Method, device and system for monitoring the capacity of a trolley battery

Abstract

The application provides a tram battery capacity monitoring method, device and tram monitoring system. The method comprises the following steps: obtaining a capacity test parameter of a tram battery; performing capacity simulation processing on the capacity test parameter and a preset test parameter to obtain an electric simulation capacity difference value; and sending a capacity estimation signal to a battery management system according to the electric simulation capacity difference value, so as to estimate the residual capacity of the tram battery by using a corresponding residual capacity estimation algorithm. The current running state of the tram battery is obtained by collecting the capacity test parameter, and then the capacity test parameter is compared with the preset test parameter to determine the difference between the current running test state and the standard running state of the tram battery, so as to determine the difference characteristic value corresponding to the residual capacity of the tram battery. Finally, the algorithm model is established according to the difference characteristic value, so as to determine the estimation algorithm of the residual capacity of the battery, and the estimation accuracy of the residual capacity of the battery is effectively improved.

Description

Technical Field

[0001] This invention relates to the field of electric vehicle technology, and in particular to a method, device, electric vehicle monitoring system, computer equipment, and storage medium for monitoring the battery capacity of electric vehicles. Background Technology

[0002] Currently, the main components of the entire electric transportation sector, such as electric bicycles, electric motorcycles, electric scooters, and some electric four-wheeled vehicles, are batteries, controllers, motors, and instruments. The development and design of a complete EV (Electric Vehicle) system is based on an electronic control unit architecture including batteries, PCUs, instruments, and motors. It utilizes advanced technologies such as the Internet of Things, cloud computing, big data, and GPS positioning to monitor and manage electric two-wheeled vehicles in real time, thereby achieving modernization in electric vehicle management, intelligent use, and upgrades in traffic information technology.

[0003] However, traditional EV systems primarily rely on the remaining battery capacity to estimate the remaining range. This remaining battery capacity is based on idealized battery usage and does not take into account driving environment, driving mode, or other interfering factors. This can easily lead to significant deviations in the estimation of the remaining battery capacity, seriously affecting actual use. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of the prior art and provide a method, device, vehicle monitoring system, computer equipment, and storage medium for monitoring the battery capacity of electric vehicles that effectively improves the accuracy of estimating the remaining battery capacity.

[0005] The objective of this invention is achieved through the following technical solution:

[0006] A method for monitoring the capacity of an electric vehicle battery, the method comprising:

[0007] Obtain the capacity test parameters of the electric vehicle battery;

[0008] The capacitance test parameters are compared with preset test parameters to obtain the electrical model capacitance difference.

[0009] Based on the difference in the electric vehicle battery capacity, a capacity estimation signal is sent to the battery management system to estimate the remaining capacity of the electric vehicle battery using the corresponding remaining capacity estimation algorithm.

[0010] In one embodiment, obtaining the capacity test parameters of the electric vehicle battery includes: obtaining the capacity tracking parameters of the electric vehicle battery; and performing capacity model processing on the capacity test parameters and preset test parameters to obtain the battery model capacity difference includes: performing tracking model processing on the capacity tracking parameters and preset tracking parameters to obtain the capacity tracking test difference.

[0011] In one embodiment, the capacity tracking parameters include dynamic capacity tracking parameters and static capacity tracking parameters.

[0012] In one embodiment, obtaining the capacity test parameters of the electric vehicle battery includes: obtaining the cell model of the electric vehicle battery; and performing capacity model processing on the capacity test parameters and preset test parameters to obtain the cell model capacity difference includes: performing core model processing on the cell model and the preset model to obtain the capacity core model test difference.

[0013] In one embodiment, obtaining the capacity test parameters of the electric vehicle battery includes: obtaining the degradation test parameters of the electric vehicle battery; and performing capacity model processing on the capacity test parameters and preset test parameters to obtain the battery model capacity difference includes: performing degradation model processing on the degradation test parameters and preset degradation parameters to obtain the capacity degradation test difference.

[0014] In one embodiment, the step of sending a capacity estimation signal to the battery management system based on the battery model capacity difference to estimate the remaining capacity of the electric vehicle battery using a corresponding remaining capacity estimation algorithm includes: detecting whether the battery model capacity difference matches a preset difference; when the battery model capacity difference matches the preset difference, sending a Kalman estimation signal to the battery management system to estimate the remaining capacity of the electric vehicle battery using a corresponding Kalman filter measurement algorithm.

[0015] A device for monitoring the battery capacity of an electric vehicle, the device comprising:

[0016] The capacity sampling module is used to acquire the capacity test parameters of the electric vehicle battery;

[0017] The capacity analysis module is used to perform capacitance-mode processing on the capacity test parameters and preset test parameters to obtain the electrical capacitance difference.

