Method and system for measuring and calculating residual endurance mileage of lead-acid battery for electric two-wheeled vehicle

Abstract

The invention discloses a method and system for measuring and calculating the remaining endurance mileage of a lead-acid battery for an electric two-wheeled vehicle, and the method comprises the steps: a controller collects the driving data of the electric two-wheeled vehicle in real time, and transmits the driving data to a data acquisition module; the data acquisition module pre-processes the driving data and transmits the pre-processed driving data to the cloud server and the model measurement and calculation module through the 4G module based on a 4G network; and the cloud server and model measurement and calculation module performs deep analysis and intelligent processing on the preprocessed driving data according to a pre-trained endurance mileage measurement and calculation model, and measures and calculates the residual endurance mileage of the electric two-wheeled vehicle. According to the invention, the endurance mileage measuring and calculating model trained by the battery charging and discharging characteristic curve of the lead-acid battery is combined, the data of the lead-acid battery is collected and monitored in real time through the 4G module, and the complex change in the vehicle driving process is fully considered, so that the actual energy consumption condition of the vehicle is accurately fed back, and the accuracy of endurance mileage prediction is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of battery testing, and particularly to a method and system for calculating the remaining cruising range of a lead-acid battery for an electric two-wheeler. Background Art

[0002] In the practical application of electric bicycles, accurately calculating the remaining cruising range of the battery is crucial for users. Currently, the calculation of the cruising range of electric bicycles mainly relies on simple mathematical formulas. Although this method has certain guiding significance in theory, especially for lead-acid batteries, the discharge curve is non-linear, and the capacity is easily affected by factors such as temperature and service life. This makes it impossible to consider the characteristics of lead-acid batteries when calculating the cruising range using the existing method, resulting in large errors in calculating the cruising range and being unable to provide users with timely and reliable cruising range information. Summary of the Invention

[0003] The present invention provides a method and system for calculating the remaining cruising range of a lead-acid battery for an electric two-wheeler to solve the problem that the existing technology estimates the cruising range of an electric two-wheeler based on a simple mathematical model or empirical formula, unable to fully consider the complex changes during vehicle driving, resulting in large errors in the calculation results.

[0004] According to one aspect of the present invention, there is provided a method for calculating the remaining cruising range of a lead-acid battery for an electric two-wheeler, which is used for a system for calculating the remaining cruising range of a lead-acid battery for an electric two-wheeler. The system includes a controller, a data acquisition module, a 4G module, and a cloud server model calculation module. The controller is connected to the data acquisition module, the data acquisition module is connected to the 4G module, and the 4G module communicates with the cloud server and the model calculation module. The method includes:

[0005] The controller continuously collects the driving data of the electric two-wheeler and sends the driving data to the data acquisition module;

[0006] The data acquisition module preprocesses the driving data and transmits the preprocessed driving data to the cloud server and the model calculation module through the 4G module based on the 4G network;

[0007] The cloud server and the model calculation module perform in-depth analysis and intelligent processing on the preprocessed driving data according to a pre-trained cruising range calculation model to calculate the remaining cruising range of the electric two-wheeler.

[0008] Optionally, before the cloud server and the model calculation module perform in-depth analysis and intelligent processing on the preprocessed driving data according to the pre-trained remaining mileage calculation model to calculate the remaining mileage of the electric two-wheeler, the following steps are also included:

[0009] The cloud server and the model calculation module train the remaining mileage calculation model according to the preprocessed driving data.

[0010] Optionally, the cloud server and the model calculation module training the remaining mileage calculation model according to the preprocessed driving data includes:

[0011] The cloud server and the model calculation module determine target features according to the preprocessed driving data;

[0012] Select a target model architecture as the remaining mileage calculation model according to the target features, and train the remaining mileage calculation model;

[0013] Evaluate the performance of the trained remaining mileage calculation model and output the evaluation result;

[0014] Deploy the remaining mileage calculation model with a qualified evaluation result to the actual application environment, and monitor and update the remaining mileage calculation model in real time.

