Battery life detection under dynamic wireless charging and related equipment

Abstract

The invention provides battery life detection under dynamic wireless charging and related equipment, and relates to the field of electric digital data processing. According to the method, characteristic curves of similar batteries of a target battery in different stages are obtained, the operation process is divided into a plurality of time slices, state classification is carried out, and state changes of the batteries under different working conditions can be captured. And determining a corresponding characteristic curve according to a classification result and extracting life influence data, so that the influence of each time slice on the life of the battery can be accurately reflected. And integrating the data of all time slices to form a life influence data set, and calculating the service life of the battery according to the life influence data set, thereby realizing the detection of the service life of the battery under the dynamic wireless charging system. According to the method, the complex working condition of dynamic wireless charging is decomposed into a plurality of simple working conditions, and the influence of each simple working condition is calculated, so that the accuracy of battery life detection under the dynamic wireless charging condition is improved.

Description

Technical Field

[0001] The present application relates to the field of electrical digital data processing, and in particular to a battery life detection under dynamic wireless charging and related equipment. Background Art

[0002] With the improvement of environmental awareness and the adjustment of energy structure, electric vehicles are becoming more and more popular as a clean and efficient means of transportation. However, traditional electric vehicles face problems such as long charging time and short driving range, which limit their widespread application. To solve these problems, the dynamic wireless charging system (DWC charging equipment status) came into being. The DWC charging equipment status is laid on the road and combined with public facilities. It can continuously provide energy for electric vehicles in a non-contact manner, realizing convenient and intelligent charging. The basic principle of the dynamic wireless charging system for electric vehicles is: using the principle of wireless power transmission, the electric energy is transmitted to the receiving coil on the car through the transmitting coil on the ground, and then the battery pack of the electric vehicle is charged. This method can greatly reduce the capacity of the battery pack, extend the driving range of electric vehicles, and make power replenishment safer and more convenient.

[0003] like Figure 1 As shown in the figure, the AC power on the road side is first converted into DC power by a rectifier, then inverted into AC power by a high-frequency inverter, and then sent to the energy transmitting coil laid on the road after passing through the compensation network. The energy transmitting terminal is composed of multiple sets of energy transmitting devices, each of which can be controlled individually by a controller. The energy transmitting device includes a high-frequency inverter, a transmitting end compensation network and an energy transmitting coil. An energy receiving coil is laid at the bottom of the electric vehicle, and the energy receiving coil picks up the energy transmitted from the energy transmitting end to charge the lithium battery on the electric vehicle. The energy receiving device is composed of a power converter such as an energy receiving main coil, a receiving end compensation network, a rectifier and a DC-DC converter for controlling the system power. With the development of detection technology and intelligent control technology, segmented dynamic wireless charging technology can effectively improve the energy utilization rate and the efficiency of system energy transmission by obtaining the location of the electric vehicle, analyzing the state of the electric vehicle battery system, and controlling the start and stop of the energy transmitting coil.

[0004] In actual application scenarios, electric vehicles using dynamic wireless charging systems will frequently switch between multiple operating conditions such as discharging, charging, and charging and discharging, forming a complex and changeable operating mode. However, the battery life detection methods currently widely used in traditional electric vehicles do not take into account these special operating conditions brought about by dynamic wireless charging systems. If these traditional methods are directly applied to electric vehicles equipped with dynamic wireless charging systems, the accuracy of the detection results will inevitably be greatly reduced. Therefore, there is currently a technical problem that needs to be solved urgently: there is a lack of a battery life detection method specifically for electric vehicles using dynamic wireless charging systems. Summary of the invention

[0005] The present application provides a battery life detection and related equipment under dynamic wireless charging, which is used to improve the accuracy of battery life detection under dynamic wireless charging conditions, thereby providing a battery life detection method specifically for electric vehicles using dynamic wireless charging systems.

[0006] In a first aspect, the present application provides a method for detecting the life of a battery under dynamic wireless charging, including: obtaining characteristic curves of batteries of the same type as a target battery in a discharge phase, a charging phase, and a charge and discharge phase, wherein the characteristic curve uses battery parameters as one indicator and life impact data as another indicator, and the same type of battery is the same type of battery as the target battery; dividing the operation process of an electric vehicle equipped with a target battery within a use cycle into multiple time segments; performing state classification on each time segment, and classifying each time segment into one of a discharge phase, a charging phase, or a charge and discharge phase; determining a corresponding characteristic curve based on the classification result of the time segment; extracting corresponding life impact data from the corresponding characteristic curve based on the battery parameters of the target battery in the time segment; integrating the life impact data of all time segments in chronological order to form a life impact data set within the use cycle; and calculating the life of the target battery based on the life impact data set.

[0007] By adopting the above technical solution, the method obtains the characteristic curves of the same type of target battery at different stages, divides the operation process into multiple time segments and performs state classification, and can capture the state changes of the battery under different working conditions. According to the classification results, the corresponding characteristic curve is determined and the life impact data is extracted, which can accurately reflect the impact of each time segment on the battery life. The data of all time segments are integrated to form a life impact data set, and the battery life is calculated based on this, realizing the detection of battery life under the dynamic wireless charging system. This method decomposes the complex working conditions of dynamic wireless charging into several simple working conditions, and calculates the impact of each simple working condition separately, thereby improving the accuracy of battery life detection under dynamic wireless charging conditions.

[0008] In combination with some embodiments of the first aspect, in some embodiments, the step of classifying the state of each time segment specifically includes: identifying the specific operating conditions of each time segment, wherein the specific operating conditions include electric vehicle driving, electric vehicle docked at a fixed wireless charging system, electric vehicle charging through a dynamic wireless charging system, electric vehicle charging downhill, electric vehicle deceleration recovery, and electric vehicle idling state; according to a preset state mapping relationship, mapping the specific operating conditions to one of the discharge stage, the charging stage, or the charge and discharge stage; according to the classification result of the time segment, determining the corresponding characteristic curve step specifically includes: based on the specific operating condition and the corresponding characteristic curve, generating a temporary characteristic curve of the specific operating condition by an interpolation method; based on the battery parameters of the target battery in the time segment, extracting the corresponding life impact data from the corresponding characteristic curve step specifically includes: based on the battery parameters of the target battery in the time segment, extracting the corresponding life impact data from the corresponding temporary characteristic curve.

