Charging remaining time estimation method and system based on self-recognition and segmented prediction

Abstract

The invention discloses a charging remaining time estimation method and system based on self-recognition and segmented prediction. The method comprises the following steps: collecting battery parameters in real time; determining a current charging mode based on the battery parameters; identifying whether thermal management is started or not according to the battery parameters; and real-time charging remaining time is settled in stages based on the identification results of the battery parameters, the charging mode and the thermal management. According to the method, the battery parameters are collected in real time, the charging mode is determined, the thermal management state is identified, and the charging remaining time is settled in stages, so that the accuracy and the stability of charging remaining time estimation can be improved, the actual charging process can be better reflected especially during charging mode switching and thermal management strategy adjustment, and the charging efficiency is improved. And the user experience and the system reliability are improved.

Description

Technical Field

[0001] This invention belongs to the field of battery charging management technology, specifically relating to a method and system for estimating remaining charging time based on self-identification and segmented prediction. Background Technology

[0002] In the application of new energy vehicles, users' requirements for the accuracy of charging time prediction are continuously increasing. Traditional methods for estimating remaining charging time mainly rely on linear extrapolation or fixed empirical formulas for the constant current / constant voltage charging stage. These methods fail to effectively handle the dynamic response differences of batteries under different thermal management modes. For example, when the ambient temperature changes, the system may trigger heating or cooling mechanisms, causing significant fluctuations in the battery temperature rise or fall rate. Traditional methods lack the ability to identify thermal management modes in real time and cannot adapt to such changes, resulting in large deviations in the estimation results under ultra-fast charging or high and low temperature conditions. At the same time, the charging process itself has obvious stage characteristics. The charging current, voltage, and power change patterns corresponding to different states of charge ranges are different. However, existing technologies often simplify the entire charging process into a single stage, ignoring the transition characteristics between stages. This makes it impossible to dynamically correct based on the real-time operating status, making it difficult to accurately reflect the actual charging progress, seriously affecting user experience and system reliability, and restricting the effectiveness of electric vehicles and energy storage systems in practical applications.

[0003] To address the aforementioned issues, existing technologies urgently need improvement. Summary of the Invention

[0004] The purpose of this invention is to address the shortcomings of the aforementioned background technology by providing a method and system for estimating remaining charging time based on self-identification and segmented prediction. This method and system can improve the accuracy and stability of remaining charging time estimation, better reflect the actual charging progress, and enhance user experience and system reliability.

[0005] The technical solution adopted in this invention is: a method for estimating remaining charging time based on self-identification and segmented prediction, comprising the following steps: Real-time battery parameter acquisition; Determine the current charging mode based on battery parameters; Identify whether thermal management should be enabled based on battery parameters; The remaining charging time is calculated in stages based on the identification results of the battery parameters, charging mode, and thermal management.

[0006] A charging remaining time estimation system based on self-identification and segmented prediction includes: The data acquisition module is used to collect battery parameters in real time. The mode determination module is used to determine the current charging mode based on battery parameters; The thermal management module is used to identify whether thermal management should be enabled based on battery parameters; The time estimation module is used to calculate the remaining charging time in stages based on the identification results of the battery parameters, charging mode, and thermal management.

[0007] The beneficial effects of this invention are as follows: This invention improves the accuracy and stability of remaining charging time estimation by collecting battery parameters in real time, determining charging mode, identifying thermal management status, and calculating the remaining charging time in stages. In particular, it can better reflect the actual charging progress when switching charging modes and adjusting thermal management strategies, thereby enhancing user experience and system reliability. Attached Figure Description

[0008] Figure 1 This is a flowchart of the charging remaining time estimation method of the present invention. Detailed Implementation

[0009] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments to facilitate a clear understanding of the present invention, but these descriptions do not constitute a limitation on the present invention.

[0010] It should be understood that, when used in this application specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or a collection thereof.

[0011] Furthermore, references to "one embodiment" or "some embodiments" in this specification mean that one or more embodiments of this application include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, phrases such as "in one embodiment," "in some embodiments," "in other embodiments," and "in still other embodiments" appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized.

[0012] like Figure 1 As shown, this application proposes a method for estimating the remaining charging time based on self-identification and segmented prediction. By collecting battery parameters in real time, the current charging mode is determined based on the battery parameters. The method identifies whether thermal management is enabled based on the battery parameters, and calculates the real-time remaining charging time in stages based on the identification results of battery parameters, charging mode, and thermal management. This effectively improves the prediction accuracy and user experience.

[0013] The following explains some key terms in this embodiment: Battery parameters: These typically refer to various physical quantities used to describe the current state and performance of a battery, such as voltage, current, and temperature. Real-time acquisition of these parameters is fundamental for assessing battery health and predicting charging behavior.

[0014] Charging mode: refers to the operating state of the battery during charging, such as fast charging mode or normal charging mode. Different charging modes usually correspond to different charging strategies and charging rates.

[0015] Thermal management refers to the process of controlling and regulating the temperature of a battery system to ensure that the battery operates within its optimal temperature range. Thermal management can include various methods such as heating and cooling, and its activation or deactivation, as well as the specific mode, has a significant impact on the battery's charging efficiency and lifespan.

[0016] Segmented prediction: This method divides the entire charging process into several stages and performs independent prediction calculations for the characteristics of each stage. This approach can capture the dynamic changes during the charging process more precisely and improve the accuracy of the prediction.

[0017] Specifically, this application provides a method for estimating remaining charging time based on self-identification and segmented prediction. This method first requires real-time acquisition of battery parameters. Battery parameter acquisition can be achieved by setting appropriate sensors in the battery pack or battery management system (BMS). For example, data such as battery voltage, current, and temperature can be acquired periodically. This data can be transmitted to a processing unit for subsequent analysis.

[0018] After obtaining the battery parameters, the current charging mode needs to be determined based on these parameters. The charging mode can be determined based on preset rules. For example, it can be determined based on the magnitude of the charging current; when the charging current exceeds a certain fixed threshold, it is determined to be in fast charging mode; when the charging current is below that threshold, it is determined to be in normal charging mode. Alternatively, it can be determined based on the trend of charging voltage changes.

[0019] Subsequently, the system identifies whether thermal management is enabled based on battery parameters. The identification of thermal management can be based on the battery's temperature status.

[0020] Finally, based on the identified battery parameters, charging mode, and thermal management results, the remaining charging time is calculated in stages. During this staged calculation, the entire charging process can be divided into multiple preset state of charge (SOC) stages. For each stage, different calculation models or empirical formulas can be used to estimate the required charging time based on the currently identified charging mode and thermal management status.

[0021] This application improves the accuracy of remaining charging time prediction by real-time sensing of battery operating status and combining charging mode and thermal management status for phased and refined prediction. Especially in complex charging scenarios such as ultra-fast charging or high and low temperatures, it provides electric vehicle users with more reliable charging time information, thereby enhancing user experience and the engineering application value of the system.

[0022] In one embodiment, this application further specifies the battery parameters, which include at least one of the following: single cell temperature, battery charging current, battery charging voltage, and battery charging power.