[0015] Optionally, before the controller collects the driving data of the electric two-wheeler in real time and sends the driving data to the data acquisition module, the following steps are also included:

[0016] When starting for the first time, enter the battery parameters of the electric two-wheeler through the terminal interface; the battery parameters include the nominal battery voltage, the first use date of the battery, the battery capacity, the battery charge and discharge characteristic curve, and the battery health curve.

[0017] Optionally, the data acquisition module preprocessing the driving data includes:

[0018] The data acquisition module performs filtering, calibration, standardization, and normalization processing on the driving data.

[0019] Optionally, the controller collecting the driving data of the electric two-wheeler in real time includes:

[0020] The controller collects the motor speed, driving current, battery voltage, battery temperature, ambient temperature, battery capacity, battery charge and discharge characteristic curve, battery health curve, and the slope of the riding section of the electric two-wheeler in real time.

[0021] Optionally, the controller, the data acquisition module, the 4G module, and the cloud server model calculation module perform data communication according to a preset cycle.

[0022] Optionally, the system further includes a display module, which is connected to the controller; after the cloud server and the model calculation module perform in-depth analysis and intelligent processing on the pre-processed driving data according to the pre-trained remaining battery range calculation model to calculate the remaining battery range of the electric two-wheeler, it further includes:

[0023] The display module displays the calculated remaining battery range.

[0024] According to another aspect of the present invention, there is provided a system for calculating the remaining battery range of a lead-acid battery for an electric two-wheeler, including a controller, a data acquisition module, a 4G module, a cloud server and a model calculation module. The controller is connected to the data acquisition module, the data acquisition module is connected to the 4G module, and the 4G module is communicatively connected to the cloud server and the model calculation module;

[0025] The controller is used to collect the driving data of the electric two-wheeler in real time and send the driving data to the data acquisition module;

[0026] The data acquisition module is used to pre-process the driving data and transmit the pre-processed driving data to the cloud server and the model calculation module through the 4G module based on the 4G network;

[0027] The cloud server and the model calculation module are used to perform in-depth analysis and intelligent processing on the pre-processed driving data according to the pre-trained remaining battery range calculation model to calculate the remaining battery range of the electric two-wheeler.

[0028] Optionally, the system further includes a display module, which is connected to the controller;

[0029] The cloud server and the model calculation module transmit the calculated remaining battery range back to the controller;

[0030] The controller sends the calculated remaining battery range to the display module for display.

[0031] An embodiment of the present invention provides a method and system for calculating the remaining driving range of a lead-acid battery for an electric two-wheeler. The method includes: the controller collects the driving data of the electric two-wheeler in real time and sends the driving data to the data acquisition module; the data acquisition module preprocesses the driving data and transmits the preprocessed driving data to the cloud server and the model calculation module via the 4G module based on the 4G network; the cloud server and the model calculation module perform in-depth analysis and intelligent processing on the preprocessed driving data according to the pre-trained driving range calculation model to calculate the remaining driving range of the electric two-wheeler. The technical solution provided by the embodiment of the present invention combines the driving range calculation model trained according to the charge and discharge characteristic curve of the lead-acid battery, and realizes the real-time collection and monitoring of the data of the lead-acid battery through the 4G module, fully considering the complex changes during the vehicle driving process, so as to accurately feedback the actual energy consumption of the vehicle, thereby improving the accuracy of the driving range prediction, and solving the problem that the existing technology estimates the driving range of the electric two-wheeler based on a simple mathematical model or empirical formula, which cannot fully consider the complex changes during the vehicle driving process, resulting in a large error in the calculation result.

[0032] It should be understood that the content described in this part is not intended to identify the key or important features of the embodiments of the present invention, nor is it used to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0034] Figure 1 It is a flowchart of a method for calculating the remaining driving range of a lead-acid battery for an electric two-wheeler provided by an embodiment of the present invention;

[0035] Figure 2 It is a schematic structural diagram of a system for calculating the remaining driving range of a lead-acid battery for an electric two-wheeler provided by an embodiment of the present invention;

[0036] Figure 3 It is a training flowchart of a driving range calculation model provided by an embodiment of the present invention;

