Estimation method of residual charging time of battery and battery management system

Abstract

The invention provides a battery residual charging time estimation method and a battery management system, and relates to the technical field of battery management. The method comprises the following steps: acquiring current state parameters of a battery, wherein the current state parameters comprise a current charge state, a health state, a temperature and a charging current; judging whether the battery is in a constant-current charging stage or a constant-voltage charging stage based on the current state parameter; starting a dynamic estimation algorithm to carry out continuous real-time estimation on the internal state of the battery; when the battery is in the constant current charging stage, calculating the remaining time of the constant current stage based on the current state parameter, predicting the remaining time of the constant voltage stage according to the real-time estimation result of the dynamic estimation algorithm, and taking the sum of the two as the total remaining charging time; and when the battery is in a constant-voltage charging stage, predicting the remaining time of the constant-voltage stage according to the real-time estimation result of the dynamic estimation algorithm, and taking the remaining time as the total remaining charging time.

Description

Technical Field

[0001] This invention relates to the field of battery management technology, and in particular to a method for estimating the remaining charging time of a battery and a battery management system. Background Technology

[0002] Accurately estimating the remaining charging time during electric vehicle charging is crucial for improving user convenience, optimizing the utilization of charging infrastructure, and protecting battery health. Currently, the accuracy of remaining charging time estimation during vehicle charging is easily affected by various factors, leading to significant deviations in practical applications.

[0003] In existing technologies, battery charging typically involves two stages: constant current and constant voltage. Traditional estimation methods often rely on static models or empirical formulas for time prediction. These methods are poorly adaptable to dynamic conditions, especially when faced with nonlinear factors such as changes in the battery's internal state and temperature fluctuations, making it difficult to accurately divide and predict the time of each stage. Furthermore, due to the lack of continuous real-time estimation of the battery's internal state, traditional methods exhibit a lag in determining the critical point of transition from the constant current stage to the constant voltage stage, further affecting the overall accuracy of time estimation.

[0004] Especially in complex application scenarios, parameters such as the battery's state of charge and health status change dynamically over time. However, existing algorithms lack a dynamic tracking mechanism for the battery's internal state, leading to inaccurate stage division judgments and an inability to establish a remaining time estimation model that matches the actual battery state. This limitation makes it difficult for existing technologies to meet users' needs for accurate charging time prediction and also hinders the development of intelligent charging management. Summary of the Invention

[0005] The purpose of this invention is to provide a method for estimating the remaining charging time of a battery and a battery management system, in order to solve the technical problems mentioned in the background art: the existing technology is difficult to meet users' needs for accurate prediction of charging time, and also restricts the development of intelligent charging management.

[0006] To achieve the above objectives, according to one aspect of the present invention, a method for estimating the remaining charging time of a battery is provided, the method comprising the following steps:

[0007] Obtain the current state parameters of the battery, including the current state of charge, health status, temperature, and charging current;

[0008] Based on the current state parameters, it is determined whether the battery is in the constant current charging stage or the constant voltage charging stage.

[0009] A dynamic estimation algorithm is enabled to continuously estimate the internal state of the battery in real time;

[0010] When the battery is in the constant current charging stage, the remaining time of the constant current stage is calculated based on the current state parameters, and the remaining time of the constant voltage stage is predicted based on the real-time estimation result of the dynamic estimation algorithm. The sum of the two is taken as the total remaining charging time.

[0011] When the battery is in the constant voltage charging stage, the remaining time of the constant voltage stage is predicted based on the real-time estimation result of the dynamic estimation algorithm, and this time is used as the total remaining charging time.

[0012] In one possible implementation, the step of calculating the remaining time of the constant current phase specifically includes:

[0013] Based on the difference between the target state of charge and the current state of charge, the charging current, the nominal capacity of the battery, and the health status, the remaining time of the constant current phase is determined, wherein the target state of charge is the state of charge corresponding to the switch from the constant current charging phase to the constant voltage charging phase.

[0014] In one possible implementation, the dynamic estimation algorithm is an extended Kalman filter, an unscented Kalman filter, or a particle filter.

[0015] In one possible implementation, the step of enabling the dynamic estimation algorithm to continuously estimate the internal state of the battery in real time specifically includes:

[0016] Construct a state vector, which includes the battery state of charge and at least one internal state parameter reflecting the internal dynamic characteristics of the battery.

[0017] Establish a state equation describing the change law of the state vector;

[0018] An observation equation is established, which takes the state vector and constant voltage charging voltage as inputs and the charging current as the observation output.

