Lithium battery endurance prediction-oriented vehicle-mounted supervision method and system and medium

Abstract

The invention relates to a lithium battery endurance prediction-oriented vehicle-mounted supervision method and system and a medium, and belongs to the technical field of vehicle-mounted supervision, and the method comprises the steps: obtaining a battery parameter change set of a target vehicle in the last charging process after the target vehicle is powered on, based on the battery parameter change set, obtaining an effective residual electricity storage amount; sending a path confirmation application to the user, and receiving initial path information fed back by the user in response to the path confirmation application; obtaining an endurance prediction result according to the initial path information and the effective residual electricity storage quantity; and performing dynamic management on the initial path information according to the endurance prediction result to obtain confirmation path information. Compared with a traditional vehicle-mounted supervision method, optimization is carried out from multiple dimensions, and the problem that the endurance prediction precision is low in a complex scene is effectively solved.

Description

Technical Field

[0001] This application relates to the field of supervision technologies, and in particular, to a vehicle-mounted supervision method, system, and medium based on the battery life guidance of lithium batteries. Background Art

[0002] At present, with the booming development of the electric vehicle industry, as the core power source, the battery life of lithium batteries directly affects the use experience and market promotion of electric vehicles; in order to ensure the reliable operation of vehicles and the accurate expectations of users for battery life, the vehicle-mounted supervision system needs to effectively monitor the state of lithium batteries and predict the battery life.

[0003] The existing vehicle-mounted supervision methods usually estimate the remaining power and driving range of lithium batteries by collecting basic parameters such as the voltage, current, and temperature of lithium batteries and combining simple mathematical models or empirical formulas; to a certain extent, this method can meet the basic supervision needs, but with the complexity of the use scenarios of lithium batteries and the improvement of the accuracy requirements for battery life prediction by users, its limitations gradually emerge. The above traditional methods can provide relatively stable prediction results under ideal laboratory conditions, but during the actual vehicle operation, since the battery life performance of lithium batteries is significantly affected by multi-dimensional dynamic factors such as real-time driving conditions (such as urban road conditions with frequent starts and stops, long-distance scenarios with high-speed cruising, terrain changes with different slopes), environmental parameters (such as extreme high and low temperatures, altitude and air pressure differences, etc.), the traditional methods often cannot dynamically calibrate the model parameters, resulting in a large deviation between the battery life prediction results obtained in actual complex scenarios and the real battery life performance, and the prediction accuracy is low; this deviation will not only make it difficult for drivers to reasonably plan their trips, but may also cause problems such as over-discharge of the battery and unreasonable charging strategies due to misjudgment of the battery life, further exacerbating battery loss and affecting the safety and reliability of the vehicle power system. Summary of the Invention

[0004] In order to improve the problem that the traditional lithium battery life prediction has low accuracy, which easily leads to unreasonable path planning and adverse effects on the lithium battery itself, this application provides a vehicle-mounted supervision method, system, and medium guided by lithium battery life prediction.

[0005] In the first aspect, this application provides a vehicle-mounted supervision method guided by lithium battery life prediction, adopting the following technical solutions: A vehicle-mounted supervision method guided by lithium battery life prediction, comprising: S100, after the target vehicle is powered on, obtain the battery parameter change set during the target vehicle's most recent charging process, and obtain the effective remaining stored power based on the battery parameter change set; S200, issuing a route confirmation request to a user, and receiving initial route information fed back by the user in response to the route confirmation request; S300, obtaining a range prediction result according to the initial route information and the effective remaining power storage capacity; S400: Dynamically manage the initial path information according to the endurance prediction result to obtain confirmed path information.

[0006] By adopting the above technical solution, after the target vehicle is powered on, this application first accurately obtains the battery parameter change set in the recent charging process to determine the effective remaining storage capacity, so as to ensure the accuracy and timeliness of the power data; then, combined with the initial route information fed back by the user, the battery life prediction is carried out based on the effective remaining storage capacity, so as to provide the user with a battery life reference that is more in line with the actual situation; finally, the initial route is dynamically managed according to the battery life prediction result, and the route planning is automatically optimized, which not only effectively avoids the risk of trip interruption due to insufficient power, but also improves the user's travel efficiency and driving experience, and realizes the intelligent and refined management of vehicle trip planning.

[0007] In a specific possible implementation scheme, the vehicle-mounted supervision method is based on a vehicle-mounted supervision system, and the vehicle-mounted supervision system includes a data acquisition device; obtaining the effective remaining storage capacity based on the battery parameter change set includes: calculating the initial remaining storage capacity according to the battery parameter change set; Acquire a correction data packet in the data acquisition device, wherein the correction data includes at least one parameter of real-time temperature, real-time air pressure, and real-time altitude; Determining whether to trigger an environment correction mechanism according to the correction data packet; When it is determined that the environment correction mechanism is triggered, the initial remaining storage capacity is corrected according to the correction data packet to obtain the effective remaining storage capacity.

[0008] By adopting the above technical solution, this application first calculates the initial remaining stored power according to a battery parameter change set including various parameters such as a charging current curve, a total charging duration, and a battery temperature sequence, and then obtains a correction data packet in a data acquisition device. This data packet covers at least one parameter among real-time temperature, real-time air pressure, and real-time altitude, and determines whether to trigger an environmental correction mechanism based on this. When the judgment result is to trigger the environmental correction mechanism, the initial remaining stored power is corrected using the correction data packet to obtain the effective remaining stored power. This method can comprehensively consider the influence of battery self-parameter changes and environmental factors, not only accurately calculate the initial remaining stored power, but also timely correct it according to the changes in environmental parameters, improving the accuracy and reliability of the remaining stored power calculation, providing more accurate data support for battery management and use, helping to more reasonably plan the charging and discharging strategy of the battery, extending the service life of the battery, and enhancing the safety and stability during battery use.

[0009] In a specific feasible implementation, before the step of sending a path confirmation application to the user and receiving the initial path information fed back by the user in response to the path confirmation application, it further includes: Set multi-level warning thresholds based on the effective remaining stored power, and actively identify the endurance risk according to the warning thresholds.

[0010] By adopting the above technical solution, after adding a warning threshold setting and risk identification link on the basis of the original technology in this application, by setting multi-level warning thresholds based on the effective remaining stored power, the endurance risk can be stratified and classified according to the severity, and then the endurance risk can be actively identified according to these thresholds, and the different urgency levels of insufficient power can be perceived in advance. In this way, before obtaining the user's initial path information, the system can predict potential endurance problems in advance. When performing endurance prediction and path dynamic management in combination with the user's planned path later, it can more accurately plan the path and adjust the itinerary arrangement, avoiding the user getting into trouble due to endurance problems, providing a more predictable and reliable travel guarantee for the user, and further enhancing the safety and reliability of the path planning system.

[0011] In a specific feasible implementation, the initial path information includes the total driving section distance; after the step of setting multi-level warning thresholds based on the effective remaining stored power and actively identifying the endurance risk according to the warning thresholds, it further includes: In the case of a first-level alarm being triggered, after the user confirms the initial path information to obtain the total driving section distance, automatically determine whether the total driving section distance conforms to the preset short-distance trip characteristics; If the total driving section distance conforms to the short-distance trip feature, cancel the primary warning and continue to execute S300 - S400; if the total driving section distance does not conform to the short-distance trip feature, preliminarily estimate whether the effective remaining battery power is sufficient to cover the total driving section distance; if the estimation result is yes, continue to execute S300 - S400; if the estimation result is no, cancel the execution of S300 and directly execute S400.