[0037] Figure 4 It is a schematic structural diagram of an electronic device of a method for calculating the remaining driving range of a lead-acid battery for an electric two-wheeler provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0038] To enable those skilled in the art to better understand the solution of the present invention, the following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0039] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged under appropriate circumstances so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device comprising a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0040] Figure 1 It is a flowchart of a method for calculating the remaining cruising range of a lead-acid battery for an electric two-wheeler provided by an embodiment of the present invention. This embodiment is applicable to predicting the cruising range of a lead-acid battery two-wheeler. This method can be executed by a system for calculating the remaining cruising range of a lead-acid battery for an electric two-wheeler. The system for calculating the remaining cruising range of a lead-acid battery for an electric two-wheeler can be implemented in the form of hardware and / or software, and the system for calculating the remaining cruising range of a lead-acid battery for an electric two-wheeler can be configured in any electronic device with communication functions. As Figure 2 shown, Figure 2 It is a schematic structural diagram of a system for calculating the remaining cruising range of a lead-acid battery for an electric two-wheeler provided by an embodiment of the present invention. The system includes a controller 110, a data acquisition module 120, a 4G module 130, and a cloud server model calculation module 140. The controller 110 is connected to the data acquisition module 120, the data acquisition module 120 is connected to the 4G module 130, and the 4G module 130 is communicatively connected to the cloud server and the model calculation module 140. The method includes:

[0041] S110. The controller continuously collects the driving data of the electric two-wheeler and sends the driving data to the data acquisition module.

[0042] Among them, the driving data includes but is not limited to parameters such as motor speed, driving current, battery voltage, battery temperature, and ambient temperature.

[0043] Specifically, the controller collects the driving data of the electric two-wheeler through sensors and sends it to the data acquisition module via CAN or 485 bus; for example, the motor speed is collected through a Hall sensor, the battery temperature and ambient temperature are collected through a temperature sensor, and the battery voltage and battery current are collected through a voltage sensor and a current sensor respectively.

[0044] S120. The data acquisition module preprocesses the driving data and transmits the preprocessed driving data to the cloud server and the model calculation module via the 4G module based on the 4G network.

[0045] Specifically, the data acquisition module preprocesses the collected driving data, such as filtering, calibration, and formatting, to ensure the accuracy and reliability of the data. The data acquisition module packs the preprocessed driving data into a format suitable for transmission and sends it to the 4G module through the USART serial port. The 4G module transmits the packed driving data to the cloud server and the model calculation module based on the 4G network to ensure the real-time and stable data transmission and guarantee data encryption and security verification.

[0046] S130. The cloud server and the model calculation module perform in-depth analysis and intelligent processing on the preprocessed driving data according to the pre-trained remaining battery range calculation model to calculate the remaining battery range of the electric two-wheeler.

[0047] Among them, the model calculation module is a functional module in the cloud server, which integrates the pre-trained remaining battery range calculation model. The main function of this module is to use the calculation model to process and analyze the input driving data to calculate the remaining battery range of the electric two-wheeler. The pre-trained remaining battery range calculation model is a model constructed based on machine learning technology. In the model training stage, a large amount of data related to the driving of electric two-wheelers will be collected, including but not limited to battery power, driving speed, driving voltage, ambient temperature, wind speed, battery charge and discharge characteristic curves, battery health curves, user riding habits such as riding acceleration speed, braking treatment methods, and riding mileage and other parameters. By deeply mining and analyzing these data, the internal relationship between these parameters and the remaining battery range is found and expressed in the form of a mathematical model. After repeated training and optimization, the model can accurately predict the remaining battery range of the electric two-wheeler according to the input driving data.

[0048] Specifically, the cloud server and the model calculation module use the pre-trained remaining battery range calculation model to deeply analyze the pre-processed driving data. According to the algorithms and logics defined in the model, it comprehensively considers the interactions between multiple factors, such as simultaneously analyzing the battery power consumption rate, the impact of the current driving speed on power consumption, and the energy loss under different road conditions, to more accurately calculate the remaining battery range. This analysis and processing process is based on the intelligent learning ability of the model, which can automatically discover the rules and patterns in the data, thereby realizing the intelligent calculation of the remaining battery range. In addition, the daily driving data of the user will be retained in the cloud server and the model calculation module. The cloud server and the model calculation module will retrieve the previously driven driving data of the user and compare it with the pre-real-time collected driving data, and comprehensively consider the battery power attenuation curve to accurately calculate the battery range.