[0019] Using the real-time measured charging current as an observation, the state vector is recursively estimated through the dynamic estimation algorithm.

[0020] In one possible implementation, the internal state parameter is a diffusion parameter characterizing the internal dynamic properties of the battery.

[0021] In one possible implementation, the state equation is used to describe the following update process:

[0022] The state of charge is updated based on the ampere-hour integral method;

[0023] The charging current is updated based on the decay characteristics, and its update pattern is related to the battery's state of charge and temperature.

[0024] Internal state parameters are updated based on the battery's health status.

[0025] In one possible implementation, the observation equation establishes the observation relationship of the charging current based on the constant voltage charging voltage, battery open-circuit voltage, internal resistance parameters, and internal state parameters.

[0026] In one possible implementation, the step of predicting the remaining time of the constant pressure phase specifically includes:

[0027] Based on the current charging current and its changing trend estimated by the dynamic estimation algorithm, the time required for the charging current to decay to the preset cutoff current is calculated.

[0028] In one possible implementation, the method for estimating the remaining battery charging time further includes a model switching step, which specifically includes:

[0029] Monitor the operational status indicators of the dynamic estimation algorithm;

[0030] When the operational status indicators exceed the normal range, switch to the backup estimation model to predict the remaining time.

[0031] According to another aspect of the present invention, a battery management system is provided, the battery management system comprising:

[0032] The data acquisition module is used to obtain the battery's status parameters;

[0033] The processing module is used to execute the battery remaining charging time estimation method as described in any of the possible implementations above;

[0034] The output module is used to output the calculated remaining charging time.

[0035] The above-described one or more technical solutions in the embodiments of this application have at least one or more of the following technical effects:

[0036] The battery remaining charging time estimation method provided in this embodiment of the invention employs a dynamic estimation algorithm to continuously estimate the internal state of the battery in real time, and intelligently determines whether it is in a constant current or constant voltage charging stage based on the current state parameters of the battery, thereby predicting the remaining time in stages. This brings several significant benefits: First, through continuous correction of the dynamic estimation algorithm, estimation errors caused by nonlinear factors such as battery aging, temperature fluctuations, and current changes can be effectively overcome, significantly improving the accuracy and reliability of remaining time prediction throughout the entire charging cycle, making the estimated time more consistent with the actual charging process; Second, this method realizes the estimation of the remaining time in both constant current and constant voltage stages. The precise identification and seamless integration, especially in the constant current stage where it proactively predicts the time required for the subsequent constant voltage stage, solves the estimation abruptness problem caused by the lag in stage transition point judgment, making the output of the total remaining time smoother and more continuous. Ultimately, this high-precision estimation capability directly translates into an excellent user experience, allowing users to efficiently plan their trips based on reliable information. It also helps the battery management system to implement optimized charging strategies based on more accurate state predictions, avoiding adverse situations such as overcharging. Thus, while improving user convenience and trust, it further ensures the health and lifespan of the battery, promoting the safety and intelligent management level of the charging process.

[0037] The above description is merely an overview of the technical solution of the present invention. In order to better understand the technical means of the present invention and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of the present invention more apparent and understandable, specific embodiments of the present invention are described below. Attached Figure Description

[0038] Figure 1 This is a schematic flowchart of a method for estimating the remaining charging time of a battery according to an exemplary embodiment.

[0039] Figure 2 This is a schematic diagram of the constituent modules of a battery management system according to an exemplary embodiment.

[0040] Explanation of reference numerals in the attached diagram: 100, data acquisition module; 200, processing module; 300, output module. Detailed Implementation

[0041] To make the objectives, technical solutions, and advantages of the present invention clearer, the embodiments of the present invention will be described in further detail below with reference to the accompanying drawings.

[0042] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present invention. Rather, they are merely examples of systems and methods consistent with some aspects of the invention as detailed in the appended claims.

[0043] Figure 1 This is a flowchart of a method for estimating the remaining charging time of a battery according to an exemplary embodiment, such as... Figure 1 As shown, the method includes the following steps:

[0044] In step S100, the current state parameters of the battery are acquired, including the current state of charge, state of health, temperature, and charging current. This step is executed by the battery management system, which acquires the real-time state parameters of the battery through its hardware interface and software algorithm. These parameters provide a data basis for subsequent charging stage determination and time estimation.

[0045] In one embodiment, the state of charge is obtained in the following manner:

[0046] The battery management system uses a built-in microcontroller to read real-time charging current data collected by the current sensor.

