Vehicle operating state adjustment method, device, apparatus and system

Abstract

The application relates to the technical field of vehicles and discloses a vehicle operation state adjustment method, device, equipment and system. The method comprises the following steps: acquiring vehicle battery state data, vehicle operation environment parameters and user usage habit information; determining a battery safety index based on the vehicle battery state data, the vehicle operation environment parameters and the user usage habit information; determining a battery risk development rate based on the battery safety index, and determining a user experience reservation degree based on the battery safety index and the battery risk development rate; determining a risk level based on the user experience reservation degree, and determining target vehicle operation parameters based on the risk level. By combining multi-dimensional information related to battery safety to determine the user experience reservation degree, the risk level is divided based on the user experience reservation degree to determine the target vehicle operation parameters, so that the traditional logic of prioritizing safety at the expense of experience is avoided, serious imbalance between battery safety and experience is avoided, and the driving and riding experience of vehicle drivers and passengers is improved.

Description

Technical Field

[0001] This application relates to the field of vehicle technology, and in particular to a method, apparatus, equipment and system for adjusting vehicle operating status. Background Technology

[0002] With the development of new energy vehicles, two independent technological systems have gradually emerged: battery safety management systems and intelligent cockpit experience systems. The core objective of the battery safety management system is to monitor battery status and protect against risks, while the core objective of the intelligent cockpit experience system is to provide a smart cockpit experience through multi-dimensional interaction.

[0003] However, current battery safety management systems and intelligent cockpit experience systems operate independently and without coordination, exhibiting three core contradictions: 1. Safety response disrupts the experience: the forced power-off and piercing alarms of the battery safety system conflict with the quietness and smoothness requirements of the intelligent cockpit; 2. Clunky safety information interaction: safety information is only conveyed through text and warning lights, failing to integrate into the cockpit's multimodal luxury interaction system, resulting in low information transmission efficiency and a disruptive experience; 3. Lack of cross-system collaboration: battery safety protection is not linked to cockpit systems (such as seats, HUD, and fragrance), leading to incomplete protection. Simultaneously, the cockpit experience does not consider battery safety status, failing to achieve a balance between safety and luxury. These core contradictions prevent the battery safety management system and intelligent cockpit experience system from working together, causing vehicles to prioritize safety at the expense of experience, resulting in a severe imbalance between battery safety and experience, significantly impacting the driving and riding experience of vehicle occupants. Summary of the Invention

[0004] In view of this, the purpose of this application is to overcome the shortcomings of the prior art and provide a method for adjusting the operating state of a vehicle, the method comprising: Acquire vehicle battery status data, vehicle operating environment parameters, and user usage habit information; Based on the vehicle battery status data, the vehicle operating environment parameters, and the user usage habit information, the battery safety index is determined. The battery risk development rate is determined based on the battery safety index, and the user experience retention rate is determined based on the battery safety index and the battery risk development rate. Based on the user experience retention rate, a risk level is determined, and based on the risk level, target vehicle operating parameters are determined so that the vehicle operates based on the target vehicle operating parameters.

[0005] In one embodiment, the step of determining the battery safety index based on the vehicle battery status data, the vehicle operating environment parameters, and the user usage habit information includes: Based on the vehicle battery status data, the health of the vehicle battery is determined; Based on the vehicle operating environment parameters, environmental risk factors are determined; Based on the user usage habit information, usage habit risk factors are determined; The battery safety index is determined based on the health of the vehicle battery, the environmental risk factors, and the usage habit risk factors.

[0006] In one embodiment, the vehicle operating environment parameters include ambient temperature, ambient humidity, and power grid voltage fluctuation amplitude. The step of determining environmental risk factors based on the vehicle operating environment parameters includes: Based on the ambient temperature and a preset temperature effect simulation algorithm, the temperature factor is determined. Based on the ambient humidity and the preset humidity effect simulation algorithm, the humidity factor is determined; Based on the voltage fluctuation amplitude of the power grid and the preset voltage fluctuation hazard simulation algorithm, the power grid factor is determined; Environmental risk factors are determined based on the temperature factor, the humidity factor, and the power grid factor.

[0007] In one embodiment, the user usage habit information includes fast charging habit information, full charging habit information, and discharge habit information. The step of determining usage habit risk factors based on the user usage habit information includes: Based on the fast charging habit information and the preset fast charging damage simulation algorithm, the fast charging frequency factor is determined; Based on the full-charge habit information and the preset full-charge damage simulation algorithm, the full-charge ratio factor is determined; Based on the discharge habit information and the preset discharge damage simulation algorithm, the discharge depth factor is determined; Based on the fast charging frequency factor, the full charge ratio factor, and the discharge depth factor, a usage habit risk factor is determined.

