the system, increase system reliability and robustness, and accurately match the best model management controller. This can ensure that the management controller is highly consistent with the working conditions of the mobile energy storage vehicle itself, and finely adjust the battery operating parameters based on the vehicle model and road condition assessment coefficient, which not only ensures stable communication and reliable data transmission during the upgrade process, prevents upgrade interruptions or errors due to poor signals and strong interference, but also maintains the battery in good condition, and does not affect the normal charging and discharging and life of the battery due to the upgrade.

Description

Technical Field

[0001] The present invention relates to the technical field of management controllers, and in particular to a battery management system and controller for a mobile energy storage vehicle used for charging a vehicle. Background Art

[0002] With the increasing popularity of electric vehicles, vehicle charging systems have become a significant research area. Mobile energy storage vehicles, as an emerging charging solution, can provide mobile charging services for electric vehicles by using battery systems. They can also provide backup power generation to meet short-term load demands in power-constrained environments such as factories and large facilities. Furthermore, the importance of battery management systems, as the core of ensuring safe and efficient battery operation, is becoming increasingly important. Consequently, a battery management system and controller for mobile energy storage vehicles is needed for vehicle charging.

[0003] Prior art, such as the invention patent application with publication number CN118363628A, discloses a battery management system upgrade method, apparatus, energy storage device, and storage medium. The method includes: upon receiving an upgrade command from a host computer, entering a boot program, receiving a source cyclic redundancy check (CRC) code and an upgrade package from the host computer; performing an upgrade using the upgrade package; calculating a local CRC code based on partial data in the upgraded application area; comparing the local CRC code with the source CRC code to determine whether the upgrade was successful; if the upgrade was successful, launching the application; otherwise, waiting for the next upgrade round. The CRC code is used to determine whether the burned program is correct, ensuring stability during the upgrade.

[0004] Regarding the above solution, the inventors of this application have discovered that the above technology has at least the following technical problems: 1. The existing technology lacks a management controller matching mechanism, which may result in a mismatch between the management controller and the actual application scenario and physical structure of the mobile energy storage vehicle. Failure to accurately match may result in resource waste or insufficient performance. For example, installing a high-end, powerful management controller on a mobile energy storage vehicle with a small load change rate and small battery capacity will increase costs and some functions will be idle; conversely, using a low-performance controller in a high-demand scenario may not meet the complex energy feedback and power distribution requirements, causing unstable battery system operation.

[0005] 2. When upgrading the management controller, existing technologies lack analysis of the upgrade section, potentially allowing upgrades to be performed on sections with poor signal quality, strong electromagnetic interference, or low traffic quality. This can lead to data transmission interruptions or errors during the upgrade process, corrupting the upgrade file and causing management controller failure or malfunction. Furthermore, failure to adjust battery operating parameters based on the road conditions and management controller model may affect the normal operation of the battery during the upgrade process. When upgrading on sections with strong electromagnetic interference, improper adjustment of charging power or battery balancing strategies may cause overcharging, over-discharging, or increased imbalance between battery cells, shortening battery life and even posing safety risks.

[0006] 3. The existing technology lacks a detailed debugging and analysis process, making it difficult to accurately determine whether the management controller upgrade is successful. Relying solely on simple functional tests may miss some potential problems, resulting in subtle changes such as a slightly longer discharge control response time after the upgrade and an increase in power distribution differences under certain operating conditions. These problems may lead to more serious failures after long-term operation. The lack of a clear failure level classification and corresponding early warning mechanism means that when a problem occurs in the management controller, the operation and maintenance personnel cannot be notified of the severity of the problem in a timely and accurate manner. This may cause minor failures to be ignored and gradually develop into serious failures; or when a serious failure occurs, emergency and effective measures such as stopping charging or discharging operations are not taken, resulting in battery damage, vehicle failures, and even safety accidents.

[0007] 4. The increasing number of modules in existing technologies increases system complexity and design costs. Furthermore, none of the modules offer independent Over-the-Air (OTA) functionality, requiring the addition of an IoT module for remote upgrades. This increases the overhead and difficulty of debugging and product deployment, making convenient remote upgrades and debugging impossible. This means that every software update or parameter adjustment for the management controller requires manual on-site operation, increasing maintenance costs and time. It can also lead to prolonged periods of suboptimal system operation due to the inability to update software in a timely manner to fix vulnerabilities or optimize performance. Summary of the Invention

[0008] In view of the above-mentioned technical deficiencies, the purpose of the present invention is to provide a mobile energy storage vehicle battery management system and controller for automobile charging.

[0009] To solve the above technical problems, the present invention adopts the following technical solution: In the first aspect, the present invention provides a mobile energy storage vehicle battery management system for automobile charging, including: a management controller matching and installation module: used to match and select the best model management controller for each mobile energy storage vehicle in the target enterprise, and install each mobile energy storage vehicle according to the matching best model management controller.

[0010] Management controller upgrade analysis module: used to analyze the optimal upgrade section corresponding to each mobile energy storage vehicle management controller when the management controller corresponding to each mobile energy storage vehicle needs to be upgraded, and adjust the corresponding battery operating parameters when the management controller of each mobile energy storage vehicle is upgraded.

[0011] Management controller debugging and analysis module: used to debug and analyze each mobile energy storage vehicle management controller after the upgrade is completed, and evaluate whether the upgrade of each mobile energy storage vehicle management controller is successful. If the upgrade of a mobile energy storage vehicle management controller fails, the failure level of the mobile energy storage vehicle management controller is analyzed and an early warning prompt is issued.

[0012] Preferably, the management controller matching installation module further includes an operating condition adaptation value analysis unit.

[0013] The operating condition data analysis unit is used to obtain application scenario parameters and physical structure parameters corresponding to each mobile energy storage vehicle. The application scenario parameters include charge and discharge cycle frequency, energy feedback efficiency and load change rate. The physical structure parameters include battery compartment volume, space utilization and the number of battery modules in series, and then analyze and obtain the operating condition adaptation value corresponding to each mobile energy storage vehicle.