[0009] By adopting the above technical solution, the method reflects the actual usage status of the battery in a more detailed manner by identifying the specific working conditions of each time segment, such as electric vehicle driving, fixed wireless charging, dynamic wireless charging, etc. According to the preset state mapping relationship, the specific working conditions are mapped to different charging and discharging stages, which improves the accuracy of classification. In particular, the temporary characteristic curve is generated by the interpolation method, which can better adapt to various complex working conditions. This method not only takes into account the traditional charging and discharging state, but also includes special cases such as downhill charging and deceleration recovery, so that it can more comprehensively and accurately evaluate the impact of various working conditions on battery life, and improve the accuracy and applicability of life detection.

[0010] In combination with some embodiments of the first aspect, in some embodiments, according to a preset state mapping relationship, the step of mapping a specific operating condition to one of the discharging stage, the charging stage or the charging and discharging stage specifically includes: when the motor is in a driving state and the vehicle speed is greater than a preset speed threshold, the energy flow is determined to be discharging; when the charging device connection status shows that it is connected, the energy flow is determined to be charging; when the slope is less than the preset slope threshold and the energy recovery system is started, the energy flow is determined to be charging; when the acceleration is less than the preset deceleration threshold and the braking system is in a regenerative braking state, the energy flow is determined to be charging; when the vehicle speed is less than the preset speed threshold and the motor is in a non-working state, the energy flow is determined to be charging and discharging; when the energy flow is discharging, the specific operating condition is mapped to the discharging stage; when the energy flow is charging, the specific operating condition is mapped to the charging stage; when the energy flow is charging and discharging, the specific operating condition is mapped to the charging and discharging stage.

[0011] By adopting the above technical solution, the method identifies the working state of the battery by judging multiple parameters such as motor state, vehicle speed, charging device connection state, slope, acceleration, etc. For example, the discharge state is determined by judging the vehicle speed and motor state, and the downhill charging state is determined by the slope and energy recovery system state. This multi-dimensional determination method can accurately capture various complex working conditions of electric vehicles in actual operation, including conventional driving, fixed charging, dynamic charging, energy recovery, etc. By mapping these specific working conditions to the corresponding charging and discharging stages, a refined classification of the battery working state is achieved, providing reliable basic data for subsequent life impact assessment.

[0012] In conjunction with some embodiments of the first aspect, in some embodiments, based on the specific working condition and the corresponding characteristic curve, the step of generating a temporary characteristic curve of the specific working condition by an interpolation method specifically includes: using an interpolation function to generate a temporary characteristic curve of the specific working condition; the interpolation function is: In the formula, is the temporary characteristic curve, is the corresponding characteristic curve, is the power coefficient, is the characteristic curve point in the charging stage, is the characteristic curve point in the discharge stage, is a dynamic factor.

[0013] By adopting the above technical solution, Provides a basic outline of battery performance, introducing the power coefficient , so that the temporary characteristic curve can be adjusted according to the energy flow intensity of the current working conditions, and the dynamic factor The introduction of further enhances the adaptability of the curve, enabling it to capture the instantaneous characteristics of changes in working conditions. The value smoothly transitions between the charge and discharge states, introducing the charge and discharge characteristics. The linear combination of the two ensures the continuity and smoothness of the temporary curve. This enables the generated data to accurately reflect the changes in battery performance under various detailed operating conditions while maintaining basic characteristics.

[0014] In combination with some embodiments of the first aspect, in some embodiments, after the step of extracting corresponding life-influencing data from the corresponding temporary characteristic curve based on the battery parameters of the target battery in the time segment, the method also includes: counting the specific operating condition changes between adjacent time segments to generate an operating condition change sequence; calculating the frequency of occurrence of each operating condition change type in the operating condition change sequence; determining the operating condition change type with the highest frequency as the main operating condition change feature; based on the frequency of occurrence of the main operating condition change feature, calculating the adjustment weight of the life-influencing data, wherein the adjustment weight is positively correlated with the frequency of the operating condition change type of the specific operating condition corresponding to the life-influencing data; adjusting the life-influencing data according to the adjustment weight to obtain adjusted life-influencing data; and using the adjusted life-influencing data for subsequent battery life prediction calculations.

[0015] By adopting the above technical solution, the method introduces an operating condition change analysis and life impact data adjustment mechanism. By counting the operating condition changes between adjacent time segments, generating an operating condition change sequence and calculating the frequency of each type of change, the main operating condition change characteristics are identified. Based on these characteristics, the method calculates the adjustment weights of the life impact data and adjusts the data accordingly. This mechanism takes into account the additional impact of operating condition changes on battery life, such as frequent operating condition switching may accelerate battery degradation. Through this dynamic adjustment, the method can more accurately reflect the battery life status under complex usage environments, improving the accuracy and reliability of life prediction.

[0016] In combination with some embodiments of the first aspect, in some embodiments, the steps of dividing the operation process of an electric vehicle equipped with a target battery within a usage cycle into multiple time segments specifically include: setting the initial time segment length; if it is detected that the battery parameter change exceeds the change threshold, ending the current time segment and starting a new time segment; if no state change is detected, checking whether the current time segment reaches the preset maximum length; if the maximum length is reached, ending the current time segment and starting a new time segment.