[0018] The metering output module is used to send a capacity estimation signal to the battery management system based on the capacity difference of the electric vehicle model, so as to estimate the remaining capacity of the electric vehicle battery using the corresponding remaining capacity estimation algorithm.

[0019] A tram monitoring system includes a whole-machine communication conversion device, a riding sensor, a big data management platform, a battery management device, a central controller for the vehicle, a data transceiver device, and the aforementioned tram battery capacity monitoring device.

[0020] A computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program performing the following steps:

[0021] Obtain the capacity test parameters of the electric vehicle battery;

[0022] The capacitance test parameters are compared with preset test parameters to obtain the electrical model capacitance difference.

[0023] Based on the difference in the electric vehicle battery capacity, a capacity estimation signal is sent to the battery management system to estimate the remaining capacity of the electric vehicle battery using the corresponding remaining capacity estimation algorithm.

[0024] A computer-readable storage medium having a computer program stored thereon, the computer program performing the following steps when executed by a processor:

[0025] Obtain the capacity test parameters of the electric vehicle battery;

[0026] The capacitance test parameters are compared with preset test parameters to obtain the electrical model capacitance difference.

[0027] Based on the difference in the electric vehicle battery capacity, a capacity estimation signal is sent to the battery management system to estimate the remaining capacity of the electric vehicle battery using the corresponding remaining capacity estimation algorithm.

[0028] Compared with the prior art, the present invention has at least the following advantages:

[0029] By collecting capacity test parameters, the current operating status of the electric vehicle battery is obtained. Then, it is compared with preset test parameters to determine the difference between the current operating test state and the standard operating state of the electric vehicle battery. This facilitates the determination of the difference characteristic value corresponding to the remaining capacity of the electric vehicle battery. Finally, based on the above difference characteristic value, an algorithm model is established, which facilitates the determination of the estimation algorithm for the remaining battery capacity. This makes it easier to select a suitable estimation algorithm, effectively improving the accuracy of the estimation of the remaining battery capacity. Attached Figure Description

[0030] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0031] Figure 1 This is a flowchart of a method for monitoring the battery capacity of an electric vehicle in one embodiment;

[0032] Figure 2 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0033] To facilitate understanding of the present invention, a more complete description will be given below with reference to the accompanying drawings. Preferred embodiments of the invention are shown in the drawings. However, the invention can be implemented in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided to provide a thorough and complete understanding of the disclosure of the invention.

[0034] It should be noted that when an element is referred to as being "fixed to" another element, it can be directly attached to the other element or there may be an intervening element. When an element is referred to as being "connected to" another element, it can be directly connected to the other element or there may be an intervening element. The terms "vertical," "horizontal," "left," "right," and similar expressions used herein are for illustrative purposes only and do not represent the only possible implementation.

[0035] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0036] This invention relates to a method for monitoring the capacity of an electric vehicle battery. In one embodiment, the method includes acquiring capacity test parameters of the electric vehicle battery; performing capacity model processing on the capacity test parameters and preset test parameters to obtain a battery model capacity difference; and sending a capacity estimation signal to a battery management system based on the battery model capacity difference to estimate the remaining capacity of the electric vehicle battery using a corresponding remaining capacity estimation algorithm. By collecting the capacity test parameters, the current operating status of the electric vehicle battery is obtained. Then, it is compared with the preset test parameters to determine the difference between the current operating test state and the standard operating state of the electric vehicle battery. This facilitates the determination of the difference characteristic value corresponding to the remaining capacity of the electric vehicle battery. Finally, an algorithm model is established based on the aforementioned difference characteristic value, thereby facilitating the determination of the estimation algorithm for the remaining battery capacity and the selection of a suitable estimation algorithm, effectively improving the accuracy of the remaining battery capacity estimation.

[0037] Please see Figure 1 This is a flowchart of a method for monitoring the battery capacity of a trolleybus according to an embodiment of the present invention. The method for monitoring the battery capacity of a trolleybus includes some or all of the following steps.

[0038] S100: Obtain the capacity test parameters of the electric vehicle battery.

[0039] In this embodiment, the capacity test parameter is the remaining capacity test data of the electric vehicle battery, that is, the capacity test parameter is the test data of the electric vehicle battery during its current operation, or the operating test parameter of the electric vehicle battery when it has remaining capacity. Obtaining the capacity test parameter facilitates the detection of the current operating state of the electric vehicle battery, thereby facilitating the collection of the operating parameters of the electric vehicle battery under the current remaining capacity. This makes it easier to determine the operating condition of the electric vehicle battery under the current remaining capacity, resulting in more comprehensive reference factors needed for subsequent estimation of the remaining capacity of the electric vehicle battery, and thus making the estimation of the remaining capacity of the electric vehicle battery more accurate.

[0040] S200: Perform capacitance model processing on the capacitance test parameters and preset test parameters to obtain the electrical model capacitance difference.