[0014] Preferably, the analysis obtains the operating condition adaptation value corresponding to each mobile energy storage vehicle. The specific analysis process is as follows: S1, inputting the charge and discharge cycle frequency, energy feedback efficiency and load change rate corresponding to each mobile energy storage vehicle into the application scenario adaptation value evaluation model, outputting the application scenario adaptation value corresponding to each mobile energy storage vehicle, and recording it as , where h represents the number corresponding to each mobile energy storage vehicle, and h is a positive integer. The battery compartment volume, space utilization rate, and number of battery modules in series corresponding to each mobile energy storage vehicle are input into the physical structure adaptation value evaluation model, and the physical structure adaptation value corresponding to each mobile energy storage vehicle is output and recorded as .

[0015] S2. Substitute the application scenario adaptation value and physical structure adaptation value corresponding to each mobile energy storage vehicle into the calculation formula The operating condition adaptation value corresponding to each mobile energy storage vehicle is obtained ,in, 、 They are the weight factors corresponding to the adaptation values ​​of the mobile energy storage vehicle application scenario and the weight factors corresponding to the adaptation values ​​of the physical structure, and e represents a natural constant.

[0016] Preferably, the matching and selection of the optimal model management controller for each mobile energy storage vehicle in the target enterprise is performed by the following specific selection process: comparing the operating condition adaptation value corresponding to each mobile energy storage vehicle with the operating condition adaptation value interval corresponding to each model management controller in the database; if the operating condition adaptation value corresponding to a certain mobile energy storage vehicle is within the operating condition adaptation value interval corresponding to a certain model management controller in the database, then the model management controller in the database is used as the optimal model management controller corresponding to the mobile energy storage vehicle in the target enterprise. In this way, the optimal model management controller is matched and selected for each mobile energy storage vehicle in the target enterprise.

[0017] Preferably, the optimal upgrade section corresponding to the management controller of each mobile energy storage vehicle is analyzed, and the specific analysis process is as follows: X1. When the management controller corresponding to each mobile energy storage vehicle needs to be upgraded, the destination of each mobile energy storage vehicle at the current moment is obtained, thereby obtaining the driving route of each mobile energy storage vehicle to the destination, and dividing the driving route of each mobile energy storage vehicle into a number of upgrade sections, and then obtaining road condition data corresponding to each upgraded section in each mobile energy storage vehicle, where the road condition data includes signal strength, road electromagnetic interference strength, and flow quality indicators.

[0018] X2. The signal strength, road electromagnetic interference intensity and flow quality index corresponding to each upgraded section of each mobile energy storage vehicle are recorded as 、 and , where h represents the number corresponding to each mobile energy storage vehicle, h is a positive integer, and f represents the number corresponding to each upgraded road section, f is a positive integer. Substitute into the calculation formula: The road condition evaluation coefficient corresponding to each upgraded section of each mobile energy storage vehicle is obtained. ,in, 、 、 They are the standard signal strength, standard road electromagnetic interference strength, and standard traffic quality index corresponding to the set road section. 、 、 They are respectively the weight factor corresponding to the set road section signal strength, the weight factor corresponding to the road electromagnetic interference strength, and the weight factor corresponding to the traffic quality index.

[0019] X3. Arrange the road condition assessment coefficients corresponding to the upgraded sections in each mobile energy storage vehicle in descending order, and use the upgraded section with the highest road condition assessment coefficient in each mobile energy storage vehicle as the optimal upgraded section corresponding to each mobile energy storage vehicle management controller. In this way, analyze the optimal upgraded section corresponding to each mobile energy storage vehicle management controller.

[0020] Preferably, the corresponding battery operating parameters of each mobile energy storage vehicle management controller are adjusted when the mobile energy storage vehicle management controller is upgraded. The specific analysis process is as follows: V1. When each mobile energy storage vehicle management controller is upgraded in the corresponding optimal upgrade section, the model corresponding to each mobile energy storage vehicle management controller is obtained, and the model corresponding to each mobile energy storage vehicle management controller is compared with the model corresponding to each battery operation adjustment parameter set in the database. If the model corresponding to a mobile energy storage vehicle management controller is the same as the model corresponding to a battery operation adjustment parameter set in the database, then the battery operation adjustment parameter set in the database is recorded as the battery operation adjustment parameter set corresponding to the mobile energy storage vehicle.

[0021] V2. Compare the road condition assessment coefficient corresponding to the optimal upgrade section of each mobile energy storage vehicle with the road condition assessment coefficient corresponding to each battery operation adjustment parameter in the corresponding battery operation adjustment parameter set in the database. If the road condition assessment coefficient corresponding to the optimal upgrade section of a mobile energy storage vehicle is the same as the road condition assessment coefficient corresponding to a battery operation adjustment parameter in the corresponding battery operation adjustment parameter set in the database, then use the battery operation adjustment parameter in the battery operation adjustment parameter set in the corresponding database as the corresponding battery operation parameter adjustment value when the mobile energy storage vehicle management controller is upgraded.

[0022] Preferably, the debugging and analysis of each mobile energy storage vehicle management controller is carried out, and the specific analysis process is as follows: after the upgrade of each mobile energy storage vehicle management controller is completed, the discharge control response time, power distribution difference value and confidence interval width reduction ratio corresponding to each mobile energy storage vehicle management controller are obtained, and the discharge control response time, power distribution difference value and confidence interval width reduction ratio corresponding to each mobile energy storage vehicle management controller are recorded as 、 and , where h represents the number corresponding to each mobile energy storage vehicle, and h is a positive integer. Substitute it into the calculation formula: In the above example, the upgrade evaluation coefficient corresponding to each mobile energy storage vehicle management controller is obtained. ,in, 、 、 They are the standard discharge control response time, standard power distribution difference value, and standard confidence interval width reduction ratio corresponding to the set mobile energy storage vehicle management controller. 、 、 They are the weight factors corresponding to the discharge control response time of the mobile energy storage vehicle management controller, the weight factors corresponding to the power distribution difference value, and the weight factors corresponding to the confidence interval width reduction ratio. 、 、 They are respectively the adjustment factor corresponding to the discharge control response time of the set mobile energy storage vehicle management controller, the adjustment factor corresponding to the power distribution difference value, and the adjustment factor corresponding to the confidence interval width reduction ratio.

