Battery available power state estimation method, computer equipment and storage medium

Abstract

The invention provides a battery available power state estimation method, computer equipment and a storage medium. The method comprises the steps that a battery model in a discrete time space state is constructed based on parameters describing dynamic characteristics of a battery, and the battery model comprises a state transition equation and a voltage prediction equation; constructing a state transition equation of a plurality of time steps according to the state transition equation; updating the voltage prediction equation based on the state transition equation of the plurality of time steps to obtain an updated voltage prediction equation; determining a limiting value current according to the updated voltage prediction equation; a battery available power limit is calculated based on the updated voltage prediction equation and the limit current. According to the method, a battery model which can fully consider the dynamic change of the battery is used for carrying out construction, updating, determination and other operations to obtain an updated voltage prediction equation and a limit value current, and the battery available power limit value which can accurately reflect the actual behavior of the battery under different working conditions is calculated, so that the estimated available power limit value is more suitable for the actual situation.

Description

Technical Field

[0001] This application relates to the field of battery management technology, specifically to a method for estimating the available power state of a battery, a computer device, and a storage medium. Background Technology

[0002] State of power (SOP) has multi-dimensional significance in a battery management system (BMS), and its value permeates core aspects such as battery safety, performance optimization, lifespan management, and system coordination. The SOP limit is particularly important.

[0003] For example, to prevent battery overcharging or over-discharging, it is necessary to estimate the maximum charging power or maximum discharging power that the battery can withstand, in order to prevent safety risks such as thermal runaway and electrolyte decomposition caused by over-power operation. However, current methods for estimating SOP limits, such as maximum charging power or maximum discharging power, are not accurate, making it impossible to accurately manage batteries based on SOP limits. Summary of the Invention

[0004] In view of this, embodiments of this application aim to provide a method for estimating the available power state of a battery, a computer device, and a storage medium.

[0005] The first aspect of this application provides a method for estimating the available power state of a battery. The method includes: constructing a discrete-time spatial state battery model based on parameters describing the dynamic characteristics of the battery, the battery model including a state transition equation and a voltage prediction equation; constructing state transition equations for multiple time steps based on the state transition equations; updating the voltage prediction equation based on the state transition equations for the multiple time steps to obtain an updated voltage prediction equation; determining a limit current based on the updated voltage prediction equation; and calculating a limit for the available power of the battery based on the updated voltage prediction equation and the limit current.

[0006] According to the first aspect, the parameters describing the dynamic characteristics of the battery include diffusion effect parameters and hysteresis effect parameters.

[0007] According to the first aspect, or any implementation of the first aspect above, the state transition equation includes the relationship equation between the state vectors at time k and time k+1, wherein the state vectors include the battery's state of charge, diffusion current, and hysteresis state; the voltage prediction equation includes the relationship equation between the output predicted voltage and the state vector at time k.

[0008] According to the first aspect, or any implementation of the first aspect above, the plurality of time steps is k. ΔTMultiple time steps; the step of updating the voltage prediction equation based on the state transition equation of the multiple time steps to obtain the updated voltage prediction equation includes: using the k ΔZ The state transition equations at time step k are used to update the state vector at time k, thereby obtaining the updated voltage prediction equation.

[0009] According to the first aspect, or any implementation of the first aspect above, determining the limit current according to the updated voltage prediction equation includes: calculating a first predicted voltage at the limiting current according to the updated voltage prediction equation; if the first predicted voltage is within a first voltage range, then determining the limiting current as the limit current, wherein the limiting current is the current of the battery in the charging / discharging state; or, calculating a second predicted voltage at zero current according to the updated voltage prediction equation; if the second predicted voltage is not within the first voltage range, then determining the zero current as the limit current, wherein the zero current is the current of the battery in the quiescent state.

[0010] According to the first aspect, or any implementation of the first aspect above, determining the limit current according to the updated voltage prediction equation includes: if the first predicted voltage is not within the first voltage range and the second predicted voltage is within the first voltage range, then searching for the limit current using the secant method.

[0011] According to the first aspect, or any implementation of the first aspect above, the limiting current includes the maximum discharge current of the battery during the discharge process, and the left end of the first voltage range is the discharge cutoff voltage of the battery; the step of searching for the limiting current using the secant method includes: determining a first predicted state-of-charge equation based on the state transition equation; obtaining a first difference function by subtracting the discharge cutoff voltage and the updated voltage prediction equation; obtaining a second difference function by subtracting the minimum allowable state of charge of the battery and the first predicted state-of-charge equation; generating a discharge target function based on the maximum value of the first difference function and the second difference function, wherein the output value of the discharge target function is less than or equal to 0 for any input value; and receiving a first root value determined in the interval from 0 to the maximum discharge current using the secant method and the discharge target function, and using the first root value as the limiting current.