[0041] In this embodiment, the capacity test parameter is the remaining capacity test data of the electric vehicle battery, that is, the test data of the electric vehicle battery during its current operation, or the operating test parameters of the electric vehicle battery when it has remaining capacity. Obtaining the capacity test parameter facilitates the detection of the current operating state of the electric vehicle battery, thereby facilitating the collection of the operating parameters of the electric vehicle battery under the current remaining capacity. This makes it easier to determine the operating status of the electric vehicle battery under the current remaining capacity, resulting in more comprehensive reference factors for subsequent estimation of the remaining capacity of the electric vehicle battery, and thus making the estimation of the remaining capacity of the electric vehicle battery more accurate. The preset test parameter is the standard test data of the electric vehicle battery, that is, the preset test parameter is the reference operating test parameter of the electric vehicle battery, or the operating test parameter corresponding to the electric vehicle battery at a specified remaining capacity. Capacitance modeling is performed on the capacity test parameters and the preset test parameters to model the capacity test parameters. The capacity test parameters serve as inputs to determine the degree of difference between the modeled output and the preset test parameters, i.e., the battery model capacity difference. Thus, after obtaining the battery model capacity difference, the appropriate estimation algorithm for the electric vehicle battery can be determined based on the current test differences reflected in the battery model capacity difference.

[0042] S300: Send a capacity estimation signal to the battery management system based on the difference in battery capacity, and use the corresponding remaining capacity estimation algorithm to estimate the remaining capacity of the electric vehicle battery.

[0043] In this embodiment, the electrical model capacity difference is the degree of difference between the modeling output of the capacity test parameter and the preset test parameter. That is, the electrical model capacity difference is the output difference obtained by modeling the capacity test parameter using the capacity test parameter as input. The capacity test parameter is the remaining capacity test data of the electric vehicle battery, that is, the test data of the electric vehicle battery during its current operation, or the operating test parameters of the electric vehicle battery when it has remaining capacity. Obtaining the capacity test parameter facilitates the detection of the current operating state of the electric vehicle battery, thereby facilitating the collection of the operating parameters of the electric vehicle battery under the current remaining capacity. This makes it easier to determine the operating status of the electric vehicle battery under the current remaining capacity, resulting in more comprehensive reference factors for subsequent estimation of the remaining capacity of the electric vehicle battery, and thus making the estimation of the remaining capacity of the electric vehicle battery more accurate. In this way, after determining the magnitude of the difference in the electric vehicle battery capacity, the difference between the current operating state and the standard operating state of the electric vehicle battery is determined. This facilitates the estimation of the subsequent capacity of the electric vehicle battery, thereby making it easier to select a high-matching remaining capacity estimation algorithm. This allows for a more accurate prediction of the remaining capacity usage of the electric vehicle battery, specifically, a more accurate prediction of the remaining usage time or remaining mileage of the electric vehicle battery.

[0044] In this embodiment, the current operating status of the electric vehicle battery is obtained by collecting capacity test parameters. These parameters are then compared with preset test parameters to determine the difference between the current operating test state and the standard operating state. This facilitates the determination of the difference characteristic value corresponding to the remaining capacity of the electric vehicle battery. Finally, an algorithm model is established based on these difference characteristic values, thereby facilitating the determination of the estimation algorithm for the remaining battery capacity and the selection of a suitable estimation algorithm, effectively improving the accuracy of the remaining battery capacity estimation. Furthermore, accurate estimation of the remaining capacity of the electric vehicle battery facilitates the estimation of the remaining mileage of the electric vehicle.

[0045] In one embodiment, obtaining the capacity test parameters of the electric vehicle battery includes: obtaining the capacity tracking parameters of the electric vehicle battery; and performing capacity model processing on the capacity test parameters and preset test parameters to obtain the battery model capacity difference includes: performing tracking model processing on the capacity tracking parameters and preset tracking parameters to obtain the capacity tracking test difference. In this embodiment, the capacity test parameters are the remaining capacity test data of the electric vehicle battery, that is, the capacity test parameters are the test data of the electric vehicle battery during its current operation, that is, the capacity test parameters are the operating test parameters of the electric vehicle battery when it has remaining capacity. By obtaining the capacity test parameters, it is convenient to detect the current operating status of the electric vehicle battery, thereby facilitating the collection of the operating parameters of the electric vehicle battery under the current remaining capacity, and thus facilitating the determination of the operating status of the electric vehicle battery under the current remaining capacity. This makes the reference factors required for subsequent estimation of the remaining capacity of the electric vehicle battery more comprehensive, thereby making the estimation of the remaining capacity of the electric vehicle battery more accurate. The capacity test parameters include capacity tracking parameters, which are used to track the remaining capacity of the electric vehicle battery during its historical usage, determining the past usage of the battery's remaining capacity. The tracking model processing of the capacity tracking parameters and preset tracking parameters involves modeling the historical remaining capacity of the electric vehicle battery against standard tracking parameters to obtain the difference between the tracked operating state of the electric vehicle battery at its current remaining capacity and the standard operating state, i.e., the capacity tracking test difference. Thus, by determining the past remaining capacity of the electric vehicle battery and the test results at the current remaining capacity, it is easier to select a remaining capacity prediction algorithm with a higher matching degree, thereby improving the accuracy of the assessment of the remaining capacity usage of the electric vehicle battery.