[0012] By adopting the above technical solution, on the basis of setting multi-level warning thresholds based on the effective remaining battery power and realizing the active identification of the endurance risk, the application further refines the processing logic after the primary warning is triggered; when the primary warning is triggered, the system will automatically determine whether the distance in the initial path information confirmed by the user conforms to the short-distance trip feature according to the total driving section distance; if it conforms to the short-distance trip feature, it indicates that in this short-distance case, the current battery state may be sufficient to meet the trip requirements, so the primary warning is cancelled and the subsequent steps (S300 - S400) are continued according to the normal process, avoiding unnecessary warning interference to the user; If it does not conform to the short-distance trip feature, further preliminarily estimate whether the effective remaining battery power is sufficient to cover the total driving section distance, so as to more accurately evaluate the endurance risk during the trip; when the estimation result shows that the effective remaining battery power is sufficient to cover the total driving section distance, it is also continued according to the normal process to ensure the normal operation of the system; when the estimation result is that it is not sufficient to cover the total driving section distance, cancel the normal path planning process of S300 - S400 and directly execute S400, so as to quickly adjust the strategy, avoid wasting resources due to ineffective path planning, and at the same time can timely remind the user to take corresponding measures (such as changing the trip plan or finding a charging facility, etc.), effectively reducing the risk of trip interruption caused by insufficient endurance, improving the adaptability of the system to different trip situations and the user experience, and making the whole endurance risk warning and response mechanism more intelligent, efficient and user-friendly.

[0013] In a specific feasible implementation, the obtaining of the endurance prediction result according to the initial path information and the effective remaining battery power includes: Structurally analyze the initial path information to obtain a set of section features corresponding to each section; Obtain the local predicted power consumption corresponding to each section according to the set of section features; Compare and analyze the sum of the local predicted power consumption with the effective remaining battery power to obtain the endurance prediction result.

[0014] In a specific feasible implementation, the road section feature set includes basic features, terrain features, road surface types, environmental features, and energy consumption features; obtaining the local predicted power consumption corresponding to each road section according to the road section feature set includes: Dividing the path into uphill road sections, flat road sections, and downhill road sections according to the terrain features; Calculating the sum of the local predicted power consumptions corresponding to all the uphill road sections according to the following formula: where E up is the sum of the local predicted power consumptions corresponding to all the uphill road sections; n up represents the total number of the uphill road sections, obtained through the basic features; C D is the air resistance coefficient, obtained through the environmental features; A represents the preset frontal area of the target vehicle; ρ represents the air density, obtained through the environmental features; v avg_i represents the average driving speed of the target vehicle on the i-th uphill road section; m represents the preset mass of the target vehicle; f represents the preset rolling resistance coefficient; a i represents the average acceleration of the target vehicle on the i-th uphill road section; L i represents the length of the i-th uphill road section, obtained through the basic features; α i represents the slope of the i-th uphill road section, obtained through the basic features; Calculating the local predicted power consumption corresponding to all the flat road sections according to the following formula: where E flat is the sum of the local predicted power consumptions corresponding to all the flat road sections; n flat represents the total number of the flat road sections; Calculating the local predicted power consumption corresponding to all the downhill road sections according to the following formula: where E down is the sum of the local predicted power consumptions corresponding to all the downhill road sections; n down represents the total number of the downhill road sections; η r represents the preset energy recovery efficiency, which is the efficiency of the target vehicle converting gravitational potential energy into electrical energy on the downhill road section.

[0015] By adopting the above technical solution, the present application can predict the endurance more accurately and comprehensively: first, the initial path information is structured and parsed, and it is refined into a set of road segment characteristics corresponding to each road segment, which covers multiple aspects such as basic characteristics, terrain characteristics, road surface type, environmental characteristics, and energy consumption characteristics. This detailed parsing method enables the system to have an in-depth understanding of the specific conditions of each section on the path, providing a rich and accurate data basis for subsequent power consumption prediction; Next, the local predicted power consumption corresponding to each road section is calculated based on the road section feature set. Since the road section feature set fully reflects the actual conditions of the road section, it can fully consider the impact of different terrain, road surface, environment and other factors on power consumption, so that the result of local predicted power consumption is closer to the actual situation; Finally, the total predicted power consumption of all sections is compared with the effective remaining power to obtain the endurance prediction result. This comparison method based on detailed section analysis and accurate power prediction can provide users with more reliable endurance information, allowing users to know in advance whether there is a risk of insufficient power on the planned route, and then adjust the itinerary or take charging measures in a targeted manner, effectively improving the convenience and reliability of users' travel and avoiding problems such as trip interruptions due to insufficient power. In addition, the present application adopts a segmented approach to calculate the power consumption of different types of road sections. This segmented and multi-factor based local prediction of power consumption calculation method can more accurately predict the energy consumption of vehicles in different sections, thereby providing more reliable data support for endurance prediction, making the endurance prediction results more in line with actual driving conditions, improving the scientificity and rationality of route planning, helping users to better plan their itineraries, and effectively avoiding problems such as itinerary interruptions due to inaccurate power estimation.

[0016] In a specific possible implementation scheme, the vehicle-mounted supervision method further includes: During the user's driving process, the user's current location information and battery parameter change set are periodically obtained; The user's travel deviation is identified according to the current position information and the confirmed path information to obtain an identification result; when different identification results are obtained, an early warning is issued on the consumption of lithium battery power according to the battery parameter change set.

[0017] By adopting the above technical solution, the present application can periodically obtain the current position information and the battery parameter change set during the user's driving process, and can thus keep track of the vehicle's driving dynamics and the battery state changes in real time. Based on the current position information, the confirmed path information, and the battery parameter change set, the present application identifies and warns about the power consumption of the lithium battery. On the one hand, by comparing the actual driving position of the vehicle with the planned confirmed path, it can accurately judge whether the actual power consumption during the vehicle's driving meets the expectation, and timely detect the abnormal power consumption caused by factors such as road condition changes and driving habits. On the other hand, by combining the battery parameter change set, such as the changes in parameters such as voltage, current, and battery percentage, it can more accurately evaluate the remaining battery power and the power consumption trend. When it identifies abnormal power consumption or the remaining power is lower than the safety threshold, it will timely issue a warning to the user, reminding the user to take corresponding measures, such as adjusting the driving mode, looking for a charging pile, etc., to avoid the predicament that the vehicle cannot continue to drive due to power exhaustion, effectively improving the user's driving experience and driving safety. At the same time, it also realizes the refined supervision of the power consumption of the lithium battery.

[0018] In a second aspect, the present application provides a supervision terminal, adopting the following technical solution: A supervision terminal, characterized by comprising a memory and a processor. At least one instruction, at least one program, a code set, or an instruction set is stored in the memory, and the at least one instruction, at least one program, the code set, or the instruction set is loaded and executed by the processor to implement an in-vehicle supervision method for lithium battery endurance prediction and guidance as described in the first aspect.

[0019] In a third aspect, the present application provides an in-vehicle supervision system for lithium battery endurance prediction and guidance, adopting the following technical solution: An in-vehicle supervision system for lithium battery endurance prediction and guidance, comprising: A data acquisition device, configured to obtain at least one parameter among real-time temperature, real-time air pressure, and real-time altitude, and obtain a data packet for correction; The supervision terminal as described in the second aspect, which is communicatively connected to the data acquisition device and the battery management system of the target vehicle, is configured to obtain the battery parameter change set in the battery management system, obtain the effective remaining storage capacity according to the battery parameter change set, and then complete the prediction and warning of the lithium battery endurance according to the effectively remaining storage capacity updated in real time.