[0023] Preferably, the evaluation of whether the upgrade of each mobile energy storage vehicle management controller is successful is performed as follows: the upgrade evaluation coefficient corresponding to each mobile energy storage vehicle management controller is compared with the upgrade evaluation coefficient corresponding to the set standard management controller. If the upgrade evaluation coefficient corresponding to a certain mobile energy storage vehicle management controller is greater than or equal to the upgrade evaluation coefficient corresponding to the set standard management controller, it indicates that the upgrade of the mobile energy storage vehicle management controller is successful. If the upgrade evaluation coefficient corresponding to a certain mobile energy storage vehicle management controller is less than the upgrade evaluation coefficient corresponding to the set standard management controller, it indicates that the upgrade of the mobile energy storage vehicle management controller is unsuccessful. In this way, whether the upgrade of each mobile energy storage vehicle management controller is successful is evaluated.

[0024] Preferably, if a mobile energy storage vehicle management controller fails to be upgraded, the failure level of the mobile energy storage vehicle management controller is analyzed and an early warning prompt is issued. The specific early warning process is as follows: H1. If a mobile energy storage vehicle management controller fails to be upgraded, the upgrade evaluation coefficient corresponding to the mobile energy storage vehicle management controller is calculated to differ from the upgrade evaluation coefficient corresponding to the set standard management controller, and the difference is recorded as the upgrade evaluation coefficient difference.

[0025] H2. Compare the upgrade assessment coefficient difference corresponding to the mobile energy storage vehicle management controller with the upgrade assessment coefficient difference interval corresponding to the failure level of each management controller in the database. If the upgrade assessment coefficient difference corresponding to the mobile energy storage vehicle management controller is within the upgrade assessment coefficient difference interval corresponding to the failure level of a certain management controller in the database, then record the failure level of the management controller in the database as the failure level of the management controller. In this way, analyze the failure level of the mobile energy storage vehicle management controller.

[0026] In a second aspect, the present invention provides a mobile energy storage vehicle battery management controller for automobile charging, comprising a management controller body, the management controller body being equipped with a 4-core Linux operating system, adopting ADI's automotive-grade AFE analog acquisition front end, communicating and controlling with the automobile charging pile and battery management system through a CAN interface, having built-in rich charging pile interface acquisition circuits, identifying and matching charging piles in different regions for charging, and supporting CCS2 / NACS and national standard bus communication protocols in three different regions to achieve efficient data transmission and real-time control. The management controller body communicates with EMS and PCS in a LAN manner, with a high transmission rate and support for flexible bus expansion. The IOT module built into the management controller body is a SIM7000 module with OTA function, which realizes remote upgrading and debugging through a wireless network, making it convenient for users to use and maintain.

[0027] The beneficial effects of the present invention are as follows: 1. In the embodiment of the present invention, the operating condition adaptation value analysis unit in the management controller matching installation module comprehensively considers the application scenario parameters such as the charge and discharge cycle frequency, energy feedback efficiency, load change rate of the mobile energy storage vehicle, as well as the physical structure parameters such as the battery compartment volume, space utilization, and the number of battery modules in series, to obtain the operating condition adaptation value, and accurately match the optimal model management controller based on this. This ensures that the management controller is highly compatible with the operating conditions of the mobile energy storage vehicle itself, avoids the situation of "overkill" or "a small horse pulling a big cart", maximizes the performance of the management controller, improves the overall efficiency of the battery management system, and reduces problems such as unstable operation and low efficiency caused by mismatch.

[0028] 2. In this embodiment of the present invention, the management controller upgrade analysis module cleverly analyzes the optimal upgrade sections based on upgrade requirements. It assesses road conditions based on signal strength, road electromagnetic interference intensity, and traffic quality indicators, selecting the most suitable sections for the upgrade. Simultaneously, it fine-tunes battery operating parameters, such as charging power and balancing strategies, based on the vehicle model and road condition assessment coefficients. This ensures stable communication and reliable data transmission during the upgrade process, preventing upgrade interruptions or errors caused by poor signal strength or strong interference. It also maintains the battery in good condition, ensuring that the upgrade does not affect normal charging and discharging, or battery life, thereby improving upgrade success and safety. After the upgrade is complete, the management controller debugging analysis module accurately determines the upgrade success by performing a multi-dimensional quantitative assessment based on discharge control response time, power allocation variance, and confidence interval reduction ratio. If the upgrade fails, it quickly compares the upgrade assessment coefficient difference with the various level ranges in the database to accurately define the failure level and issue a timely warning. From in-system prompts and remote notifications for minor faults to local emergency braking and comprehensive remote alerts for severe faults, this comprehensive approach ensures that operations and maintenance personnel are immediately aware of and address the problem, reducing the scope and duration of the fault and improving system availability.