[0023] Specifically, the individual cell temperature refers to the temperature of a single cell within the battery pack. Its function is to precisely monitor the internal heat distribution of the battery, identify localized overheating or overcooling, and help prevent thermal runaway or low-temperature performance degradation. It is a crucial basis for determining whether thermal management needs to be activated and for identifying the thermal management mode (such as heating or cooling). This individual cell temperature can be collected in real time by directly attaching thermistors or thermocouples inside the battery module or on the cell surface. The battery charging current refers to the magnitude of the current flowing through the battery during charging. It directly reflects the charging rate and intensity and is a key parameter for determining the charging mode (fast charging / slow charging) and calculating the charging capacity. This battery charging current can be measured in real time by connecting current sensors such as Hall effect sensors or shunts in series in the charging circuit; alternatively, it can be measured by the voltage drop in the charging circuit through the current sampling module within the Battery Management System (BMS) and calculated using known resistance. The battery charging voltage refers to the voltage value across the battery terminals during charging. It reflects the battery's state of charge (SOC) and charging stage, and is an important parameter for determining charging cutoff conditions and assessing battery health. This charging voltage can be measured in real-time by directly connecting a voltage sampling module in parallel across the battery terminals; alternatively, it can be measured with high precision by the voltage detection circuit within the BMS (Battery Management System) to sample the voltage of the battery pack or individual cells. The battery charging power refers to the rate at which the battery receives electrical energy during charging, typically the product of the charging current and charging voltage. It comprehensively reflects the energy input capability of the charging system and is an important indicator for evaluating charging efficiency and predicting charging time, especially in fast-charging scenarios where power variations significantly impact charging time. This charging power can be obtained by multiplying the real-time battery charging current and voltage data in the controller; or, it can be obtained by directly measuring the instantaneous power of the charging circuit using a dedicated power sensor.

[0024] Through the above technical solution, this application clearly defines battery parameters as at least one of the following: single cell temperature, battery charging current, battery charging voltage, and battery charging power. The individual or combined application of these parameters enables the remaining charging time estimation method to capture the dynamic characteristics of the battery under different operating conditions more precisely. Especially when thermal management is enabled or charging mode is switched, it can effectively cope with the impact of temperature changes and charging dynamics on prediction accuracy, thereby improving the accuracy and adaptability of remaining charging time estimation.

[0025] In one embodiment, this application further proposes determining the current charging mode based on battery parameters, including: determining the charging mode based on any one of battery charging current, charging voltage, and charging power, wherein the charging mode is a fast charging mode or a slow charging mode.

[0026] Specifically, this solution determines the current charging mode by monitoring any one of the core electrical parameters: battery charging current, charging voltage, and charging power. These parameters directly reflect the battery's state of charge and charging power level, serving as the direct basis for identifying the charging mode. In practical applications, one or more parameters can be flexibly selected for judgment based on system design, sensor availability, or data reliability. Clearly classifying charging modes into fast charging or slow charging modes effectively simplifies and categorizes the complex charging process. Fast charging typically refers to charging with high current or high power to shorten charging time; while slow charging typically refers to charging with lower current or power to protect the battery or for use in non-emergency situations. These two modes differ significantly in charging characteristics, battery response, and the requirements for the remaining charging time estimation model.

[0027] Understandably, this application flexibly determines the current charging mode based on any one of the parameters—battery charging current, charging voltage, and charging power—avoiding misjudgments that might result from relying on a single parameter, thus significantly improving the accuracy and adaptability of charging mode identification. Clearly classifying charging modes into fast charging or slow charging modes provides a clear and targeted classification basis for subsequent phased calculation of remaining charging time. This classification allows the system to use different estimation models and parameters for different charging modes, thereby improving the overall accuracy and reliability of remaining charging time estimation. Especially when facing different charging scenarios and operating conditions, it can more accurately reflect the dynamic charging characteristics of the battery, providing users with more reliable information on remaining charging time.

[0028] In one embodiment, this application further proposes identifying whether thermal management is enabled based on battery parameters, including: Set ultra-low temperature range, low temperature range, normal temperature range, high temperature range, and ultra-high temperature range; If the temperature of a single battery cell is within the normal temperature range, thermal management will not be activated; otherwise, thermal management will be activated and the thermal management mode will be determined. When thermal management is enabled, if the minimum temperature of a single cell is in the ultra-low temperature range, the thermal management mode is determined to be heating mode only; if the minimum temperature of a single cell is in the low temperature range, the thermal management mode is determined to be charging and heating mode simultaneously; if the maximum temperature of a single cell is in the high temperature range, the thermal management mode is determined to be charging and cooling mode simultaneously; if the maximum temperature of a single cell is in the ultra-high temperature range, the thermal management mode is determined to be cooling mode only.

[0029] The setting of ultra-low temperature range, low temperature range, normal temperature range, high temperature range, and ultra-high temperature range constitutes a preset range for finely dividing the temperature of a single battery cell. This aims to provide a basis for thermal management strategies based on the battery's performance and safety characteristics at different temperatures. By setting these ranges, the system can identify the specific temperature state of the battery and thus take targeted thermal management measures. These temperature ranges can be pre-defined in the firmware or software of the battery management system (BMS). Furthermore, machine learning models or adaptive algorithms can be used to dynamically adjust the boundaries of these temperature ranges based on the battery's historical operating data and environmental conditions to optimize thermal management effectiveness. Furthermore, there are slight differences in the temperature ranges set for fast charging mode and slow charging mode. For example, for slow charging mode, the temperature ranges can be set as: (-∞, -20°C], (-20°C, -3°C], (-3°C, 43°C], (43°C, 55°C], (55°C, +∞); for fast charging mode, the temperature ranges can be set as: (-∞, -20°C], (-20°C, -10°C], (-10°C, 43°C], (43°C, 55°C], (55°C, +∞).

[0030] If the temperature of a single battery cell is within the normal temperature range, thermal management is not activated; otherwise, thermal management is activated, and the thermal management mode is determined. This feature describes the basic judgment logic for whether the thermal management system is activated. Here, the temperature of a single battery cell refers to the maximum and minimum values ​​among all single battery cell temperatures. That is, when both the minimum and maximum temperatures of a single battery cell are within the normal temperature range, the battery performance is usually at its optimal state, requiring no additional thermal management intervention, thus saving energy consumption. Once the minimum or maximum temperature of a single battery cell deviates from the normal temperature range, it indicates that the battery may face the risk of being too cold or too hot. At this time, the thermal management function must be activated, and the corresponding thermal management mode is further determined based on the specific temperature state. This is achieved by continuously monitoring the temperature of the single battery cell and comparing it with the preset upper and lower limits of the normal temperature range. If the temperature is within the normal temperature range, the thermal management actuators (such as heaters, cooling pumps, etc.) remain inactive; if the temperature exceeds the range, a thermal management activation signal is triggered, and the subsequent mode judgment process is entered according to the direction and degree of temperature deviation.

[0031] When thermal management is activated, if the minimum temperature of a single battery cell is in the ultra-low temperature range, the thermal management mode is set to heating-only mode. The charging process may be restricted or paused, prioritizing and only performing heating to quickly raise the battery temperature to a suitable charging range, ensuring charging safety and efficiency. Upon detecting that the minimum temperature of a single battery cell is in the ultra-low temperature range, the Battery Management System (BMS) will activate the battery heating system, such as a PTC heater, liquid-cooled heater, or utilize the charging current for self-heating. Simultaneously, it may restrict or pause the charging current until the battery temperature reaches the preset heating target temperature.