[0047] The change in state of charge is calculated using the ampere-hour integral method. The formula is as follows:

[0048]

[0049] in, The rated capacity of the battery. This is the real-time charging current. The calculated change in state of charge is added to the initial state of charge value to obtain the current state of charge value.

[0050] In practice, the initial state of charge can be determined by looking up the open-circuit voltage-state of charge correspondence table pre-stored in the battery management system, based on the measured open-circuit voltage at the start of charging.

[0051] In one embodiment, health status is obtained in the following way:

[0052] The pre-calculated and stored state of health value is read from the non-volatile memory of the battery management system. The state of health value is calculated based on the battery's historical complete charging cycle data, specifically by comparing the total amount of electricity charged in the current charging cycle with the battery's rated capacity.

[0053] In one embodiment, the temperature is obtained in the following manner:

[0054] The surface or internal temperature of the battery is directly measured by at least one temperature sensor connected to the battery management system.

[0055] Preferably, when multiple temperature sensors are set, the maximum value among all sensor readings is taken as the current temperature value for subsequent calculations.

[0056] In one embodiment, the charging current is obtained in the following manner:

[0057] Measurements are taken using a Hall current sensor connected in series in the charging circuit.

[0058] The analog front end of the battery management system conditions and converts the analog signals output by the sensors into digital form to obtain the current charging current value.

[0059] After obtaining all the above status parameters, the battery management system temporarily stores these parameters in internal registers or memory for subsequent steps to call.

[0060] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention.

[0061] In step S200, the battery is determined to be in either a constant current charging stage or a constant voltage charging stage based on the current state parameters. This step is executed by the microcontroller of the battery management system, which automatically identifies the current charging stage of the battery based on the current state parameters obtained in step S100 and through preset logical judgment rules. For example, the implementation method of the judgment process is as follows:

[0062] When the detected charging current value remains near the constant current value set by the charging device, and the detected battery voltage is lower than a preset voltage threshold, the battery is determined to be in the constant current charging stage.

[0063] When the detected battery voltage reaches or exceeds the preset voltage threshold, and at the same time, the detected charging current shows a decreasing trend, it is determined that the battery is in the constant voltage charging stage.

[0064] Preferably, to ensure the reliability of the judgment, the control system continuously monitors the above parameters, and only confirms the judgment result of the charging stage when the corresponding judgment conditions are met in multiple consecutive sampling periods.

[0065] Through the above judgment mechanism, the system can accurately identify the charging stage of the battery, providing a basis for subsequent use of appropriate remaining time estimation methods.

[0066] In step S300, a dynamic estimation algorithm is enabled to continuously estimate the internal state of the battery in real time; this step is the core of improving the accuracy of remaining charging time estimation. The dynamic estimation algorithm is used to perform high-precision, adaptive real-time tracking and correction of the battery's internal state, especially the state of charge (SOC), to overcome the initial and cumulative error problems existing in traditional methods such as the ampere-hour integration method.

[0067] In step S400, when the battery is in the constant current charging stage, the remaining time of the constant current stage is calculated based on the current state parameters, and the remaining time of the constant voltage stage is predicted based on the real-time estimation result of the dynamic estimation algorithm. The sum of the two is taken as the total remaining charging time. After confirming that the battery is in the constant current charging stage, the battery management system starts the estimation process in this step. First, based on the real-time acquired current state of charge, target state of charge, and charging current parameters of the battery, the remaining charging capacity at the current charging rate is calculated to obtain the estimated remaining time of the constant current charging stage. At the same time, the system predicts the time required for the constant voltage stage from the end time of the constant current charging stage until the charging is completed, based on the real-time estimation result of the dynamic estimation algorithm described in step S300. The prediction is based on the battery internal state estimation value and battery model parameters provided in real time by the dynamic estimation algorithm. In one embodiment, the predicted time of the constant voltage stage is obtained by using the internal state estimation value as the initial condition and performing forward calculation based on the dynamic characteristics of the battery under constant voltage charging until the charging termination condition is met. Finally, the system adds the calculated remaining time of the constant current stage to the predicted remaining time of the constant voltage stage to obtain the total remaining charging time and outputs it to the display device or communication interface.