[0020] In a fourth aspect, the present application provides a computer-readable storage medium, adopting the following technical solution: A computer-readable storage medium stores at least one instruction, at least one program, a code set or an instruction set, and the at least one instruction, at least one program, code set or instruction set is loaded and executed by a processor to implement a vehicle supervision method guided by lithium battery endurance prediction as described in the first aspect.

[0021] In summary, the present application includes at least one of the following beneficial technical effects: 1. Compared with the limitation of traditional vehicle supervision methods that only collect basic parameters of lithium batteries and rely on simple models or empirical formulas to estimate the remaining power and endurance mileage, the technical solution of the present application is optimized from multiple dimensions, effectively solving the problem of low accuracy of endurance prediction in complex scenarios: Determine the effective remaining storage capacity by obtaining the battery parameter change set during the most recent charging process of the target vehicle, avoiding the power data deviation caused by traditional static estimation methods; Divide the road sections according to the terrain features and calculate the local predicted power consumption by integrating multiple factors, dynamically considering the impact of real-time driving conditions on endurance; In the path planning link, combine the user's initial path information with the effective remaining storage capacity to predict the endurance, and dynamically manage the path based on the prediction results. At the same time, set multiple warning thresholds to actively identify endurance risks, enabling the system to flexibly adjust according to the actual scenario; During the driving process, periodically obtain the position information and the battery parameter change set, and real-time identify and warn of the power consumption situation to ensure continuous and accurate monitoring of the lithium battery status; The above technical means achieve dynamic and refined supervision of the lithium battery status, significantly improving the accuracy of endurance prediction, helping the driver to reasonably plan the itinerary, greatly avoiding battery loss and safety problems caused by endurance misjudgment, providing a strong guarantee for the reliable operation of electric vehicles and the good user experience, and promoting the further development of the electric vehicle industry. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 Shows a schematic structural diagram of a vehicle supervision system guided by lithium battery endurance prediction provided by an exemplary embodiment of the present application.

[0023] Figure 2 Shows a schematic flowchart of a vehicle supervision method guided by lithium battery endurance prediction provided by an exemplary embodiment of the present application.

[0024] Figure 3 Is a schematic structural diagram of an electronic device disclosed in another embodiment of the present application.

[0025] Description of the reference numerals: 500, electronic device; 501, processor; 502, communication bus; 503, user interface; 504, network interface; 505, memory. Detailed Implementation Modes

[0026] In order to enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings in the embodiments of this specification. Obviously, the described embodiments are only a part of the embodiments of this application, rather than all of the embodiments.

[0027] In the description of the embodiments of this application, words such as "for example" or "for illustration" are used to give examples, illustrations or explanations. Any embodiment or design solution described as "for example" or "for illustration" in the embodiments of this application should not be construed as being more preferred or having more advantages than other embodiments or design solutions. Rather, the use of words such as "for example" or "for illustration" is intended to present the relevant concepts in a specific manner.

[0028] In the description of the embodiments of this application, the meaning of the term "a plurality of" refers to two or more. For example, a plurality of systems refers to two or more systems, and a plurality of screen terminals refers to two or more screen terminals. In addition, the terms "first" and "second" are only used for descriptive purposes and cannot be construed as indicating or implying relative importance or implicitly indicating the technical features indicated. Thus, the features defined with "first" and "second" may explicitly or implicitly include one or more of such features. The terms "include", "comprise", "have" and their variants all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0029] The technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of this application. Obviously, the described embodiments are only a part of the embodiments of this application, rather than all of the embodiments.

[0030] An embodiment of this application discloses a vehicle-mounted supervision system guided by lithium battery endurance prediction.

[0031] Refer to Figure 1 , a vehicle-mounted supervision system guided by lithium battery endurance prediction mainly includes data acquisition devices and a supervision terminal communicatively connected to the data acquisition devices; Among them, the data acquisition devices mainly include at least one of a temperature sensor, a humidity sensor, a barometric pressure sensor, and an altitude sensor, and are used to obtain at least one parameter of real-time temperature, real-time barometric pressure, and real-time altitude to obtain a correction data packet; The supervision terminal is communicatively connected to a preset battery management system (i.e., BMS, which is prior art and will not be elaborated here) in an electric vehicle or an electric car, and is used to obtain a set of battery parameter changes in the battery management system at the first moment when the vehicle starts, and obtain the effective remaining storage capacity according to the set of battery parameter changes, and then perform a battery life prediction and warning for the lithium battery according to the effectively updated remaining storage capacity; The supervision terminal is also communicatively connected to the user's personal terminal, and is used to send a path confirmation request to the user at the first moment when the vehicle starts, and receive the initial path information sent by the user in response to the path confirmation request; Obtain a battery life prediction result according to the initial path information and the effective remaining storage capacity, and perform dynamic management on the initial path information according to the battery life prediction result to obtain the confirmed path information.

[0032] The supervision terminal includes a processor and a memory, and the memory is used to store at least one instruction, at least one program, a code set or an instruction set; when the processor runs at least one instruction, at least one program, a code set or an instruction set, it executes the steps of the following on-vehicle supervision method guided by lithium battery life prediction.

[0033] The implementation of the method of this application will be described in detail below in combination with the above system: Refer to Figure 2 , another embodiment of this application provides an on-vehicle supervision method guided by lithium battery life prediction, including: S100, after the target vehicle is powered on, obtain a set of battery parameter changes during the target vehicle's most recent charging process, and obtain the effective remaining storage capacity based on the set of battery parameter changes; Among them, the target vehicle being powered on in this embodiment means that the vehicle (electric vehicle or electric car) is started by a key. At this time, the supervision terminal is automatically activated, establishes a communication connection with the battery management system of the target vehicle through the CAN bus, and encapsulates and transmits data parameters in a unified protocol (such as SAE J1939) to ensure the real-time and accuracy of the data; the set of battery parameter changes includes a charging current curve, a total charging duration, and a battery temperature sequence, and the above data can be directly obtained through the BMS; Specifically, the charging current curve is collected in real time by a high-precision current sensor (such as a Hall effect sensor) built into the BMS during the charging of the lithium battery, and the changes in current at each moment during the charging process are recorded at a frequency of ≥10 Hz. Generally speaking, the charging current curve includes two parts: the constant current charging stage (i.e., a fixed current value) and the constant voltage charging stage (the current decay process); the BMS calculates the total charging duration through the timestamps at the start and end of charging, where the timestamps are generated by the clock module inside the BMS; the battery temperature sequence is mainly collected through the NTC thermistor sensor integrated in the BMS. This sensor is arranged at key positions of the battery module (such as the surface of the battery cell, the connection of the electrode posts) to monitor the battery temperature changes during the charging process in real time and form the battery temperature sequence; Generally speaking, in S100, based on the existing battery parameter change set, the effective remaining storage capacity can be calculated by adopting the following existing technologies: one is the coulomb counting method (ampere-hour integration method), which calculates the charge amount charged into the battery by integrating the charging current over time, that is, performing an integration operation on the charging current curve within the total charging duration range, and then combining with the initial battery charge to obtain the effective remaining storage capacity; the other is the open-circuit voltage method, which utilizes the corresponding relationship between the open-circuit voltage of the battery and the remaining charge. After the battery is charged and left standing for a period of time, the BMS obtains the open-circuit voltage of the battery, and then determines the effective remaining storage capacity according to the pre-established voltage-charge calibration curve; the third is the method based on the battery model, such as the equivalent circuit model or the neural network model. The former simulates the internal dynamic characteristics of the battery by establishing a circuit model containing components such as resistors and capacitors, and combines the battery parameter change set to solve the model parameters to estimate the remaining charge. The latter trains the neural network through a large amount of historical data to learn the complex mapping relationship between the battery parameters and the remaining charge, and then predicts the effective remaining storage capacity; these methods have been widely used in the field of battery charge estimation, so they will not be elaborated here. S200, send a path confirmation request to the user and receive the initial path information feedback by the user in response to the path confirmation request; among them, after calculating the effective remaining storage capacity of the lithium battery, the supervision terminal will actively initiate a path confirmation request to the user; the user can set the trip destination in the navigation software in various ways, such as using the in-vehicle central control screen for touch input and directly entering the destination name on the screen; or through voice commands, telling the in-vehicle voice assistant where to go; it can also rely on mobile phone interconnection to send the trip planned on the mobile phone to the in-vehicle system through the APP or small program; after the user selects and confirms one of the recommended multiple routes in the navigation software, the supervision terminal connected to the in-vehicle system and the user's personal terminal (such as mobile phone, tablet, etc.) can obtain this finally selected route, and integrate and analyze the data such as the destination information, the distance of each section, the estimated driving time, the road type, and the real-time road conditions included in it, so as to obtain the initial path information for subsequent operations such as endurance prediction.

