Mobile energy storage vehicle battery management system and controller for vehicle charging

Abstract

The invention discloses a mobile energy storage vehicle battery management system for automobile charging and a controller, and relates to the technical field of management controllers, the hardware configuration of a management controller body is excellent, a four-core Linux operating system endows strong operation and multi-task processing capability, and the system is guaranteed to stably operate without blockage. A mobile energy storage vehicle battery system is managed in a mode of assembling a local controller, a built-in IOT module can conveniently and quickly realize an OTA function, the system is simplified and efficiently controlled, the reliability and robustness of the system are improved, and the optimal model management controller is accurately matched. The management controller can be ensured to be highly matched with the working condition of the mobile energy storage vehicle, battery operation parameters are finely adjusted according to the vehicle type and road condition evaluation coefficient, stable communication and reliable data transmission in the upgrading process are ensured, upgrading interruption or errors caused by poor signals and strong interference are prevented, the good state of the battery is maintained, and the service life of the battery is prolonged. And normal charging and discharging and service life of the battery are not influenced by upgrading.

Description

Technical Field

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

[0002] With the popularization of electric vehicles, the vehicle charging system has become an important research field. Among them, as an emerging charging solution, a mobile energy storage vehicle can provide mobile charging services for electric vehicles by carrying a battery system, and can also generate backup power under the condition of power shortage in factories / large facilities to meet short-term load requirements. At the same time, as the core to ensure the safe and efficient operation of the battery, the battery management system has also been paid more and more attention. Therefore, a battery management system and a controller for a mobile energy storage vehicle used for vehicle charging are needed.

[0003] The prior art, such as an invention application patent with the publication number of CN118363628A, discloses a method, device, energy storage device and storage medium for upgrading a battery management system. The method includes: when receiving an upgrade instruction from a host computer, entering a bootloader program, receiving a source cyclic redundancy check code and an upgrade program package from the host computer; performing an upgrade through the upgrade program package; calculating a local cyclic redundancy check code according to part of the data in the upgraded application program area; comparing the local cyclic redundancy check code with the source cyclic redundancy check code to determine whether the upgrade is successful; if the upgrade is successful, starting the application program, and if the upgrade fails, waiting for the next round of upgrade. By using the cyclic redundancy check code to determine the burn-in program, it is ensured whether the burn-in program is correct, and the stability during the upgrade is ensured.

[0004] Regarding the above solution, the inventors of the present application found that the above technology has at least the following technical problems: 1. The prior art lacks a management controller matching mechanism, and there may be a situation where the management controller does not match the actual application scenario and physical structure of the mobile energy storage vehicle. Without precise matching, it may cause resource waste or insufficient performance. For example, installing a high-end and powerful management controller on a mobile energy storage vehicle with a small load change rate and a small battery capacity will increase costs and some functions will be idle; on the contrary, using a low-performance controller in a high-demand scenario may not be able to meet the complex energy feedback and power distribution requirements, resulting in unstable operation of the battery system.

[0005] 2. The prior art lacks analysis of the upgrade section when upgrading the management controller, and may upgrade on sections with poor signals, strong electromagnetic interference, or low traffic quality. This will cause data transmission interruptions or errors during the upgrade process, damage the upgrade file, and further cause the management controller to malfunction or malfunction. At the same time, failure to adjust the battery operating parameters according to the road conditions of the upgrade section and the model of the management controller may affect the normal operation of the battery during the upgrade process. When upgrading on sections with strong electromagnetic interference, if the charging power or battery balancing strategy is not adjusted appropriately, it may cause overcharging or over-discharging of the battery, or aggravate the imbalance between battery cells, shorten the battery life, and even cause safety hazards.

[0006] 3. The existing technology does not have a detailed debugging and analysis process, so it is difficult to accurately determine whether the management controller upgrade is successful. Relying solely on simple functional tests may miss some potential problems, causing slight changes such as a slightly longer discharge control response time after the upgrade and an increase in the power distribution difference value under certain working conditions. These problems may lead to more serious failures after long-term operation. There is a lack of clear failure level classification and corresponding early warning mechanism. 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. Minor failures may be ignored and gradually develop into serious failures; or when a serious failure occurs, no emergency and effective measures are taken, such as stopping charging or discharging operations, which may cause battery damage, vehicle failures, or even safety accidents.