[0046] In another embodiment, the capacity tracking parameters include dynamic capacity tracking parameters and static capacity tracking parameters. In this embodiment, the dynamic capacity tracking parameters are obtained based on the open-circuit voltage versus remaining capacity characteristic curve. Specifically, by collecting the static calibration region in the open-circuit voltage versus remaining capacity characteristic curve, for example, selecting the region with a large slope as the calibrable voltage range, the dynamic parameters in the capacity tracking parameters are calibrated to ensure the accuracy of the dynamic capacity tracking parameters. Moreover, the dynamic capacity tracking parameters also simultaneously reference the error caused by voltage sampling, further improving the accuracy of the dynamic capacity tracking parameters. The static capacity tracking parameters are the operating state parameters of the electric vehicle battery in a shallow dormant state. Specifically, by establishing a small current detection circuit for the electric vehicle battery, the power consumption in the shallow dormant state is collected, and the change of small current is tracked in real time, which facilitates the collection of the static capacity tracking parameters, thereby facilitating the accurate estimation of the remaining capacity of the electric vehicle battery.

[0047] In one embodiment, obtaining the capacity test parameters of the electric vehicle battery includes: obtaining the cell model of the electric vehicle battery; and performing capacity model processing on the capacity test parameters and preset test parameters to obtain the cell model capacity difference includes: performing core model processing on the cell model and the preset model to obtain the capacity core model test difference. In this embodiment, the capacity test parameters are the remaining capacity test data of the electric vehicle battery, that is, the capacity test parameters are the test data of the electric vehicle battery during its current operation, that is, the capacity test parameters are the operating test parameters of the electric vehicle battery when it has remaining capacity. By obtaining the capacity test parameters, it is convenient to detect the current operating status of the electric vehicle battery, thereby facilitating the collection of the operating parameters of the electric vehicle battery under the current remaining capacity, and thus facilitating the determination of the operating status of the electric vehicle battery under the current remaining capacity. This makes the reference factors required for subsequent estimation of the remaining capacity of the electric vehicle battery more comprehensive, thereby making the estimation of the remaining capacity of the electric vehicle battery more accurate. The capacity test parameters include the cell model, which is used to distinguish the product type of the electric vehicle battery and determine the remaining capacity usage of the electric vehicle battery under the corresponding model. The cell modeling process involves modeling the current model of the electric vehicle battery against a standard signal to obtain the difference between the operating state of the electric vehicle battery at its current remaining capacity and that of the specified model battery, i.e., the capacity cell model test difference. Thus, after determining the difference between the current model and the specified signal of the electric vehicle battery at its current remaining capacity, it is easier to select a remaining capacity estimation algorithm with a higher matching degree, thereby improving the accuracy of the remaining capacity usage of the electric vehicle battery.

[0048] In one embodiment, obtaining the capacity test parameters of the electric vehicle battery includes: obtaining the degradation test parameters of the electric vehicle battery; and performing capacity model processing on the capacity test parameters and preset test parameters to obtain the battery model capacity difference includes: performing degradation model processing on the degradation test parameters and preset degradation parameters to obtain the capacity degradation test difference. In this embodiment, the capacity test parameters are the remaining capacity test data of the electric vehicle battery, that is, the capacity test parameters are the test data of the electric vehicle battery during its current operation, that is, the capacity test parameters are the operating test parameters of the electric vehicle battery when it has remaining capacity. By obtaining the capacity test parameters, it is convenient to detect the current operating state of the electric vehicle battery, thereby facilitating the collection of the operating parameters of the electric vehicle battery under the current remaining capacity, and thus facilitating the determination of the operating status of the electric vehicle battery under the current remaining capacity. This makes the reference factors required for subsequent estimation of the remaining capacity of the electric vehicle battery more comprehensive, thereby making the estimation of the remaining capacity of the electric vehicle battery more accurate. The capacity test parameters include degradation test parameters, which are used to track the degradation of the electric vehicle battery and determine its usage status when degradation occurs. Degradation modeling is performed on the degradation test parameters and preset degradation parameters. This involves modeling the degradation state of the electric vehicle battery against standard degradation state parameters to obtain the difference between the current degradation operating state and the standard operating state, i.e., the capacity degradation test difference. By determining the degradation mechanism of the electric vehicle battery, it is easier to select a more accurate remaining capacity estimation algorithm, thereby improving the accuracy of the remaining capacity usage of the electric vehicle battery.