[0008] In one embodiment, the step of determining the battery risk development rate based on the battery safety index includes: The process involves repeatedly acquiring vehicle battery status data, vehicle operating environment parameters, and user usage habit information, and determining the battery safety index based on the vehicle battery status data, vehicle operating environment parameters, and user usage habit information, thereby obtaining a preset number of battery safety indices. The rate of battery risk development is determined by fitting a preset number of battery safety indices using the least squares method.

[0009] In one embodiment, the step of determining user experience retention based on the battery safety index and the battery risk development rate includes: Based on a preset weighting rule, the first weight of the battery safety index and the second weight of the battery risk development rate are determined. User experience retention is determined based on the battery safety index, the battery risk development rate, the first weight, and the second weight.

[0010] In one embodiment, the step of determining a risk level based on the user experience retention rate and determining target vehicle operating parameters based on the risk level includes: The battery risk development rate is compared with a preset battery risk development rate threshold. If the battery risk development rate is greater than the preset battery risk development rate threshold, the risk level is determined to be an emergency level, and based on the mapping table between the risk level and vehicle operating parameters, the vehicle operating parameters corresponding to the emergency level are determined as the target vehicle operating parameters. If the battery risk development rate is not greater than the preset battery risk development rate threshold, then the risk level is determined based on the user experience retention rate and the preset user experience retention rate threshold, and the target vehicle operating parameters are determined based on the risk level and the mapping table between the risk level and vehicle operating parameters.

[0011] This application also provides a vehicle operating state adjustment device, the vehicle operating state adjustment device comprising: The acquisition module is used to acquire vehicle battery status data, vehicle operating environment parameters, and user usage habit information. The first determining module is used to determine the battery safety index based on the vehicle battery status data, the vehicle operating environment parameters, and the user usage habit information; The second determining module is used to determine the battery risk development rate based on the battery safety index, and to determine the user experience retention rate based on the battery safety index and the battery risk development rate. The third determining module is used to determine the risk level based on the user experience retention rate, and to determine the target vehicle operating parameters based on the risk level, so that the vehicle operates based on the target vehicle operating parameters.

[0012] This application also provides a computer device, which includes a processor and a memory, the memory storing a computer program, and the processor executing the computer program to implement the above-described vehicle operating state adjustment method.

[0013] This application also provides a vehicle operating status adjustment system, including a vehicle and a computer device, wherein the computer device implements the above-described vehicle operating status adjustment method to adjust the vehicle's operating status.

[0014] The embodiments of this application have the following beneficial effects: This application's embodiments acquire vehicle battery status data, vehicle operating environment parameters, and user usage habit information; based on these data, a battery safety index is determined; the battery risk development rate is determined based on the battery safety index, and the user experience retention rate is determined based on both the battery safety index and the battery risk development rate; based on the user experience retention rate, a risk level is determined, and the target vehicle operating parameters are determined based on the risk level. By combining multi-dimensional information related to battery safety to determine user experience retention, and classifying risk levels based on user experience retention to determine target vehicle operating parameters, this approach avoids the traditional logic of prioritizing safety at the expense of user experience, which leads to a severe imbalance between battery safety and user experience, thereby improving the driving and riding experience for vehicle drivers and passengers. Attached Figure Description

[0015] To more clearly illustrate the technical solutions of this application, the accompanying drawings used in the embodiments will be briefly described below. It should be understood that the following drawings only show some embodiments of this application and therefore should not be considered as a limitation on the scope of protection of this application. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 A flowchart illustrating the first embodiment of the vehicle operating status adjustment method provided in this application; Figure 2 A flowchart illustrating the second embodiment of the vehicle operating status adjustment method provided in this application; Figure 3 A flowchart illustrating the third embodiment of the vehicle operating status adjustment method provided in this application; Figure 4 A flowchart illustrating the fourth embodiment of the vehicle operating status adjustment method provided in this application; Figure 5 A flowchart illustrating the fifth embodiment of the vehicle operating status adjustment method provided in this application; Figure 6 This is a schematic diagram of the vehicle operating status adjustment device provided in this application. Detailed Implementation

[0017] The technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments.

[0018] The components of the embodiments of this application described and illustrated in the accompanying drawings can be arranged and designed in a variety of different configurations. Therefore, the following detailed description of the embodiments of this application provided in the drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of the application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0019] In the following, the terms “comprising,” “having,” and their cognates, which may be used in various embodiments of this application, are intended only to indicate a particular feature, number, step, operation, element, component, or combination thereof, and should not be construed as excluding, firstly, the presence of one or more other features, numbers, steps, operations, elements, components, or combinations thereof, or adding the possibility of one or more features, numbers, steps, operations, elements, components, or combinations thereof.

[0020] Furthermore, the terms "first," "second," and "third" are used only to distinguish descriptions and should not be interpreted as indicating or implying relative importance.

[0021] Unless otherwise specified, all terms used herein (including technical and scientific terms) shall have the same meaning as commonly understood by one of ordinary skill in the art to which the various embodiments of this application pertain. Terms (such as those defined in commonly used dictionaries) shall be interpreted as having the same meaning as in their contextual meaning in the relevant technical field and shall not be construed as having an idealized or overly formal meaning, unless clearly defined in the various embodiments of this application.