[0068] In step S500, when the battery is in the constant-voltage charging stage, the remaining time of the constant-voltage stage is predicted based on the real-time estimation result of the dynamic estimation algorithm, and this is taken as the total remaining charging time. After confirming that the battery has entered the constant-voltage charging stage, the remaining time of the constant-voltage stage is predicted based on the real-time estimation result of the dynamic estimation algorithm described in step S300. The prediction is based on the battery internal state estimation value provided in real time by the dynamic estimation algorithm. Specifically, using the battery internal state estimation value at the current moment as the initial condition, forward calculation is performed based on the dynamic characteristics of the battery under constant-voltage charging until the charging termination condition is met, thereby obtaining the remaining time of the constant-voltage stage. Finally, the remaining time of the constant-voltage stage is output as the total remaining charging time.

[0069] By employing a dynamic estimation algorithm to continuously estimate the internal state of the battery in real time, and intelligently determining whether it is in a constant current or constant voltage charging stage based on the current state parameters, the remaining time is predicted in stages, resulting in several significant benefits: First, through continuous correction of the dynamic estimation algorithm, estimation errors caused by nonlinear factors such as battery aging, temperature fluctuations, and current changes can be effectively overcome, significantly improving the accuracy and reliability of the remaining time prediction throughout the entire charging cycle, making the estimated time more consistent with the actual charging process; Second, this method achieves accurate identification and seamless connection between the constant current and constant voltage stages, especially in the constant current stage, proactively predicting the time required for the subsequent constant voltage stage, solving the estimation abruptness problem caused by the lag in stage transition point judgment, making the output of the total remaining time smoother and more continuous; Finally, this high-precision estimation capability directly translates into an excellent user experience, allowing users to efficiently plan their trips based on reliable information, while also helping the battery management system to implement optimized charging strategies based on more accurate state predictions, avoiding adverse situations such as overcharging. This enhances user convenience and trust, further ensuring the health and lifespan of the battery, and promoting the safety and intelligent management level of the charging process.

[0070] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0071] In an exemplary embodiment, step S400, specifically including the step of calculating the remaining time of the constant current phase, includes:

[0072] Based on the difference between the target state of charge and the current state of charge, the charging current, the battery's nominal capacity, and its health status, the remaining time of the constant current phase is determined. The target state of charge is the state of charge corresponding to the transition from the constant current charging phase to the constant voltage charging phase. Specifically, the remaining time of the constant current phase is calculated using the following formula:

[0073]

[0074] in, Indicates the remaining time of the constant current charging phase; This indicates the target state of charge when switching from the constant current charging stage to the constant voltage charging stage. This indicates the current state of charge obtained through the battery management system; This indicates the battery's nominal capacity, a parameter pre-stored in the system. This indicates the battery's health status, which is obtained through the battery management system. This indicates the charging current value obtained in real time through the battery management system.

[0075] In this embodiment, the target state of charge These are fixed values ​​predetermined based on battery characteristics, stored in the non-volatile memory of the battery management system. The calculation process is executed by the system's microcontroller, and the calculation result is output as the remaining time of the constant current phase.

[0076] In an exemplary embodiment, in step S300, the dynamic estimation algorithm is an extended Kalman filter, an unscented Kalman filter, or a particle filter. In a preferred embodiment, the dynamic estimation algorithm employs an extended Kalman filter. This algorithm handles the nonlinear characteristics of the battery system by linearizing the nonlinear system.

[0077] In alternative embodiments, the unscented Kalman filter algorithm can also be used, which approximates nonlinear distributions with higher accuracy through lossless transformation; or the particle filter algorithm can be used, which handles strongly nonlinear systems through the Monte Carlo method and is suitable for non-Gaussian noise environments.

[0078] In an exemplary embodiment, step S300, the step of enabling the dynamic estimation algorithm to continuously estimate the internal state of the battery in real time, specifically includes:

[0079] Construct a state vector, which includes the battery state of charge and at least one internal state parameter reflecting the internal dynamic characteristics of the battery.

[0080] Establish a state equation describing the change law of the state vector;

[0081] An observation equation is established, which takes the state vector and constant voltage charging voltage as inputs and the charging current as the observation output.

[0082] Using the real-time measured charging current as an observation, the state vector is recursively estimated through the dynamic estimation algorithm.

[0083] In one embodiment, the internal state parameter is a diffusion parameter characterizing the dynamic properties inside the battery. In the dynamic estimation algorithm, the internal state parameter is specifically a diffusion parameter, which characterizes the dynamic properties of ion diffusion during the electrochemical process inside the battery. In a preferred embodiment, the diffusion parameter is specifically embodied in diffusion resistance. .