[0017] By adopting the above technical solution, this method can capture the changes in battery status more accurately by setting the initial time segment length and dynamically adjusting the segment length according to the changes in battery parameters. When it is detected that the battery parameter changes exceed the threshold, the current segment is immediately ended and a new segment is started to ensure that the status changes can be captured in time. At the same time, by setting the maximum segment length, information loss caused by excessive segment length when the status is stable is prevented. This adaptive time segment division method improves the accuracy and sensitivity of battery status capture and provides a more reliable and detailed data basis for subsequent life impact assessment.

[0018] In combination with some embodiments of the first aspect, in some embodiments, if a battery parameter change is detected to exceed a change threshold, then after the step of ending the current time segment and starting a new time segment, the method also includes: sampling the target battery using standard mode sampling; if no state change is detected, then after the step of checking whether the current time segment reaches a preset maximum length, sampling the battery using high-frequency mode sampling.

[0019] By adopting the above technical solution, the method introduces a dual-mode sampling strategy. When a change in battery parameters is detected, standard mode sampling is used to obtain stable and representative data, while when no change is detected, high-frequency mode sampling is used to monitor small changes more carefully. This dynamically adjusted sampling strategy can not only accurately capture changes in battery status, but also provide more detailed data when the status is relatively stable.

[0020] In a second aspect, the present application provides a life detection system for a battery under dynamic wireless charging, the life detection system for a battery under dynamic wireless charging comprising: one or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code comprises computer instructions, the one or more processors call the computer instructions to enable the life detection system for a battery under dynamic wireless charging to perform the method described in the first aspect and any possible implementation manner of the first aspect.

[0021] In a third aspect, the present application provides a computer program product comprising instructions. When the computer program product is run on a battery life detection system under dynamic wireless charging, the battery life detection system under dynamic wireless charging performs the method described in the first aspect and any possible implementation method of the first aspect.

[0022] In a fourth aspect, the present application provides a computer-readable storage medium comprising instructions, which, when executed on a battery life detection system under dynamic wireless charging, enables the battery life detection system under dynamic wireless charging to perform the method described in the first aspect and any possible implementation method of the first aspect.

[0023] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages: 1. This method obtains the characteristic curves of similar batteries of the target battery at different stages, divides the operation process into multiple time segments and performs state classification, and can capture the state changes of the battery under different working conditions. According to the classification results, the corresponding characteristic curve is determined and the life impact data is extracted, which can accurately reflect the impact of each time segment on the battery life. The data of all time segments are integrated to form a life impact data set, and the battery life is calculated based on this, realizing the detection of battery life under the dynamic wireless charging system. This method decomposes the complex working conditions of dynamic wireless charging into several simple working conditions, and calculates the impact of each simple working condition separately, thereby improving the accuracy of battery life detection under dynamic wireless charging conditions.

[0024] 2. This method reflects the actual usage status of the battery in more detail by identifying the specific working conditions of each time segment, such as electric vehicle driving, fixed wireless charging, dynamic wireless charging, etc. According to the preset state mapping relationship, the specific working conditions are mapped to different charging and discharging stages, which improves the accuracy of classification. In particular, the temporary characteristic curve is generated by the interpolation method, which can better adapt to various complex working conditions. This method not only takes into account the traditional charging and discharging state, but also includes special situations such as downhill charging and deceleration recovery, so that it can more comprehensively and accurately evaluate the impact of various working conditions on battery life, and improve the accuracy and applicability of life detection.

[0025] 3. This method can capture changes in battery status more accurately by setting the initial time segment length and dynamically adjusting the segment length according to changes in battery parameters. When it is detected that the battery parameter changes exceed the threshold, the current segment is immediately ended and a new segment is started to ensure that the status changes can be captured in a timely manner. At the same time, by setting the maximum segment length, information loss caused by excessively long segments when the status is stable is prevented. This adaptive time segment division method improves the accuracy and sensitivity of battery status capture and provides a more reliable and detailed data basis for subsequent life impact assessment. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 This is a schematic diagram of an exemplary application scenario of the method for detecting the life of a battery under dynamic wireless charging in an embodiment of the present application; Figure 2 It is a flow chart of a method for detecting the life of a battery under dynamic wireless charging in an embodiment of the present application; Figure 3 This is a flow chart of step S202 in the embodiment of the present application; Figure 4 is another flow chart of a method for detecting the life of a battery under dynamic wireless charging in an embodiment of the present application; Figure 5It is a schematic diagram of an exemplary hardware structure of a battery life detection system under dynamic wireless charging in an embodiment of the present application. DETAILED DESCRIPTION

[0027] The terms used in the following embodiments of the present application are only for the purpose of describing specific embodiments, and are not intended to be used as limitations to the present application. As used in the specification and appended claims of the present application, the singular expressions "one", "a kind of", "said", "above", "the" and "this" are intended to also include plural expressions, unless there is a clear indication to the contrary in the context. It should also be understood that the term "and / or" used in the present application refers to and includes any or all possible combinations of one or more listed items.

[0028] In the following, the terms "first" and "second" are used for descriptive purposes only and are not to be understood as suggesting or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features, and in the description of the embodiments of the present application, unless otherwise specified, "plurality" means two or more.

[0029] See also Figure 2 , Figure 2 It is a flow chart of a method for detecting the life of a battery under dynamic wireless charging in an embodiment of the present application; S201, obtaining characteristic curves of similar batteries of the target battery in the discharge stage, the charge stage, and the charge-discharge stage, wherein the characteristic curve uses battery parameters as one indicator and life impact data as another indicator, and the similar batteries are batteries of the same type as the target battery; The target battery refers to a specific battery that needs to be tested for life. The same type of battery refers to a battery type with the same chemical composition, structure and specifications as the target battery. The characteristic curve is used to represent the performance changes of the battery under different working conditions. Battery parameters refer to measurable indicators that can reflect the battery status, such as voltage, current, temperature, etc. Life impact data is used to represent quantitative indicators that affect the battery life.