[0022] It is understood that the method of this application is applied to a vehicle operating status adjustment system, and the computer equipment in the vehicle operating status adjustment system can be installed in the vehicle or in a remote server.

[0023] The following detailed description of some embodiments of this application is provided in conjunction with the accompanying drawings. Unless otherwise specified, the following embodiments and features can be combined with each other.

[0024] Please refer to Figure 1 , Figure 1 This is a flowchart illustrating a first embodiment of the vehicle operating status adjustment method provided in this application. The method includes: Step S101: Obtain vehicle battery status data, vehicle operating environment parameters, and user usage habit information.

[0025] In this embodiment, the vehicle operation status adjustment system acquires vehicle battery status data, vehicle operating environment parameters, and user usage habit information. It should be noted that the vehicle operation status adjustment system adopts a three-tier architecture of "perception layer - decision layer - execution layer." Each layer interacts with the CANFD bus (Controller Area Network with Flexible Data-Rate, an upgraded version of the traditional CAN 2.0 bus protocol, which addresses the pain points of "low speed and short frame length" while maintaining compatibility with CAN 2.0 hardware topology, achieving a maximum of 64 bytes of data per frame and a maximum data segment rate of 8Mbps. It has been incorporated into the ISO 11898-1:2015 standard and is the mainstream choice for high-speed communication in vehicles and industry) via in-vehicle Ethernet. Vehicle battery status data includes data such as vehicle battery voltage, capacity, and current; vehicle operating environment parameters include the temperature, humidity, and power grid conditions of the vehicle's environment; and user usage habit information includes the user's charging and discharging habits when using the vehicle.

[0026] Step S102: Determine the battery safety index based on the vehicle battery status data, the vehicle operating environment parameters, and the user usage habit information.

[0027] In this embodiment, the vehicle operating status adjustment system determines the battery safety index based on vehicle battery status data, vehicle operating environment parameters, and user usage habit information. It should be noted that the vehicle operating status adjustment system has pre-set rules for calculating the battery safety index. After obtaining vehicle battery status data, vehicle operating environment parameters, and user usage habit information, the vehicle can calculate the battery safety index based on these rules. The battery safety index indicates the safety level of the vehicle's battery at the current moment; the lower the battery safety index, the higher the safety level of the battery at the current moment.

[0028] Step S103: Determine the battery risk development rate based on the battery safety index, and determine the user experience retention rate based on the battery safety index and the battery risk development rate.

[0029] In this embodiment, the vehicle operating status adjustment system determines the battery risk development rate based on the battery safety index, and determines the user experience retention rate based on both the battery safety index and the battery risk development rate. It should be noted that the battery risk development rate describes the probability of a sudden risk occurring to the vehicle battery; a higher battery risk development rate indicates a greater probability of such a risk. The user experience retention rate describes how much user experience needs to be retained; a higher user experience retention rate indicates a greater amount of user experience needs to be retained.

[0030] Understandably, user experience is determined by specific vehicle operating parameters related to various decision-making aspects, such as seat posture and seatbelt pretensioning adjustment, cabin function access control, privacy and lighting system control, child lock and seat heating / ventilation control, charging power limitation and cooling control, battery emergency protection actions, battery pack temperature equalization adjustment, low-voltage power supply priority control, suspension stiffness and height adjustment, braking system pre-preparation, drive power limitation, steering system assist adjustment, vehicle lighting and warning system control, V2X surrounding warning information transmission, door unlocking and emergency access control, vehicle posture stability control, steering wheel vibration frequency and intensity adjustment, seat surface temperature fine adjustment, carbon fiber and bamboo fiber composite center console trim feedback force control, door interior panel tactile cues, electrochromic crystal trim color and transparency adjustment, AR-HUD display content and field of view control, cabin ambient lighting mode switching, center console screen safety information presentation, fragrance type and release amount adjustment, customized sound effects and volume control, active noise cancellation intensity adjustment, and external sound and light cues.

[0031] Step S104: Based on the user experience retention rate, determine the risk level, and based on the risk level, determine the target vehicle operating parameters so that the vehicle operates based on the target vehicle operating parameters.

[0032] In this embodiment, the vehicle operation status adjustment system determines the risk level based on user experience retention and then determines the target vehicle operation parameters based on the risk level, so that the vehicle operates according to the target vehicle operation parameters. It should be noted that the vehicle operation status adjustment system pre-stores a mapping table between risk levels and vehicle operation parameters. Different user experience retention rates represent different risk levels, different risk levels correspond to different core response principles, and different core response principles correspond to different vehicle operation parameters.