[0049] Exemplarily, the cloud server and the model calculation module input the received pre-processed driving data into a pre-trained DeepSeek or other Artificial Intelligence (AI) model. The AI model deeply analyzes and intelligently processes the data to estimate the remaining battery range of the electric two-wheeler.

[0050] The technical solution provided by the embodiment of the present invention combines the remaining battery range calculation model trained according to the battery charge and discharge characteristic curve of the lead-acid battery, and realizes the real-time collection and monitoring of the data of the lead-acid battery through the 4G module. It fully considers the complex changes during the vehicle driving process, thereby realizing the accurate feedback of the actual energy consumption situation of the vehicle, improving the accuracy of the remaining battery range prediction, and solving the problem that the existing technology estimates the remaining battery range of the electric two-wheeler based on a simple mathematical model or empirical formula, which cannot fully consider the complex changes during the vehicle driving process, resulting in a large error in the calculation result.

[0051] In some other embodiments, optionally, before step S130, it further includes:

[0052] The cloud server and the model calculation module train the remaining battery range calculation model according to the pre-processed driving data.

[0053] Specifically, referring to Figure 3 , Figure 3 which is the training flowchart of the remaining battery range calculation model provided by the embodiment of the present invention. Optionally, the cloud server and the model calculation module train the remaining battery range calculation model according to the pre-processed driving data, including:

[0054] S310. The cloud server and the model calculation module determine the target features according to the pre-processed driving data.

[0055] Specifically, the cloud server and the model calculation module determine target features that are helpful for predicting the cruising range or factors related to the prediction result of the cruising range based on the preprocessed driving data and professional knowledge. Such as the charge and discharge characteristic curve of lead-acid batteries, the battery health curve, the slope of the riding road conditions, the ambient temperature, the wind speed, etc.

[0056] S320. Select a target model architecture as the cruising range calculation model according to the target features, and train the cruising range calculation model.

[0057] Specifically, select a model architecture suitable for the task requirements according to the target features. For cruising range prediction, deep learning models, such as the Long-Short Term Memory network (LSTM), are very suitable for processing time series data, while Convolutional Neural Networks (CNNs) can be used to analyze spatially related information, such as road condition information on a map. Train the cruising range calculation model using the dataset composed of target features. During the training process, use the loss function to evaluate the difference between the predicted value and the actual value, and adjust the model weights through backpropagation to minimize the loss. To prevent overfitting, techniques such as cross-validation are usually adopted.

[0058] S330. Evaluate the performance of the trained cruising range calculation model and output the evaluation result.

[0059] Specifically, evaluate the performance of the trained cruising range calculation model on an independent test set to ensure its generalization ability. Commonly used evaluation metrics include mean squared error and mean absolute error, etc. Output the evaluation result. When the evaluation result is unqualified, such as the mean squared error or mean absolute error does not reach the preset threshold, further adjust the model structure or hyperparameters. Among them, the model structure refers to the hierarchical structure of the model, the number of neurons, the size of the convolutional kernel, and other architectural settings. When the evaluation result is unqualified, you can try to adjust the model structure, such as increasing or decreasing the number of layers of the model, changing the number of neurons, adjusting the size of the convolutional kernel, etc., to improve the expressive ability and performance of the model. Hyperparameters refer to the parameters that need to be manually set before model training, such as learning rate, batch size, number of iterations, etc. The selection of hyperparameters will directly affect the training effect and performance of the model. When the evaluation result is unqualified, you can optimize the model training process by adjusting the hyperparameters. For example, reducing the learning rate can make the model converge more stably, and increasing the batch size can improve the training efficiency. By continuously adjusting the model structure and hyperparameters, retraining the model and evaluating its performance until the evaluation result of the model meets the qualified standard.

[0060] S340. Deploy the range calculation model with a qualified evaluation result to the actual application environment, and monitor and update the range calculation model in real time.

[0061] Specifically, once the model has been fully verified and considered accurate enough, it can be deployed to the actual application environment. At the same time, continuously monitor the performance of the model and retrain it when necessary to adapt to new data patterns or changes in external conditions. As new data accumulates, regularly updating the model can improve its prediction accuracy. In addition, more advanced algorithms can be explored or combined with other types of sensor data to further optimize the model.