[0029] 3. This embodiment of the present invention boasts superior hardware configuration for the management controller. Its quad-core Linux operating system provides powerful computing and multitasking capabilities, ensuring stable and seamless system operation. By integrating a local controller to manage the mobile energy storage vehicle battery system, it replaces the complex and redundant system design of a vehicle BMS, energy storage BMS, European and American charging protocol converter, and communication converter. This significantly optimizes the control system and communication topology, reduces the number of electronic modules, lowers design costs, and saves structural space. The CAN interface adapts to multiple charging pile protocols, seamlessly connecting the vehicle charging pile and the battery system to ensure a smooth charging process. The LAN interface enables high-speed communication and flexible expansion with the EMS and PCS, meeting complex energy management and power scheduling requirements and ensuring efficient coordinated operation of all components. Furthermore, the built-in IoT module enables convenient and fast OTA functionality, streamlining the control system, increasing system reliability and robustness, and addressing remote upgrade and debugging issues. The built-in SIM7000 module's OTA functionality transcends time and space limitations, enabling remote upgrades and debugging over a wireless network without the need for on-site maintenance personnel. Whether updating control algorithms, fixing software vulnerabilities, or adapting to new charging standards, these can all be accomplished quickly and easily. This greatly saves manpower and material costs, shortens the system iteration cycle, allows the mobile energy storage vehicle battery management system to always maintain optimal performance, keep pace with industry development, enhance product market competitiveness, and conform to the trend of intelligent operation and maintenance. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. 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 any creative work.

[0031] Figure 1 This is a schematic diagram of the system module connection of the present invention. DETAILED DESCRIPTION

[0032] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0033] The present invention is implemented as follows Figure 1As shown, a mobile energy storage vehicle battery management system for automobile charging includes: a management controller matching and installation module, a management controller upgrade analysis module, a management controller debugging and analysis module and a database.

[0034] The management controller upgrade analysis module is connected to the management controller matching installation module and the management controller debugging analysis module respectively, and the database is connected to the management controller matching installation module and the management controller upgrade analysis module respectively.

[0035] Management controller matching and installation module: used to match and select the best model management controller for each mobile energy storage vehicle in the target enterprise, and install each mobile energy storage vehicle according to the matching best model management controller.

[0036] In a specific embodiment, the management controller matching and installation module further includes an operating condition adaptation value analysis unit.

[0037] The operating condition data analysis unit is used to obtain application scenario parameters and physical structure parameters corresponding to each mobile energy storage vehicle. The application scenario parameters include charge and discharge cycle frequency, energy feedback efficiency and load change rate. The physical structure parameters include battery compartment volume, space utilization and the number of battery modules in series, and then analyze and obtain the operating condition adaptation value corresponding to each mobile energy storage vehicle.

[0038] It should be noted that a counter is set up in the battery management system of the mobile energy storage vehicle to first count the number of complete charging and discharging processes within a certain period of time. A complete charging process is from a low-power state to a set high-power state, and a complete discharging process is from a high-power state to a set low-power state. This number is then divided by the statistical time period to obtain the charge and discharge cycle frequency. The energy actually fed back to the energy storage system during the energy regeneration process is first measured, along with the maximum energy that the entire energy storage system can theoretically receive and store. The energy regeneration efficiency is calculated by dividing the actual regenerated energy by the theoretical maximum received and stored energy, and then multiplying by 100%. This is achieved by recording the load current or power at different times. The load current or power is first recorded at one time, then recorded at another time. The value at the latter time is subtracted from the value at the previous time, and then divided by the time interval between the two times to obtain the rate of change of the load current or power.

[0039] It should also be noted that the length, width, and height of the battery compartment are measured. Multiplying these three lengths together gives the volume of the battery compartment. To calculate space utilization, first calculate the actual volume of space occupied by the battery modules in the battery compartment. This is determined by measuring the module dimensions and considering their arrangement. Then, divide the actual volume of space occupied by the batteries by the total volume of the battery compartment, and multiply the result by 100% to obtain the space utilization rate. The number of battery modules connected in series can be determined by checking the records in the mobile energy storage vehicle's battery management system or by viewing the energy storage system's circuit diagram.

[0040] In another specific embodiment, the analysis obtains the operating condition adaptation value corresponding to each mobile energy storage vehicle. The specific analysis process is as follows: S1, the charge and discharge cycle frequency, energy feedback efficiency and load change rate corresponding to each mobile energy storage vehicle are input into the application scenario adaptation value evaluation model, and the application scenario adaptation value corresponding to each mobile energy storage vehicle is output and recorded as , where h represents the number corresponding to each mobile energy storage vehicle, and h is a positive integer. The battery compartment volume, space utilization rate, and number of battery modules in series corresponding to each mobile energy storage vehicle are input into the physical structure adaptation value evaluation model, and the physical structure adaptation value corresponding to each mobile energy storage vehicle is output and recorded as .

[0041] It should be noted that the analysis process of the application scenario adaptation value corresponding to each mobile energy storage vehicle is as follows: the charge and discharge cycle frequency, energy feedback efficiency and load change rate corresponding to each mobile energy storage vehicle are normalized, and the charge and discharge cycle frequency, energy feedback efficiency and load change rate corresponding to each mobile energy storage vehicle after processing are recorded as 、 and , substitute into the analytical formula , get the application scenario adaptation value corresponding to each mobile energy storage vehicle , 、 、 They are respectively the weight factor corresponding to the charging and discharging cycle frequency of the set mobile energy storage vehicle, the weight factor corresponding to the energy feedback efficiency, and the weight factor corresponding to the load change rate.

[0042] It should be noted that 、 、 Both are greater than 0 and less than 1.

[0043] It should also be noted that, based on the expertise and research of field experts, and after discussion and confirmation with industry organizations or professional institutions, the experts set the weighting factors corresponding to the charge and discharge cycle frequency, energy regenerative efficiency, and load change rate of mobile energy storage vehicles based on their own experience and knowledge.

[0044] It should be noted once again that the physical structure adaptation value corresponding to each mobile energy storage vehicle is obtained by analyzing the application scenario adaptation value corresponding to each mobile energy storage vehicle according to the above-mentioned analysis process.