[0007] 4. The increase in the number of modules in the existing technology makes the system more complex and the design cost increases. At the same time, none of the modules has the OTA (Over-the-Air) function independently, and an additional IOT module is required for remote upgrade, which increases the cost and difficulty of debugging and product deployment, and cannot achieve convenient remote upgrade and debugging. This means that every time the management controller software is updated or the parameters are adjusted, manual on-site operation is required, which not only increases the maintenance cost and time, but also may cause the system to be in a non-optimal operating state for a long time due to the inability to update the software in time to fix vulnerabilities or optimize performance. Summary of the invention

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

[0009] In order to solve the above technical problems, the present invention adopts the following technical solutions: 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: When the management controllers corresponding to each mobile energy storage vehicle need to be upgraded, it analyzes the optimal upgrade sections corresponding to the management controllers of each mobile energy storage vehicle and adjusts the battery operation parameters corresponding to the upgrade of the management controllers of each mobile energy storage vehicle.

[0011] Management controller debugging analysis module: After the upgrade of the management controllers of each mobile energy storage vehicle is completed, it conducts debugging analysis on the management controllers of each mobile energy storage vehicle and evaluates whether the upgrade of the management controllers of each mobile energy storage vehicle is successful. If the upgrade of a certain mobile energy storage vehicle management controller fails, it analyzes the failure level of the mobile energy storage vehicle management controller and gives a warning prompt.

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

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

[0014] Preferably, the process of analyzing and obtaining the operating condition adaptation value corresponding to each mobile energy storage vehicle 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, denoted as Q h , where h represents the number corresponding to each mobile energy storage vehicle, and h is a positive integer. Input the battery compartment volume, space utilization rate, and the number of battery modules in series corresponding to each mobile energy storage vehicle into the physical structure adaptation value evaluation model, and output the physical structure adaptation value corresponding to each mobile energy storage vehicle, denoted 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 to obtain the operating condition adaptation value α corresponding to each mobile energy storage vehicle h , where ζ 1 , ζ 2 are respectively the weight factors corresponding to the application scenario adaptation value of the mobile energy storage vehicle and the weight factors corresponding to the physical structure adaptation value, and e represents the natural constant.

[0015] Preferably, the matching selection of the optimal model management controller for each mobile energy storage vehicle in the target enterprise is carried out as follows: compare the operation condition adaptation value corresponding to each mobile energy storage vehicle with the operation condition adaptation value range corresponding to each model management controller in the database. If the operation condition adaptation value corresponding to a certain mobile energy storage vehicle is within the operation condition adaptation value range corresponding to a certain model management controller in the database, then the model management controller in the database is taken as the optimal model management controller corresponding to the mobile energy storage vehicle in the target enterprise. In this way, the matching selection of the optimal model management controller for each mobile energy storage vehicle in the target enterprise is carried out.

[0016] Preferably, the analysis of the best upgrade section corresponding to the management controller of each mobile energy storage vehicle is carried out as follows: X1. When the management controller corresponding to each mobile energy storage vehicle needs to be upgraded, obtain the destination of each mobile energy storage vehicle at the current moment, so as to obtain the driving route of each mobile energy storage vehicle to the destination, and divide the driving route of each mobile energy storage vehicle into several upgrade sections, and then obtain the road condition data corresponding to each upgrade section in each mobile energy storage vehicle. The road condition data includes signal strength, road electromagnetic interference strength and traffic quality index.

[0017] X2. Denote the signal strength, road electromagnetic interference strength and traffic quality index corresponding to each upgrade section in each mobile energy storage vehicle 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 upgrade section, f is a positive integer. Substitute into the calculation formula: to obtain the road condition evaluation coefficient β hf corresponding to each upgrade section in each mobile energy storage vehicle, where R′, Y′, K′ are the standard signal strength, standard road electromagnetic interference strength and standard traffic quality index corresponding to the set section respectively, and υ 1 , υ 2 , υ 3 are the weight factors corresponding to the signal strength of the set section, the weight factor corresponding to the road electromagnetic interference strength and the weight factor corresponding to the traffic quality index respectively.

[0018] X3. Arrange the road condition evaluation coefficients corresponding to each upgrade section in each mobile energy storage vehicle in descending order, and take the upgrade section with the highest road condition evaluation coefficient in each mobile energy storage vehicle as the best upgrade section corresponding to the management controller of each mobile energy storage vehicle. In this way, the analysis of the best upgrade section corresponding to the management controller of each mobile energy storage vehicle is carried out.