[0084] The diffusion resistance The physical significance of this parameter lies in characterizing the resistance encountered by lithium ions during diffusion and migration in the electrolyte and electrode materials inside the battery; this parameter directly affects the decay characteristics of the current during the constant voltage charging stage, and its numerical change reflects the dynamic evolution of the internal state of the battery.

[0085] In the equation of state, the diffusion resistance The update rule is described by the following equation:

[0086]

[0087] in, The aging effect coefficient is determined through battery aging experiments, and its value ranges from 0.1 to 0.3. This indicates the current health status of the battery. This is the baseline health status value of the battery at the time of manufacture. This represents process noise, characterizing model uncertainty.

[0088] The diffusion resistance Real-time identification is achieved using an extended Kalman filter algorithm, with initial values ​​derived from battery factory test data. During constant voltage charging, as... The diffusion resistance increases with the rise and temperature change. The system will adjust accordingly to accurately reflect the impact of the battery's internal dynamic characteristics on the charging process.

[0089] Furthermore, the state vector is constructed to include the battery state of charge. Charging current and diffusion resistance The state-space model is defined as follows:

[0090]

[0091] in This characterizes the percentage relationship between the battery's remaining capacity and its rated capacity, providing a fundamental parameter for calculating the remaining charging time. It directly affects the decay rate of the charging current during the constant voltage stage, and at the same time determines the corresponding relationship between the open-circuit voltage and the state of charge curve. Real-time estimation can be performed using the ampere-hour integration method, and periodic calibration can be performed using the open-circuit voltage-state-of-charge lookup table method.

[0092] As a core variable in the constant voltage charging stage, its decay characteristics directly determine the change law of charging power. The current decay rate objectively reflects the dynamic change of the battery's internal polarization characteristics with the charging process. It can be directly measured by a high-precision current sensor, preferably with a sampling frequency of not less than 1Hz, to ensure accurate capture of the dynamic changes in current.

[0093] The state equation describes the evolution of the state vector; more specifically, the state equation describes the following update process:

[0094] The state of charge (SOC) is updated based on the ampere-hour integral method; the SOC update is based on the current integral principle and is achieved through the following state equations:

[0095]

[0096] in, The nominal capacity of the battery is initially taken from the nominal capacity value in the battery datasheet. During use, the actual capacity is calibrated periodically through complete charge and discharge tests to reflect the capacity decay. This parameter determines the contribution of unit charging current to the change in state of charge and is the basic reference value for capacity calculation.

[0097] The sampling time interval is recommended to be set to a value within the range of 1-10 seconds, depending on processor performance and system real-time requirements. In practical applications, it can be adjusted according to specific needs. As the time step of the discretized system, it is used to determine the time granularity of state estimation and parameter updates;

[0098] The charging current is updated based on its decay characteristics, and its update pattern is related to the battery's state of charge and temperature; the charging current... The update follows an exponential decay law, and its state equation is:

[0099]

[0100] Wherein, time constant It is a charged state and temperature The parameter is a composite function of the state of charge and temperature, comprehensively reflecting the dynamic characteristics of the battery under different operating conditions. It is used to control the rate of current decay during the constant-voltage charging phase and characterize the dynamic response characteristics of the system. The parameterized form adopts the following relationship:

[0101]

[0102] In one embodiment, the reference time constant Approximately 1800 seconds was obtained by fitting historical charging data; Influence coefficient Approximately 2.0 is used to characterize the differences in decay characteristics under different states of charge; activation energy Approximately 30,000 J / mol is used to reflect the system's sensitivity to temperature changes; gas constant. Take 8.314 J / mol·K; reference temperature Set to 298.15K.

[0103] Internal state parameters are updated based on the battery's health status; diffusion resistance As an internal state parameter, its update is related to the battery's health status. The relevant state equation is:

[0104]

[0105] in, The aging effect coefficient is a weighted coefficient used to quantify the impact of battery aging on diffusion resistance. As the battery's health condition declines, this coefficient causes the diffusion resistance to increase accordingly, accurately reflecting the battery aging effect. This coefficient is usually determined through systematic aging test data, and a value range of 0.1-0.3 is recommended.

[0106] This is the baseline health status of the battery at the time of manufacture. This update process reflects the direct impact of battery aging on internal state parameters;

[0107] System process noise These correspond to the uncertainties of each state variable in the state equation, and are used to absorb unmodeled dynamics and parameter errors. The process noise covariance matrix is ​​configured as follows:

[0108]

[0109] The aforementioned state equations together constitute the dynamic model of the battery system, providing a mathematical basis for subsequent state estimation.