[0045] S2. Substitute the application scenario adaptation value and physical structure adaptation value corresponding to each mobile energy storage vehicle into the calculation formula The operating condition adaptation value corresponding to each mobile energy storage vehicle is obtained ,in, 、 They are the weight factors corresponding to the adaptation values ​​of the mobile energy storage vehicle application scenario and the weight factors corresponding to the adaptation values ​​of the physical structure, and e represents a natural constant.

[0046] It should be noted that 、 Both are greater than 0 and less than 1.

[0047] It should also be noted that, based on the expertise and research of domain experts, and after discussion and confirmation with industry organizations or professional institutions, the experts set the weighting factors corresponding to the application scenario adaptation value and the weighting factors corresponding to the physical structure adaptation value of the mobile energy storage vehicle based on their own experience and knowledge.

[0048] In another specific embodiment, the matching and selection of the optimal model management controller for each mobile energy storage vehicle in the target enterprise is performed as follows: the operating condition adaptation value corresponding to each mobile energy storage vehicle is compared with the operating condition adaptation value interval corresponding to each model management controller in the database. If the operating condition adaptation value corresponding to a mobile energy storage vehicle is within the operating condition adaptation value interval corresponding to a model management controller in the database, the model management controller in the database is used as the optimal model management controller corresponding to the mobile energy storage vehicle in the target enterprise. In this way, the optimal model management controller is matched and selected for each mobile energy storage vehicle in the target enterprise.

[0049] Management controller upgrade analysis module: used to analyze the optimal upgrade section corresponding to each mobile energy storage vehicle management controller when the management controller corresponding to each mobile energy storage vehicle needs to be upgraded, and adjust the corresponding battery operating parameters when the management controller of each mobile energy storage vehicle is upgraded.

[0050] In a specific embodiment, the optimal upgrade section corresponding to the management controller of each mobile energy storage vehicle is analyzed. The specific analysis process is as follows: X1. When the management controller corresponding to each mobile energy storage vehicle needs to be upgraded, the destination of each mobile energy storage vehicle at the current moment is obtained, thereby obtaining the driving route of each mobile energy storage vehicle to the destination, and dividing the driving route of each mobile energy storage vehicle into a number of upgrade sections, and then obtaining road condition data corresponding to each upgraded section of each mobile energy storage vehicle. The road condition data includes signal strength, road electromagnetic interference strength, and flow quality indicators.

[0051] It should be noted that telecommunications operators provide signal strength coverage maps. Based on the location of the mobile energy storage vehicle, you can view the estimated signal strength of the corresponding road section on the map software. The local electromagnetic interference database records information on electromagnetic interference sources near different road sections (such as power and frequency). Based on the route and location of the mobile energy storage vehicle, you can query the database to obtain the estimated electromagnetic interference strength of the corresponding road section. Use existing network performance monitoring tools such as Wireshark. Before the software upgrade, start Wireshark for packet capture analysis. It can capture all data packets passing through the vehicle's network interface and can filter according to various conditions such as protocol, source address, and destination address to obtain traffic quality indicators for the corresponding road section.

[0052] X2. The signal strength, road electromagnetic interference intensity and flow quality index corresponding to each upgraded section of each mobile energy storage vehicle are recorded as 、 and , where h represents the number corresponding to each mobile energy storage vehicle, h is a positive integer, and f represents the number corresponding to each upgraded road section, f is a positive integer. Substitute into the calculation formula: The road condition evaluation coefficient corresponding to each upgraded section of each mobile energy storage vehicle is obtained. ,in, 、 、 They are the standard signal strength, standard road electromagnetic interference strength, and standard traffic quality index corresponding to the set road section. 、 、 They are respectively the weight factor corresponding to the set road section signal strength, the weight factor corresponding to the road electromagnetic interference strength, and the weight factor corresponding to the traffic quality index.

[0053] It should be noted that 、 、 Both are greater than 0 and less than 1.

[0054] It should also be noted that, through the summary of extensive research and experimental data, professional organizations and research institutions set the standard signal strength, standard road electromagnetic interference intensity, and standard traffic quality indicators for road sections. Furthermore, based on the expertise and research of field experts, and after discussion and confirmation with industry organizations and professional institutions, the experts set the weighting factors for road section signal strength, road electromagnetic interference intensity, and traffic quality indicators based on their own experience and knowledge.

[0055] X3. Arrange the road condition assessment coefficients corresponding to the upgraded sections in each mobile energy storage vehicle in descending order, and use the upgraded section with the highest road condition assessment coefficient in each mobile energy storage vehicle as the optimal upgraded section corresponding to each mobile energy storage vehicle management controller. In this way, analyze the optimal upgraded section corresponding to each mobile energy storage vehicle management controller.

[0056] In another specific embodiment, the corresponding battery operating parameters of each mobile energy storage vehicle management controller are adjusted when the mobile energy storage vehicle management controller is upgraded. The specific analysis process is as follows: V1. When each mobile energy storage vehicle management controller is upgraded in the corresponding optimal upgrade section, the model corresponding to each mobile energy storage vehicle management controller is obtained, and the model corresponding to each mobile energy storage vehicle management controller is compared with the model corresponding to each battery operation adjustment parameter set in the database. If the model corresponding to a mobile energy storage vehicle management controller is the same as the model corresponding to a battery operation adjustment parameter set in the database, then the battery operation adjustment parameter set in the database is recorded as the battery operation adjustment parameter set corresponding to the mobile energy storage vehicle.

[0057] V2. Compare the road condition assessment coefficient corresponding to the optimal upgrade section of each mobile energy storage vehicle with the road condition assessment coefficient corresponding to each battery operation adjustment parameter in the corresponding battery operation adjustment parameter set in the database. If the road condition assessment coefficient corresponding to the optimal upgrade section of a mobile energy storage vehicle is the same as the road condition assessment coefficient corresponding to a battery operation adjustment parameter in the corresponding battery operation adjustment parameter set in the database, then use the battery operation adjustment parameter in the battery operation adjustment parameter set in the corresponding database as the corresponding battery operation parameter adjustment value when the mobile energy storage vehicle management controller is upgraded.