[0032] If the minimum temperature of a single battery cell is within the low-temperature range, the thermal management mode is determined to be a simultaneous charging and heating mode. This feature is designed for charging scenarios where the battery is at a low but not extremely low temperature. Within this range, although the battery can be charged, its efficiency and lifespan will still be affected. Therefore, the simultaneous charging and heating mode is adopted to gradually increase the battery temperature while ensuring charging, optimizing charging performance and reducing damage to the battery. The system activates the heating system for auxiliary heating while allowing the charging current to pass. The heating power and charging current can be coordinated and controlled according to the battery's real-time temperature, SOC, and charging power requirements. For example, when the temperature is low, the heating power can be appropriately increased, while the charging current may be slightly limited; as the temperature rises, the heating power gradually decreases.

[0033] If the maximum temperature of a single battery cell is within the high-temperature range, the thermal management mode is determined to be a simultaneous charging and cooling mode. This feature is used to address situations where the battery is charged at high temperatures. High temperatures accelerate battery aging and may even trigger thermal runaway. Therefore, simultaneous cooling during charging effectively controls battery temperature, prevents overheating, and ensures charging safety and battery life. The cooling system, such as an air-cooled system, liquid-cooled system, or refrigerant-cooled system, is activated during charging. The cooling intensity can be dynamically adjusted based on factors such as battery pack temperature, charging current, and ambient temperature to maintain the battery temperature within a safe and efficient operating range.

[0034] If the maximum temperature of a single battery cell is in the ultra-high temperature range, the thermal management mode is set to cooling-only mode. This feature is designed for emergency situations involving the battery at extreme temperatures. Continuing to charge when the maximum temperature of a single battery cell reaches the ultra-high temperature range poses a serious safety hazard. In this situation, charging must be stopped immediately, and cooling must be initiated at full speed to reduce the battery temperature as quickly as possible and prevent thermal runaway. Once the maximum temperature of a single battery cell is detected to have entered the ultra-high temperature range, the Battery Management System (BMS) will immediately cut off the charging circuit and activate the cooling system at maximum power until the battery temperature drops below the safe threshold.

[0035] Based on the example data for each temperature range and the thermal management mode determination method mentioned above, the determination conditions for each thermal management mode can be determined as follows: Under slow charging mode, only cooling mode (T max >55°C), charging and cooling mode (43°C < T max ≤55°C), charging and heating mode (-20°C < T min ≤-3°C), heating only mode (T) min ≤-20°C); In fast charging mode, cooling mode only (T max >55°C), charging and cooling mode (43°C < T max ≤55°C), charging and heating mode (-20°C < T min ≤-10°C), heating only mode (T min (≤-20°C). Through the above technical solution, this application can dynamically and accurately identify whether thermal management needs to be activated and which thermal management mode should be adopted based on the real-time temperature status of individual battery cells. Specifically, by setting finely defined temperature ranges such as ultra-low temperature, low temperature, normal temperature, high temperature, and ultra-high temperature, the system can accurately classify the temperature environment in which the battery is located. This segmented, adaptive thermal management mode identification mechanism enables the thermal management strategy to be highly matched with the actual temperature state of the battery cell, avoiding the problems of inaccurate or inconsistent thermal management identification in traditional methods. Given the significant impact of thermal management mode on the battery temperature rise / fall rate, this application provides more accurate input parameters for subsequent phased calculation of remaining charging time by accurately identifying the thermal management mode, thereby improving the accuracy and reliability of remaining charging time estimation. Especially under extreme conditions such as ultra-low temperature, low temperature, high temperature, or ultra-high temperature, it can effectively reduce estimation errors and meet users' high requirements for the predictability of charging time.

[0036] In one embodiment, this application proposes a method for determining the remaining charging time using the following formula when the charging mode is fast charging mode: ; Among them, T RDCT Remaining charging time in fast charging mode; T TMM The expected duration of the current thermal management mode; T TMMPre The expected duration of the next phase of thermal management mode; T SOCTemp The required charging time at the current SOC stage; T Q－SOCTemp This refers to the charging time required for the remaining SOC at room temperature after removing the current SOC stage.

[0037] Specifically, T RDCTThis represents the total time required for the battery to fully charge from the current moment in fast charging mode. This value is the most important prediction for users, as its accuracy directly impacts the user experience. T TMM The estimated duration of the current thermal management mode quantifies how long the currently operating thermal management strategy (such as heating, cooling, or no thermal management) is expected to last. Accurate estimation of this duration is crucial for capturing the immediate impact of thermal management on charging efficiency. TMMPre The prediction of the duration of the next thermal management mode helps to anticipate the duration of the next thermal management mode after the current one ends. This allows for advance consideration of the impact of future changes in thermal management strategies on the charging process, thereby improving the continuity and accuracy of the prediction.

[0038] T SOCTemp The charging time required for the current SOC stage is used to accurately calculate the charging time needed to reach the upper limit of the preset SOC stage from the current state of charge (SOC). This takes into account the nonlinearity of the battery's charging characteristics in different SOC ranges. Q－SOCTemp This function serves as a reference to determine the charging time required for the remaining SOC at room temperature after removing the current SOC stage. It provides a baseline time required to complete the remaining charging capacity under ideal room temperature conditions. This helps separate the impact of thermal management from the pure charging capacity requirement, simplifying calculations and improving robustness.

[0039] Through the above technical solution, this application decomposes the remaining charging time in fast charging mode into four key components: the estimated duration of the current thermal management mode, the estimated duration of the next stage of thermal management mode, the charging time required for the current SOC stage, and the charging time required for the remaining SOC at room temperature after removing the current SOC stage. This decomposition method enables the system to dynamically and precisely consider the real-time changes in thermal management strategies and their impact on the charging rate, as well as the charging characteristics of the battery at different states of charge (SOC) stages. By incorporating these dynamically changing factors into the calculation, this application can achieve precise dynamic adjustment of the remaining charging time, improving the accuracy and real-time performance of the remaining charging time estimation, thereby meeting users' high requirements for the predictability of charging time and improving the user experience.

[0040] In one embodiment, this application proposes a method for estimating the remaining charging time. When the charging mode is slow charging mode, the remaining charging time is determined by the following formula: When the charging mode is slow charging, the remaining charging time is determined by the following formula: ; Among them, T RACT Remaining charging time in slow charging mode; T TMMThe expected duration of the current thermal management mode; T TMMPre The expected duration of the next phase of thermal management mode; The charging time required to charge the SOC from the end threshold to 100%; Charge the current SOC to the end threshold SOC. chrgEnd The required charging time. End-of-life threshold SOC. chrgEnd The value is 90%-99%, preferably 95%.

[0041] Specifically, when the charging mode is slow charging, this condition limits the applicable scenarios of this method, ensuring that the estimation logic is specifically adapted to low charging rate conditions and avoiding confusion with the estimation logic of fast charging mode. The overall remaining charging time is predicted by accumulating the estimated values ​​from multiple time periods, reflecting the idea of ​​phased integration. RACT The remaining charging time in slow charging mode serves to define the output of the formula, i.e., the charging completion time in slow charging mode. This result can be directly displayed to the user or used as an input parameter for subsequent system decisions (such as adjusting the charging strategy).