[0033] For example, the mapping table between risk level and vehicle operating parameters is shown in the table below:

[0034] The vehicle operation status adjustment system in this embodiment acquires vehicle battery status data, vehicle operating environment parameters, and user usage habit information; based on the vehicle battery status data, vehicle operating environment parameters, and user usage habit information, it determines a battery safety index; based on the battery safety index, it determines the battery risk development rate, and based on the battery safety index and battery risk development rate, it determines the user experience retention rate; based on the user experience retention rate, it determines the risk level, and based on the risk level, it determines the target vehicle operating parameters. By combining multi-dimensional information related to battery safety to determine the user experience retention rate, and classifying the risk level based on the user experience retention rate to determine the target vehicle operating parameters, it avoids the serious imbalance between battery safety and experience caused by the traditional logic of prioritizing safety at the expense of experience, thereby improving the driving and riding experience of vehicle drivers and passengers.

[0035] Please refer to Figure 2 , Figure 2 This is a flowchart illustrating a second embodiment of the vehicle operating status adjustment method provided in this application. The difference between the second embodiment and the first embodiment is that the step of determining the battery safety index based on the vehicle battery status data, the vehicle operating environment parameters, and the user usage habit information includes: Step S201: Determine the health status of the vehicle battery based on the vehicle battery status data.

[0036] In this embodiment, the vehicle operating status adjustment system determines the health of the vehicle battery based on the vehicle battery status data. It should be noted that traditional methods for calculating vehicle battery health include capacity decay methods, internal resistance measurement methods, capacity definition methods, and cycle count methods. Current battery health estimation technology is developing towards higher accuracy, real-time performance, and intelligence, with hybrid methods combining physical models and data-driven approaches becoming mainstream, such as pulse analysis, digital twin methods, hybrid model methods, and AI-driven methods. This embodiment will not focus on the calculation of battery health; it will simply estimate the battery health based on the specific scenario.

[0037] Step S202: Determine environmental risk factors based on the vehicle operating environment parameters.

[0038] In this embodiment, the vehicle operating status adjustment system determines environmental risk factors based on vehicle operating environment parameters. It should be noted that the impact of environmental parameters on the battery has a threshold effect (such as a sharp increase in the risk of thermal runaway after the ambient temperature exceeds a certain threshold) and a coupling effect (the synergistic harm of high temperature and high humidity is greater than the sum of a single factor). The vehicle operating status adjustment system adopts a two-level computing architecture of "single-factor nonlinear mapping + multi-factor coupling correction" to determine environmental risk factors by combining multiple environmental parameters.

[0039] Step S203: Based on the user usage habit information, determine the usage habit risk factor.

[0040] In this embodiment, the vehicle operating status adjustment system determines usage habit risk factors based on user usage habit information. It should be noted that the impact of user usage habits on the battery has a cumulative effect (such as the damage from frequent fast charging increasing exponentially) and a memory effect (long-term full charging leads to irreversible capacity decay). Therefore, the vehicle operating status adjustment system adopts a calculation architecture of "nonlinear mapping of behavior frequency + long-term trend correction", introducing time decay to correct short-term behavior fluctuations, highlighting the cumulative effect of long-term habits, and determining usage habit risk factors.

[0041] Step S204: Determine the battery safety index based on the health of the vehicle battery, the environmental risk factor, and the usage habit risk factor.

[0042] In this embodiment, the vehicle operating status adjustment system determines the battery safety index based on the vehicle battery's health, environmental risk factors, and usage habit risk factors. The formula for calculating the battery safety index is as follows:

[0043] in, For battery safety index, For the health of the vehicle battery. As an environmental risk factor, To use the habitual risk factor.

[0044] In one embodiment, the vehicle operating environment parameters include ambient temperature, ambient humidity, and... The step of determining environmental risk factors based on the vehicle operating environment parameters includes: Step S2021: Determine the temperature factor based on the ambient temperature and the preset temperature effect simulation algorithm.

[0045] In this embodiment, the vehicle operating status adjustment system determines the temperature factor based on the ambient temperature and a preset temperature effect simulation algorithm. It should be noted that the preset temperature effect simulation algorithm uses a sigmoid curve to simulate the nonlinear effect of temperature on the battery (rapid increase in risk at low or high temperatures). The specific formula for calculating the temperature factor is as follows:

[0046] in, Temperature factor ; Ambient temperature; Inflection point temperature (default is) ),yes ; Temperature sensitivity coefficient ( ).

[0047] Step S2022: Determine the humidity factor based on the ambient humidity and the preset humidity effect simulation algorithm.

[0048] In this embodiment, the vehicle operating status adjustment system determines the temperature factor based on ambient humidity and a preset humidity effect simulation algorithm. It should be noted that the preset humidity effect simulation algorithm uses a quadratic curve to simulate the accelerated effects of high humidity (the baseline humidity is 40%-60%RH; the greater the humidity deviation, the greater the risk of the electrolyte absorbing moisture or drying out). The specific formula for calculating the humidity factor is as follows:

[0049] in, Humidity factor, ; Humidity coefficient (default is 5); For ambient humidity; for .