[0058] Management controller debugging and analysis module: used to debug and analyze each mobile energy storage vehicle management controller after the upgrade is completed, and evaluate whether the upgrade of each mobile energy storage vehicle management controller is successful. If the upgrade of a mobile energy storage vehicle management controller fails, the failure level of the mobile energy storage vehicle management controller is analyzed and an early warning prompt is issued.

[0059] In a specific embodiment, the debugging and analysis of each mobile energy storage vehicle management controller is performed, and the specific analysis process is as follows: after the upgrade of each mobile energy storage vehicle management controller is completed, the discharge control response time, power distribution difference value and confidence interval width reduction ratio corresponding to each mobile energy storage vehicle management controller are obtained, and the discharge control response time, power distribution difference value and confidence interval width reduction ratio corresponding to each mobile energy storage vehicle management controller are recorded as 、 and , where h represents the number corresponding to each mobile energy storage vehicle, and h is a positive integer. Substitute it into the calculation formula: In the above example, the upgrade evaluation coefficient corresponding to each mobile energy storage vehicle management controller is obtained. ,in, 、 、 They are the standard discharge control response time, standard power distribution difference value, and standard confidence interval width reduction ratio corresponding to the set mobile energy storage vehicle management controller. 、 、 They are the weight factors corresponding to the discharge control response time of the mobile energy storage vehicle management controller, the weight factors corresponding to the power distribution difference value, and the weight factors corresponding to the confidence interval width reduction ratio. 、 、 They are respectively the adjustment factor corresponding to the discharge control response time of the set mobile energy storage vehicle management controller, the adjustment factor corresponding to the power distribution difference value, and the adjustment factor corresponding to the confidence interval width reduction ratio.

[0060] It should be noted that 、 、 Both are greater than 0 and less than 1.

[0061] It should also be noted that through the summary of a large amount of research data and experimental data. According to the standard discharge control response time, standard power distribution difference value, and standard confidence interval width reduction ratio corresponding to the mobile energy storage vehicle management controller set by professional institutions and research institutions, at the same time, based on the professional knowledge and research basis of experts in the field, and discussed and confirmed with industry organizations or professional institutions. Experts set the weight factors corresponding to the discharge control response time of the mobile energy storage vehicle management controller, the weight factors corresponding to the power distribution difference value, and the weight factors corresponding to the confidence interval width reduction ratio based on their own experience and knowledge, and set the adjustment factors corresponding to the discharge control response time of the mobile energy storage vehicle management controller, the adjustment factors corresponding to the power distribution difference value, and the adjustment factors corresponding to the confidence interval width reduction ratio.

[0062] Once again, it's important to note that the moment the management controller receives a discharge request from an external device is considered the start time. The end time is then determined, when the energy storage system actually begins discharging in response to this discharge request. Finally, the difference between the start time and the end time is the discharge control response time. After the management controller upgrade is complete, the actual allocated power is measured. The actual allocated power is subtracted from the preset power, and the absolute value of the difference is taken. This value is the power allocation difference. Before the management controller upgrade, multiple measurements of key performance indicators are performed. The average of these measurements is calculated, followed by the sample standard deviation. The confidence interval width before the upgrade is then calculated using a formula corresponding to a specific confidence level. This formula is the distribution quantile multiplied by the sample standard deviation divided by the square root of the number of measurements, multiplied by 2. After the management controller upgrade, the same performance indicator is measured multiple times using the same method, and the average, sample standard deviation, and confidence interval width are again calculated. Finally, subtract the width of the confidence interval after the upgrade from the width of the confidence interval before the upgrade, divide the difference by the width of the confidence interval before the upgrade, and multiply the result by 100%, which is the reduction ratio of the confidence interval width.

[0063] In another specific embodiment, the evaluation of whether the upgrade of each mobile energy storage vehicle management controller is successful is performed as follows: the upgrade evaluation coefficient corresponding to each mobile energy storage vehicle management controller is compared with the upgrade evaluation coefficient corresponding to the set standard management controller. If the upgrade evaluation coefficient corresponding to a mobile energy storage vehicle management controller is greater than or equal to the upgrade evaluation coefficient corresponding to the set standard management controller, it indicates that the upgrade of the mobile energy storage vehicle management controller is successful. If the upgrade evaluation coefficient corresponding to a mobile energy storage vehicle management controller is less than the upgrade evaluation coefficient corresponding to the set standard management controller, it indicates that the upgrade of the mobile energy storage vehicle management controller is unsuccessful. In this way, whether the upgrade of each mobile energy storage vehicle management controller is successful is evaluated.

[0064] In another specific embodiment, if a mobile energy storage vehicle management controller fails to be upgraded, the failure level of the mobile energy storage vehicle management controller is analyzed and an early warning prompt is issued. The specific early warning process is as follows: H1. If a mobile energy storage vehicle management controller fails to be upgraded, the upgrade evaluation coefficient corresponding to the mobile energy storage vehicle management controller is calculated to differ from the upgrade evaluation coefficient corresponding to the set standard management controller, and the difference is recorded as the upgrade evaluation coefficient difference.

[0065] H2. Compare the upgrade assessment coefficient difference corresponding to the mobile energy storage vehicle management controller with the upgrade assessment coefficient difference interval corresponding to the failure level of each management controller in the database. If the upgrade assessment coefficient difference corresponding to the mobile energy storage vehicle management controller is within the upgrade assessment coefficient difference interval corresponding to the failure level of a certain management controller in the database, then record the failure level of the management controller in the database as the failure level of the management controller. In this way, analyze the failure level of the mobile energy storage vehicle management controller.