[0019] Preferably, when the management controllers of the mobile energy storage vehicles are upgraded, the corresponding battery operation parameters are adjusted. The specific analysis process is as follows: V1. When the management controllers of the mobile energy storage vehicles are upgraded on the corresponding optimal upgrade sections, obtain the models corresponding to the management controllers of the mobile energy storage vehicles, and compare the models corresponding to the management controllers of the mobile energy storage vehicles with the models corresponding to the battery operation adjustment parameter sets in the database. If the model corresponding to a certain management controller of the mobile energy storage vehicle is the same as the model corresponding to a certain 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.

[0020] V2. Compare the road condition evaluation coefficients corresponding to the optimal upgrade sections of the mobile energy storage vehicles with the road condition evaluation coefficients corresponding to the battery operation adjustment parameters in the corresponding battery operation adjustment parameter sets in the database. If the road condition evaluation coefficient corresponding to the optimal upgrade section of a certain mobile energy storage vehicle is the same as the road condition evaluation coefficient corresponding to a certain battery operation adjustment parameter in the corresponding battery operation adjustment parameter set in the database, then use the battery operation adjustment parameter in the corresponding battery operation adjustment parameter set in the database as the battery operation parameter adjustment value corresponding to the upgrade of the management controller of the mobile energy storage vehicle.

[0021] Preferably, the debugging and analysis of the management controllers of the mobile energy storage vehicles are as follows: After the management controllers of the mobile energy storage vehicles are upgraded, obtain the discharge control response duration, power distribution difference value, and confidence interval width reduction ratio corresponding to the management controllers of the mobile energy storage vehicles, and record the discharge control response duration, power distribution difference value, and confidence interval width reduction ratio corresponding to the management controllers of the mobile energy storage vehicles as G h 、J h and M h respectively, where h represents the number corresponding to each mobile energy storage vehicle, and h is a positive integer. Substitute it into the calculation formula:

[0022] to obtain the upgrade evaluation coefficient Φ h corresponding to the management controllers of the mobile energy storage vehicles, where G′, J′, and M′ are the standard discharge control response duration, standard power distribution difference value, and standard confidence interval width reduction ratio corresponding to the set management controllers of the mobile energy storage vehicles, and γ 1 、γ 2 、γ 3 are the weight factors corresponding to the discharge control response duration, power distribution difference value, and confidence interval width reduction ratio of the set management controllers of the mobile energy storage vehicles respectively, and λ 1 、λ 2 、λ 3They are the adjustment factors corresponding to the set discharge control response duration of the mobile energy storage vehicle management controller, the adjustment factor corresponding to the power distribution difference value, and the adjustment factor corresponding to the reduction ratio of the confidence interval width respectively.

[0023] Preferably, to evaluate whether the upgrade of each mobile energy storage vehicle management controller is successful, the specific evaluation process is as follows: Compare the upgrade evaluation coefficient corresponding to each mobile energy storage vehicle management controller 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 this 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 this mobile energy storage vehicle management controller is not successful. In this way, evaluate whether the upgrade of each mobile energy storage vehicle management controller is successful.

[0024] Preferably, if the upgrade of a certain mobile energy storage vehicle management controller fails, then analyze the failure level of this mobile energy storage vehicle management controller and give a warning prompt. The specific warning process is as follows: H1. If the upgrade of a certain mobile energy storage vehicle management controller fails, calculate the difference between the upgrade evaluation coefficient corresponding to this mobile energy storage vehicle management controller and the upgrade evaluation coefficient corresponding to the set standard management controller, and record this difference as the upgrade evaluation coefficient difference.