[0042] Among them, T TMM T TMMPre The parameter calculation method is the same as that in fast charging mode, only the historical data used is different. One is the data corresponding to fast charging mode, and the other is the data corresponding to slow charging mode. The charging time required to charge the SOC from the end threshold to 100% is used to optimize the charging characteristics at the end of slow charging. Charge the current SOC to the end threshold SOC. chrgEnd The required charging time is used to cover the main charging phase from the current State of Charge (SOC) to the end threshold. End threshold SOC chrgEnd A threshold for the end of the slow charging mode is defined as a calculation benchmark to ensure that the time segmentation is consistent with the actual charging behavior. This threshold can be written into the system configuration as a fixed parameter, or it can be dynamically adjusted according to battery type, charging strategy, or user preference, but the default or typical value is 95%.

[0043] Through the above technical solution, this application, in slow charging mode, introduces T... TMM and T TMMPreThis approach quantifies and incorporates the immediate and future switching effects of thermal management modes into the estimation. Simultaneously, by dividing the SOC charging process into two stages, particularly the independent calculation of the final charging stage, it effectively addresses the issues of nonlinear SOC changes and decreased charging efficiency at the end of the charging process under slow charging, significantly improving the prediction accuracy of the final charging time. This strategy of subdividing the entire slow charging process into a thermal management stage and a SOC charging stage, and estimating and accumulating them separately, enables refined and adaptive prediction of the remaining charging time under slow charging mode, thereby significantly improving estimation accuracy and enhancing the user experience.

[0044] In one embodiment, this application further proposes the expected duration T of the current thermal management mode. TMM The process of determining [the value] specifically includes: Obtain the most recent first historical temperature change rates under the current thermal management mode; The average rate of change of the first historical temperature is calculated based on multiple first historical temperature change rates. Once the first temperature difference is determined, the expected duration of the current thermal management mode is the first temperature difference divided by the average rate of change of the first historical temperature; the first temperature difference is the absolute value of the difference between the current battery pack temperature and the temperature threshold corresponding to exiting the current thermal management mode.

[0045] The purpose of acquiring multiple recent historical temperature change rates under the current thermal management mode is to collect data reflecting the actual trend of battery pack temperature changes under this mode. By acquiring multiple recent historical data, it is ensured that the analyzed temperature change rates are timely and reflect the dynamic characteristics under current operating conditions, rather than being based on outdated or irrelevant historical data. The temperature change rate is obtained by dividing the temperature change in the corresponding thermal management mode during previous charging processes by time. For example, during a fast charging session in a simultaneous charging and cooling mode, if the time taken for the battery pack temperature to rise from 0℃ to 10℃ is 10 minutes, then the temperature change rate under this mode is 1℃ / min.

[0046] The average of multiple historical temperature change rates is calculated to eliminate the influence of instantaneous measurement noise and short-term fluctuations, thereby obtaining a more stable and representative indicator of temperature change trend. This average better reflects the overall rate of temperature change of the battery pack under the current thermal management mode, providing a reliable input for subsequent duration estimation.

[0047] Determining the first temperature difference aims to quantify the gap between the current battery pack temperature and the target temperature required to exit the current thermal management mode. This first temperature difference directly reflects how much the battery pack temperature needs to change to meet the conditions for switching thermal management modes, and is a key input for estimating the duration. For example, different temperature thresholds are preset for different thermal management modes. If the current mode is charging while heating, its exit threshold might be a certain upper limit of low temperature; if the current mode is charging while cooling, its exit threshold might be a certain lower limit of high temperature. The first temperature difference is calculated as the absolute value of the difference between the current battery pack temperature and the temperature threshold corresponding to exiting the current thermal management mode.

[0048] Ultimately, the expected duration of the current thermal management mode is calculated by dividing the first temperature difference by the average rate of change of the first historical temperature. This step estimates the duration of the thermal management mode based on the fundamental relationship in physics (time = distance / velocity). By dividing the temperature difference to be eliminated (the first temperature difference) by the average rate of change of the battery pack temperature (the average rate of change of the first historical temperature), the time required for the battery pack temperature to reach the target threshold can be scientifically and reasonably predicted. For example, by directly performing numerical calculations, if the first temperature difference is 5°C and the average rate of change of the first historical temperature is 0.1°C / minute, then the expected duration is 5 / 0.1 = 50 minutes.

[0049] Through the above technical solution, this application dynamically acquires and averages historical temperature change rates, combines the temperature difference between the current temperature and the target temperature threshold, and uses a physical model (time = temperature difference / rate) to predict the duration of the current thermal management mode. This solution, by introducing multiple recent first historical temperature change rates and calculating their average, can capture the actual dynamic change trend of the battery pack temperature, effectively filtering out instantaneous noise, thereby obtaining a more stable and representative temperature change rate. Combined with the accurate calculation of the first temperature difference, the estimated duration is closer to actual operating conditions, improving the accuracy of the estimation results. In the method for estimating the remaining charging time, the accurate estimated duration of the current thermal management mode is one of the key inputs. With the accurate estimated duration provided by this solution, the remaining charging time estimation formula can obtain more reliable parameters for both fast and slow charging modes, making the overall remaining charging time prediction result more accurate and stable. Especially in complex temperature environments where thermal management frequently intervenes, it can effectively reduce prediction errors and improve user experience.

[0050] In one embodiment, this application further proposes a method for determining the expected duration T of the next stage of thermal management mode. TMMPreThe method specifically includes: determining the temperature of a single cell; if the minimum temperature of a single cell is less than the ultra-low temperature threshold or the maximum temperature of a single cell is greater than the ultra-high temperature threshold, then determining the expected duration of the next stage of thermal management mode based on the second historical temperature change rate; otherwise, the expected duration of the next stage of thermal management mode is 0.

[0051] First, the temperature of each individual battery cell needs to be determined. Individual cell temperature is fundamental to assessing the battery's thermal state and thermal management requirements, and its accuracy directly impacts the reliability of the remaining charge time estimation. Specifically, this can be achieved by deploying multiple temperature sensors within the battery pack to directly measure the surface or internal temperature of each or a portion of representative individual cells, and then transmitting this data in real time to the battery management system (BMS) for processing.

[0052] After obtaining the temperature of a single battery cell, the system will determine the minimum temperature T of the single battery cell. min Is it below the ultra-low temperature threshold or the maximum temperature T of a single cell? max Whether the temperature exceeds the ultra-high temperature threshold. This judgment aims to identify whether the battery system is in an extreme temperature condition requiring the activation or adjustment of thermal management strategies. For example, fixed ultra-low temperature thresholds and ultra-high temperature thresholds can be preset, such as setting the ultra-low temperature threshold to -20℃ and the ultra-high temperature threshold to 55℃. The system compares the maximum and minimum values ​​of the collected individual cell temperatures with these preset thresholds in real time. max >55℃ or T min When the temperature is below -20℃, it indicates that the battery system needs to activate or adjust the thermal management strategy for extreme temperature conditions, i.e., it needs to activate the cooling-only or heating-only mode.

[0053] If the above conditions are met, i.e., the battery is under extreme temperature conditions, the expected duration of the next stage of thermal management mode is determined based on the second historical temperature change rate. This step utilizes historical data to predict the duration of the thermal management mode, enabling the system to dynamically estimate the time consumed by the thermal management process based on the battery's actual thermal characteristics and historical performance. Specifically, the system can store and analyze data on the rate of change of battery pack temperature or individual cell temperature over time under a specific thermal management mode. When it is necessary to predict the duration of the next stage of thermal management, a temperature change rate similar to the current operating condition is selected from historical data and calculated in conjunction with the target temperature difference.