[0066] Embodiment 2 of the present invention is a mobile energy storage vehicle battery management controller for car charging, including a management controller body. The management controller body is equipped with a quad-core Linux operating system and adopts ADI's automotive-grade AFE analog acquisition front end. It communicates and controls with the car charging pile and battery management system through a CAN interface. It has built-in rich charging pile interface acquisition circuits to identify and match charging piles in different regions for charging. The charging protocol supports three bus communication protocols for different regions: CCS2 / NACS and national standard, to achieve efficient data transmission and real-time control. The management controller body communicates with EMS and PCS using LAN, with a high transmission rate and support for flexible bus expansion. The IOT module built into the management controller body is a SIM7000 module with OTA function, which can achieve remote upgrade and debugging through a wireless network, making it convenient for users to use and maintain.

[0067] The above content is merely an example and explanation of the concept of the present invention. Those skilled in the art may make various modifications or additions to the described specific embodiments or replace them in a similar manner. As long as they do not deviate from the concept of the invention or exceed the scope defined in this specification, they should all fall within the scope of protection of the present invention.

Claims

1. A mobile energy storage vehicle battery management system for automobile charging, characterized in that: include: Management controller matching and installation module: used to match and select the best management controller for each mobile energy storage vehicle in the target enterprise, and install each mobile energy storage vehicle according to the matching best management controller; Management controller upgrade analysis module: used to analyze the optimal upgrade section corresponding to each mobile energy storage vehicle's management controller when the management controller corresponding to each mobile energy storage vehicle needs to be upgraded, and adjust the corresponding battery operating parameters when the management controller of each mobile energy storage vehicle is upgraded; The optimal upgrade section corresponding to each mobile energy storage vehicle management controller is analyzed, and the specific analysis process is as follows: X1. When the management controller corresponding to each mobile energy storage vehicle needs to be upgraded, the current destination of each mobile energy storage vehicle is obtained, thereby obtaining the driving route of each mobile energy storage vehicle to the destination, and dividing the driving route of each mobile energy storage vehicle into several upgraded sections, thereby obtaining the road condition data corresponding to each upgraded section of each mobile energy storage vehicle. The road condition data includes signal strength, road electromagnetic interference intensity, and traffic quality indicators; X2, the signal strength, road electromagnetic interference intensity and flow quality index corresponding to each upgraded section of each mobile energy storage vehicle are recorded as R hf 、Y hf and K hf , where h represents the number corresponding to each mobile energy storage vehicle, h is a positive integer, and f represents the number corresponding to each upgraded road section, f is a positive integer. Substitute into the calculation formula: The road condition evaluation coefficient β corresponding to each upgraded section of each mobile energy storage vehicle is obtained. hf , where R′, Y′, and K′ are the standard signal strength, standard road electromagnetic interference strength, and standard flow quality index corresponding to the set road section, respectively; υ1, υ2, and υ3 are the weight factors corresponding to the set road section signal strength, road electromagnetic interference strength, and flow quality index, respectively; X3. Arrange the road condition assessment coefficients corresponding to the upgraded sections in each mobile energy storage vehicle in descending order, and use the upgraded section with the highest road condition assessment coefficient as the optimal upgraded section corresponding to each mobile energy storage vehicle management controller. In this way, analyze the optimal upgraded section corresponding to each mobile energy storage vehicle management controller. Management controller debugging and analysis module: used to debug and analyze each mobile energy storage vehicle management controller after the upgrade is completed, and evaluate whether the upgrade of each mobile energy storage vehicle management controller is successful. If the upgrade of a mobile energy storage vehicle management controller fails, the failure level of the mobile energy storage vehicle management controller is analyzed and an early warning prompt is issued.

2. A mobile energy storage vehicle battery management system for automobile charging according to claim 1, characterized in that: The management controller matching installation module also includes an operating condition adaptation value analysis unit; The operating condition data analysis unit is used to obtain application scenario parameters and physical structure parameters corresponding to each mobile energy storage vehicle. The application scenario parameters include charge and discharge cycle frequency, energy feedback efficiency and load change rate. The physical structure parameters include battery compartment volume, space utilization and the number of battery modules in series, and then analyze and obtain the operating condition adaptation value corresponding to each mobile energy storage vehicle.

3. A mobile energy storage vehicle battery management system for automobile charging according to claim 2, characterized in that: The analysis obtains the operating condition adaptation value corresponding to each mobile energy storage vehicle. The specific analysis process is as follows: S1. Input the charge and discharge cycle frequency, energy feedback efficiency and load change rate corresponding to each mobile energy storage vehicle into the application scenario adaptation value evaluation model, and output the application scenario adaptation value corresponding to each mobile energy storage vehicle, which is recorded as Q h , where h represents the number corresponding to each mobile energy storage vehicle, and h is a positive integer. The battery compartment volume, space utilization rate, and number of battery modules in series corresponding to each mobile energy storage vehicle are input into the physical structure adaptation value evaluation model, and the physical structure adaptation value corresponding to each mobile energy storage vehicle is output and recorded as E h ; S2. Substitute the application scenario adaptation value and physical structure adaptation value corresponding to each mobile energy storage vehicle into the calculation formula The operating condition adaptation value α corresponding to each mobile energy storage vehicle is obtained h , where ζ1 and ζ2 are the weight factors corresponding to the set mobile energy storage vehicle application scenario adaptation value and the weight factor corresponding to the physical structure adaptation value, respectively, and e represents a natural constant.

4. A mobile energy storage vehicle battery management system for automobile charging according to claim 3, characterized in that: The specific selection process for matching the best model management controller for each mobile energy storage vehicle in the target enterprise is as follows: The operating condition adaptation value corresponding to each mobile energy storage vehicle is compared with the operating condition adaptation value range corresponding to each model management controller in the database. If the operating condition adaptation value corresponding to a mobile energy storage vehicle is within the operating condition adaptation value range corresponding to a certain model management controller in the database, the model management controller in the database is used as the optimal model management controller corresponding to the mobile energy storage vehicle in the target enterprise. In this way, the optimal model management controller is matched and selected for each mobile energy storage vehicle in the target enterprise.