[0030] It should be noted that this method innovatively introduces the concept of a composite charging and discharging stage. Under normal circumstances, electric vehicles are usually in a single charging or discharging state. However, when an electric vehicle passes through a dynamic wireless charging system, the vehicle receives energy through the wireless charging device on the one hand, and continuously consumes energy due to driving on the other hand, forming a unique composite state where charging and discharging coexist.

[0031] In some embodiments, this step is achieved by collecting and analyzing a large amount of data on similar batteries in a laboratory or actual use environment. These characteristic curves reflect the performance change law of the battery under three typical working conditions: discharge, charge, and charge-discharge. Each characteristic curve takes the battery parameters (such as remaining capacity, internal resistance, etc.) as the horizontal axis and the life-influencing data (such as number of cycles, capacity attenuation rate, etc.) as the vertical axis to form a two-dimensional chart. These curves provide an important reference for subsequent life evaluation.

[0032] S202, dividing the operation process of the electric vehicle equipped with the target battery in a use cycle into multiple time segments; Among them, the usage cycle refers to the entire process of an electric vehicle starting from a starting point and arriving at a destination.

[0033] Before starting a detailed analysis of the battery status, the entire use cycle of the electric vehicle needs to be divided into smaller time units. In some embodiments, this step divides the time segments by setting time intervals or according to specific events. This division method allows the system to more finely capture the performance of the battery at different time points and under different working conditions. Each time segment can contain records of battery voltage, current, temperature and other parameters, as well as vehicle operating status information. In this way, the continuous operation process can be converted into a discrete sequence of data points for subsequent analysis and processing.

[0034] In some specific embodiments, a fixed time interval is set, such as every 5 minutes or every 10 minutes as a time segment; the status data of the battery and the vehicle are recorded at the end of each time interval; and all recorded data points are arranged in chronological order to form a time segment sequence.

[0035] In some specific embodiments, key events during the operation of the electric vehicle, such as starting, stopping, charging start and end, etc., are identified; these key events are used as demarcation points for time segments; and all relevant data between each event are recorded as a time segment.

[0036] It is understandable that other methods may be used to divide the time segments, such as dynamically adjusting the length of the time segments according to the magnitude of the battery parameter change, or a hybrid method combining fixed time intervals and key events, which is not limited here.

[0037] See also Figure 3 , Figure 3 This is a flow chart of step S202 in the embodiment of the present application; In some specific embodiments, step S202 specifically includes: S2021, setting the initial time segment length; The initial time segment length refers to a basic time unit predetermined when the battery status monitoring starts.

[0038] S2022: if it is detected that the battery parameter change exceeds the change threshold, then end the current time segment and start a new time segment; In some embodiments, the system monitors key battery parameters such as voltage, current, temperature, etc. in real time. For each parameter, the system is set with a corresponding change threshold. When the change amplitude of any parameter exceeds its corresponding threshold, the system considers that the battery state has changed. At this time, the system will immediately end the current time segment, save the collected data, and start a new time segment. This method of dynamically adjusting the time segment can better capture important changes in the battery state and improve the time resolution and relevance of the data. At the same time, it can also reduce unnecessary data storage and processing when the battery state is relatively stable.

[0039] S2023. If no state change is detected, check whether the current time segment reaches a preset maximum length; During the continuous monitoring process of the battery management system, this step is performed when the condition in S2022 is not triggered. In some embodiments, the system checks whether there is a state change in each data acquisition cycle. If no change exceeding the threshold is detected, the system will not end the current time segment immediately, but continue to monitor. At the same time, the system checks whether the duration of the current time segment has reached the preset maximum length. This maximum length is a safety mechanism that ensures that the system can regularly save data and update the status even when the battery state is stable for a long time. This approach balances the sensitivity to changes and the continuity of data acquisition to prevent the time segment from being extended indefinitely.

[0040] S2024: If the maximum length is reached, end the current time segment and start a new time segment.

[0041] This step is performed when the check result of executing step S2023 shows that the current time segment has reached the preset maximum length. In some embodiments, the system first confirms that the duration of the current time segment has indeed reached or exceeded the preset maximum length. Then, the system immediately triggers the switching operation of the time segment. This includes several key steps: First, the system saves all collected data in the current time segment to ensure that no information is lost. Next, the system may perform some data processing or aggregation operations, such as calculating the average value within the time segment or identifying key trends. Finally, the system initializes a new time segment, resets all related timers and status flags, and prepares to start a new round of data collection.

[0042] It can be seen that this method can capture changes in battery status more accurately by setting the initial time segment length and dynamically adjusting the segment length according to changes in battery parameters. When it is detected that the battery parameter changes exceed the threshold, the current segment is immediately ended and a new segment is started to ensure that the status changes can be captured in a timely manner. At the same time, by setting the maximum segment length, information loss caused by excessively long segments when the status is stable is prevented. This adaptive time segment division method improves the accuracy and sensitivity of battery status capture and provides a more reliable and detailed data basis for subsequent life impact assessments.

[0043] In some embodiments, steps S2023 and S2025 may be performed after step S2022.

[0044] S2025, sampling the target battery using standard mode sampling; Among them, standard mode sampling refers to the process of collecting data on battery parameters according to a preset normal frequency and method.

[0045] S2026: If no state change is detected, after the step of checking whether the current time segment reaches a preset maximum length, the battery is sampled using high frequency mode sampling.

[0046] High-frequency mode sampling refers to the process of collecting data on battery parameters at a higher frequency than the standard mode.

[0047] This step is performed after checking whether the current time segment has reached the preset maximum length, especially when the system determines that the battery may be about to change state. In some embodiments, the system first evaluates the current battery state and the duration of the time segment. If the time segment is close to the preset maximum length, but has not yet reached the conditions for triggering a new time segment, the system will start high-frequency mode sampling. In this mode, the system will increase the sampling frequency, possibly from once a minute in standard mode to multiple times per second. At the same time, the system may increase the data type sampled or improve the sampling accuracy. The purpose of high-frequency mode sampling is to capture small but important state changes that may be about to occur, providing more refined data to support more accurate decisions and predictions.