[0054] Otherwise, if the above conditions are not met, meaning the battery system is not under extreme temperature conditions, then there is no next-stage thermal management mode, and the expected duration of the corresponding next-stage thermal management mode is 0. This processing mechanism avoids unnecessary complex calculations and improves the system's response speed and computational efficiency.

[0055] Through the above technical solution, this application can dynamically and accurately determine the expected duration of the next stage of thermal management mode. By acquiring the temperature of individual battery cells in real time and combining it with preset ultra-low temperature thresholds and ultra-high temperature thresholds, the system can accurately identify whether the battery is in an extreme temperature range requiring special thermal management. When the battery is in an extreme temperature, historical temperature change rates are used for prediction, making the estimated duration of the next stage of thermal management mode closer to reality, thereby improving the accuracy of the remaining charging time estimation. Under non-extreme temperature conditions, this duration is directly set to 0, avoiding unnecessary computational burden and ensuring prediction efficiency. Overall, this method, through conditional calculation and historical data combination, allows the remaining charging time estimation to better adapt to the dynamic changes in battery thermal management, especially under extreme high and low temperature conditions, accurately reflecting the impact of thermal management behavior on the charging process, thereby improving the prediction accuracy of the remaining charging time and user experience. For example, only when the current thermal management mode is heating-only or cooling-only will there be a next stage of thermal management mode and its corresponding duration, in which case the next stage of thermal management mode is charging while heating or charging while cooling. If the current thermal management mode is any other than heating only or cooling only, there is no next stage of thermal management mode, and the expected duration of the next stage of thermal management mode is 0.

[0056] In one embodiment, this application further proposes a method for determining the expected duration of the next stage of thermal management mode based on a second historical temperature change rate. This method is similar to the method described above for determining the expected duration of the current thermal management stage, both being based on historical data. Specifically, it involves: obtaining multiple recent second historical temperature change rates under the next stage of thermal management mode; calculating the average of the second historical temperature change rates based on the multiple second historical temperature change rates; determining a second temperature difference, whereby the expected duration of the next stage of thermal management mode is the second temperature difference divided by the average of the second historical temperature change rates; the second temperature difference is the absolute value of the difference between the temperature threshold corresponding to exiting the current thermal management mode and the temperature threshold corresponding to exiting the next stage of thermal management mode.

[0057] Specifically, once the current thermal management mode is determined, the next thermal management mode can be determined. This allows for the acquisition of historical data under the same mode, thereby predicting the duration of the mode. When acquiring the second historical temperature change rate, the system can periodically record parameters such as battery pack temperature and individual cell temperature. Combined with the thermal management mode switching records, the system calculates and stores the temperature change rate under that mode each time a thermal management mode is switched or a specific time point is reached. For example, the temperature increase or decrease value per unit time.

[0058] When calculating the average of the second historical temperature change rates based on multiple rates, an arithmetic mean method can be used. This involves summing all the acquired rates and then dividing by the number of rates. When determining the second temperature difference, a preset temperature threshold table can be used to find the exit temperature threshold for the current thermal management mode and the exit temperature threshold for the next stage of the thermal management mode. The absolute value of the difference between the two is then calculated. This clarifies the specific calculation method for the second temperature difference, ensuring the accuracy and consistency of the calculation. For example, the ultra-low temperature range may correspond to a heating exit threshold, and the low temperature range may correspond to a simultaneous charging and heating exit threshold. Based on the current mode and the next stage mode, the corresponding exit temperature threshold is looked up from the table, and the absolute value of the difference is calculated.

[0059] When calculating the expected duration of the next stage of the thermal management mode, the previously calculated second temperature difference and the average of the second historical temperature change rates are directly substituted into the formula. In practical applications, a correction coefficient can also be introduced to fine-tune the calculation results to address potential nonlinear factors or systematic errors in actual operating conditions.

[0060] Through the above technical solution, this application can more accurately predict the duration of the next stage of thermal management mode, thereby significantly improving the accuracy of the overall remaining charging time estimation. Especially in scenarios where thermal management modes frequently switch or temperatures change drastically, it can effectively cope with the impact of temperature changes on the remaining charging time estimation and provide users with more reliable charging information.

[0061] In one embodiment, this application further proposes the charging time T required for the current SOC stage. SOCTemp The process of determining is as follows: Determine the current SOC stage by looking up a table; The difference between the upper limit of the preset SOC stage and the actual SOC value at the previous moment is calculated as the remaining power in the current SOC stage; Determine the required charging capacity Q corresponding to the remaining charge at the current SOC stage. SOCTemp ; The charging time required for the current SOC stage is Q. SOCTemp Divided by the steady charging current I real ; The stable charging current I real It is obtained by applying a moving average filter to the battery charging current within a set time window prior to the current moment.

[0062] Specifically, determining the current SOC stage can be achieved using a lookup table pre-stored in system memory. This lookup table divides the entire SOC range into multiple discrete, pre-defined stages based on the battery's state of charge (SOC), such as each stage representing 10% or 5%. After acquiring the current battery SOC value in real time, the system can quickly locate the specific stage corresponding to the current SOC value by querying this table.

[0063] When calculating the remaining battery capacity for the current SOC stage as the difference between the upper limit of the preset SOC stage and the actual SOC value at the previous moment, the upper limit of the SOC is first obtained from the predetermined preset SOC stage. For example, if the current SOC is 35% and the preset stage is 30%-40%, then the upper limit is 40%. Then, this upper limit is subtracted from the real-time collected actual SOC value from the previous moment; the difference represents the percentage of battery capacity still needed within the current SOC stage. For example, 40% - 35% = 5%. This step transforms the abstract SOC stage into a specific battery demand, laying the foundation for subsequent capacity calculations.

[0064] Determine the required charging capacity Q corresponding to the remaining power at the current SOC stage. SOCTemp When calculating the remaining charge percentage, multiply the calculated percentage by the battery's rated capacity C. For example, if the battery's rated capacity is 50Ah and the remaining charge is 5%, then the required charging capacity Q is... SOCTemp The result is 50Ah * 5% = 2.5Ah. This conversion transforms the relative percentage of charge into an absolute unit of charge, making subsequent charging time calculations more intuitive and accurate.

[0065] When calculating the required charging time for the current SOC stage, the determined required charging capacity Q is used. SOCTemp Divide by the steady-state charging current Ireal. For example, if Q SOCTemp It has a capacity of 2.5Ah and a stable charging current I. real If the current is 5A, then the charging time is 2.5Ah / 5A = 0.5 hours. This direct calculation method based on capacity and current can effectively reflect the charging needs at the current stage.

[0066] In order to obtain the stable charging current I real This application obtains the current by applying a moving average filter to the battery charging current within a set time window prior to the current moment. Moving average filtering is a commonly used signal processing technique. Its principle is to average all current samples within a fixed-size time window and use the average result as the stable charging current I at the current moment. real For example, a 10-second time window can be set, and the charging current can be sampled once per second, thus ensuring a stable charging current I.real It is the average of all current values ​​over the past 10 seconds.