[0034] S300. Obtain the endurance prediction result based on the initial path information and the effective remaining battery capacity. Among them, this step mainly constructs an endurance prediction model by integrating the correlation between the multi-dimensional features of the initial path (such as terrain, road conditions, environmental parameters, etc.) and the effective remaining battery capacity of the lithium battery, so as to comprehensively evaluate the power consumption situation and endurance ability of the vehicle on the target path, and provide a core decision-making basis for subsequent path dynamic management (i.e., S400) and real-time verification during driving (i.e., S500).

[0035] S400. Dynamically manage the initial path information according to the endurance prediction result to obtain the confirmed path information. Among them, this step is based on the endurance prediction result output by S300, and combines information such as the real-time distribution of charging piles and road energy consumption characteristics to intelligently adjust the initial path (such as inserting charging plans and avoiding high-energy consumption sections), and finally generates confirmed path information that takes into account endurance safety and user experience, providing a benchmark solution for path execution and dynamic verification during vehicle driving.

[0036] It should be particularly noted that in order to realize the real-time monitoring and dynamic verification of the lithium battery power consumption during vehicle driving, timely respond to the impact of dynamic factors such as route deviation and road condition changes on endurance, and ensure the safety of user trips and the continuous accuracy of endurance prediction, a vehicle-mounted supervision method guided by lithium battery endurance prediction in this embodiment further includes the following steps: S500. During the user's driving process, periodically obtain the user's current position information and the battery parameter change set. S600. Identify the user's travel deviation situation according to the current position information and the confirmed path information to obtain the identification result; S700. In the case of obtaining different identification results, give an early warning of the lithium battery power consumption situation according to the battery parameter change set.

[0037] Among them, steps S500 - S600 mainly judge whether the user deviates from the established route by periodically collecting the vehicle's current position information and comparing the current position information with the confirmed path. This step has been applied in existing navigation systems for re-planning routes for temporarily adjusted routes by users, and will not be elaborated here; in this embodiment, the identification and early warning of the lithium battery power consumption situation in step S700 are mainly aimed at real-time monitoring of the actual remaining battery power of the lithium battery after the user deviates from the established route, and after it gradually decreases to enter the early warning range, by re-planning the route (such as including the charging piles near the path points on the way into the new route, etc.), to ensure the real-time performance and reliability of endurance supervision during vehicle driving as much as possible.

[0038] To address the dynamic impact of real-time environmental parameters on the available capacity of lithium batteries and improve the calculation accuracy of the remaining stored electricity to support subsequent accurate range prediction and vehicle safety supervision, in the effective remaining stored electricity obtained by S100 based on the battery parameter change set, this implementation method includes the following sub-steps: S110, calculate the initial remaining stored electricity according to the battery parameter change set; Among them, the battery parameter change set further includes the battery percentage reported by the BMS at the end of charging (such as 100% when fully charged), the temperature-related Coulomb efficiency function (this data can be queried in real time through the lookup table provided by the battery manufacturer. For example, at 25°C, η = 100%; at 0°C, η = 92%; at 40°C, η = 95%), the rated capacity of the battery, the current cycle life number, and the battery health status; specifically, in this embodiment, the ampere-hour integration method is used to complete the calculation of the initial remaining stored electricity, and the formula is as follows: Among them, SOC initial represents the initial remaining stored electricity; SOH represents the battery health status; SOC end_charge represents the battery percentage reported by the BMS at the end of charging; Q n represents the rated capacity of the battery; t now represents the time stamp of the current power-on; T end represents the end-of-charge time stamp; I(t) represents the charging current curve; η(θ) represents the temperature-related Coulomb efficiency function.

[0039] S120, obtain the correction data packet in the data acquisition device; Among them, the correction data includes at least one parameter among real-time temperature, real-time air pressure, and real-time altitude; the real-time temperature refers to the external environmental temperature, which can be collected by the temperature sensor outside the vehicle body; the real-time air pressure mainly refers to the atmospheric pressure, which is collected by the air pressure sensor integrated in the vehicle navigation module; the real-time altitude can be obtained by parsing the positioning data of the GPS.

[0040] S130, determine whether to trigger the environmental correction mechanism according to the correction data packet; Among them, when any parameter in the correction data packet meets the following corresponding preset conditions, the environmental correction mechanism is triggered, otherwise, it is not triggered: |T0 - T last | ≥ 5°C; |H0 - H last | ≥ 200 m; |P0 - P last | ≥ 10 kPa; Among them, T0 represents the standard temperature in the experimental environment, for example; P0 represents the standard air pressure in the experimental environment, for example; H0 represents the standard altitude in the experimental environment, for example; Tlast represents the real-time temperature; P last represents the real-time air pressure; H last represents the real-time altitude.

[0041] S140, when it is determined that the environmental correction mechanism is triggered, the initial remaining battery charge is corrected according to the correction data packet to obtain the effective remaining battery charge; Among them, the effective remaining battery charge refers to the actual available capacity of the lithium battery under the influence of the current environment; specifically, the calculation formula of the effective remaining battery charge is as follows: SOC effective = SOC initial ·k T ·k H ·k P ; k H = 1 + 0.0005·(H - 1000); k P = 1 + 0.001·(P amb_std - P amb ); Among them, SOC effective represents the effective remaining battery charge; SOC initial represents the initial remaining battery charge; k T , k H , k P are the temperature correction coefficient, altitude correction coefficient, and air pressure correction coefficient respectively; R bat represents the actual internal resistance of the battery; E a is the activation energy, which is taken as 7.5×10 4 J / mol in this embodiment; k is the Boltzmann constant, which is taken as 1.38×10 -23 J / K in this embodiment; represents the reference internal resistance of the battery at 25°C; T amb represents the real-time temperature; P amb_std represents the standard atmospheric pressure; P amb represents the real-time air pressure.