[0048] It can be seen that this method introduces a dual-mode sampling strategy. When a change in battery parameters is detected, standard mode sampling is used to obtain stable and representative data, and when no change is detected, high-frequency mode sampling is used to monitor small changes more carefully. This dynamically adjusted sampling strategy can not only accurately capture changes in battery status, but also provide more detailed data when the status is relatively stable.

[0049] S203, classifying each time segment into a discharging stage, a charging stage, or a charging and discharging stage; Among them, state classification means classifying time segments according to the working state of the battery. The discharge phase refers to the process of the battery supplying power to the electric vehicle. The charging phase is used to indicate the process of the battery receiving charging from an external power source. The charge and discharge phase refers to the composite state in which the battery is charged and discharged at the same time, such as during energy recovery braking.

[0050] After obtaining the time segment division, the battery working state of each time segment needs to be classified. In some embodiments, this step determines the working stage to which it belongs by analyzing the changes in battery parameters within each time segment. The system will check key indicators such as current direction and voltage change, and determine whether the time segment belongs to the discharge, charge or charge-discharge stage according to the preset judgment rules. This classification can help the subsequent steps to select the appropriate characteristic curve for life impact analysis and improve the accuracy of the evaluation.

[0051] In some specific embodiments, a current threshold is set, such as a positive current represents discharge and a negative current represents charge; the average current value in each time segment is analyzed; and the time segments are classified into corresponding stages according to the direction and magnitude of the current, which is not limited here.

[0052] S204, determining a corresponding characteristic curve according to the classification result of the time segment; In some embodiments, this step is implemented by establishing a mapping relationship between the time segment state and the characteristic curve. The system selects the corresponding curve from the characteristic curve set obtained in step S201 according to the classification result of the time segment (discharging, charging, or charging and discharging). This matching ensures that the most relevant performance data is used in subsequent analysis, improving the accuracy of life assessment.

[0053] S205, extracting corresponding lifespan impact data from a corresponding characteristic curve based on the battery parameters of the target battery in the time segment; After determining the characteristic curve corresponding to each time segment, it is necessary to obtain the corresponding life impact data from the curve based on the actual battery parameters. In some embodiments, this step is implemented by interpolating or searching on the characteristic curve. The system uses the battery parameters recorded in the time segment as input, locates the corresponding point on the corresponding characteristic curve, and then reads the life impact data corresponding to the point. This process associates the real-time working status of the battery with its potential life impact, providing key data for subsequent life calculations.

[0054] S206, integrating the life impact data of all time segments in chronological order to form a life impact data set within the use cycle; After obtaining the life impact data for each time segment, it is necessary to integrate these data into a complete data set. In some embodiments, this step is achieved by arranging and organizing the life impact data of all time segments in chronological order. The system will maintain the temporal order of the data to ensure that each data point is correctly associated with its corresponding time segment. This integration process creates a data set that comprehensively reflects the life changes of the battery throughout its entire use cycle, providing a basis for the final life calculation.

[0055] S207: Calculate the life of the target battery according to the life impact data set.

[0056] After obtaining the complete life impact data set, the final life calculation step is required. In some embodiments, this step estimates the overall life of the battery by analyzing the cumulative effect of the life impact data set. The system may consider multiple factors, such as capacity decay rate, internal resistance increase, number of cycles, etc., to comprehensively evaluate the health status and remaining service life of the battery. This calculation process converts all previously collected and processed data into a specific life prediction result, providing an important reference for the maintenance and battery management of electric vehicles.

[0057] In some specific embodiments, the method accumulates the life impact data of all time segments in the entire operation cycle to obtain a final total life impact value. This total value reflects the comprehensive impact of the entire journey from the starting point to the end point on the battery life. Subsequently, by subtracting this total life impact value from the original life data of the battery, the actual remaining life of the target battery is accurately calculated.

[0058] It can be seen that this method obtains the characteristic curves of the same type of target battery at different stages, divides the operation process into multiple time segments and performs state classification, and can capture the state changes of the battery under different working conditions. Determining the corresponding characteristic curve based on the classification results and extracting the life impact data can accurately reflect the impact of each time segment on the battery life. The data of all time segments are integrated to form a life impact data set, and the battery life is calculated based on this, realizing the detection of battery life under the dynamic wireless charging system. This method decomposes the complex working conditions of dynamic wireless charging into several simple working conditions, and calculates the impact of each simple working condition separately, thereby improving the accuracy of battery life detection under dynamic wireless charging conditions.

[0059] In actual use, electric vehicles have complex operating conditions (such as charging downhill, accelerating, decelerating, idling, etc.). Simply dividing the battery status into discharge, charging, and charge and discharge stages cannot accurately reflect the actual situation, which may lead to insufficient assessment of the impact on battery life.

[0060] See also Figure 4 , Figure 4is another flow chart of a method for detecting the life of a battery under dynamic wireless charging in an embodiment of the present application; Therefore, in some embodiments, step S203 is replaced by step S301: S301, identifying specific operating conditions for each time segment, wherein the specific operating conditions include electric vehicle driving, electric vehicle docked at a fixed wireless charging system, electric vehicle charging through a dynamic wireless charging system, electric vehicle downhill charging, electric vehicle deceleration recovery, and electric vehicle idling; Among them, specific working conditions refer to the operating status and usage scenarios of electric vehicles within a specific time period. Electric vehicle driving refers to the state of the vehicle driving on normal roads. Fixed wireless charging systems are used to represent static wireless charging facilities in parking lots or specific locations. Dynamic wireless charging systems refer to road-embedded charging devices that can charge electric vehicles while driving. Downhill charging refers to the process of charging the battery of an electric vehicle through an energy recovery system when going downhill. Deceleration recovery is used to describe the process of converting kinetic energy into electrical energy and storing it in the battery when the vehicle decelerates. Idle state refers to a low-power state where the electric vehicle stops driving but the system is still running.