[0050] Step S2023, based on the above And a preset voltage fluctuation hazard simulation algorithm is used to determine the power grid factor.

[0051] In this embodiment, the vehicle operating status adjustment system is based on A preset voltage fluctuation hazard simulation algorithm is used to determine the grid factor. It should be noted that the preset voltage fluctuation hazard simulation algorithm uses an exponential function to simulate the cumulative hazard of voltage fluctuations (after fluctuations exceed 5%, battery charge / discharge protection is frequently triggered, accelerating aging). The specific formula for calculating the grid factor is as follows:

[0052] in, For grid factors, ; The base fluctuation is 3% (default). for ; This is the volatility coefficient (default is 0.8).

[0053] Step S2024: Determine the environmental risk factor based on the temperature factor, the humidity factor, and the power grid factor.

[0054] In this embodiment, the vehicle operating status adjustment system determines the environmental risk factor based on temperature, humidity, and power grid factors. The formula for calculating the environmental risk factor is as follows:

[0055] in, As an environmental risk factor, ; Temperature factor; Humidity factor; For grid factors; , , For the weighting coefficients, under the temperature-dominated scenario, the weighting coefficients are set to w_T=0.4, w_H=0.2, and w_E=0.4; C is the coupling coefficient. k_c represents the amplification synergistic effect; the larger the value, the more obvious the amplification effect (the basic value is k_c=0.3).

[0056] In one embodiment, the user usage habit information includes fast charging habit information, full charging habit information, and discharge habit information. The step of determining usage habit risk factors based on the user usage habit information includes: Step S1031: Determine the fast charging frequency factor based on the fast charging habit information and the preset fast charging damage simulation algorithm.

[0057] In this embodiment, the vehicle operation status adjustment system determines the fast charging frequency factor based on fast charging habit information and a preset fast charging damage simulation algorithm. It should be noted that the fast charging habit information refers to the number of times the user fast charges the vehicle per week, and the preset fast charging damage simulation algorithm uses a power function to simulate the cumulative damage of fast charging (after more than 3 fast charges per week, the aging rate increases quadratically). The specific formula for calculating the fast charging frequency factor is as follows:

[0058] in, For fast charging frequency factor, ; The number of times a user fast charges their vehicle per week; This is the baseline frequency (default 3 times per week, the battery tolerance threshold). It is the power exponent (default is 2, reflecting increasing marginal damage).

[0059] Step S1032: Based on the full-charge habit information and the preset full-charge damage simulation algorithm, determine the full-charge ratio factor.

[0060] In this embodiment, the vehicle operation status adjustment system determines the full-charge ratio factor based on full-charge habit information and a preset full-charge damage simulation algorithm. It should be noted that the full-charge habit information refers to the percentage of times a user charges the vehicle to 90% or higher capacity each month. The preset full-charge damage simulation algorithm uses a logarithmic function to simulate the long-term damage of full charging (the higher the frequency of charging to 90% or higher, the faster the capacity decays, and this decays logarithmically). The specific formula for calculating the full-charge ratio factor is as follows:

[0061] in, For full filling ratio factor, ; This is the scaling factor (default is 0.8). The percentage of times each user charges their vehicle to 90% or higher per month; The base percentage is 20% (default).

[0062] Step S1033: Determine the discharge depth factor based on the discharge habit information and the preset discharge damage simulation algorithm.

[0063] In this embodiment, the vehicle operating status adjustment system determines the depth of discharge factor based on discharge habit information and a preset discharge damage simulation algorithm. It should be noted that the discharge habit information refers to the vehicle's average depth of discharge, where average depth of discharge = used battery capacity / total battery capacity; the preset discharge damage simulation algorithm uses a quadratic curve to simulate the impact of deep discharge (the risk of electrode structure damage increases sharply after the depth of discharge > 80%). The specific formula for calculating the depth of discharge factor is as follows:

[0064] in, For the depth of discharge factor, ; This is the coefficient (default is 10); The average depth of discharge for the vehicle; The reference depth of discharge (default is 60%). This is a constant term (default value is 0.01).

[0065] Step S1034: Determine the usage habit risk factor based on the fast charging frequency factor, the full charge ratio factor, and the discharge depth factor.

[0066] In this embodiment, the vehicle operating status adjustment system determines the usage habit risk factor based on the fast charging frequency factor, the full charge ratio factor, and the depth of discharge factor. The specific formula for calculating the usage habit risk factor is as follows:

[0067] in, To use habitual risk factors, ; For fast charging frequency factor; This is the full-fill ratio factor; This is the depth of discharge factor; , , The weighting coefficients are w_F=0.4, w_C=0.3, and w_D=0.3. The basic weights are w_F=0.4, w_C=0.3, and w_D=0.3. t is the number of days the habit is maintained (the default is 90 days as the stable period, and the smaller t is, the smaller the impact).