5. A mobile energy storage vehicle battery management system for automobile charging according to claim 4, characterized in that: When the management controllers of each mobile energy storage vehicle are upgraded, the corresponding battery operating parameters are adjusted. The specific analysis process is as follows: V1. When each mobile energy storage vehicle management controller is upgraded on the corresponding optimal upgrade section, obtain the model corresponding to each mobile energy storage vehicle management controller, and compare the model corresponding to each mobile energy storage vehicle management controller with the model corresponding to each battery operation adjustment parameter set in the database. If the model corresponding to a mobile energy storage vehicle management controller is the same as the model corresponding to a battery operation adjustment parameter set in the database, then record the battery operation adjustment parameter set in the database as the battery operation adjustment parameter set corresponding to the mobile energy storage vehicle; V2. Compare the road condition assessment coefficient corresponding to the optimal upgrade section of each mobile energy storage vehicle with the road condition assessment coefficient corresponding to each battery operation adjustment parameter in the corresponding battery operation adjustment parameter set in the database. If the road condition assessment coefficient corresponding to the optimal upgrade section of a mobile energy storage vehicle is the same as the road condition assessment coefficient corresponding to a battery operation adjustment parameter in the corresponding battery operation adjustment parameter set in the database, then use the battery operation adjustment parameter in the battery operation adjustment parameter set in the corresponding database as the corresponding battery operation parameter adjustment value when the mobile energy storage vehicle management controller is upgraded.

6. A mobile energy storage vehicle battery management system for automobile charging according to claim 5, characterized in that: The debugging and analysis of each mobile energy storage vehicle management controller is carried out, and the specific analysis process is as follows: When the upgrade of each mobile energy storage vehicle management controller is completed, the discharge control response time, power distribution difference value and confidence interval width reduction ratio corresponding to each mobile energy storage vehicle management controller are obtained, and the discharge control response time, power distribution difference value and confidence interval width reduction ratio corresponding to each mobile energy storage vehicle management controller are recorded as G h 、J h and M h , where h represents the number corresponding to each mobile energy storage vehicle, and h is a positive integer. Substitute it into the calculation formula: The upgrade evaluation coefficient Φ corresponding to each mobile energy storage vehicle management controller is obtained h , where G′, J′, and M′ are the standard discharge control response time, standard power allocation difference value, and standard confidence interval width reduction ratio corresponding to the set mobile energy storage vehicle management controller, respectively; γ1, γ2, and γ3 are the weight factors corresponding to the discharge control response time, power allocation difference value, and confidence interval width reduction ratio of the set mobile energy storage vehicle management controller, respectively; λ1, λ2, and λ3 are the adjustment factors corresponding to the discharge control response time, power allocation difference value, and confidence interval width reduction ratio of the set mobile energy storage vehicle management controller, respectively.

7. A mobile energy storage vehicle battery management system for automobile charging according to claim 6, characterized in that: The specific evaluation process for evaluating whether the upgrade of each mobile energy storage vehicle management controller is successful is as follows: The upgrade evaluation coefficient corresponding to each mobile energy storage vehicle management controller is compared with the upgrade evaluation coefficient corresponding to the set standard management controller. If the upgrade evaluation coefficient corresponding to a mobile energy storage vehicle management controller is greater than or equal to the upgrade evaluation coefficient corresponding to the set standard management controller, it indicates that the upgrade of the mobile energy storage vehicle management controller is successful. If the upgrade evaluation coefficient corresponding to a mobile energy storage vehicle management controller is less than the upgrade evaluation coefficient corresponding to the set standard management controller, it indicates that the upgrade of the mobile energy storage vehicle management controller is unsuccessful. In this way, whether the upgrade of each mobile energy storage vehicle management controller is successful is evaluated.

8. A mobile energy storage vehicle battery management system for automobile charging according to claim 7, characterized in that: If a mobile energy storage vehicle management controller fails to upgrade, the failure level of the mobile energy storage vehicle management controller is analyzed and an early warning is issued. The specific early warning process is as follows: H1. If a mobile energy storage vehicle management controller fails to upgrade, the upgrade evaluation coefficient corresponding to the mobile energy storage vehicle management controller is calculated to differ from the upgrade evaluation coefficient corresponding to the set standard management controller, and the difference is recorded as the upgrade evaluation coefficient difference; H2. Compare the upgrade assessment coefficient difference corresponding to the mobile energy storage vehicle management controller with the upgrade assessment coefficient difference interval corresponding to the failure level of each management controller in the database. If the upgrade assessment coefficient difference corresponding to the mobile energy storage vehicle management controller is within the upgrade assessment coefficient difference interval corresponding to the failure level of a certain management controller in the database, then record the failure level of the management controller in the database as the failure level of the management controller. In this way, analyze the failure level of the mobile energy storage vehicle management controller.

9. A battery management controller for a mobile energy storage vehicle battery management system for automobile charging according to any one of claims 1 to 8, comprising a management controller body, characterized in that: The management controller body is equipped with a quad-core Linux operating system and adopts ADI's automotive-grade AFE analog acquisition front-end. It communicates and controls with the vehicle charging pile and battery management system through the CAN interface. It has built-in rich charging pile interface acquisition circuits to identify and match charging piles in different regions for charging. The charging protocol supports three bus communication protocols for different regions: CCS2 / NACS and national standard, to achieve efficient data transmission and real-time control. The management controller body communicates with EMS and PCS using LAN, with high transmission rate and support for flexible bus expansion. The built-in IOT module of the management controller body uses a module model SIM7000, which has OTA function and can achieve remote upgrade and debugging through wireless network, which is convenient for user use and maintenance.

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