[0042] It should be noted that if the battery parameter change set data during the charging process in S100 is missing (such as a communication failure of the BMS), the backup estimation mode is enabled at this time: the remaining battery charge at the last stop is deducted by the static power consumption (such as the standby current of the low-voltage system); at the same time, the supervision terminal will trigger a malfunction indicator light (MIL) to prompt the user to check the BMS connection, or prompt "Battery data is abnormal, and the accuracy of the endurance prediction may decrease" on the interaction interface.

[0043] It should be specifically noted that, in order to give an early warning in the low battery scenario to ensure driving safety, this embodiment adds a battery power warning mechanism before S200 (path confirmation). The specific logic is as follows: S150, set multiple warning thresholds based on the effective remaining battery capacity, and actively identify the driving range risk according to the warning thresholds. Among them, this embodiment takes two levels as an example for the multiple levels: the trigger condition for the first-level warning is that the effective remaining battery power is lower than the preset conventional safety threshold (for example, the battery percentage corresponding to the effective remaining battery capacity is between 10% and 30%), and the step S200 has not been completed yet (that is, the initial path information confirmed by the user in S200 has not been obtained, mainly the total driving section distance). At this time, the first-level warning is triggered. In this embodiment, it is taken as an example that the supervision terminal controls the yellow battery icon on the central control screen to flash + voice prompt "The remaining battery power is low. It is recommended to plan for charging"; the trigger condition for the second-level warning is that the battery power enters the preset critical range (for example, the battery percentage corresponding to the effective remaining battery capacity is lower than 5%). At this time, regardless of the length of the total driving section distance obtained by S200 later, the second-level warning is triggered. In this embodiment, it is taken as an example that the supervision terminal controls a red pop-up window to cover the main interface of the central control screen + buzzer alarm + the red light on the dashboard is always on, showing "The battery power is urgent. Please charge immediately" (the specific warning method can be adjusted according to the actual situation. This is only an example here).

[0044] At the same time, in order to avoid interfering with users due to invalid warnings caused by short trips, this embodiment also adds a warning dynamic cancellation mechanism after S150. The specific logic is as follows: S160, after the first-level warning is triggered, after the user confirms the initial path information to obtain the total driving section distance, automatically judge whether it conforms to the short-distance trip feature; S170, if it conforms to the short-distance trip feature, cancel the first-level warning and continue to execute S300 - S700; Among them, the initial path information includes the total driving section distance; for electric vehicles, this embodiment takes 2 km as an example for the short-distance trip feature; for trams, this embodiment takes 10 km as an example for the short-distance trip feature; conforming means that the total driving section distance is less than the short-distance trip feature, otherwise it is considered non-conforming; the short-distance trip judgment here mainly considers that users may sometimes temporarily need to drive a tram / electric vehicle to complete daily needs such as picking up express delivery, having a meal, and getting a haircut near their location. At this time, even if the vehicle's battery power is not very sufficient, it is already enough for the user to complete the corresponding operations.

[0045] S180, if it does not conform to the short-distance trip feature, preliminarily estimate whether the effective remaining battery capacity is sufficient to cover the total driving section distance; if the estimation result is yes, continue to execute S300 - S400 according to the normal process; if the estimation result is no, cancel the execution of S300 and directly execute S400.

[0046] S190. After triggering a secondary alarm, cancel the execution of the regular path planning process of S300 - S400 and directly execute S500 - S700.

[0047] It should be noted that after triggering an alarm, if the initial path information in S200 has not been generated (such as the user has not confirmed the path, etc.), then cancel the execution of S300 - S400. Periodically (in this embodiment, every 2 minutes as an example), repeat the prompt "Please confirm the travel destination. The current battery level only supports short - distance travel" through voice and pop - up window on the central control screen to effectively prevent the user from ignoring the alarm. If the user clicks "Not to confirm for now", the supervision terminal records the user's preference and extends the reminder interval to 5 minutes. During the above process, the supervision terminal always executes S500 - S700 to continuously monitor the power consumption during the user's subsequent driving process.

[0048] In addition, in this embodiment, the cancellation of the secondary alarm requires the user to actively confirm (to avoid misoperation), and the cancellation of the primary alarm can be automatically executed by the supervision terminal based on the short - distance travel characteristics (to improve the interaction efficiency).

[0049] In order to achieve accurate prediction of the lithium - battery endurance ability under complex road conditions through refined analysis of the initial path and segmented energy consumption calculation, and provide a scientific decision - making basis for user travel planning and system dynamic supervision, in the process of obtaining the endurance prediction result based on the initial path information and the effective remaining battery charge in S300, this embodiment includes the following sub - steps: S310. Structurally analyze the initial path information to obtain a set of road - segment characteristics corresponding to each road segment; Among them, the supervision terminal can divide the initial path into continuous sub - road segments according to geographical features through a high - precision map API (such as the Baidu Map Open Platform). To ensure the operation speed, in this embodiment, it is default that each 1 km is a segment, and the complex mountainous road segments are refined to every 500 m. In actual applications, the supervision terminal can also automatically adaptively divide the initial path through automatic recognition technology, etc., to ensure that parameters such as slope, speed limit, and road type within each sub - road segment are relatively stable as much as possible. The set of road - segment characteristics includes terrain features, road surface types, and environmental features. Specifically, the basic features include the starting and ending coordinates of the road segment and the distance of the road segment from the starting point to the ending point. The terrain features include the average slope (positive for uphill and negative for downhill). The road surface types include urban roads / highways / mountain roads. The environmental features include the real - time temperature, atmospheric pressure, and wind speed (taking positive values in the direction of travel and negative values against the direction of travel).

[0050] S320. Obtain the local predicted power consumption corresponding to each road segment according to the set of road - segment characteristics; Among them, in this embodiment, the power consumption that the vehicle may consume when passing through each road segment (i.e., the locally predicted power consumption) is mainly calculated according to the road segment characteristics of each road segment.

[0051] S330. Compare and analyze the sum of the locally predicted power consumptions with the effective remaining power storage to obtain the endurance prediction result. Among them, if the effective remaining power storage * 80% < the sum of the locally predicted power consumptions, the endurance prediction result is insufficient endurance; if the effective remaining power storage * 80% ≤ the sum of the locally predicted power consumptions < the effective remaining power storage, the endurance prediction result is critical endurance; if the effective remaining power storage * 80% ≥ the sum of the locally predicted power consumptions, the endurance prediction result is sufficient endurance.

[0052] In order to achieve refined calculation of the power consumption of each road segment based on different terrain characteristics, ensure the accuracy and reliability of the endurance prediction result under complex road conditions, and provide a scientific basis for subsequent path dynamic management and driving safety warning. In the step of obtaining the locally predicted power consumption corresponding to each road segment according to the road segment feature set in S320, this embodiment includes the following sub-steps: S321. Divide the path into uphill road segments, flat road segments, and downhill road segments according to the terrain characteristics. Among them, the supervision terminal mainly regards the road segments with a slope between -5 degrees and 5 degrees as flat road segments; regards the road segments with a slope greater than 5 degrees as uphill road segments; regards the road segments with a slope less than -5 degrees as downhill road segments.