[0067] Through the above technical solution, this application provides a method for accurately calculating the charging time required for the current SOC stage. Specifically, by looking up a table, the preset SOC stage of the current SOC is determined, ensuring the standardization and consistency of stage division, avoiding subjective division errors, and laying the foundation for subsequent calculations; the difference between the upper limit of the preset SOC stage and the actual SOC value at the previous moment is calculated as the remaining power of the current SOC stage, quantifying the remaining charging demand, and using the actual SOC value improves data accuracy; the required charging capacity Q corresponding to this remaining power is then determined. SOCTemp Converting electricity into capacity units facilitates time calculations; Q SOCTemp Divided by the steady charging current I real The required charging time is obtained, and time estimation is achieved through current conversion; while the stable charging current I... real This method, achieved by applying a moving average filter to the battery charging current within a pre-defined time window prior to the current moment, smooths out current fluctuations, reduces noise interference, and ensures a stable and reliable current value. Ultimately, this makes the estimation of remaining charging time more robust and accurate. This approach is particularly important in fast charging mode, where current fluctuations can be significant. Introducing a stable current can significantly improve the accuracy and stability of the remaining charging time estimation, thus providing users with more reliable charging information.

[0068] In one embodiment, this application further proposes to determine T by the following formula. Q－SOCTemp : Where N is the number of SOC stages contained in the remaining SOC at room temperature after removing the current SOC stage; i is the number of the SOC stages contained in the remaining SOC at room temperature; ΔSOC is the preset interval length of the SOC stages; C is the rated capacity of the battery; I map_i This represents the charging rate corresponding to the i-th SOC stage.

[0069] Specifically, N in the formula represents the number of SOC stages contained in the remaining SOC at room temperature after removing the current SOC stage. This number N is used to define the total number of SOC stages that need to be charged from the current state of charge (SOC) to a fully charged battery (usually 100% SOC) under room temperature conditions. N can be determined by pre-setting an SOC stage division table and traversing the table to count the number of stages based on the current SOC value and the target SOC value (e.g., 100%); or by using a dynamic algorithm to calculate the number of stages that can be divided from the current SOC to 100% based on the actual charging characteristics of the battery and the preset SOC interval length ΔSOC. For example, if the current SOC is 57%, which is in the 55%-60% stage range, and ΔSOC is 5%, then the remaining SOC is in the 60%-100% interval, which contains 8 SOC stages, and N is 8. i represents the SOC stage number contained within the remaining SOC at room temperature. It is typically an integer index from 1 to N, used to iterate over different SOC stages in a loop calculation. Alternatively, it can be a unique identifier associated with a specific SOC range (e.g., 80%-85%) to facilitate finding the corresponding charge rate I. map_i Used at specific times. ΔSOC is the preset interval length of the SOC stage, referring to the percentage length of SOC represented by each stage when the battery's state of charge (SOC) range is divided into several stages. ΔSOC can be set to a fixed value, such as 5% or 10%, so that the entire SOC range is evenly divided. C is the battery's rated capacity, referring to the total amount of electricity the battery can release from a fully charged state under specific discharge conditions, usually expressed in ampere-hours (Ah) or kilowatt-hours (kWh). This parameter is usually provided by the battery manufacturer and stored in the battery management system (BMS) or vehicle controller. I map_i The charging rate corresponding to the i-th SOC stage refers to the ratio of the maximum charging current that the battery can accept to the battery's rated capacity in the i-th SOC stage, reflecting the charging speed potential of that stage. map_i The maximum charging current of the battery can be measured at different SOC stages through pre-conducted battery charging characteristic tests, and the result can be calculated in combination with the battery's rated capacity. This data can be stored in a lookup table (map).

[0070] Through the above technical solution, this application introduces a precise formula to calculate T. Q－SOCTemp This formula comprehensively considers N (the number of remaining SOC stages), i (stage number), ΔSOC (preset interval length), C (battery rated capacity), and I. map_i(Charging rate corresponding to the i-th SOC stage). By dividing the remaining SOC into multiple stages and assigning a specific charging rate to each stage, the system can simulate the battery's charging behavior in different SOC ranges more precisely. This segmented calculation method can dynamically adapt to changes in the battery's charging characteristics at different SOC stages; for example, the charging rate may be higher at lower SOCs, while it may gradually decrease at higher SOCs. By accurately considering these dynamic characteristics, this method significantly improves T... Q－SOCTemp The estimation accuracy is improved. Combining the above-mentioned charging remaining time estimation method based on self-identification and segmented prediction, especially when the charging mode is fast charging, this accurate T... Q－SOCTemp The calculation can more accurately predict the total remaining charging time T in fast charging mode. RDCT This enables the entire remaining charging time estimation system to provide more reliable and realistic predictions, thereby improving user experience and charging efficiency management.

[0071] In one embodiment, this application further proposes the charging time required to charge the SOC from the end threshold to 100%. The determination process is as follows: obtain the most recent historical end-charge times when the SOC is charged from the end threshold to 100%; use the average of the multiple historical end-charge times as the... .

[0072] Specifically, when determining the charging time required to charge from the final threshold to 100% of the State of Charge (SOC), it is first necessary to obtain the most recent historical charging times at which the SOC reached 100%. This step aims to collect data on the time required for the battery to charge from the final threshold of a specific SOC to full charge (100%) during actual charging. This historical data reflects the charging characteristics of the battery under different operating conditions. For example, when the charge reaches 95%, the timer starts and resets when charging is complete. The onboard battery management system (BMS) can automatically record and store the actual charging time from the final SOC threshold to 100% during each charge. This data can be stored in the vehicle's local memory and managed in chronological order.

[0073] After obtaining multiple historical end-point charging durations, this application further uses the average of the multiple historical end-point charging durations as the... By averaging these historical data, random fluctuations and outliers can be effectively smoothed out, resulting in a more stable and representative estimate. This helps improve the robustness and accuracy of predictions and reduces the impact of single charging anomalies or measurement errors.

[0074] Through the above technical solution, this application determines the charging time required to charge from the final threshold to 100% SOC using a method based on historical data averaging. This significantly improves the accuracy and robustness of estimating the remaining charging time in slow charging mode. Overall, this method makes calculating the remaining charging time T in slow charging mode much easier. RACT At the same time, the estimation of its key components is more accurate, so that the prediction of the overall remaining charging time is more in line with the actual working conditions, thus improving the user experience.

[0075] In one embodiment, this application further proposes a formula for determining the required charging time from the current SOC to the end threshold. : ; Where E is the battery pack energy at full charge under normal temperature; SOC chrgEnd End threshold; SOC real P represents the actual SOC value at the current moment. charger This refers to the charger's rated power.

[0076] Specifically, the battery pack energy E at full charge under normal temperature refers to the total energy that the battery pack can store when fully charged under standard ambient temperature conditions (e.g., 25°C). This parameter serves as a benchmark for calculating battery energy differences, aiming to ensure the stability and accuracy of the calculation and reduce the impact of ambient temperature changes on energy estimation. End-of-line threshold SOC chrgEnd This refers to the percentage of State of Charge (SOC) set when the battery charging process is considered to be close to completion or has reached the user's desired charging level in slow charging mode.

[0077] The actual SOC value at the current moment. real This refers to the actual state-of-charge percentage of the battery pack at the current moment. This parameter is introduced to provide real-time battery state data, support dynamic updates in the calculation process, and ensure that the estimation results accurately reflect the battery's immediate condition. Charger rated power P charger This refers to the maximum output power that the currently connected charger can provide under normal operating conditions. This parameter reflects the performance limitations of the actual charging equipment, ensuring that the estimated charging time matches the actual charging capacity.