[0049] In one embodiment, the step of sending a capacity estimation signal to the battery management system based on the battery model capacity difference to estimate the remaining capacity of the electric vehicle battery using a corresponding remaining capacity estimation algorithm includes: detecting whether the battery model capacity difference matches a preset difference; when the battery model capacity difference matches the preset difference, sending a Kalman estimation signal to the battery management system to estimate the remaining capacity of the electric vehicle battery using a corresponding Kalman filter measurement algorithm. In this embodiment, the battery model capacity difference is the degree of difference between the modeling output of the capacity test parameter and the preset test parameter, that is, the battery model capacity difference is the output difference obtained by data modeling of the capacity test parameter, with the capacity test parameter as the input. The capacity test parameter is the remaining capacity test data of the electric vehicle battery, that is, the capacity test parameter is the test data of the electric vehicle battery during current operation, that is, the capacity test parameter is the operating test parameter of the electric vehicle battery when there is remaining capacity. By acquiring the capacity test parameters, the current operating status of the electric vehicle battery can be easily detected. This facilitates the collection of the battery's operating parameters under the current remaining capacity, making it easier to determine the battery's operating condition under the current remaining capacity. This results in a more comprehensive set of reference factors for subsequent estimation of the battery's remaining capacity, leading to a more accurate estimation. The preset difference is the standard battery model capacity difference, used to differentiate the selection of the battery estimation algorithm. Specifically, the preset difference matches the corresponding remaining capacity prediction algorithm; specifically, the algorithm corresponding to the preset difference is the Kalman filter measurement algorithm. When the battery model capacity difference matches the preset difference, it indicates that the feature value of the estimation algorithm corresponding to the electric vehicle battery is the same as the feature value of the estimation algorithm corresponding to the preset difference. A Kalman prediction signal is then sent to the battery management system to estimate the remaining capacity of the electric vehicle battery using the corresponding Kalman filter measurement algorithm, making the selection of the battery estimation algorithm more accurate.

[0050] Further, the step of detecting whether the difference in the battery model capacity matches a preset difference includes: when the difference in the battery model capacity does not match the preset difference, sending a uniform signal to the battery management system. In this embodiment, the difference in the battery model capacity is the degree of difference in the modeling output between the capacity test parameter and the preset test parameter. That is, the difference in the battery model capacity is the output difference obtained by data modeling of the capacity test parameter, with the capacity test parameter as the input. The capacity test parameter is the remaining capacity test data of the electric vehicle battery, that is, the capacity test parameter is the test data of the electric vehicle battery during its current operation, or the operating test parameter of the electric vehicle battery when it has remaining capacity. By acquiring the capacity test parameter, it is convenient to detect the current operating state of the electric vehicle battery, thereby facilitating the collection of the operating parameters of the electric vehicle battery under the current remaining capacity, and thus facilitating the determination of the operating status of the electric vehicle battery under the current remaining capacity. This makes the reference factors required for subsequent estimation of the remaining capacity of the electric vehicle battery more comprehensive, thereby making the estimation of the remaining capacity of the electric vehicle battery more accurate. The preset difference is the standard analog capacity difference of the electric vehicle battery. This preset difference is used to differentiate the selection of the estimation algorithm for the electric vehicle battery; specifically, it matches the preset difference with the corresponding remaining capacity estimation algorithm. The algorithm corresponding to the preset difference is the Kalman filter metering algorithm. If the analog capacity difference does not match the preset difference, it indicates that the feature value of the estimation algorithm corresponding to the electric vehicle battery is different from the feature value of the estimation algorithm corresponding to the preset difference. In this case, a uniform signal is sent to the battery management system to use another metering algorithm to estimate the remaining capacity of the electric vehicle battery, making the selection of the estimation algorithm for the electric vehicle battery more accurate.

[0051] In another embodiment, based on the difference in battery capacity, a new battery equalization signal and a decay signal can be sent to the battery management system to estimate the battery performance of the electric vehicle battery using a new equalization algorithm and a decay algorithm, thereby facilitating the assessment of the electric vehicle battery's lifespan.

[0052] Understandably, when estimating the remaining capacity of the electric vehicle battery, the selected Kalman filter measurement algorithm can achieve estimation even in the presence of interfering parameters, effectively improving the prediction accuracy of the remaining battery capacity, i.e., accurately estimating the usage of the remaining battery capacity. However, the environment and operating state of the electric vehicle battery can both affect its performance. For example, the operating state of the electric vehicle battery is completely different when the vehicle is on a highway versus a mountain road, which can easily lead to some deviation in the estimation of the remaining battery capacity, and consequently, reduce the accuracy of the prediction.