[0061] Before starting a detailed assessment of battery life, it is necessary to first identify the specific operating status of the electric vehicle in each time segment. In some embodiments, this step is achieved by analyzing information from multiple data sources such as on-board sensors, GPS, and on-board diagnostic systems (OBD). The system will comprehensively consider multiple parameters such as vehicle speed, acceleration, slope, battery current, voltage, etc., and classify each time segment in combination with preset operating condition judgment rules. This detailed operating condition identification can capture the complex operating status of electric vehicles in actual use, and provide more accurate basic data for subsequent life impact assessments.

[0062] In some specific embodiments, the vehicle sensor system is used to collect key parameter data, including vehicle speed, acceleration, slope, battery current, voltage, etc. These parameters provide basic data support for working condition identification. Based on the collected parameter data, a series of logical judgment conditions are designed to identify specific working conditions: Electric vehicle driving: The vehicle speed is greater than zero and no other special operating conditions are met.

[0063] The electric vehicle is parked at a fixed wireless charging system: the vehicle speed is zero and the signal of the wireless charging system is detected.

[0064] The electric vehicle is charged by a dynamic wireless charging system: the vehicle speed is greater than zero and the signal of the wireless charging system is detected.

[0065] Electric vehicle charging downhill: the vehicle speed is greater than zero, the slope is negative, and the battery current is displayed as charging status.

[0066] Electric vehicle deceleration recovery: The vehicle speed decreases and the battery current is displayed as charging status.

[0067] Electric vehicle idling state: the vehicle speed is close to zero and the engine is running.

[0068] This is not a limitation.

[0069] S302, mapping a specific working condition to one of a discharging stage, a charging stage, or a charging and discharging stage according to a preset state mapping relationship; After identifying the specific working conditions of each time segment, these working conditions need to be converted into more general battery working state categories. In some embodiments, this step is achieved by using predefined mapping rules. The system will classify each specific working condition into one of the three basic states of discharge, charge, or charge and discharge according to the characteristics of the specific working condition, such as energy flow direction, battery current direction, etc. This mapping process simplifies subsequent analysis while retaining the essential characteristics of the working condition, so that it can better correspond to the standardized battery characteristic curve.

[0070] In some embodiments, in some embodiments, step S302 specifically includes: When the motor is in the driving state and the vehicle speed is greater than a preset speed threshold, the energy flow is determined to be discharge; When the charging device connection status shows connected, it is determined that the energy flow direction is charging; When the slope is less than a preset slope threshold and the energy recovery system is activated, it is determined that the energy flow is charging; When the acceleration is less than the preset deceleration threshold and the braking system is in a regenerative braking state, it is determined that the energy flow is charging; When the vehicle speed is less than the preset speed threshold and the motor is in a non-operating state, the energy flow is determined to be charging and discharging; When the energy flow is discharging, the specific working condition is mapped to the discharging stage; When the energy flow is charging, the specific working condition is mapped to the charging stage; When the energy flow is charging and discharging, the specific working conditions are mapped to the charging and discharging stages.

[0071] It can be seen that this method identifies the working state of the battery by judging multiple parameters such as motor state, vehicle speed, charging device connection status, slope, acceleration, etc. For example, the discharge state is determined by judging the vehicle speed and motor state, and the downhill charging state is determined by the slope and energy recovery system state. This multi-dimensional determination method can accurately capture various complex working conditions of electric vehicles in actual operation, including conventional driving, fixed charging, dynamic charging, energy recovery, etc. By mapping these specific working conditions to the corresponding charging and discharging stages, a refined classification of the battery working state is achieved, providing reliable basic data for subsequent life impact assessment.

[0072] S303, based on the specific working condition and the corresponding characteristic curve, generate a temporary characteristic curve of the specific working condition by an interpolation method; The temporary characteristic curve refers to a more accurate battery performance curve temporarily generated for a specific working condition. Generate is used to indicate the process of creating or generating new data or information.

[0073] After determining the battery operating status for each time segment, it is necessary to create a more specific characteristic curve for each specific operating condition. In some embodiments, this step is achieved by correcting and adjusting the standard characteristic curve. The system will take into account the characteristics of specific operating conditions, such as discharge depth, charging power, temperature conditions, etc., and use appropriate interpolation algorithms to generate a temporary curve that is closer to the actual situation based on the standard characteristic curve. This method can more accurately reflect the performance and life impact of the battery under specific operating conditions, and improve the accuracy of subsequent evaluations.

[0074] In some specific embodiments, a standard characteristic curve that is closest to the specific working condition is selected as a basis; the portion of the curve that needs to be adjusted is determined based on specific parameters of the working condition (such as temperature, current rate, etc.); new data points are generated within the selected interval using methods such as linear interpolation or spline interpolation to form a temporary characteristic curve, which is not limited here.

[0075] In some specific embodiments, step S303 specifically includes: Use interpolation functions to generate temporary characteristic curves for specific working conditions; The interpolation function is: In the formula, is the temporary characteristic curve, is the corresponding characteristic curve, is the power coefficient, is the characteristic curve point of the charging stage, is the characteristic curve point in the discharge stage, is a dynamic factor.

[0076] By adopting the above technical solution, Provides a basic outline of battery performance, introducing the power coefficient , so that the temporary characteristic curve can be adjusted according to the energy flow intensity of the current working conditions, and the dynamic factor The introduction of further enhances the adaptability of the curve, enabling it to capture the instantaneous characteristics of changes in working conditions. The value smoothly transitions between the charge and discharge states, introducing the charge and discharge characteristics. The linear combination of the two ensures the continuity and smoothness of the temporary curve. This enables the generated data to accurately reflect the changes in battery performance under various detailed operating conditions while maintaining basic characteristics.