[0062] Optionally, before S110, it also includes:

[0063] When starting for the first time, enter the battery parameters of the electric two-wheeler through the terminal interface; the battery parameters include the battery nominal voltage, the first use date of the battery, the battery capacity, the battery charge and discharge characteristic curve, and the battery health curve.

[0064] Specifically, when the user uses it for the first time, they need to enter the battery parameters of the whole vehicle in advance through the Xiaodi Zhixing app installed on the mobile phone: the battery nominal voltage, commonly 48V, 60V, 72V, etc., the first use date of the battery, the battery capacity, the battery charge and discharge characteristic curve, and the battery health curve. These parameters are crucial for accurately calculating the range of the electric vehicle. The above parameters only need to be entered once when using it for the first time. If the battery model is replaced midway, the battery parameters after replacement need to be entered again. Among them, the battery charge and discharge characteristic curve and the battery health curve can be directly retrieved after entering the battery nominal voltage, the first use date of the battery, and the battery capacity.

[0065] Optionally, the data acquisition module preprocesses the driving data including:

[0066] The data acquisition module filters, calibrates, standardizes, and normalizes the driving data.

[0067] Specifically, the driving data sent from the controller to the data acquisition module may have problems such as noise, missing values, and inconsistent data formats. Before inputting the data into the range calculation model, it needs to be preprocessed, including operations such as data cleaning, normalization, and feature extraction, to improve the quality and usability of the data and make it more suitable for the input requirements of the model.

[0068] Optionally, the controller real-time collects the driving data of the electric two-wheeler including:

[0069] The controller real-time collects the motor speed, driving current, battery voltage, battery temperature, ambient temperature, battery capacity, battery charge and discharge characteristic curve, battery health curve, and the slope of the riding section of the electric two-wheeler.

[0070] Optionally, data communication is performed between the controller, the data acquisition module, the 4G module, and the cloud server model calculation module at a preset period.

[0071] Among them, the preset period can be set in advance according to actual requirements.

[0072] Exemplarily, the period for the controller to collect and send driving data to the data acquisition module is 100 milliseconds, the period for the data acquisition module to send driving data to the 4G module is 200 milliseconds, the period for the 4G module to send driving data to the cloud server and the model calculation module is 200 milliseconds, and the period for the cloud server to send back the calculation result to the 4G module is 5 seconds, that is, the cloud server and the model calculation module estimate the remaining driving range of the whole vehicle every 5 seconds.

[0073] Continue to refer to Figure 2 , optionally, the remaining driving range calculation system for the lead-acid battery of the electric two-wheeler further includes a display module 150, and the display module 150 is connected to the controller 110; after step S130, it further includes:

[0074] The cloud server and the model calculation module send the calculated remaining driving range back to the controller.

[0075] The controller sends the calculated remaining driving range to the display module for display.

[0076] Specifically, the cloud server and the model calculation module send the estimation result back to the 4G module, the 4G module sends the estimation result back to the data acquisition module through the USART serial port, the data acquisition module sends it to the controller through the CAN / 485 bus, and the controller sends the driving range data to the display module through the one-line communication for display, or provides it to the user through other channels (such as a mobile phone APP).

[0077] The technical solution provided by the embodiment of the present invention preprocesses the driving data, and the cloud server and the model calculation module train the remaining driving range calculation model according to the preprocessed driving data, improving the generalization ability and prediction performance of the model and the accuracy and reliability of the remaining driving range calculation. It organically integrates various modules such as data acquisition, communication transmission, and model calculation together to form an efficient, stable, and scalable overall system.

[0078] Continue to refer to Figure 2 , Figure 2Schematic diagram of the remaining cruising range calculation system for lead-acid batteries used in electric two-wheel vehicles provided by an embodiment of the present invention. The system includes a controller 110, a data acquisition module 120, a 4G module 130, and a cloud server model calculation module 140. The controller 110 is connected to the data acquisition module 120, the data acquisition module 120 is connected to the 4G module 130, and the 4G module 130 is communicatively connected to the cloud server and the model calculation module 140.