[0053] To further improve the accuracy of estimating the remaining capacity of the electric vehicle battery, when the difference in the battery model capacity matches the preset difference, a Kalman prediction signal is sent to the battery management system to estimate the remaining capacity of the electric vehicle battery using the corresponding Kalman filter metering algorithm. This specifically includes the following steps:

[0054] When the difference in the electric model capacity matches the preset difference, the real-time operating parameters of the electric vehicle battery are obtained;

[0055] The remaining capacity jump value is obtained based on the operating parameters;

[0056] Detect whether the remaining capacity jump value is equal to the preset jump value;

[0057] When the remaining capacity jump value is equal to the preset jump value, an unscented prediction signal is sent to the battery management system to estimate the remaining capacity of the electric vehicle battery using an unscented Kalman filter algorithm.

[0058] In this embodiment, the real-time operating condition parameter is the current operating state parameter of the electric vehicle battery, that is, the real-time operating condition parameter corresponds to the current operating state of the electric vehicle battery, and also represents the movement mode of the electric vehicle using the electric vehicle battery. As the operating state of the electric vehicle battery changes, the change in the movement state of the electric vehicle can be reflected by the operating condition parameter, indicating the current movement mode of the electric vehicle. The operating condition parameter converts the operating characteristics of the electric vehicle battery. Specifically, the operating condition parameter is the voltage or current of the electric vehicle battery under different movement modes of the electric vehicle, and the operating condition parameter corresponds to a working time, meaning that the voltage or current on the electric vehicle battery also changes under different working modes, i.e., the remaining capacity jump value. For example, the remaining capacity jump value is the period of the operating voltage or current of the electric vehicle battery. The preset jump value is the remaining capacity jump value of the electric vehicle battery under a specified working state, that is, the preset jump value corresponds to the specified movement mode of the electric vehicle, i.e., the preset jump value corresponds to the standard movement mode of the electric vehicle. When the remaining capacity jump value is equal to the preset jump value, it indicates that the electric vehicle battery is in standard operating condition, meaning the electric vehicle corresponding to the battery is in pulse motion mode. In this case, the battery management system sends an unscented prediction signal to estimate the remaining capacity of the electric vehicle battery using an unscented Kalman filter algorithm. This facilitates a more accurate estimation of the remaining capacity, further improving the accuracy of the remaining capacity estimation.

[0059] Further, the step of detecting whether the remaining capacity jump value is equal to a preset jump value also includes:

[0060] When the remaining capacity jump value is greater than the preset jump value, an extended prediction signal is sent to the battery management system to estimate the remaining capacity of the electric vehicle battery using an extended Kalman filter algorithm.

[0061] In this embodiment, the real-time operating condition parameter is the current operating state parameter of the electric vehicle battery, that is, the real-time operating condition parameter corresponds to the current operating state of the electric vehicle battery, and also represents the movement mode of the electric vehicle using the electric vehicle battery. As the operating state of the electric vehicle battery changes, the change in the movement state of the electric vehicle can be reflected by the operating condition parameter, indicating the current movement mode of the electric vehicle. The operating condition parameter converts the operating characteristics of the electric vehicle battery. Specifically, the operating condition parameter is the voltage or current of the electric vehicle battery under different movement modes of the electric vehicle, and the operating condition parameter corresponds to a working time, meaning that the voltage or current on the electric vehicle battery also changes under different working modes, i.e., the remaining capacity jump value. For example, the remaining capacity jump value is the period of the operating voltage or current of the electric vehicle battery. The preset jump value is the remaining capacity jump value of the electric vehicle battery under a specified working state, that is, the preset jump value corresponds to the specified movement mode of the electric vehicle, i.e., the preset jump value corresponds to the standard movement mode of the electric vehicle. When the remaining capacity jump value is greater than the preset jump value, it indicates that the electric vehicle battery is in a stable operating condition, meaning the electric vehicle corresponding to the battery is in a constant current operation mode. At this point, the battery management system sends an extended prediction signal to estimate the remaining capacity of the electric vehicle battery using an extended Kalman filter algorithm. This facilitates a more accurate estimation of the remaining capacity, further improving the accuracy of the remaining capacity estimation.

[0062] Furthermore, the step of detecting whether the remaining capacity jump value is equal to a preset jump value further includes:

[0063] When the remaining capacity jump value is less than the preset jump value, an integral estimation signal is sent to the battery management system to estimate the remaining capacity of the electric vehicle battery using the ampere-hour integral Kalman filter algorithm.