[0068] The vehicle operation status adjustment system in this embodiment introduces a high-order calculation model for calculating environmental risk factors and usage habit risk factors, which significantly reduces the error of environmental risk assessment, improves the accuracy of usage habit risk assessment, and the risk quantification accuracy is significantly better than that of the traditional linear model. This helps to improve the accuracy of determining user experience retention, classifies risk levels based on user experience retention to determine target vehicle operation parameters, and improves the driving and riding experience of vehicle drivers and passengers.

[0069] Please refer to Figure 3 , Figure 3 This is a flowchart illustrating a third embodiment of the vehicle operating state adjustment method provided in this application. The difference between the third embodiment and the first to second embodiments is that the step of determining the battery risk development rate based on the battery safety index includes: Step S301 involves repeatedly executing the steps of acquiring vehicle battery status data, vehicle operating environment parameters, and user usage habit information, and determining the battery safety index based on the vehicle battery status data, vehicle operating environment parameters, and user usage habit information, thereby obtaining a preset number of battery safety indices.

[0070] Step S302: The battery risk development rate is determined by fitting a preset number of battery safety indices using the least squares method.

[0071] In this embodiment, the vehicle operating status adjustment system cyclically executes steps to acquire vehicle battery status data, vehicle operating environment parameters, and user usage habit information. Based on these data, it determines a battery safety index, resulting in a preset number of battery safety indices. The preset number of battery safety indices are then fitted using the least squares method to determine the battery risk development rate. It should be noted that the formula for calculating the battery risk development rate is as follows:

[0072] in, To the rate of development of battery risks, n is the number of battery safety indices acquired by the vehicle operating status adjustment system, and t is the time point at which the battery safety indices are acquired. This refers to the battery safety index.

[0073] The vehicle operating status adjustment system in this embodiment determines the battery risk development rate by fitting a preset number of battery safety indices using the least squares method. This improves the accuracy of the battery risk development rate calculation, laying the foundation for subsequent calculations of user experience retention and contributing to improving the accuracy of user experience retention.

[0074] Please refer to Figure 4 , Figure 4 This is a flowchart illustrating the fourth embodiment of the vehicle operating state adjustment method provided in this application. The difference between the fourth embodiment and the first to third embodiments is that the step of determining the user experience retention rate based on the battery safety index and the battery risk development rate includes: Step S401: Based on the preset weight allocation rules, determine the first weight of the battery safety index and the second weight of the battery risk development rate.

[0075] Step S402: Determine the user experience retention rate based on the battery safety index, the battery risk development rate, the first weight, and the second weight.

[0076] In this embodiment, the vehicle operating status adjustment system determines a first weight for the battery safety index and a second weight for the battery risk development rate based on a preset weight allocation rule. Based on the battery safety index, battery risk development rate, first weight, and second weight, the user experience retention rate is determined. Specifically, the formula for calculating the user experience retention rate is as follows:

[0077] in, To improve user experience retention, As the first weighting factor in the battery safety index, For battery safety index, As the second weighting factor for the rate of development of battery risks, The rate of development of battery risks.

[0078] The vehicle operation status adjustment system in this embodiment determines the user experience retention rate based on the battery safety index, battery risk development rate, first weight, and second weight. By calculating the user experience retention rate using two dimensions of parameters—battery safety index and battery risk development rate—the accuracy of the user experience retention rate is improved. This helps to improve the accuracy of classifying risk levels based on user experience retention rate to determine the target vehicle's operating parameters, thereby improving the driving and riding experience for vehicle drivers and passengers.

[0079] Please refer to Figure 5 , Figure 5This is a flowchart illustrating the fifth embodiment of the vehicle operating status adjustment method provided in this application. The difference between the fifth embodiment and the first to fourth embodiments is that the step of determining the risk level based on the user experience retention rate and determining the target vehicle operating parameters based on the risk level includes: Step S501: Compare the battery risk development rate with a preset battery risk development rate threshold.

[0080] Step S502: If the battery risk development rate is greater than the preset battery risk development rate threshold, the risk level is determined to be an emergency level, and based on the mapping table between the risk level and vehicle operating parameters, the vehicle operating parameters corresponding to the emergency level are determined as the target vehicle operating parameters.

[0081] In this embodiment, the vehicle operation status adjustment system first compares the battery risk development rate with a preset battery risk development rate threshold. If the battery risk development rate is greater than the preset battery risk development rate threshold, the risk level is determined to be an emergency level. Based on the mapping table between risk level and vehicle operation parameters, the vehicle operation parameters corresponding to the emergency level are determined as target vehicle operation parameters so that the vehicle operates based on the target vehicle operation parameters.

[0082] It should be noted that the battery risk development rate is used as a cutoff indicator. When the battery risk development rate exceeds the preset battery risk development rate threshold (default RDR>0.5 levels / minute), regardless of the current user experience retention level, the emergency level is forcibly upgraded. The vehicle operating parameters corresponding to the emergency level are then determined as the target vehicle operating parameters to avoid missing sudden risks and help ensure the safety of drivers and passengers.