[0079] The controller 110 is configured to collect the driving data of the electric two-wheel vehicle in real time and send the driving data to the data acquisition module 120;

[0080] The data acquisition module 120 is configured to preprocess the driving data and transmit the preprocessed driving data to the cloud server and the model calculation module 140 via the 4G module 130 based on the 4G network;

[0081] The cloud server and the model calculation module 140 are configured to perform in-depth analysis and intelligent processing on the preprocessed driving data according to a pre-trained cruising range calculation model to calculate the remaining cruising range of the electric two-wheel vehicle.

[0082] Continue to refer to Figure 2 Optionally, the remaining cruising range calculation system for lead-acid batteries used in electric two-wheel vehicles provided by an embodiment of the present invention further includes a display module 150, and the display module 150 is connected to the controller 110;

[0083] The cloud server and the model calculation module 140 transmit the calculated remaining cruising range back to the controller 110;

[0084] The controller 110 sends the calculated remaining cruising range to the display module 150 for display.

[0085] The remaining cruising range calculation system for lead-acid batteries used in electric two-wheel vehicles provided by an embodiment of the present invention can execute the remaining cruising range calculation method for lead-acid batteries used in electric two-wheel vehicles provided by any embodiment of the present invention, and has the corresponding functional modules and beneficial effects for executing the method.

[0086] Figure 4Schematic structural diagram of an electronic device for a method of calculating the remaining cruising range of a lead-acid battery for an electric two-wheeler. The electronic device is intended to represent various forms of digital computers, such as, laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as, personal digital processors, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0087] As Figure 4 shown, the electronic device 10 includes at least one processor 11, and a memory communicatively connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc. Among them, the memory stores a computer program executable by the at least one processor. The processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 into the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. The input / output (I / O) interface 15 is also connected to the bus 14.

[0088] Multiple components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a magnetic disk, an optical disc, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0089] The processor 11 can be various general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as a method for a system of calculating the remaining cruising range of a lead-acid battery for an electric two-wheeler.

[0090] In some embodiments, the method for calculating the remaining cruising range of a lead-acid battery for an electric two-wheeler can be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed onto the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the method for calculating the remaining cruising range of a lead-acid battery for an electric two-wheeler described above can be performed. Alternatively, in other embodiments, the processor 11 can be configured to execute the method for calculating the remaining cruising range of a lead-acid battery for an electric two-wheeler in any other suitable manner (e.g., by means of firmware).

[0091] The various embodiments of the systems and techniques described above in this document can be implemented in digital electronic circuitry, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-chip (SOCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special-purpose or general-purpose programmable processor, receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting the data and instructions to the storage system, the at least one input device, and the at least one output device.

[0092] The computer programs for implementing the methods of the present invention can be written in any combination of one or more programming languages. These computer programs can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when the computer programs are executed by the processor, the functions / operations specified in the flowcharts and / or block diagrams are implemented. The computer programs can be executed entirely on the machine, partially on the machine, as a stand-alone software package partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0093] In the context of the present invention, a computer-readable storage medium can be a tangible medium that can contain or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. The computer-readable storage medium can include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. Alternatively, the computer-readable storage medium can be a machine-readable signal medium. More specific examples of the machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0094] For providing interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the electronic device. Other kinds of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0095] The systems and techniques described herein can be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer having a graphical user interface or a web browser through which the user can interact with an implementation of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0096] A computing system may include a client and a server. The client and the server are generally far from each other and usually interact via a communication network. The client-server relationship is created by computer programs running on respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or a cloud host, which is a host product in the cloud computing service system, solving the defects of difficult management and weak business scalability existing in traditional physical hosts and VPS services.

[0097] It should be understood that various forms of the processes shown above can be used, with steps reordered, added, or deleted. For example, the steps recited in the present invention can be executed in parallel, sequentially, or in a different order, as long as the desired results of the technical solution of the present invention can be achieved, and no limitations are imposed herein.

[0098] The above specific embodiments do not constitute a limitation on the protection scope of the present invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention shall be included within the protection scope of the present invention.