[0078] Through the above technical solution, this application directly utilizes the battery pack energy E when fully charged at room temperature and the end-of-life threshold SOC. chrgEnd The actual SOC value at the current moment. real and the charger's rated power P chargerBased on physical parameters, a formula for calculating the required charging time to reach the final threshold of the current State of Charge (SOC) in slow charging mode was constructed. This formulaic method based on physical parameters overcomes the problem of insufficient dynamic adaptability in traditional empirical estimations, ensuring that the calculation results can reflect the actual demand for battery energy transfer in real time. Overall, this solution significantly improves the accuracy and reliability of estimating the remaining charging time in slow charging mode, enabling users to obtain more accurate charging time predictions and thus optimizing the charging experience.

[0079] In one embodiment, this application further proposes to include updating the corresponding historical data based on the temperature change and timing data of this charging process at the end of the charging process. The historical data includes the temperature rise / fall rate of the corresponding thermal management mode and the end charging time in slow charging mode.

[0080] Specifically, the end of charging refers to the moment when the system detects the completion of the charging process. This can be determined by monitoring the charging current to drop below a preset threshold or by the battery's state of charge (SOC) reaching a target value (e.g., 100%). Temperature changes and timing data during this charging process refer to the battery-related temperature data (such as battery pack temperature, individual cell temperature, etc.) and charging duration data (such as the duration of thermal management mode) collected and recorded by the system in real time throughout the entire charging cycle.

[0081] Updating historical data refers to correcting or replacing the historical data stored in the system that is used for subsequent charging estimations, using the actual temperature changes and timing data measured during the current charging process. For example, the system can directly overwrite or replace the old historical records in the database corresponding to a specific thermal management mode calculated during the current charging process.

[0082] The historical data, including the temperature rise / fall rate for the corresponding thermal management mode and the final charging time in slow charging mode, clarifies the specific data types that need to be updated. The "temperature rise / fall rate for the corresponding thermal management mode" refers to the rate at which the battery temperature changes over time under different thermal management modes (e.g., heating only, heating while charging, cooling while charging, cooling only, etc.). These rates are key parameters for estimating the expected duration of the thermal management mode. The final charging time in slow charging mode refers to the actual time required for the battery to charge from a certain final state of charge (SOC) threshold (e.g., 95%) to 100% in slow charging mode. This historical data is typically stored in the non-volatile memory of the battery management system (BMS) or managed and synchronized via a cloud platform.

[0083] Through the above technical solution, after each charge, the system adaptively updates key historical parameters using the actual temperature changes and timing data that occurred during the charging process. This continuous learning and correction mechanism enables the system to dynamically adapt to battery performance degradation, changes in external ambient temperature, and differences in user charging habits, thereby ensuring that the historical data used for estimating the remaining charging time is always up-to-date and highly accurate. Overall, this solution enhances the adaptability and robustness of the remaining charging time estimation system, avoids estimation errors caused by outdated historical data, and provides users with a more accurate and reliable charging experience.

[0084] In one embodiment, this application further proposes a step for adjusting the remaining charging time displayed on the screen, including at least one of the following: When charging, if the charging current is zero or the discharging current is greater than or equal to the charging current, the remaining charging time displayed remains unchanged. When the thermal management mode is heating only or cooling mode, or when the actual SOC reaches the limit threshold, the displayed remaining charging time decreases according to the actual time elapsed rate. When the actual SOC is lower than the limit threshold, the rate of change of the displayed remaining charging time is limited. This limit is set with different maximum and minimum rates of change based on the current SOC value. Under normal charging conditions, the remaining charging time displayed on the meter can only decrease.

[0085] Specifically, the technical feature that maintains the displayed remaining charging time if the charging current is zero or the discharging current is greater than or equal to the charging current during charging is designed to handle situations where actual charging has not occurred or is ineffective. Zero charging current indicates that no current is flowing into the battery, and the charging process has been paused or stopped; a discharging current greater than or equal to the charging current means that the battery is discharging, or the net current is insufficient for effective charging.

[0086] The technical feature that reduces the displayed remaining charging time according to the actual elapsed time when the thermal management mode is heating-only or cooling-only, or when the actual SOC reaches the limit threshold, primarily addresses situations where charging efficiency may be limited or the estimation model may have significant biases under specific thermal management states or extreme states of charge (SOC). Heating-only or cooling-only modes refer to the battery system undergoing temperature regulation, during which effective charging may not be taking place, or the charging efficiency may be extremely low. Reaching the limit threshold for the actual SOC means the battery's SOC is very high (e.g., close to 100%), at which point the charging rate is usually limited, and the estimation model may have significant biases. To achieve this function, one approach is for the system to acquire the current thermal management mode status in real time. If the current mode is identified as heating-only or cooling-only, the displayed remaining charging time is directly set to countdown at a rate of one second less per second. Another approach is for the system to continuously monitor the battery's actual SOC value. When the SOC value reaches a preset upper limit threshold (e.g., 98%), the system switches to a mode that reduces the displayed remaining charging time according to the actual elapsed time. Under thermal management or extreme SOC conditions, the estimated remaining charging time may be inaccurate. In such cases, reducing the displayed time according to the actual elapsed time rate can more realistically reflect the user's waiting time, reduce the impact of estimation deviation on the display, and avoid user confusion due to inaccurate estimates.

[0087] Regarding the rate of change limitation of the displayed remaining charging time when the actual SOC is below the limit threshold, this technical feature sets different maximum and minimum change rates based on the current SOC value. Its purpose is to smooth the display during low SOC phases and prevent drastic fluctuations in the time display. The rate of change limitation constrains the amount by which the displayed remaining charging time decreases per unit of time, keeping it between the set maximum and minimum values. For example, when the SOC is below 90%, the maximum rate of change of the displayed remaining charging time is 5 / s (meaning that for every 12 seconds of absolute time, the displayed remaining charging time decreases by 1 minute), and the minimum is 0.2 / s; when the SOC is between 90% and 99%, the maximum rate of change of the displayed remaining charging time is 2 / s, and the minimum is 0.5 / s. By limiting the rate of change, drastic fluctuations in the time display can be prevented, ensuring a smooth and predictable display and improving the user experience.

[0088] The technical feature that allows the displayed remaining charging time to only decrease under normal charging conditions ensures a consistent and intuitive user experience during normal charging. Normal charging conditions refer to charging the battery under non-extreme temperature, non-extreme SOC, and stable charging current. Allowing only decreases means the displayed remaining charging time cannot increase, even if the estimation model might calculate a slight increase at certain moments. To achieve this, after calculating the new remaining charging time, it is compared with the displayed value at the previous moment. If the new value is greater than the previous displayed value, the system will maintain the previous displayed value, or only allow it to decrease in minimum steps (e.g., 1 minute) to ensure monotonicity. This aligns with the user's intuitive understanding of the charging process, avoids non-monotonic changes in the time display due to minor fluctuations in the estimation model, thereby eliminating user confusion and enhancing trust.

[0089] Through the above technical solution, this application introduces an adjustment step for the remaining charging time display to ensure more reliable, smooth, and user-expected time display. Specifically, during charging, if the charging current is zero or the discharging current is greater than or equal to the charging current, the remaining charging time display remains unchanged. This avoids incorrect time updates due to lack of actual charging progress when charging is paused or ineffective, thus improving display accuracy. When the thermal management mode is heating only or cooling mode, or when the actual SOC reaches the limit threshold, the remaining charging time display decreases according to the actual time elapsed. This more accurately reflects the user's waiting time under thermal management or extreme SOC conditions, reducing the impact of estimation errors on the display. When the actual SOC is below the limit threshold, the rate of change of the remaining charging time display is limited. This limit sets different maximum and minimum change rates based on the current SOC value. This prevents drastic time fluctuations when the SOC is low, ensuring smooth and predictable display. Under normal charging conditions, the remaining charging time display is only allowed to decrease, which aligns with the user's intuitive understanding of the charging process and avoids confusion caused by non-monotonic changes. These adjustment steps, combined with the aforementioned method for estimating remaining charging time, can effectively improve the accuracy, stability, and user experience of the remaining charging time display, especially under complex operating conditions, providing more reliable charging information.