[0110] Furthermore, the observation equation establishes the observation relationship of the charging current based on the constant voltage charging voltage, battery open-circuit voltage, internal resistance parameters, and internal state parameters. The observation equation is specifically expressed as follows:

[0111]

[0112] in, express The charging current value measured at any time; This indicates the constant voltage value provided by the charging equipment during the constant voltage charging phase. The set voltage value of the charging equipment can be read in real time via the battery management system communication bus. ; Indicates the current state of charge. and temperature The corresponding battery open-circuit voltage;

[0113] The instantaneous voltage response characteristics of a battery, reflecting the resistance to carrier conduction, can be obtained as a baseline value through pulse testing, and corrected for the effects of temperature and health status. The relationship is expressed as follows:

[0114]

[0115] in, The aging effect coefficient is approximately 0.5, determined through battery cycle aging tests.

[0116] express Diffusion resistance at time; Observation noise represents the random error present in the current sensor measurement process, affecting the convergence performance and estimation accuracy of the state estimation algorithm; covariance. The value is determined based on the actual accuracy of the current sensor used, and is usually in the range of 0.1-1.0.

[0117] The open circuit voltage The functional relationship is obtained through a pre-established two-dimensional lookup table:

[0118]

[0119] in This is the open-circuit voltage-state-of-charge curve at the reference temperature. This is the temperature compensation coefficient.

[0120] The above observation equation establishes the state vector. With observation The mathematical relationship between them provides the basis for measurement updates in the state estimation algorithm.

[0121] More specifically, in the implementation of recursively estimating the state vector using the dynamic estimation algorithm, the extended Kalman filter algorithm is used to recursively estimate the state vector, specifically including the following steps:

[0122] In the prediction step, state prediction is first performed, calculating the predicted state value for the current time step based on the state estimate from the previous time step:

[0123]

[0124] in, Indicates based on Time information Prior estimation of the state at time step. It is a nonlinear state transition function.

[0125] Next, the Jacobian matrix is ​​calculated, which represents the relationship between the partial derivatives of the state transition function and the state variables:

[0126]

[0127] The partial derivative of the current with respect to the state of charge is calculated as follows:

[0128]

[0129] Finally, covariance prediction is performed:

[0130]

[0131] in, This is the prior estimate of the error covariance matrix.

[0132] In the update step, the first step is to perform observation prediction, calculating the predicted values ​​of the observations based on the state prior estimate:

[0133]

[0134] Next, calculate the observation Jacobian matrix:

[0135]

[0136] The partial derivative of the open-circuit voltage with respect to the state of charge is calculated using the central difference method:

[0137]

[0138] Then calculate the Kalman gain:

[0139]

[0140] State update based on Kalman gain:

[0141]

[0142] Finally, update the covariance matrix:

[0143]

[0144] In one embodiment, the step of predicting the remaining time of the constant pressure phase specifically includes:

[0145] Based on the current charging current and its changing trend estimated by the dynamic estimation algorithm, the time required for the charging current to decay to the preset cutoff current is calculated. Specifically, based on the converged state estimation results, the remaining charging time in the constant voltage stage is predicted, and the remaining time in the constant voltage stage is calculated using the following formula:

[0146]

[0147] in, This indicates the predicted remaining charging time during the constant voltage phase. The time constant for the current state is the state of charge. and temperature The function; The current charging current value is estimated using a dynamic estimation algorithm. The preset charging cutoff current is typically set to a value within the range of 0.02C to 0.05C.

[0148] This prediction method fully considers the nonlinear decay characteristics of the charging current during the constant voltage stage and adaptively adjusts the prediction results by updating the state estimate in real time, thereby significantly improving the accuracy of the remaining charging time prediction.

[0149] Preferably, to assess the reliability of the prediction results, the system simultaneously calculates the confidence interval for the remaining time:

[0150]

[0151]

[0152] in, The standard deviation of the time constant is the estimated value. This represents the estimated standard deviation of the charging current.

[0153] In one embodiment, the method further includes a model switching step, which specifically includes:

[0154] Monitor the operational status indicators of the dynamic estimation algorithm;

[0155] When the operational status indicators exceed the normal range, switch to the backup estimation model to predict the remaining time.

[0156] Monitor the operational status indicators of the dynamic estimation algorithm; when the operational status indicators exceed the normal range, switch to the backup estimation model to predict the remaining time.