[0083] Step S503: If the battery risk development rate is not greater than the preset battery risk development rate threshold, then the risk level is determined based on the user experience retention rate and the preset user experience retention rate threshold, and the target vehicle operating parameters are determined based on the risk level and the mapping table between the risk level and vehicle operating parameters.

[0084] In this embodiment, if the vehicle operation status adjustment system determines that the battery risk development rate is not greater than the preset battery risk development rate threshold, it determines the risk level based on the user experience retention rate and the preset user experience retention rate threshold, and determines the target vehicle operation parameters based on the risk level and the mapping table between the risk level and the vehicle operation parameters.

[0085] For example, the target vehicle operating parameters corresponding to different user experience retention rates are shown in the table below:

[0086] The vehicle operation status adjustment system in this embodiment uses the battery risk development rate as a cutoff indicator. When the battery risk development rate exceeds a preset threshold, regardless of the current user experience retention level, the emergency level is forcibly upgraded. The vehicle operation parameters corresponding to this emergency level are then determined as the target vehicle operation parameters. This avoids missing detection of sudden risks and helps balance battery safety and user experience to improve the driving and riding experience for vehicle occupants while ensuring their safety. Conversely, when the battery risk development rate does not exceed the preset threshold, the target vehicle operation parameters are determined based on the risk level corresponding to the current user experience retention level to balance battery safety and user experience and improve the driving and riding experience for vehicle occupants.

[0087] refer to Figure 6 , Figure 6 This is a schematic diagram of the vehicle operating state adjustment device provided in this application. The vehicle operating state adjustment device includes: The acquisition module 10 is used to acquire vehicle battery status data, vehicle operating environment parameters, and user usage habit information.

[0088] The first determining module 20 is used to determine the battery safety index based on the vehicle battery status data, the vehicle operating environment parameters, and the user usage habit information.

[0089] The second determining module 30 is used to determine the battery risk development rate based on the battery safety index, and to determine the user experience retention rate based on the battery safety index and the battery risk development rate.

[0090] The third determining module 40 is used to determine the risk level based on the user experience retention rate, and to determine the target vehicle operating parameters based on the risk level, so that the vehicle operates based on the target vehicle operating parameters.

[0091] It is understood that the vehicle operating state adjustment device in this embodiment corresponds to the vehicle operating state adjustment method in the above embodiment. The options in the above embodiment are also applicable to this embodiment, so they will not be described again here.

[0092] This application also provides a computer device, exemplary of which includes a processor and a memory, wherein the memory stores a computer program, and the processor executes the computer program to enable the computer device to perform the functions of the various modules in the above-described vehicle operating state adjustment method or the above-described vehicle operating state adjustment device.

[0093] The processor can be an integrated circuit chip with signal processing capabilities. The processor can be a general-purpose processor, including at least one of a Central Processing Unit (CPU), Graphics Processing Unit (GPU), Network Processor (NP), Digital Signal Processor (DSP), Application-Specific Integrated Circuit (ASIC), Field-Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. The general-purpose processor can be a microprocessor or any conventional processor, capable of implementing or executing the methods, steps, and logic block diagrams disclosed in the embodiments of this application.

[0094] The memory can be, but is not limited to, Random Access Memory (RAM), Read Only Memory (ROM), Programmable Read-Only Memory (PROM), Erasable Programmable Read-Only Memory (EPROM), Electrically Erasable Programmable Read-Only Memory (EEPROM), etc. The memory is used to store computer programs, and the processor can execute the computer programs accordingly after receiving execution instructions.

[0095] This application also provides a computer device including a processor and a memory, wherein the memory stores a computer program, and the processor is used to execute the computer program to implement the above-described vehicle operating state adjustment method.

[0096] This application also provides a vehicle operating status adjustment system, including a vehicle and a computer device, wherein the computer device implements the above-described vehicle operating status adjustment method to adjust the vehicle's operating status.

[0097] This application also provides a computer storage medium for storing the computer program used in the aforementioned computer device. The computer storage medium can be a readable storage medium, a non-volatile storage medium, or a volatile storage medium. For example, the computer storage medium may include, but is not limited to, various media capable of storing program code, such as a USB flash drive, a portable hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0098] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can also be implemented in other ways. The apparatus embodiments described above are merely illustrative. For example, the flowcharts and block diagrams in the accompanying drawings show the architecture, functionality, and operation of possible implementations of apparatus, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that, in alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and / or flowchart, and combinations of blocks in the block diagram and / or flowchart, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0099] In addition, the functional modules or units in the various embodiments of this application can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.

[0100] If the aforementioned functions are implemented as software functional modules and sold or used as independent products, they can be stored in a readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a smartphone, personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application.

[0101] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application.