[0090] In one embodiment, this application proposes a charging remaining time estimation system based on self-identification and segmented prediction. The system includes: The data acquisition module is used to collect battery parameters in real time. The mode determination module is used to determine the current charging mode based on battery parameters; The thermal management module is used to identify whether thermal management should be enabled based on battery parameters; The time estimation module is used to calculate the remaining charging time in stages based on the identification results of the battery parameters, charging mode, and thermal management.

[0091] Specifically, the data acquisition module periodically acquires battery parameters such as voltage, current, and temperature through sensors in the battery management system, ensuring that all subsequent processing is based on the latest dynamic data and avoiding estimation distortion caused by data lag. The mode judgment module distinguishes between fast charging mode and regular charging mode based on preset charging current thresholds or voltage change trends, enabling the system to adopt differentiated strategies for different charging rate characteristics, thereby improving adaptability to dynamic operating conditions. The thermal management module identifies whether thermal management should be activated based on whether the battery pack temperature exceeds a preset range, and determines the specific mode, such as heating or cooling, directly addressing the impact of thermal management on the battery temperature rise / fall rate, enabling estimation to respond to temperature changes in real time. The time estimation module divides the entire charging process into multiple state-of-charge stages. For each stage, combining the currently identified charging mode and thermal management state, it uses a corresponding calculation model to estimate the charging time required for that stage and dynamically updates the remaining time prediction, thus handling the multi-stage characteristics of the charging process.

[0092] Through the above technical solutions, this system achieves accurate capture of multi-stage characteristics of the charging process and real-time compensation for the impact of thermal management, effectively improving the accuracy of remaining charging time prediction, providing electric vehicle users with more reliable charging time information, and significantly enhancing its engineering application value.

[0093] It should be noted that the information interaction and execution process between the above modules / units are based on the same concept as the method embodiments of this application. For details on their specific functions and technical effects, please refer to the method embodiments section, and they will not be repeated here.

[0094] It is understood that those skilled in the art will clearly recognize that, for the sake of convenience and brevity, the above-described division of functional units and modules is used as an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0095] The above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit it. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. These modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application. Content not described in detail in this specification belongs to the prior art known to those skilled in the art.

Claims

1. A method for estimating remaining charging time based on self-identification and segmented prediction, characterized in that: Real-time battery parameter acquisition; Determine the current charging mode based on battery parameters; Identify whether thermal management should be enabled based on battery parameters; The remaining charging time is calculated in stages based on the identification results of the battery parameters, charging mode, and thermal management.

2. The charging remaining time estimation method based on self-identification and segmented prediction according to claim 1, characterized in that: The process of determining the current charging mode based on battery parameters includes: determining the charging mode based on any one of battery charging current, charging voltage, and charging power, wherein the charging mode is either a fast charging mode or a slow charging mode.

3. The method for estimating remaining charging time based on self-identification and segmented prediction according to claim 1, characterized in that: The method of identifying whether thermal management is enabled based on battery parameters includes: Set ultra-low temperature range, low temperature range, normal temperature range, high temperature range, and ultra-high temperature range; If the temperature of a single battery cell is within the normal temperature range, thermal management will not be activated; otherwise, thermal management will be activated and the thermal management mode will be determined. When thermal management is enabled, if the minimum temperature of a single cell is in the ultra-low temperature range, the thermal management mode is determined to be heating mode only. If the minimum temperature of a single battery cell is in the low-temperature range, then the thermal management mode is determined to be the simultaneous charging and heating mode. If the maximum temperature of a single battery cell is in the high-temperature range, then the thermal management mode is determined to be the charging-while-cooling mode. If the maximum temperature of a single cell is in the ultra-high temperature range, then the thermal management mode is determined to be cooling-only mode.

4. The method for estimating remaining charging time based on self-identification and segmented prediction according to claim 1, characterized in that: When the charging mode is fast charging, the remaining charging time is determined by the following formula: ； Among them, T RDCT Remaining charging time in fast charging mode; T TMM The expected duration of the current thermal management mode; T TMMPre The expected duration of the next phase of thermal management mode; T SOCTemp The required charging time at the current SOC stage; T Q－SOCTemp This refers to the charging time required for the remaining SOC at room temperature after removing the current SOC stage.

5. The method for estimating remaining charging time based on self-identification and segmented prediction according to claim 4, characterized in that: The charging time T required at the current SOC stage SOCTemp The process of determining is as follows: Determine the current SOC stage by looking up a table; The difference between the upper limit of the preset SOC stage and the actual SOC value at the previous moment is calculated as the remaining power in the current SOC stage; Determine the required charging capacity Q corresponding to the remaining charge at the current SOC stage. SOCTemp ; The charging time required for the current SOC stage is Q. SOCTemp Divided by the steady charging current I real ; The stable charging current I real It is obtained by applying a moving average filter to the battery charging current within a set time window prior to the current moment.

6. The method for estimating remaining charging time based on self-identification and segmented prediction according to claim 4, characterized in that: The T is determined by the following formula. Q－SOCTemp : Where N is the number of SOC stages contained in the remaining SOC at room temperature after removing the current SOC stage; i is the number of the SOC stages contained in the remaining SOC at room temperature; ΔSOC is the preset interval length of the SOC stages; C is the rated capacity of the battery; I map_i This represents the charging rate corresponding to the i-th SOC stage.

7. The method for estimating remaining charging time based on self-identification and segmented prediction according to claim 1, characterized in that: When the charging mode is slow charging, the remaining charging time is determined by the following formula: ； Among them, T RACT Remaining charging time in slow charging mode; T TMM The expected duration of the current thermal management mode; T TMMPre The expected duration of the next phase of thermal management mode; The charging time required to charge the SOC from the end threshold to 100%; To charge from the current SOC to the end threshold SOC chrgEnd The charging time required.

8. The method for estimating remaining charging time based on self-identification and segmented prediction according to claim 7, characterized in that: The charging time required to charge the SOC from the end threshold to 100% The process of determining is as follows: Obtain the most recent historical end-charge times when the SOC was charged from the end-threshold to 100%; The average of multiple historical end-point charging times is used as the... .

9. The method for estimating remaining charging time based on self-identification and segmented prediction according to claim 7, characterized in that: The required charging time from the current SOC to the final charging threshold is determined using the following formula. : ； Where E is the battery pack energy at full charge under normal temperature; SOC chrgEnd End threshold; SOC real P represents the actual SOC value at the current moment. charger This refers to the charger's rated power.

10. A charging remaining time estimation system based on self-identification and segmented prediction, characterized in that: include The data acquisition module is used to collect battery parameters in real time. The mode determination module is used to determine the current charging mode based on battery parameters. The thermal management module is used to identify whether thermal management should be enabled based on battery parameters; The time estimation module is used to calculate the remaining charging time in stages based on the identification results of the battery parameters, charging mode, and thermal management.

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