[0025] H2. Then compare the upgrade evaluation coefficient difference corresponding to this mobile energy storage vehicle management controller with the upgrade evaluation coefficient difference intervals corresponding to the failure levels of each management controller in the database. If the upgrade evaluation coefficient difference corresponding to this mobile energy storage vehicle management controller is within the upgrade evaluation coefficient difference interval corresponding to the failure level of a certain management controller in the database, then record the failure level of this management controller in the database as the failure level of this management controller. In this way, analyze the failure level of this 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, including a management controller body, the management controller body is equipped with a 4-core Linux operating system, adopts ADI's automotive-grade AFE analog acquisition front end, communicates and controls with the automobile charging pile and battery system through a CAN interface, has a built-in rich charging pile interface acquisition circuit, identifies and matches charging piles in different regions for charging, and the charging protocol supports CCS2 / NACS and national standard bus communication protocols in three different regions to achieve efficient data transmission and real-time control. The local controller communicates with EMS and PCS in LAN mode, has a high transmission rate and supports flexible bus expansion, and the built-in IOT module of the local controller uses a module of model SIM7000, which has an OTA function and realizes remote upgrading and debugging through a wireless network, which is 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 is used to comprehensively consider the application scenario parameters such as the charging and discharging cycle frequency, energy feedback efficiency, load change rate of the mobile energy storage vehicle, and the physical structure parameters such as the battery compartment volume, space utilization, and the number of battery modules in series, and the operating condition adaptation value is obtained, and the optimal model management controller is accurately matched based on this. This can ensure that the management controller is highly consistent with the working conditions of the mobile energy storage vehicle itself, avoid the situation of "overkill" or "a small horse pulling a big cart", maximize the performance of the management controller, improve the overall performance of the battery management system, and reduce problems such as unstable operation and low efficiency caused by mismatch.

[0028] 2. In the embodiment of the present invention, the management controller upgrade analysis module can skillfully analyze the best upgrade section according to the upgrade requirements. The road conditions are evaluated by comprehensive signal strength, road electromagnetic interference strength and flow quality indicators, and the most suitable section is selected for upgrading. At the same time, the battery operation parameters, such as charging power and balancing strategy, are finely adjusted according to the vehicle model and road condition evaluation coefficient. It not only ensures stable communication and reliable data transmission during the upgrade process, prevents upgrade interruption or error due to poor signal and strong interference, but also maintains the battery in good condition, does not affect the normal charging and discharging and life of the battery due to the upgrade, and improves the success rate and safety of the upgrade. After the upgrade is completed, the management controller debugging analysis module conducts multi-dimensional quantitative evaluation from the discharge control response time, power allocation difference value and confidence interval width reduction ratio to accurately determine whether the upgrade is successful. Once the upgrade fails, the upgrade evaluation coefficient difference can be quickly compared with the various level intervals in the database, the failure level can be accurately defined, and the corresponding early warning prompt can be issued in time. From system prompts and remote notifications for minor faults to local emergency braking and comprehensive remote alarms for serious faults, it is fully guaranteed that the operation and maintenance personnel can know and deal with the problem in the first time, reduce the scope and duration of the fault impact, and improve the system availability.

[0029] 3. In the embodiment of the present invention, the hardware configuration of the management controller body is excellent. The 4-core Linux operating system endows powerful computing and multitasking capabilities, ensuring the stable operation of the system without lag. By assembling a local controller to manage the battery system of the mobile energy storage vehicle, it replaces the complex and redundant system design of the original automotive BMS + energy storage BMS + European and American standard charging protocol converter + communication converter, greatly optimizing the control system and communication topology, reducing the number of electronic modules, lowering the design cost, and saving structural space. The CAN interface adapts to a variety of charging pile protocols, seamlessly connecting the automotive 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 the requirements of complex energy management and power scheduling, and coordinating the efficient operation of each component. At the same time, the built-in IOT module can conveniently and quickly implement the OTA function. The concise and efficient control system increases the reliability and robustness of the system, solves the problems of remote upgrade and debugging. The OTA function of the built-in SIM7000 module breaks through time and space limitations, enabling remote upgrade and debugging via a wireless network without the need for maintenance personnel to be on-site. Whether it is updating the control algorithm, fixing software vulnerabilities, or adapting to new charging standards, it can be achieved conveniently and quickly. This greatly saves labor and material costs, shortens the system iteration cycle, keeps the battery management system of the mobile energy storage vehicle in optimal performance at all times, keeps up with the industry development pace, enhances the market competitiveness of the product, and conforms to the trend of intelligent operation and maintenance. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0031] Figure 1 It is a schematic diagram of the connection of the system modules of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0032] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, rather than all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