[0064] In this embodiment, the real-time operating condition parameter is the current operating state parameter of the electric vehicle battery, that is, the real-time operating condition parameter corresponds to the current operating state of the electric vehicle battery, and also represents the movement mode of the electric vehicle using the electric vehicle battery. As the operating state of the electric vehicle battery changes, the change in the movement state of the electric vehicle can be reflected by the operating condition parameter, indicating the current movement mode of the electric vehicle. The operating condition parameter converts the operating characteristics of the electric vehicle battery. Specifically, the operating condition parameter is the voltage or current of the electric vehicle battery under different movement modes of the electric vehicle, and the operating condition parameter corresponds to a working time, meaning that the voltage or current on the electric vehicle battery also changes under different working modes, i.e., the remaining capacity jump value. For example, the remaining capacity jump value is the period of the operating voltage or current of the electric vehicle battery. The preset jump value is the remaining capacity jump value of the electric vehicle battery under a specified working state, that is, the preset jump value corresponds to the specified movement mode of the electric vehicle, i.e., the preset jump value corresponds to the standard movement mode of the electric vehicle. If the remaining capacity jump value is less than the preset jump value, it indicates that the trolley battery is in an abnormal operating condition, meaning that the trolley corresponding to the battery is in UDDS (Urban Dynamometer Driving Schedule) mode. In this case, the battery management system sends an integral estimation signal to estimate the remaining capacity of the trolley battery using an ampere-hour integral Kalman filter algorithm. This facilitates a more accurate estimation of the remaining capacity, further improving the accuracy of the remaining capacity estimation.

[0065] All the above-mentioned preset variables are set in the database for easy retrieval, and different preset variables are placed in different storage units, that is, in different storage stacks.

[0066] In one embodiment, this application also provides a vehicle battery capacity monitoring device, which is implemented using the vehicle battery capacity monitoring method described in any of the above embodiments. In one embodiment, the vehicle battery capacity monitoring device has functional modules for implementing each step of the vehicle battery capacity monitoring method. The vehicle battery capacity monitoring device includes a capacity sampling module, a capacity analysis module, and a metering output module; the capacity sampling module is used to acquire the capacity test parameters of the vehicle battery; the capacity analysis module is used to perform capacity-model processing on the capacity test parameters and preset test parameters to obtain the battery model capacity difference; the metering output module is used to send a capacity prediction signal to the battery management system based on the battery model capacity difference, so as to estimate the remaining capacity of the vehicle battery using a corresponding remaining capacity prediction algorithm.

[0067] In this embodiment, the capacity sampling module collects capacity test parameters to obtain the current operating status of the electric vehicle battery. Then, the capacity analysis module compares it with preset test parameters to determine the difference between the current operating test state and the standard operating state of the electric vehicle battery. This facilitates the determination of the difference characteristic value corresponding to the remaining capacity of the electric vehicle battery. Finally, the metering output module establishes an algorithm model based on the above difference characteristic value, which facilitates the determination of the estimation algorithm for the remaining battery capacity and the selection of a suitable estimation algorithm, effectively improving the accuracy of the estimation of the remaining battery capacity.

[0068] In one embodiment, this application also provides a tram monitoring system, including a whole-machine communication conversion device, a riding sensor, a big data management platform, a battery management device, a central controller for driving, a data transceiver device, and the tram battery capacity monitoring device described in the above embodiments. In this embodiment, the whole-machine communication conversion device is used to enable communication between the major components of the tram and the whole vehicle, so as to enable interoperability between different communication protocols of the components and achieve rapid information exchange. Specifically, a whole-machine communication architecture based on the Canopen communication protocol is constructed.

[0069] Furthermore, the cycling sensors are numerous and distributed throughout the vehicle to collect various information data, such as vehicle status, riding statistics, historical trajectory, battery level, and battery health status in real time. This information is transmitted to the battery via the central controller, and then uploaded to a big data management platform via a data transceiver. Specifically, the battery uploads data to the app and backend via a 4G or Bluetooth module. The big data management platform analyzes and processes the riding data, specifically optimizing the FOC vector controller algorithm to precisely drive the motor and accurately adjust power output for smoother acceleration. The data collected by the cycling sensors includes at least one of the following: riding mileage, latitude and longitude, acceleration, light intensity, temperature, humidity, voltage, current, SOC, and overall vehicle health status.

[0070] In another embodiment, the big data management platform utilizes cycling data collected from various sensors and the analysis results to assess the lifespan of all vehicle components, calculate the balance of the overall vehicle design, and evaluate the remaining lifespan of each component. Specifically, a least-squares surface fitting method is used to construct a continuous surface equation for the core parameters of the battery cells (DCR, OCV, etc.) in relation to temperature and SOC. Based on this, combined with the power requirements of the vehicle during use and the operating voltage range of the motor and inverter, a reasonable battery state of operation (SOP) table is calculated. According to the actual driving conditions and performance requirements of the vehicle, the optimal values ​​of various parameters of the on-board power battery pack and major vehicle components are matched through multiple constraints such as capacitive load, temperature rise parameters, and static power consumption. This ensures optimal power performance of the entire vehicle while matching the best safety protection performance, thereby improving the energy utilization efficiency of the power battery pack.

[0071] Specific limitations regarding the trolley battery capacity monitoring device can be found in the above description and will not be repeated here. Each module in the aforementioned trolley battery capacity monitoring device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the corresponding operations of each module.