Claims

1. A method of adjusting a running state of a vehicle, characterized by, The method includes: Acquire vehicle battery status data, vehicle operating environment parameters, and user usage habit information; Based on the vehicle battery status data, the vehicle operating environment parameters, and the user usage habit information, the battery safety index is determined. The battery risk development rate is determined based on the battery safety index, and the user experience retention rate is determined based on the battery safety index and the battery risk development rate. Based on the user experience retention rate, a risk level is determined, and based on the risk level, target vehicle operating parameters are determined so that the vehicle operates based on the target vehicle operating parameters.

2. The vehicle running state adjustment method according to claim 1, characterized by, The step of determining the battery safety index based on the vehicle battery status data, the vehicle operating environment parameters, and the user usage habit information includes: Based on the vehicle battery status data, the health of the vehicle battery is determined; Based on the vehicle operating environment parameters, environmental risk factors are determined; Based on the user usage habit information, usage habit risk factors are determined; The battery safety index is determined based on the health of the vehicle battery, the environmental risk factors, and the usage habit risk factors.

3. The vehicle running state adjustment method according to claim 2, characterized by, The vehicle operating environment parameters include ambient temperature, ambient humidity, and power grid voltage fluctuation amplitude. The step of determining environmental risk factors based on the vehicle operating environment parameters includes: Based on the ambient temperature and a preset temperature effect simulation algorithm, the temperature factor is determined. Based on the ambient humidity and the preset humidity effect simulation algorithm, the humidity factor is determined; Based on the voltage fluctuation amplitude of the power grid and the preset voltage fluctuation hazard simulation algorithm, the power grid factor is determined; Environmental risk factors are determined based on the temperature factor, the humidity factor, and the power grid factor.

4. The vehicle running state adjustment method according to claim 2, characterized by, The user usage habit information includes fast charging habit information, full charging habit information, and discharge habit information. The step of determining usage habit risk factors based on the user usage habit information includes: Based on the fast charging habit information and the preset fast charging damage simulation algorithm, the fast charging frequency factor is determined; Based on the full-charge habit information and the preset full-charge damage simulation algorithm, the full-charge ratio factor is determined; Based on the discharge habit information and the preset discharge damage simulation algorithm, the discharge depth factor is determined; Based on the fast charging frequency factor, the full charge ratio factor, and the discharge depth factor, a usage habit risk factor is determined.

5. The vehicle running state adjustment method according to claim 1, characterized by, The step of determining the battery risk development rate based on the battery safety index includes: The process involves repeatedly acquiring vehicle battery status data, vehicle operating environment parameters, and user usage habit information, and determining the battery safety index based on the vehicle battery status data, vehicle operating environment parameters, and user usage habit information, thereby obtaining a preset number of battery safety indices. The rate of battery risk development is determined by fitting a preset number of battery safety indices using the least squares method.

6. The vehicle running state adjustment method according to claim 1, characterized by, The step of determining user experience retention based on the battery safety index and the battery risk development rate includes: Based on a preset weighting rule, the first weight of the battery safety index and the second weight of the battery risk development rate are determined. User experience retention is determined based on the battery safety index, the battery risk development rate, the first weight, and the second weight.

7. The vehicle running state adjustment method according to claim 1, characterized by, The steps of determining the risk level based on the user experience retention rate and determining the target vehicle operating parameters based on the risk level include: The battery risk development rate is compared with a preset battery risk development rate threshold. If the battery risk development rate is greater than the preset battery risk development rate threshold, the risk level is determined to be an emergency level, and based on the mapping table between the risk level and vehicle operating parameters, the vehicle operating parameters corresponding to the emergency level are determined as the target vehicle operating parameters. If the battery risk development rate is not greater than the preset battery risk development rate threshold, then the risk level is determined based on the user experience retention rate and the preset user experience retention rate threshold, and the target vehicle operating parameters are determined based on the risk level and the mapping table between the risk level and vehicle operating parameters.

8. A vehicle running state adjusting apparatus characterized by comprising: The vehicle operating status adjustment device includes: The acquisition module is used to acquire vehicle battery status data, vehicle operating environment parameters, and user usage habit information. The first determining module is used to determine the battery safety index based on the vehicle battery status data, the vehicle operating environment parameters, and the user usage habit information; The second determining module is used to determine the battery risk development rate based on the battery safety index, and to determine the user experience retention rate based on the battery safety index and the battery risk development rate. The third determining module is used to determine the risk level based on the user experience retention rate, and to determine the target vehicle operating parameters based on the risk level, so that the vehicle operates based on the target vehicle operating parameters.

9. A computer device, characterized in that, The computer device includes a processor and a memory, the memory storing a computer program, and the processor executing the computer program to implement the vehicle operating state adjustment method according to any one of claims 1-7.

10. A vehicle operating status adjustment system, characterized in that, The system includes a vehicle and a computer device, wherein the computer device implements the vehicle operating state adjustment method according to any one of claims 1-7 to adjust the vehicle operating state.

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