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

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

[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 matched 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 the application scenario parameters and physical structure parameters corresponding to each mobile energy storage vehicle. The application scenario parameters include the charge and discharge cycle frequency, energy feedback efficiency, and load change rate, and the physical structure parameters include the battery compartment volume, space utilization rate, and the number of battery modules in series, so as to 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 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 battery state to a set high battery state, and a complete discharging process is from a high battery state to a set low battery state. Then divide this number by the statistical time period to obtain the charge and discharge cycle frequency. First, measure the energy actually fed back to the energy storage system during the energy feedback process and the maximum energy that the entire energy storage system can theoretically receive and store. Divide the actually fed back energy by the theoretical maximum received and stored energy, and then multiply by 100% to obtain the energy feedback efficiency, which is achieved by recording the load current or power at different times. First, record the load current or power at one moment, then record the load current or power at another moment, subtract the value at the previous moment from the value at the latter moment, and then divide by the time interval between these two moments to obtain the change rate of the load current or power.

[0039] It should also be noted that the lengths of the length, width, and height of the battery compartment are measured. Then, the three lengths of length, width, and height are multiplied, and the result obtained is the volume of the battery compartment. To calculate the space utilization rate, first calculate the actual occupied space volume of the battery modules in the battery compartment, which is determined by measuring the dimensions of the battery modules and considering their arrangement. Then, divide the actual occupied space volume of the battery 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 battery management system of the mobile energy storage vehicle or by checking the circuit diagram of the energy storage system.

[0040] In another specific embodiment, the analysis to obtain the operation condition adaptation values corresponding to each mobile energy storage vehicle is as follows: S1. Input the charge-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 values corresponding to each mobile energy storage vehicle, which are denoted as Q h , where h represents the number corresponding to each mobile energy storage vehicle, and h is a positive integer. Input the battery compartment volume, space utilization rate, and number of battery modules connected in series corresponding to each mobile energy storage vehicle into the physical structure adaptation value evaluation model, and output the physical structure adaptation values corresponding to each mobile energy storage vehicle, which are denoted as E h .

[0041] It should be noted that the analysis process of the application scenario adaptation values corresponding to each mobile energy storage vehicle is as follows: The charge-discharge cycle frequency, energy feedback efficiency, and load change rate corresponding to each mobile energy storage vehicle are subjected to normalization processing, and the processed charge-discharge cycle frequency, energy feedback efficiency, and load change rate corresponding to each mobile energy storage vehicle are respectively denoted as a h , b h , and c h . Substitute them into the analysis formula Q h = a h * ρ 1 + b h * ρ 2 + c h * ρ 3 to obtain the application scenario adaptation values Q h corresponding to each mobile energy storage vehicle. ρ 1 , ρ 2 , and ρ 3 are respectively the weight factors corresponding to the charge-discharge cycle frequency, energy feedback efficiency, and load change rate of the set mobile energy storage vehicle.

[0042] It should be noted that ρ 1 , ρ 2 , and ρ 3 are all greater than 0 and less than 1.

[0043] It should also be noted that, based on the professional knowledge and research of domain experts, discussions and confirmations are carried out with industry organizations or professional institutions. Experts set the weight factors corresponding to the charge-discharge cycle frequency of the mobile energy storage vehicle, the weight factors corresponding to the energy feedback efficiency, and the weight factors corresponding to the load change rate according to their own experience and knowledge.

[0044] Once again, it should be noted that the physical structure adaptation values corresponding to each mobile energy storage vehicle are analyzed according to the analysis process of the application scenario adaptation values corresponding to each mobile energy storage vehicle as described above.

[0045] S2. Substitute the application scenario adaptation values and physical structure adaptation values corresponding to each mobile energy storage vehicle into the calculation formula to obtain the operating condition adaptation value α corresponding to each mobile energy storage vehicle h , where ζ 1 , ζ 2 are respectively the weight factors corresponding to the application scenario adaptation values of the mobile energy storage vehicle and the weight factors corresponding to the physical structure adaptation values, and e represents the natural constant.

[0046] It should be noted that ζ 1 , ζ 2 are both greater than 0 and less than 1.

[0047] It should also be noted that, based on the professional knowledge and research of domain experts, discussions and confirmations are carried out with industry organizations or professional institutions. Experts set the weight factors corresponding to the application scenario adaptation values of the mobile energy storage vehicle and the weight factors corresponding to the physical structure adaptation values according to their own experience and knowledge.