[0077] visible, Provides a basic outline of battery performance, introducing the power coefficient , so that the temporary characteristic curve can be adjusted according to the energy flow intensity of the current working conditions, and the dynamic factor The introduction of further enhances the adaptability of the curve, enabling it to capture the instantaneous characteristics of changes in working conditions. The value smoothly transitions between the charge and discharge states, introducing the charge and discharge characteristics. The linear combination of the two ensures the continuity and smoothness of the temporary curve. This enables the generated data to accurately reflect the changes in battery performance under various detailed operating conditions while maintaining basic characteristics.

[0078] Step S205 is replaced by step S304: S304 . Based on the battery parameters of the target battery in the time segment, extract corresponding lifespan impact data from the corresponding temporary characteristic curve.

[0079] It should be noted that the principle and process of this step are similar to those of step S205 , and the relevant principles and processes may refer to step S304 , which are not limited here.

[0080] It can be seen that this method reflects the actual usage status of the battery in more detail by identifying the specific working conditions of each time segment, such as electric vehicle driving, fixed wireless charging, dynamic wireless charging, etc. According to the preset state mapping relationship, the specific working conditions are mapped to different charging and discharging stages, which improves the accuracy of classification. In particular, the temporary characteristic curve is generated by the interpolation method, which can better adapt to various complex working conditions. This method not only takes into account the traditional charging and discharging state, but also includes special cases such as downhill charging and deceleration recovery, so that it can more comprehensively and accurately evaluate the impact of various working conditions on battery life, and improve the accuracy and applicability of life detection.

[0081] In some embodiments, after step S304, the method further includes: S305, counting specific operating condition changes between adjacent time segments to generate an operating condition change sequence; The operating condition change sequence refers to a series of operating condition change records arranged in chronological order.

[0082] This step is performed when the battery management system needs to analyze the battery usage pattern. In some embodiments, the system first divides the battery usage time into multiple consecutive time segments, which can be fixed time intervals (such as every 5 minutes) or dynamically divided according to the change of battery status. Then, the system compares the specific operating conditions between adjacent time segments, including but not limited to charging and discharging status, power level, temperature change, etc. The system records these changes and organizes them into a sequence in chronological order. This sequence reflects the dynamic changes of the operating conditions during battery use and provides basic data for subsequent analysis.

[0083] S306, calculating the occurrence frequency of each operating condition change type in the operating condition change sequence; After the generation of the operating condition change sequence is completed, this step is performed when statistical analysis is required. In some embodiments, the system first defines a series of operating condition change types, such as from charging to discharging, from low power to high power, rapid temperature rise, etc. Then, the system traverses the entire operating condition change sequence, identifies which type each change belongs to, and records the number of occurrences of each type. Finally, the system calculates the frequency of occurrence of each type, that is, the number of occurrences of the type divided by the total length of the sequence. These frequency data reflect the characteristics of the battery usage pattern and provide an important basis for subsequent analysis and decision-making.

[0084] S307, determining the operating condition change type with the highest frequency as the main operating condition change feature; S308, calculating the adjustment weight of the life-influencing data based on the occurrence frequency of the main operating condition change characteristics, wherein the adjustment weight is positively correlated with the frequency of the operating condition change type of the specific operating condition corresponding to the life-influencing data; After the main operating condition change characteristics are determined, this step is performed when it is necessary to evaluate the impact of different operating conditions on battery life. In some embodiments, the system first analyzes the frequency of occurrence of the main operating condition change characteristics and uses it as a benchmark. Then, the system calculates an adjustment weight for each operating condition change type, which reflects the relative importance of the operating condition change type and the main characteristics. The closer the frequency is to the main operating condition change characteristics, the greater the adjustment weight. This method ensures that more common operating condition changes receive higher attention in the life impact assessment. The system uses these weights to adjust the original life impact data so that the final life prediction more accurately reflects the actual use of the battery.

[0085] S309, adjusting the lifespan impact data according to the adjustment weight to obtain adjusted lifespan impact data; S310: Use the adjusted lifespan impact data for subsequent battery life prediction calculations.

[0086] It can be seen that in some embodiments, the operation process of an electric vehicle equipped with a target battery within a usage cycle is divided into multiple time segments, specifically including: setting the initial time segment length; if it is detected that the battery parameter change exceeds the change threshold, then ending the current time segment and starting a new time segment; if no state change is detected, checking whether the current time segment reaches the preset maximum length; if the maximum length is reached, then ending the current time segment and starting a new time segment.

[0087] The following introduces an exemplary dynamic wireless charging battery life detection system 500 provided in an embodiment of the present application. Figure 5 It is a schematic diagram of an exemplary hardware structure of a battery life detection system 500 under dynamic wireless charging provided in an embodiment of the present application.

[0088] In some embodiments, the life detection system 500 of the battery under dynamic wireless charging is a computer device or the life detection system 500 of the battery under dynamic wireless charging includes a computer device. The computer device includes a processor, a memory and a network interface connected by a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store data. The network interface of the computer device is used to communicate with other external terminals or servers through a network connection. In some embodiments, the network interface can be a wired network interface, and in some embodiments, the network interface can also be a wireless network interface. When the computer program is executed by the processor, the method in the embodiment of the present application is implemented.

[0089] Those skilled in the art will understand that Figure 5 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0090] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present application.

[0091] As used in the above embodiments, the term "when..." may be interpreted to mean "if..." or "after..." or "in response to determining..." or "in response to detecting...", depending on the context. Similarly, the phrases "upon determining..." or "if (the stated condition or event) is detected" may be interpreted to mean "if determining..." or "in response to determining..." or "upon detecting (the stated condition or event)" or "in response to detecting (the stated condition or event)", depending on the context.

[0092] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented by software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions may be transmitted from one website site, computer, server or data center to another website site, computer, server or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium may be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more available media integrated. The available medium may be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., D vehicle speed D), or a semiconductor medium (e.g., a solid state drive), etc.