[0157] Specifically, the model switching step is implemented in the following manner:

[0158] The system monitors the operational status indicators of the extended Kalman filter algorithm in real time, including:

[0159] Trace of covariance matrix , used to characterize the degree of uncertainty in state estimation;

[0160] The statistical properties of the innovation sequence are used to detect model mismatch or measurement anomalies;

[0161] The physical rationality of the parameter estimates is ensured, guaranteeing that the estimates are within a preset reasonable range.

[0162] The operating status indicator is determined to be outside the normal range when any of the following conditions are met:

[0163] Trace of covariance matrix Exceeding the preset threshold ;

[0164] The mean or variance of the new sequence continuously deviates from the theoretical statistical properties;

[0165] State estimates are outside their physically reasonable range, such as SOC > 100% or < 0%;

[0166] When operational status indicators exceed the normal range, the system automatically switches to the backup estimation model, including:

[0167] When the extended Kalman filter algorithm shows a divergence trend, it switches to a simplified model based on a second-order RC equivalent circuit. This model identifies the model parameters online using the recursive least squares method and predicts the remaining charging time based on the identification results.

[0168] When current measurement is abnormal or data quality is poor, the system switches to a regression model based on historical charging data. This model predicts the remaining charging time by searching for historical charging curves under similar operating conditions and using a pattern matching method.

[0169] Through the model switching mechanism, the system can maintain the normal operation of the remaining time prediction function when the extended Kalman filter algorithm malfunctions, which significantly improves the robustness and reliability of the system.

[0170] To verify the effectiveness and accuracy of the adaptive estimation method for remaining battery charging time described in this invention, a series of experimental tests were conducted. The experimental conditions and results are as follows:

[0171] Experimental conditions: The test battery type is an NMC ternary lithium battery; the nominal battery capacity is 20Ah; the test temperature range is -10℃ to 45℃; the battery health status is 100% (initial state); the test cycle is 10 complete charging cycles; the charging strategy is to request charging current according to the preset charge and discharge MAP table.

[0172] The following experimental data were obtained through 10 complete charging cycles:

[0173] Experimental cycle number Predicted remaining charging time (minutes) Actual charging time (minutes) 1 53 59 2 51 56 3 49 54 4 48 51 5 44 47 6 43 45 7 43 43 8 43 44 9 43 43 10 42 41

[0174] During the experiment, the actual charging time showed a gradual decreasing trend. This phenomenon is due to the fact that the battery system was not allowed to rest sufficiently during the continuous charge and discharge test, which caused the battery pack temperature to gradually rise and eventually reach a thermal equilibrium state. The increase in temperature improved the kinetic conditions of the electrochemical reaction inside the battery, thereby improving the charging efficiency and shortening the charging time. This phenomenon is consistent with the thermodynamic characteristics of the battery.

[0175] The average prediction error of ten tests is 5-6 minutes, which meets the design requirements of practical applications. As the test cycle progresses, the deviation between the predicted value and the actual value gradually decreases, indicating that the adaptive estimation method has good learning ability and convergence characteristics.

[0176] In summary, the adaptive estimation method for remaining battery charging time described in this invention demonstrates good prediction accuracy and adaptability in actual testing, providing reliable technical support for battery charging management.

[0177] Please see Figure 2 The present invention also provides a battery management system, comprising:

[0178] The data acquisition module 100 is used to acquire the state parameters of the battery; the state parameters include, but are not limited to, battery voltage, charging current, temperature, state of charge and health status; the module includes a high-precision sensor array and a signal conditioning circuit. In specific implementation, a Hall current sensor can be used to measure the charging current, a high-precision voltage sampling circuit can be used to measure the battery terminal voltage, and thermistors distributed at key locations on the battery can be used to measure the temperature.

[0179] The processing module 200 is used to execute the battery remaining charging time estimation method as described in any of the exemplary embodiments above; the module is implemented by a microcontroller unit and includes a processor and a memory, the memory storing executable instructions, which, when executed by the processor, implement functions including charging stage judgment, constant current stage time calculation, constant voltage stage time prediction, and model adaptive switching;

[0180] The output module 300 is used to output the calculated remaining charging time. This module includes a display driver interface and a communication interface. In specific implementations, it can be configured to drive the vehicle's on-board display to show the remaining time, and at the same time transmit the remaining time information to the vehicle's central control system or charging pile management system via a CAN bus or Ethernet interface.

[0181] The battery management system can be integrated into the battery pack as the main controller, or it can be set up independently as a component of the charging management system. This system is suitable for various lithium-ion battery applications, including electric vehicles and energy storage systems.