[0048] In another specific embodiment, the matching selection of the best model management controller for each mobile energy storage vehicle in the target enterprise is as follows: Compare the operating condition adaptation values corresponding to each mobile energy storage vehicle with the operating condition adaptation value ranges 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 range corresponding to a certain model management controller in the database, then use the model management controller in the database as the best model management controller corresponding to the mobile energy storage vehicle in the target enterprise. In this way, the matching selection of the best model management controller for each mobile energy storage vehicle in the target enterprise is carried out.

[0049] Management controller upgrade analysis module: Used to analyze the best upgrade section corresponding to each mobile energy storage vehicle management controller and adjust the battery operating parameters corresponding to the upgrade of each mobile energy storage vehicle management controller when the management controllers corresponding to each mobile energy storage vehicle need to be upgraded.

[0050] In a specific embodiment, the analysis of the best upgrade sections corresponding to the management controllers of each mobile energy storage vehicle is as follows: X1. When the management controller corresponding to each mobile energy storage vehicle needs to be upgraded, obtain the destinations of each mobile energy storage vehicle at the current moment, so as to obtain the driving routes of each mobile energy storage vehicle to the destinations, divide the driving routes of each mobile energy storage vehicle into several upgrade sections, and then obtain the road condition data corresponding to each upgrade section in each mobile energy storage vehicle. The road condition data includes signal strength, road electromagnetic interference strength, and traffic quality index.

[0051] It should be noted that the communication operator provides a signal strength coverage map. The estimated signal strength value of the corresponding section can be viewed on the map software according to the driving position of the mobile energy storage vehicle. There is a local electromagnetic interference database, which records the information of electromagnetic interference sources near different sections (such as power, frequency, etc.). The estimated electromagnetic interference strength value of the corresponding section can be queried from the database according to the driving route and position of the mobile energy storage vehicle. Use existing network performance monitoring tools, such as Wireshark, etc. Start Wireshark for packet capture analysis before software upgrade. It can capture all data packets passing through the vehicle network interface and can be filtered according to various conditions such as protocol, source address, and destination address to obtain the traffic quality index of the corresponding section.

[0052] X2. Denote the signal strength, road electromagnetic interference strength, and traffic quality index corresponding to each upgrade section in each mobile energy storage vehicle as R hf , Y hf and K hf respectively, 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 upgrade section, f is a positive integer. Substitute into the calculation formula: to obtain the road condition evaluation coefficient β hf corresponding to each upgrade section in each mobile energy storage vehicle, where R′, Y′, and K′ are the standard signal strength, standard road electromagnetic interference strength, and standard traffic quality index corresponding to the set section respectively, and υ 1 , υ 2 , υ 3 are the weight factors corresponding to the set section signal strength, road electromagnetic interference strength, and traffic quality index respectively.

[0053] It should be noted that υ 1 , υ 2 , υ 3 are all greater than 0 and less than 1.

[0054] It should also be noted that through the summary of a large amount of research data and experimental data. According to the standard signal strength, standard road electromagnetic interference intensity, and standard traffic quality index corresponding to the road sections set by professional institutions and research institutions, at the same time, based on the professional knowledge and research basis of domain experts, and through discussions and confirmations with industry organizations or professional institutions. Experts set the weight factors corresponding to the signal strength of the road section, the weight factors corresponding to the road electromagnetic interference intensity, and the weight factors corresponding to the traffic quality index according to their own experience and knowledge.

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

[0056] In another specific embodiment, the battery operation parameters corresponding to the upgrade of each mobile energy storage vehicle management controller are adjusted. The specific analysis process is as follows: V1. When each mobile energy storage vehicle management controller is upgraded on the corresponding best upgraded road 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 models corresponding to each battery operation adjustment parameter set in the database. If the model corresponding to a certain mobile energy storage vehicle management controller is the same as the model corresponding to a certain 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 that mobile energy storage vehicle.

[0057] V2. Compare the road condition evaluation coefficient corresponding to the best upgraded road section of each mobile energy storage vehicle with the road condition evaluation coefficients corresponding to each battery operation adjustment parameter in the corresponding battery operation adjustment parameter set in the database. If the road condition evaluation coefficient corresponding to the best upgraded road section of a certain mobile energy storage vehicle is the same as the road condition evaluation coefficient corresponding to a certain battery operation adjustment parameter in the corresponding battery operation adjustment parameter set in the database, then take the battery operation adjustment parameter in the corresponding database battery operation adjustment parameter set as the battery operation parameter adjustment value corresponding to the upgrade of that mobile energy storage vehicle management controller.