[0053] S322. Calculate the sum of the locally predicted power consumptions corresponding to all uphill road segments according to the following formula: Among them, E up is the sum of the locally predicted power consumptions corresponding to the uphill road segments; n up represents the total number of uphill road segments; C D is the air resistance coefficient, which is used to reflect the influence degree of air resistance on the energy consumption of the vehicle during driving. It is a preset value, and this value is part of the vehicle design parameters and can be directly obtained from the vehicle manufacturer; A represents the preset frontal area of the target vehicle, and this data is one of the vehicle design parameters and can be directly obtained from the vehicle manufacturer; ρ represents the air density, which can be measured by an on-vehicle sensor or obtained in combination with local meteorological data (i.e., the environmental characteristics obtained in S310); v avg_i represents the average driving speed of the target vehicle on the i-th uphill road segment, and this data can be obtained from the historical driving data of the user on different road surface types. It is a prior art and will not be elaborated here; m represents the mass of the target vehicle, and this data is one of the vehicle design parameters and can be directly obtained from the vehicle manufacturer; f represents the preset rolling resistance coefficient, and this data is one of the vehicle design parameters and can be directly obtained from the vehicle manufacturer; ai represents the average acceleration of the target vehicle on the i-th uphill section, which is used to reflect the impact of frequent vehicle starts and stops on energy consumption; L i represents the length of the i-th uphill section (i.e., the section distance in the basic features); α i represents the slope of the i-th uphill section, which can be obtained from the terrain features.

[0054] S323. Calculate the local predicted power consumption corresponding to all flat sections according to the following formula: where, E flat is the sum of the local predicted power consumptions corresponding to the flat sections; n flat represents the total number of flat sections; the definitions of the remaining parameters are the same as those in S322.

[0055] S324. Calculate the local predicted power consumption corresponding to all downhill sections according to the following formula: where, E down is the sum of the local predicted power consumptions corresponding to the downhill sections; η down represents the total number of downhill sections; η r represents the energy recovery efficiency, which indicates the efficiency of the vehicle converting gravitational potential energy into electrical energy during downhill driving. It is one of the vehicle design parameters and can be directly obtained from the vehicle manufacturer; the definitions of the remaining parameters are the same as those in S322.

[0056] It should be particularly noted that the C D , A, m, f, and η r in the calculation formulas of S322, S323, and S324 above all belong to vehicle design parameters, which can be obtained from the vehicle manufacturer or corrected through actual test data.

[0057] In order to achieve intelligent optimization and safety guarantee of the path under different endurance prediction results and ensure that the vehicle can reach the destination efficiently while meeting the endurance requirements, in S400, dynamically manage the initial path information according to the endurance prediction result to obtain the confirmed path information. This embodiment includes the following sub-steps: S410. If the endurance prediction result is insufficient endurance, trigger the first charging pile retrieval mechanism to obtain the preferred coordinates for charging on the way, and obtain the confirmed path information based on the preferred coordinates for charging on the way and the initial path information; Among them, when the battery life is insufficient, the supervision terminal inserts a charging node in the middle of the path, so that when the user arrives at the charging node, the vehicle charging can be arranged to avoid the vehicle breaking down on the way due to power exhaustion; in this embodiment, by preferentially selecting a fast charging pile that is closer to the midpoint node of the first half of the initial path and has a higher power (the preferred charging pile can be selected by using a weight distribution formula to set a comprehensive score for the charging piles near the passing points of the initial path to obtain the preferred coordinates for mid-way charging), the power can be quickly replenished while minimizing the detour distance, ensuring the continuity of the journey; when the preferred coordinates for mid-way charging are obtained, the supervision terminal mainly obtains the confirmed path information by adding the preferred coordinates for mid-way charging into the initial path; in this embodiment, adding the coordinates into the initial path specifically means marking the preferred coordinates for mid-way charging as a necessary passing point. If the initial path in the initial path information is A→B→C→D→E and the preferred coordinates for mid-way charging are F, which is closer to both B and C, then the confirmed path in the confirmed path information is A→F→D→E.

[0058] S420, if the battery life prediction result is critical battery life, trigger the second charging pile retrieval mechanism to obtain the preferred coordinates for end charging, and obtain the confirmed path information based on the preferred coordinates for end charging and the initial path information; Among them, when the battery life is critical, it means that although the vehicle's power can reach the destination (or near the destination), there is probably no redundancy. Therefore, it is necessary to pre-plan the charging piles around the destination so that the user can charge in time after arrival, avoiding additional energy consumption due to searching for charging piles; in this embodiment, the preferred coordinates for end charging are mainly obtained by preferentially selecting a fast charging pile that is closer to the destination of the initial path, and the preferred coordinates for end charging are used as the end destination of the initial path to obtain the confirmed path information; specifically, in this embodiment, adding the coordinates into the initial path means marking the preferred coordinates for end charging as a necessary passing point. If the initial path in the initial path information is A→B→C→D→E and the preferred coordinates for end charging are F, which is closest to E, then the confirmed path in the confirmed path information is A→B→C→D→F.

[0059] S430, if the battery life prediction result is sufficient battery life, directly use the initial path information as the confirmed path information.

[0060] In order to realize the intelligent screening and path optimization of the mid-way charging node, in the first charging pile retrieval mechanism of S410, this embodiment includes the following sub-steps: S411, delimit the mid-way charging retrieval range; Among them, the supervision terminal takes the first half of the initial path as the benchmark (in this embodiment, the first 60% of the initial distance is regarded as the first half), and expands a certain width to both sides (in this embodiment, a total width of 1 km for the retrieval area is taken as an example) as the mid-way charging retrieval range to ensure that the charging pile is within a reasonable detour range.

[0061] S412. Within the mid - journey charging search range, search the covered charging piles to obtain the first alternative charging location information. Among them, in this embodiment, the search is mainly segmented according to the driving direction corresponding to the initial journey, and the charging piles in the first half of the journey are preferentially searched to effectively avoid the vehicle being forced to stop due to too low battery power in the second half of the journey. The first alternative charging location information includes the position coordinates of all charging locations that can be used to charge the vehicle within the mid - journey charging search range, and the charging power of the charging piles in the charging location.

[0062] S413. Screen the first alternative charging location information to obtain the preferred coordinates for mid - journey charging. Among them, screening mainly refers to evaluating the priority of each charging location in the first alternative charging location information. The priority calculation formula in this embodiment is: Priority = w1 * Power coefficient+w2 * Deviation distance coefficient; Power coefficient = Actual power of the charging pile / 120kW, Deviation distance coefficient = 2d1 / d0; d represents the driving distance required to return from the charging pile to the initial path; w1 and w2 are weight coefficients, and w1 + w2 = 1; In this embodiment, if the requirement for charging speed is relatively high, the value of w1 is greater than w2; If the requirement is to minimize the total driving distance, the value of w1 is less than w2; The supervision terminal takes the position information of the charging location corresponding to the maximum priority as the preferred coordinates for mid - journey charging.

[0063] In order to realize the intelligent screening and path optimization of the end - charging node, in the second charging pile search mechanism of S420, this embodiment includes the following sub - steps: S421. Define the end - charging search range. Among them, the supervision terminal takes the destination of the initial path as the center of the circle, and the area within a radius of 500 meters is used as the end - charging search range.

[0064] S422. Within the end - charging search range, search the covered charging piles to obtain the second alternative charging location information. Among them, the second alternative charging location information in this embodiment includes the position coordinates of all charging locations that can be used to charge the vehicle within the end - charging search range, and the charging power of the charging piles in the charging location.

[0065] S423. Screen the second alternative charging location information to obtain the preferred coordinates for end - charging. Among them, the screening principle of this step is the same as that of S413 and will not be elaborated here.