Claims

1. A method for calculating the remaining cruising range of a lead-acid battery for an electric two-wheeler, characterized in that, Remaining battery life calculation system for lead-acid batteries used in electric two-wheel vehicles. The system includes a controller, a data acquisition module, a 4G module, and a cloud server model calculation module. The controller is connected to the data acquisition module, the data acquisition module is connected to the 4G module, and the 4G module is communicatively connected to the cloud server and the model calculation module. The method includes: The controller collects the driving data of the electric two-wheel vehicle in real time and sends the driving data to the data acquisition module; The data acquisition module preprocesses the driving data and transmits the preprocessed driving data to the cloud server and the model calculation module via the 4G module based on the 4G network; The cloud server and the model calculation module perform in-depth analysis and intelligent processing on the preprocessed driving data according to a pre-trained remaining battery life calculation model to calculate the remaining battery life of the electric two-wheel vehicle.

2. The method according to claim 1, characterized in that, Before the cloud server and the model calculation module perform in-depth analysis and intelligent processing on the preprocessed driving data according to the pre-trained remaining battery life calculation model to calculate the remaining battery life of the electric two-wheel vehicle, it further includes: The cloud server and the model calculation module train the remaining battery life calculation model according to the preprocessed driving data.

3. The method according to claim 2, wherein The cloud server and the model calculation module training the remaining battery life calculation model according to the preprocessed driving data includes: The cloud server and the model calculation module determine target features according to the preprocessed driving data; Select a target model architecture as the remaining battery life calculation model according to the target features and train the remaining battery life calculation model; Perform performance evaluation on the trained remaining battery life calculation model and output the evaluation result; Deploy the remaining battery life calculation model with a qualified evaluation result to the actual application environment and monitor and update the remaining battery life calculation model in real time.

4. The method according to claim 1, wherein Before the controller collects the driving data of the electric two-wheel vehicle in real time and sends the driving data to the data acquisition module, it further includes: When starting for the first time, enter the battery parameters of the electric two-wheel vehicle through the terminal interface; the battery parameters include the battery nominal voltage, the battery's first use date, the battery capacity, the battery charge and discharge characteristic curve, and the battery health curve.

5. The method according to claim 1, characterized in that, The data acquisition module preprocessing the driving data includes: The data acquisition module performs filtering, calibration, standardization, and normalization on the driving data.

6. The method according to claim 1, wherein The controller collecting the driving data of the electric two-wheel vehicle in real time includes: The controller collects the motor speed, driving current, battery voltage, battery temperature, ambient temperature, battery capacity, battery charge and discharge characteristic curve, battery health curve, and the slope of the riding section of the electric two-wheel vehicle in real time.

7. The method according to claim 1, characterized in that Data communication is carried out between the controller, the data acquisition module, the 4G module, and the cloud server model calculation module according to a preset period.

8. The method according to claim 1, wherein The system further includes a display module, and the display module is connected to the controller; after the cloud server and the model calculation module perform in-depth analysis and intelligent processing on the preprocessed driving data according to the pre-trained remaining battery range calculation model to calculate the remaining battery range of the electric two-wheeler, it further includes: The cloud server and the model calculation module send the calculated remaining battery range back to the controller; The controller sends the calculated remaining battery range to the display module for display.

9. A remaining cruising range calculation system for lead-acid batteries used in electric two-wheel vehicles, characterized in that, It includes a controller, a data acquisition module, a 4G module, and a cloud server model calculation module. The controller is connected to the data acquisition module, the data acquisition module is connected to the 4G module, and the 4G module is communicatively connected to the cloud server and the model calculation module; The controller is used to collect the driving data of the electric two-wheeler in real time and send the driving data to the data acquisition module; The data acquisition module is used to preprocess the driving data and transmit the preprocessed driving data to the cloud server and the model calculation module through the 4G module based on the 4G network; The cloud server and the model calculation module are used to perform in-depth analysis and intelligent processing on the preprocessed driving data according to the pre-trained remaining battery range calculation model to calculate the remaining battery range of the electric two-wheeler.

10. The system according to claim 9, wherein The system further includes a display module, and the display module is connected to the controller; The cloud server and the model calculation module send the calculated remaining battery range back to the controller; The controller sends the calculated remaining battery range to the display module for display.

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