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

[0059] In a specific embodiment, the debugging and analysis of each mobile energy storage vehicle management controller are as follows: After the upgrade of each mobile energy storage vehicle management controller is completed, obtain the discharge control response duration, power distribution difference value, and confidence interval width reduction ratio corresponding to each mobile energy storage vehicle management controller, and record the discharge control response duration, power distribution difference value, and confidence interval width reduction ratio corresponding to each mobile energy storage vehicle management controller as G h 、J h and M h respectively, where h represents the number corresponding to each mobile energy storage vehicle, and h is a positive integer. Substitute it into the calculation formula: to obtain the upgrade evaluation coefficient Φ h corresponding to each mobile energy storage vehicle management controller, where G′, J′, and M′ are the standard discharge control response duration, standard power distribution difference value, and standard confidence interval width reduction ratio corresponding to the set mobile energy storage vehicle management controller respectively, and γ 1 、γ 2 、γ 3 are the weight factors corresponding to the discharge control response duration of the set 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 respectively. λ 1 、λ 2 、λ 3 are the adjustment factors corresponding to the discharge control response duration of the set 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 respectively.

[0060] It should be noted that γ 1 、γ 2 、γ 3 are all 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 duration, 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 domain experts, and through discussions and confirmations with industry organizations or professional institutions. The experts set the weight factors corresponding to the discharge control response duration 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, as well as the adjustment factors corresponding to the discharge control response duration 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 should be noted that when the management controller receives the discharge request instruction from the external device, this moment is regarded as the starting time. Then, determine the moment when the energy storage system actually starts discharging due to this discharge request, which is the ending time. Finally, subtract the starting time from the ending time, and the difference obtained is the discharge control response duration. After the management controller upgrade is completed, measure the actually allocated power. Subtract the pre-set power from the actually allocated power, and take the absolute value of the difference. This value is the power allocation difference value. Before the management controller upgrade, conduct multiple measurements for key performance indicators. First, calculate the average of these measurement values, and then calculate the sample standard deviation. Then, according to the calculation formula corresponding to a certain confidence level, calculate the width of the confidence interval before the upgrade. This calculation formula is to multiply the distribution quantile by the sample standard deviation, divide by the square root of the number of measurements, and then multiply by 2. After the management controller upgrade, use the same method to conduct multiple measurements for the same performance indicators, and calculate the average, sample standard deviation, and confidence interval width again. 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 management controller of each mobile energy storage vehicle is successfully upgraded is as follows: Compare the upgrade evaluation coefficient corresponding to the management controller of each mobile energy storage vehicle with the upgrade evaluation coefficient corresponding to the set standard management controller. If the upgrade evaluation coefficient corresponding to the management controller of a certain mobile energy storage vehicle is greater than or equal to the upgrade evaluation coefficient corresponding to the set standard management controller, it indicates that the management controller of this mobile energy storage vehicle is successfully upgraded. If the upgrade evaluation coefficient corresponding to the management controller of a certain mobile energy storage vehicle is less than the upgrade evaluation coefficient corresponding to the set standard management controller, it indicates that the management controller of this mobile energy storage vehicle is not successfully upgraded. In this way, evaluate whether the management controller of each mobile energy storage vehicle is successfully upgraded.

[0064] In another specific embodiment, if the management controller of a certain mobile energy storage vehicle fails to be upgraded, then analyze the failure level of the management controller of this mobile energy storage vehicle and give a warning prompt. The specific warning process is as follows: H1. If the management controller of a certain mobile energy storage vehicle fails to be upgraded, calculate the difference between the upgrade evaluation coefficient corresponding to the management controller of this mobile energy storage vehicle and the upgrade evaluation coefficient corresponding to the set standard management controller, and record this difference as the upgrade evaluation coefficient difference.

[0065] H2, and 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 management controller in the database, the failure level of the management controller in the database is recorded as the failure level of the management controller. In this way, the failure level of the mobile energy storage vehicle management controller is analyzed.