[0066] In order to monitor the actual power consumption of the lithium battery during vehicle driving in real time, timely detect the deviation from the predicted value and respond to emergencies, and ensure the safety of the journey and the accuracy of the endurance prediction, during the user's driving of S500 - S700, the current position information of the user and the battery parameter change set are periodically obtained, and the travel deviation situation of the user is identified according to the current position information and the confirmed path information to obtain an identification result; in the case of obtaining different identification results, early warning is carried out on the power consumption situation of the lithium battery according to the battery parameter change set. This embodiment includes the following sub - steps: S610, obtain the battery parameter change set in real time, and obtain the actual power consumption value of the lithium battery according to the battery parameter change set; Among them, the battery parameter change set includes the real - time current I(t), real - time voltage V(t) obtained from the BMS, and the time stamp t corresponding to each group of real - time current and real - time voltage. The actual power consumption value E of the lithium battery is obtained according to the above battery parameter change set actual This is prior art. In this embodiment, the calculation formula of the actual power consumption value is as follows: t1 represents the start time stamp of sampling, and t2 represents the end time stamp of sampling.

[0067] S620, update the effective remaining stored power in real time according to the actual power consumption value; Among them, the updated effective remaining stored power = the effective remaining stored power in S100 - the actual power consumption value.

[0068] S630, periodically obtain the current position information of the user, and identify the travel deviation situation of the user according to the current position information and the confirmed path information to obtain an identification result; Among them, the identification result includes that the user deviates from the established route and the user does not deviate from the established route; the supervision terminal can judge the route deviation by calculating the vertical distance between the user's current position and the confirmed path. In this embodiment, when the vertical distance exceeds 500 meters and the duration exceeds 2 minutes, it is determined as a route deviation; at the same time, the supervision terminal can also identify the user's active lane change (such as continuing to go straight after the navigation prompts "please make a U - turn") or non - active deviation (such as detouring due to road closure), mark the deviation type, and after marking as a non - active deviation, the duration of the route deviation judgment can be appropriately extended.

[0069] S640, if the identification result is that the user deviates from the established route, repeat S200, and after obtaining the new initial path information, re - execute S300 - S700 according to the updated effective remaining stored power and the initial path information; Among them, when it is determined that the user has deviated from the route, the supervision terminal will trigger the operation of repeatedly executing S200, that is, prompting the user to re-confirm the initial path information; at this time, the user can input a new destination on the vehicle's central control screen or select a new path recommended by the system; after the user confirms the new initial path information, the supervision terminal will re-execute the battery life prediction operation of S300 in combination with the updated effective remaining battery power, predict the power consumption during driving according to the road segment feature set of the new path, and then execute the path dynamic management of S400 to dynamically manage the initial path information according to the new battery life prediction result to obtain a new confirmed path information; finally, enter the S500-S700 process again to continue to monitor the power consumption situation in real time and perform identification and warning.

[0070] S650, if no new initial path information is obtained in S640, it is determined in real time according to the effective remaining battery power whether to trigger an alarm; Among them, if in S640, the user fails to confirm the new initial path information within the specified time (such as 3 minutes), the supervision terminal will issue a hierarchical alarm according to the real-time updated effective remaining battery power: Level 1 alarm (5% < effective remaining battery power ≤ 20%): The central control screen displays a yellow warning icon, and the voice prompts "The current power consumption is slightly fast, and the remaining battery life is XX kilometers. Please pay attention to the power"; at the same time, the supervision terminal will mark the positions of available charging piles within 3 kilometers ahead on the navigation interface; Level 2 alarm (effective remaining battery power ≤ 5%): Trigger a red pop-up alarm, the buzzer beeps 3 times short, and display "Abnormal power consumption! Please go to the nearest available charging pile to charge as soon as possible"; at this time, the supervision terminal can automatically execute S400, plan the nearest available charging pile as the new destination, and display this path on the navigation interface.

[0071] Based on the above same inventive concept, the embodiment of the present application also discloses a supervision terminal, which includes a memory and a processor. At least one instruction, at least one program, a code set or an instruction set is stored in the memory, and the at least one instruction, at least one program, the code set or the instruction set is loaded and executed by the processor to implement a vehicle-mounted supervision method guided by lithium battery life prediction as provided in the above method embodiment.

[0072] Based on the above same inventive concept, the embodiment of the present application also discloses a computer-readable storage medium, in which at least one instruction, at least one program, a code set or an instruction set is stored, and the at least one instruction, at least one program, the code set or the instruction set can be loaded and executed by a processor to implement a vehicle-mounted supervision method guided by lithium battery life prediction as provided in the above method embodiment.

[0073] Refer to Figure 3, yet another embodiment of the present application also discloses an electronic device. The electronic device 500 may include: at least one processor 501, at least one network interface 504, a user interface 503, a memory 505, and at least one communication bus 502; Among them, the communication bus 502 is used to realize the connection and communication between these components.

[0074] Among them, the user interface 503 may include a display screen (Display) and a camera (Camera). Optionally, the user interface 503 may further include a standard wired interface and a wireless interface.

[0075] Among them, the network interface 504 may optionally include a standard wired interface and a wireless interface (such as a WI-FI interface).

[0076] Among them, the processor 501 may include one or more processing cores. The processor 501 connects various parts within the entire server through various interfaces and lines. By running or executing instructions, programs, code sets, or instruction sets stored in the memory 505, and by calling data stored in the memory 505, it executes various functions of the server and processes data. Optionally, the processor 501 may be implemented in at least one hardware form of digital signal processing (DSP), field-programmable gate array (FPGA), or programmable logic array (PLA). The processor 501 may integrate one or several combinations of a central processing unit (CPU), a graphics processing unit (GPU), and a modem, etc. Among them, the CPU mainly processes the operating system, user interface, and application programs, etc.; the GPU is responsible for the rendering and drawing of the content to be displayed on the display screen; the modem is used to process wireless communication. It can be understood that the above-mentioned modem may not be integrated into the processor 501 and may be implemented separately by a single chip.

[0077] Among them, the memory 505 may include a Random Access Memory (RAM), or may also include a Read-Only Memory. Optionally, the memory 505 includes a non-transitory computer-readable storage medium. The memory 505 can be used to store instructions, programs, codes, code sets or instruction sets. The memory 505 may include a program storage area and a data storage area. Among them, the program storage area may store instructions for implementing the operating system, instructions for at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area may store the data involved in the above-mentioned various method embodiments. Optionally, the memory 505 may also be at least one storage device located far from the aforementioned processor 501.

[0078] The memory 505 as a computer storage medium may include an operating system, a network communication module, a user interface module, and an application program of a vehicle supervision method guided by lithium battery endurance prediction.

[0079] In Figure 3 In the electronic device 500 shown, the user interface 503 is mainly used to provide an input interface for the user and obtain the data input by the user; and the processor 501 can be used to call the application program of a vehicle supervision method guided by lithium battery endurance prediction stored in the memory 505. When executed by one or more processors 501, the electronic device 500 executes the method as one or more of the above embodiments. It should be noted that for the foregoing method embodiments, for the sake of simple description, they are all expressed as a series of action combinations. However, those skilled in the art should know that the present application is not limited by the described action sequence, because according to the present application, certain steps can be performed in other sequences or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the present application.

[0080] In the above embodiments, the descriptions of the various embodiments have their own emphases. For the parts not detailed in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0081] In several implementation manners provided by this application, it should be understood that the disclosed device can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed coupling or direct coupling or communication connection to each other can be through some service interfaces. The indirect coupling or communication connection of the device or unit can be in an electrical or other form.