[0182] In an exemplary embodiment, the present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method for estimating the remaining charging time of a battery as described in any of the exemplary embodiments above. Optionally, the storage medium is a non-transitory computer-readable storage medium, such as a ROM (Read-Only Memory), RAM (Random Access Memory), CD-ROM (Compact Disc Read-Only Memory), magnetic tape, floppy disk, and optical data storage device, etc.

[0183] In an exemplary embodiment, the present invention also provides a computer program product comprising computer program code stored in a computer-readable storage medium, wherein a processor of a computer device reads the computer program code from the computer-readable storage medium and executes the computer program code, causing the computer device to perform the operations performed in the above-described method for estimating the remaining battery charging time.

[0184] Any aspects of this invention not described in detail are well-known to those skilled in the art.

[0185] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered in all respects as exemplary and non-limiting, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the present invention. No reference numerals in the claims should be construed as limiting the scope of the claims.

Claims

1. A method for estimating the remaining charging time of a battery, characterized in that, Includes the following steps: Obtain the current state parameters of the battery, including the current state of charge, health status, temperature, and charging current; Based on the current state parameters, it is determined whether the battery is in the constant current charging stage or the constant voltage charging stage. A dynamic estimation algorithm is enabled to continuously estimate the internal state of the battery in real time; When the battery is in the constant current charging stage, the remaining time of the constant current stage is calculated based on the current state parameters, and the remaining time of the constant voltage stage is predicted based on the real-time estimation result of the dynamic estimation algorithm. The sum of the two is taken as the total remaining charging time. When the battery is in the constant voltage charging stage, the remaining time of the constant voltage stage is predicted based on the real-time estimation result of the dynamic estimation algorithm, and this time is used as the total remaining charging time.

2. The method for estimating the remaining charging time of a battery according to claim 1, characterized in that, The steps for calculating the remaining time of the constant current phase specifically include: Based on the difference between the target state of charge and the current state of charge, the charging current, the nominal capacity of the battery, and the health status, the remaining time of the constant current phase is determined, wherein the target state of charge is the state of charge corresponding to the switch from the constant current charging phase to the constant voltage charging phase.

3. The method for estimating the remaining charging time of a battery according to claim 1, characterized in that, The dynamic estimation algorithm is an extended Kalman filter algorithm, an unscented Kalman filter algorithm, or a particle filter algorithm.

4. The method for estimating the remaining charging time of a battery according to claim 1, characterized in that, The step of enabling the dynamic estimation algorithm to continuously estimate the internal state of the battery in real time specifically includes: Construct a state vector, which includes the battery state of charge and at least one internal state parameter reflecting the internal dynamic characteristics of the battery. Establish a state equation describing the change law of the state vector; An observation equation is established, which takes the state vector and constant voltage charging voltage as inputs and the charging current as the observation output. Using the real-time measured charging current as an observation, the state vector is recursively estimated through the dynamic estimation algorithm.

5. The method for estimating the remaining charging time of a battery according to claim 4, characterized in that, The internal state parameters are diffusion parameters that characterize the internal dynamic properties of the battery.

6. The method for estimating the remaining charging time of a battery according to claim 4, characterized in that, The state equation is used to describe the following update process: The state of charge is updated based on the ampere-hour integral method; The charging current is updated based on the decay characteristics, and its update pattern is related to the battery's state of charge and temperature. Internal state parameters are updated based on the battery's health status.

7. The method for estimating the remaining charging time of a battery according to claim 4, characterized in that, The observation equation establishes the observation relationship of the charging current based on the constant voltage charging voltage, battery open circuit voltage, internal resistance parameters, and internal state parameters.

8. The method for estimating the remaining charging time of a battery according to claim 1, characterized in that, The steps for predicting the remaining time of the constant pressure phase specifically include: Based on the current charging current and its changing trend estimated by the dynamic estimation algorithm, the time required for the charging current to decay to the preset cutoff current is calculated.

9. The method for estimating the remaining charging time of a battery according to claim 1, characterized in that, It also includes a model switching step, which specifically includes: Monitor the operational status indicators of the dynamic estimation algorithm; When the operational status indicators exceed the normal range, switch to the backup estimation model to predict the remaining time.

10. A battery management system, characterized in that, include: The data acquisition module is used to obtain the battery's status parameters; A processing module is configured to execute the method for estimating the remaining battery charging time as described in any one of claims 1 to 9; The output module is used to output the calculated remaining charging time.

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