[0066] Embodiment 2 of the present invention is a mobile energy storage vehicle battery management controller for automobile charging, comprising a management controller body, wherein the management controller body is equipped with a 4-core Linux operating system, adopts ADI's automotive-grade AFE analog acquisition front end, communicates and controls with the automobile charging pile and battery system through a CAN interface, has a built-in rich charging pile interface acquisition circuit, identifies and matches charging piles in different regions for charging, and the charging protocol supports three bus communication protocols of CCS2 / NACS and national standards in different regions, realizes efficient data transmission and real-time control, and the local controller communicates with EMS and PCS in LAN mode, has a high transmission rate and supports flexible bus expansion, and the built-in IOT module of the local controller adopts a module of model SIM7000, which has an OTA function, realizes remote upgrading and debugging through a wireless network, and is convenient for users to use and maintain.

[0067] The above contents are merely examples and explanations of the concept of the present invention. Those skilled in the art may make various modifications or additions to the specific embodiments described 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 shall all fall within the protection scope 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 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; Management controller upgrade analysis module: used to analyze the best 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; 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 as claimed in 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 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 as claimed in 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, output the application scenario adaptation value corresponding to each mobile energy storage vehicle, and record it 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 adaptation value of the mobile energy storage vehicle application scenario and the weight factors corresponding to the adaptation value of the physical structure, respectively, and e represents a natural constant.

4. A mobile energy storage vehicle battery management system for automobile charging as claimed in claim 3, characterized in that: The matching and selection of the best model management controller for each mobile energy storage vehicle in the target enterprise is carried out, and the specific selection process is 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 best model management controller corresponding to the mobile energy storage vehicle in the target enterprise. In this way, the best 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 as claimed in claim 4, characterized in that: 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 destination of each mobile energy storage vehicle at the current moment is obtained, so as to obtain the driving route of each mobile energy storage vehicle to the destination, and divide the driving route of each mobile energy storage vehicle into several upgraded sections, and then obtain the road condition data corresponding to each upgraded section in each mobile energy storage vehicle, and the road condition data includes signal strength, road electromagnetic interference strength and flow quality index; X2, the signal strength, road electromagnetic interference intensity and flow quality index corresponding to each upgraded section in 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 section, f is a positive integer, and is substituted 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, the weight factors corresponding to the road electromagnetic interference strength, and the weight factors corresponding to the flow quality index, respectively; X3. Arrange the road condition assessment coefficients corresponding to each upgraded section 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.

6. A mobile energy storage vehicle battery management system for automobile charging as claimed in claim 5, characterized in that: When each mobile energy storage vehicle management controller is 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 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, 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; V2. Compare the road condition assessment coefficient corresponding to the best upgrade section of each mobile energy storage vehicle with the road condition assessment coefficient corresponding to each battery operation adjustment parameter in the battery operation adjustment parameter set in the corresponding database. If the road condition assessment coefficient corresponding to the best 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 battery operation adjustment parameter set in the corresponding 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.

7. A mobile energy storage vehicle battery management system for automobile charging as claimed in claim 6, characterized in that: The debugging and analysis of each mobile energy storage vehicle management controller is performed, 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 allocation 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 allocation 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, which is substituted 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.

8. A mobile energy storage vehicle battery management system for automobile charging as claimed in claim 7, characterized in that: The specific evaluation process of 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 mobile energy storage vehicle management controller has been successfully upgraded. 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 mobile energy storage vehicle management controller has not been successfully upgraded. In this way, whether the upgrade of each mobile energy storage vehicle management controller is successful is evaluated.

9. A mobile energy storage vehicle battery management system for automobile charging as claimed in claim 8, 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 be different from the upgrade evaluation coefficient corresponding to the set standard management controller, and the difference is recorded as the upgrade evaluation coefficient difference; H2, and 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 management controller in the database, the failure level of the management controller in the database is recorded as the failure level of the management controller. In this way, the failure level of the mobile energy storage vehicle management controller is analyzed.

10. A mobile energy storage vehicle battery management controller for vehicle charging that implements the mobile energy storage vehicle battery management system for vehicle charging according to any one of claims 1 to 9, comprising a management controller body, characterized in that: The management controller body is equipped with a 4-core Linux operating system and adopts ADI's automotive-grade AFE analog acquisition front end. It communicates and controls the car charging pile and battery 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 in different regions: CCS2 / NACS and national standard, to achieve efficient data transmission and real-time control. The local controller communicates with EMS and PCS via LAN, with high transmission rate and support for flexible bus expansion. The built-in IOT module of the local controller uses a module model SIM7000. This module has OTA function, which can achieve remote upgrade and debugging through wireless network, which is convenient for users to use and maintain.

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