[0082] The units described as separate components may or may not be physically separated. The components displayed as units may or may not be physical units, that is, they can be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0083] In addition, each functional unit in various embodiments of this application can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit. The above-mentioned integrated units can be implemented in the form of hardware or in the form of software functional units.

[0084] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on such an understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes several instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods in various embodiments of this application. And the aforementioned memory includes: various media such as USB flash drives, mobile hard disks, magnetic disks, or optical discs that can store program codes.

[0085] The above are only exemplary embodiments of the present disclosure and cannot be used to limit the scope of the present disclosure. That is, all equivalent changes and modifications made according to the teachings of the present disclosure still fall within the scope covered by the present disclosure. Those skilled in the art will easily think of other implementation schemes of the present disclosure after considering the specification and the disclosed practice.

[0086] This application aims to cover any variations, uses, or adaptive changes of the present disclosure. These variations, uses, or adaptive changes follow the general principles of the present disclosure and include common general knowledge or conventional technical means in the technical field not recorded in the present disclosure. The specification and embodiments are only regarded as exemplary, and the scope and spirit of the present disclosure are defined by the claims.

Claims

1. A vehicle supervision method guided by lithium battery endurance prediction, characterized in that, Including: S100, after the target vehicle is powered on, obtain the battery parameter change set during the target vehicle's most recent charging process, and obtain the effective remaining power storage based on the battery parameter change set; S200, send a path confirmation application to the user, and receive the initial path information fed back by the user in response to the path confirmation application; S300, obtain a battery life prediction result based on the initial path information and the effective remaining power storage; S400, dynamically manage the initial path information according to the battery life prediction result to obtain the confirmed path information.

2. The vehicle-mounted supervision method guided by lithium battery endurance prediction according to claim 1, characterized in that The vehicle-mounted supervision method is based on a vehicle-mounted supervision system, and the vehicle-mounted supervision system includes data acquisition equipment; the obtaining of the effective remaining power storage based on the battery parameter change set includes: Calculate the initial remaining power storage according to the battery parameter change set; Obtain the correction data packet in the data acquisition equipment, and the correction data includes at least one parameter of real-time temperature, real-time air pressure, and real-time altitude; Judge whether to trigger the environment correction mechanism according to the correction data packet; When it is judged that the environment correction mechanism is triggered, correct the initial remaining power storage according to the correction data packet to obtain the effective remaining power storage.

3. The vehicle-mounted supervision method guided by lithium battery endurance prediction according to claim 1, characterized in that, Before the step of sending a path confirmation application to the user and receiving the initial path information fed back by the user in response to the path confirmation application, it further includes: Set multi-level warning thresholds based on the effective remaining power storage, and actively identify the battery life risk according to the warning thresholds.

4. The vehicle-mounted supervision method guided by lithium battery endurance prediction according to claim 3, characterized in that The initial path information includes the total driving section distance; after the step of setting multi-level warning thresholds based on the effective remaining power storage and actively identifying the battery life risk according to the warning thresholds, it further includes: In the case of triggering a first-level alarm, after the user confirms the initial path information to obtain the total driving section distance, automatically judge whether the total driving section distance conforms to the preset short-distance trip characteristics; If the total driving section distance conforms to the short-distance trip characteristics, cancel the first-level alarm and continue to execute S300 - S400; if the total driving section distance does not conform to the short-distance trip characteristics, preliminarily estimate whether the effective remaining power storage is sufficient to cover the total driving section distance; if the estimation result is yes, continue to execute S300 - S400; if the estimation result is no, cancel the execution of S300 and directly execute S400.

5. The vehicle-mounted supervision method guided by the prediction of the battery life of a lithium battery according to claim 4, characterized in that, The obtaining of the battery life prediction result based on the initial path information and the effective remaining power storage includes: Structurally analyze the initial path information to obtain a section feature set corresponding to each section one by one; Obtain the sum of the local predicted power consumption corresponding to each section according to the section feature set; Compare and analyze the sum of the local predicted power consumption with the effective remaining power storage to obtain the battery life prediction result.

6. The vehicle-mounted supervision method guided by lithium battery endurance prediction according to claim 5, characterized in that The section feature set includes basic features, terrain features, road surface types, and environmental features; the obtaining of the local predicted power consumption corresponding to each section according to the section feature set includes: Divide the path into uphill sections, flat sections, and downhill sections according to the terrain features; Calculate the sum of the locally predicted power consumptions corresponding to all the uphill road sections according to the following formula: Among them, E up is the sum of the locally predicted power consumptions corresponding to all the uphill sections; n up represents the total number of the uphill sections, obtained from the basic features; C D is the air resistance coefficient, obtained from the environmental features; A represents the preset frontal area of the target vehicle; ρ represents the air density, obtained from the environmental features; v avg_i represents the average driving speed of the target vehicle when driving on the i-th uphill section; m represents the preset mass of the target vehicle; f represents the preset rolling resistance coefficient; a i represents the average acceleration of the target vehicle when driving on the i-th uphill section; L i represents the length of the i-th uphill section, obtained from the basic features; α i represents the slope of the i-th uphill section, obtained from the basic features. Calculate the locally predicted power consumption corresponding to all the flat road sections according to the following formula: Among them, E flat is the sum of the locally predicted power consumptions corresponding to all the flat slope sections; n flat represents the total number of the flat slope sections; Calculate the locally predicted power consumption corresponding to all the downhill road sections according to the following formula: Among them, E down is the sum of the locally predicted power consumption corresponding to all the downhill sections; n down represents the total number of the downhill sections; η r represents a preset energy recovery efficiency, which is the efficiency of the target vehicle converting gravitational potential energy into electric energy on the downhill section.

7. The vehicle-mounted supervision method guided by lithium battery endurance prediction according to claim 1, characterized in that The vehicle-mounted supervision method further includes: During the user's driving, periodically obtain the user's current location information and the battery parameter change set; Identify the user's travel deviation situation according to the current location information and the confirmed path information to obtain an identification result; in the case of obtaining different identification results, give an early warning of the power consumption of the lithium battery according to the battery parameter change set.

8. A regulatory terminal, characterized in that, It includes a memory and a processor. At least one instruction, at least one program, a code set or an instruction set is stored in the memory. The at least one instruction, at least one program, the code set or the instruction set is loaded and executed by the processor to implement a vehicle-mounted supervision method oriented to lithium battery endurance prediction as described in any one of claims 1 to 7.

9. An in-vehicle supervision system guided by lithium battery endurance prediction, characterized in that, It includes: A data acquisition device, configured to obtain at least one parameter among real-time temperature, real-time air pressure, and real-time altitude to obtain a correction data packet; The supervision terminal as described in claim 8 is communicatively connected to the data acquisition device and the battery management system of the target vehicle, configured to obtain the battery parameter change set in the battery management system, and obtain the effective remaining storage capacity according to the battery parameter change set, and then complete the prediction and early warning of the lithium battery endurance according to the effectively updated remaining storage capacity.

10. A computer-readable storage medium, characterized in that, At least one instruction, at least one program, a code set or an instruction set is stored in the readable storage medium. The at least one instruction, at least one program, the code set or the instruction set is loaded and executed by the processor to implement a vehicle-mounted supervision method oriented to lithium battery endurance prediction as described in any one of claims 1 to 7.

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