[0093] Those skilled in the art can understand that to implement all or part of the processes in the above-mentioned embodiments, the processes can be completed by computer programs to instruct related hardware, and the programs can be stored in computer-readable storage media. When the programs are executed, they can include the processes of the above-mentioned method embodiments. The aforementioned storage media include: ROM or random access memory RAM, magnetic disk or optical disk and other media that can store program codes.

Claims

1. A method for detecting battery life under dynamic wireless charging, characterized in that: include: Obtaining characteristic curves of similar batteries of the target battery in the discharge stage, the charge stage, and the charge-discharge stage, wherein the characteristic curve uses battery parameters as one indicator and life impact data as another indicator, and the similar batteries are batteries of the same type as the target battery; Dividing the operation process of an electric vehicle equipped with the target battery in a use cycle into multiple time segments; Performing state classification on each of the time segments, and classifying each of the time segments as one of the discharging stage, the charging stage, or the charging and discharging stage; Determining the corresponding characteristic curve according to the classification result of the time segment; extracting corresponding lifespan impact data from the corresponding characteristic curve based on the battery parameters of the target battery in the time segment; Integrate the life impact data of all the time segments in chronological order to form a life impact data set within the use cycle; The life of the target battery is calculated according to the life impact data set.

2. The method according to claim 1, characterized in that: The step of classifying the state of each time segment specifically includes: Identify the specific operating conditions of each of the time segments, wherein the specific operating conditions include electric vehicle driving, electric vehicle docked at a fixed wireless charging system, electric vehicle charging through a dynamic wireless charging system, electric vehicle downhill charging, electric vehicle deceleration recovery, and electric vehicle idling; According to a preset state mapping relationship, the specific operating condition is mapped to one of the discharging stage, the charging stage or the charging and discharging stage; The step of determining the corresponding characteristic curve according to the classification result of the time segment specifically includes: generating a temporary characteristic curve of the specific working condition by an interpolation method based on the specific working condition and the corresponding characteristic curve; The step of extracting the corresponding life-influencing data from the corresponding characteristic curve based on the battery parameters of the target battery in the time segment specifically includes: extracting the corresponding life-influencing data from the corresponding temporary characteristic curve based on the battery parameters of the target battery in the time segment.

3. The method according to claim 2, characterized in that The step of mapping the specific operating condition to one of the discharging stage, the charging stage or the charging and discharging stage according to the preset state mapping relationship specifically includes: When the motor is in the driving state and the vehicle speed is greater than a preset speed threshold, the energy flow is determined to be discharge; When the charging device connection status shows connected, the energy flow direction is determined to be charging; When the slope is less than a preset slope threshold and the energy recovery system is activated, it is determined that the energy flow is charging; When the acceleration is less than the preset deceleration threshold and the braking system is in a regenerative braking state, it is determined that the energy flow is charging; When the vehicle speed is less than the preset speed threshold and the motor is in a non-operating state, determining that the energy flow direction is charging and discharging; When the energy flow direction is discharge, mapping the specific working condition to the discharge stage; When the energy flow direction is charging, mapping the specific working condition to the charging stage; When the energy flow direction is charging and discharging, the specific working condition is mapped to the charging and discharging stage.

4. The method according to claim 2, characterized in that: The step of generating a temporary characteristic curve for the specific working condition by an interpolation method based on the specific working condition and the corresponding characteristic curve specifically includes: Using an interpolation function to generate a temporary characteristic curve for the specific working condition; The interpolation function is: In the formula, is the temporary characteristic curve, is the corresponding characteristic curve, is the power coefficient, is the characteristic curve point in the charging stage, is the characteristic curve point in the discharge stage, is the dynamic factor.

5. The method according to claim 2, characterized in that: After the step of extracting the corresponding lifespan impact data from the corresponding temporary characteristic curve based on the battery parameters of the target battery in the time segment, the method further includes: Counting the specific operating condition changes between adjacent time segments to generate an operating condition change sequence; Calculating the occurrence frequency of each operating condition change type in the operating condition change sequence; Determine the operating condition change type with the highest frequency as the main operating condition change feature; Based on the occurrence frequency of the main operating condition change characteristics, calculating the adjustment weight of the life-influencing data, wherein the adjustment weight is positively correlated with the frequency of the operating condition change type of the specific operating condition corresponding to the life-influencing data; Adjusting the life impact data according to the adjustment weight to obtain adjusted life impact data; The adjusted life impact data is used for subsequent battery life prediction calculations.

6. The method according to claim 1, characterized in that The step of dividing the operation process of the electric vehicle equipped with the target battery in a use cycle into multiple time segments specifically includes: Set the initial time segment length; If it is detected that the battery parameter changes exceed the change threshold, the current time segment ends and a new time segment begins; If no state change is detected, check whether the current time segment reaches the preset maximum length; If the maximum length is reached, the current time segment ends and a new time segment begins.

7. The method according to claim 6, characterized in that After the step of ending the current time segment and starting a new time segment if it is detected that the battery parameter change exceeds the change threshold, the method further includes: Sample the target battery using standard mode sampling; After the step of checking whether the current time segment reaches a preset maximum length if no state change is detected, the battery is sampled using high frequency mode sampling.

8. A battery life detection system under dynamic wireless charging, characterized in that: The life detection system of the battery under dynamic wireless charging includes: one or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code includes computer instructions, and the one or more processors call the computer instructions to enable the life detection system of the battery under dynamic wireless charging to execute the method described in any one of claims 1-7.

9. A computer program product comprising instructions, characterized in that When the computer program product runs on a system for detecting the life of a battery under dynamic wireless charging, the system for detecting the life of a battery under dynamic wireless charging executes the method according to any one of claims 1 to 7.

10. A computer-readable storage medium comprising instructions, characterized in that: When the instruction is executed on a system for detecting the life of a battery under dynamic wireless charging, the system for detecting the life of a battery under dynamic wireless charging executes the method according to any one of claims 1 to 7.

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