[0072] In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 2 As shown, the computer device includes a processor, memory, and a network interface connected via a system bus. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The database stores capacity test parameters, battery capacity differences, and capacity prediction signals. The network interface communicates with external terminals via a network connection. When executed by the processor, the computer program implements a method for monitoring the capacity of a vehicle battery.

[0073] Those skilled in the art will understand that Figure 2 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0074] In one embodiment, this application also provides a computer device including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments.

[0075] In one embodiment, this application also provides a computer-readable storage medium having a computer program stored thereon that, when executed by a processor, implements the steps in the above-described method embodiments.

[0076] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the methods described above. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, or optical storage, etc. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0077] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these all fall within the protection scope of the present invention. Therefore, the protection scope of this invention patent should be determined by the appended claims.

Claims

1. A method of monitoring the capacity of a trolley battery, characterized by, include: Obtain the capacity test parameters of the electric vehicle battery; The capacitance test parameters are compared with preset test parameters to obtain the electrical model capacitance difference. Based on the difference in the electric model capacity, a capacity estimation signal is sent to the battery management system to estimate the remaining capacity of the electric vehicle battery using the corresponding remaining capacity estimation algorithm. The acquisition of the electric vehicle battery capacity test parameters includes: The capacity tracking parameters, cell model, and degradation test parameters of the electric vehicle battery are obtained. The capacity tracking parameters include dynamic capacity tracking parameters and static capacity tracking parameters. The dynamic capacity tracking parameters are obtained based on the characteristic curve of open circuit voltage and remaining capacity. The static capacity tracking parameters are the operating status parameters of the electric vehicle battery in a shallow dormant state. The step of performing capacitance model processing on the capacitance test parameters and preset test parameters to obtain the capacitance model difference includes: The capacity tracking parameters and preset tracking parameters are processed by a tracking model to obtain the capacity tracking test difference; The cell model is processed with a preset model to obtain the capacity-cell model test difference. The cell model processing is to model the current model of the electric vehicle battery with a standard signal to obtain the difference between the operating state of the electric vehicle battery and the specified model battery under the current remaining capacity. The degradation test parameters and preset degradation parameters are subjected to degradation modeling to obtain the capacity degradation test difference. Degradation modeling involves modeling the degradation state of the electric vehicle battery with standard degradation state parameters to obtain the difference between the current degradation operating state and the standard operating state of the electric vehicle battery.

2. The electric vehicle battery capacity monitoring method of claim 1, wherein, The step of sending a capacity estimation signal to the battery management system based on the capacity difference of the electric vehicle model, and estimating the remaining capacity of the electric vehicle battery using a corresponding remaining capacity estimation algorithm, includes: Detect whether the difference in the electrical model capacity matches a preset difference; When the difference in the battery module capacity matches the preset difference, a Kalman prediction signal is sent to the battery management system to estimate the remaining capacity of the electric vehicle battery using the corresponding Kalman filter metering algorithm.

3. A trolley battery capacity monitoring device, characterized by, include: The capacity sampling module is used to acquire the capacity test parameters of the electric vehicle battery. The capacity test parameters include capacity tracking parameters, cell model and degradation test parameters. The capacity tracking parameters include dynamic capacity tracking parameters and static capacity tracking parameters. The dynamic capacity tracking parameters are obtained based on the open circuit voltage and remaining capacity characteristic curve. The static capacity tracking parameters are the operating status parameters of the electric vehicle battery in a shallow dormant state. The capacity analysis module is used to perform capacitive model processing on the capacity test parameters and preset test parameters to obtain the electric model capacity difference. This capacitive model processing includes: performing tracking model processing on the capacity tracking parameters and preset tracking parameters to obtain the capacity tracking test difference; performing cell model processing on the cell type and preset type to obtain the capacity-cell type test difference, where the cell type model processing involves modeling the current type of the electric vehicle battery with a standard signal to obtain the difference between the operating state of the electric vehicle battery at its current remaining capacity and that of a battery of a specified type; and performing degradation model processing on the degradation test parameters and preset degradation parameters to obtain the capacity degradation test difference, where the degradation model processing involves modeling the degradation state of the electric vehicle battery with standard degradation state parameters to obtain the difference between the current degraded operating state and the standard operating state of the electric vehicle battery. The metering output module is used to send a capacity estimation signal to the battery management system based on the capacity difference of the electric vehicle model, so as to estimate the remaining capacity of the electric vehicle battery using the corresponding remaining capacity estimation algorithm.

4. A trolley monitoring system, characterized by It includes a whole-machine communication conversion device, a riding sensor, a big data management platform, a battery management device, a vehicle central controller, a data transceiver device, and a vehicle battery capacity monitoring device as described in claim 3.

5. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 2.

6. A computer-readable storage medium having stored thereon a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 2.

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