[0012] According to the first aspect, or any implementation of the first aspect above, the step of receiving a first root value determined from the interval from 0 to the maximum discharge current using the secant method and the discharge target function includes: defining a secantmethod function, wherein the secantmethod function is used to implement the secant method to solve the equation, the secantmethod function includes a discharge target function, an iterative first guess value, a second guess value, and a first convergence tolerance, the first guess value and the second guess value being the two endpoints of the interval from 0 to the maximum discharge current; substituting the first guess value and the second guess value into the discharge target function to obtain a first function value and a second function value, wherein... The mathematical signs of the first function value and the second function value are opposite. Substituting the first guess value, the second guess value, the first function value, and the second function value into the secant method formula, a third guess value in the interval is obtained. The third guess value is then substituted into the discharge target function to obtain a third function value. If the third function value is greater than or equal to the first convergence tolerance, the interval from 0 to the maximum discharge current is updated based on the third guess value. Based on the updated interval, the step of defining the secantmethod function is returned until the third function value is less than the first convergence tolerance. Then, the iteration stops, and the third guess value determined from the interval is determined as the first root value and returned.

[0013] According to the first aspect, or any implementation of the first aspect above, the limiting current includes the minimum charging current of the battery during the charging process, and the right end of the first voltage range is the charging cutoff voltage of the battery; the step of searching for the limiting current using the secant method includes: determining a second predicted state-of-charge equation based on the state transition equation; obtaining a third difference function by subtracting the charging cutoff voltage and the updated voltage prediction equation; obtaining a fourth difference function by subtracting the maximum permissible state of charge of the battery and the second predicted state-of-charge equation; generating a charging target function based on the minimum value of the third difference function and the fourth difference function, wherein the output value of the charging target function is greater than or equal to 0 for any input value; and receiving a second root value determined from the interval from the minimum charging current to 0 using the secant method and the charging target function, and using the second root value as the limiting current.

[0014] According to the first aspect, or any implementation of the first aspect above, the step of receiving a second root value determined from the interval from the minimum charging current to 0 using the secant method and the charging target function includes: defining a secantmethod function, wherein the secantmethod function is used to implement the secant method to solve the equation, the secantmethod function includes a discharge target function, an iterative fourth guess value, a fifth guess value, and a second convergence tolerance, wherein the fourth guess value and the fifth guess value are the two endpoints of the interval from the minimum charging current to 0; substituting the fourth guess value and the fifth guess value into the charging target function to obtain the fourth function value and the fifth function value, wherein... The fourth and fifth function values ​​have opposite mathematical signs. Substituting the fourth, fifth, fourth, and fifth guess values ​​into the secant method formula yields the sixth guess value in the interval. This sixth guess value is then substituted into the charging objective function to obtain the sixth function value. If the sixth function value is greater than or equal to the second convergence tolerance, the interval for the minimum charging current to 0 is updated based on the sixth guess value. Based on the updated interval, the step of defining the secantmethod function is returned until the sixth function value is less than the second convergence tolerance. Then, the iteration stops, and the sixth guess value determined from the interval is identified as the second root value and returned.

[0015] According to the first aspect, or any implementation of the first aspect above, the battery includes multiple batteries, which are connected in parallel and / or in series; the step of calculating the battery's available power limit based on the updated voltage prediction equation and the limit current includes: substituting the limit current into the updated voltage prediction equation to obtain a predicted voltage value; and multiplying the predicted voltage value, the limit current, the number of batteries connected in series, and / or the number of batteries connected in parallel to obtain the battery's available power limit.

[0016] In a second aspect, this application provides a computer device, including a memory and a processor, wherein the memory is used to store a computer program, and the processor is used, when executing the computer program, to perform a battery available power state estimation method, including the first aspect and any possible implementation thereof.

[0017] Thirdly, this application provides a computer-readable storage medium storing program code for computer execution, the program code including a battery available power state estimation method for performing the first aspect and any of the possible implementations of the first aspect.

[0018] Fourthly, embodiments of this application provide a computer program including instructions for executing the battery available power state estimation method in the first aspect and any possible implementation thereof.

[0019] In this application, a discrete-time spatial state battery model is constructed based on parameters describing the battery's dynamic characteristics. This ensures the constructed battery model fully considers the battery's dynamic response under various states, tracks the battery's internal dynamics, and adapts to various complex operating conditions (such as battery states during vehicle acceleration and hill climbing). The state transition equations included in the battery model are used to construct state transition equations for multiple time steps. These equations are then used to update the voltage prediction equations included in the battery model, resulting in an updated voltage prediction equation. A limit current is determined based on the updated voltage prediction equation. Finally, the battery's usable power limit is calculated based on the updated voltage prediction equation and the limit current. In other words, the construction, updating, and determination processes utilize a battery model that fully considers the battery's dynamic changes to obtain an updated voltage prediction equation and a limit current. These equations and the limit current are then used to calculate a battery's usable power limit that accurately reflects the battery's actual behavior under different operating conditions, making the estimated usable power limit more closely aligned with reality. Furthermore, using a more realistic usable power limit allows for better battery management. Attached Figure Description

[0020] Figure 1 This is a schematic flowchart of a battery available power state estimation method provided in an embodiment of this application.

[0021] Figure 2 This is a flowchart illustrating the processing procedure of a battery available power state estimation method provided in an embodiment of this application.

[0022] Figure 3 This is a schematic structural diagram of a computer device provided in an embodiment of this application. Detailed Implementation

[0023] 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, and 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.

[0024] In this article, the term "and / or" is merely a description of the relationship between related objects, indicating that there can be three relationships. For example, A and / or B can represent three situations: A exists alone, A and B exist simultaneously, and B exists alone.

[0025] The terms "first" and "second," etc., used in the specification and claims of this application are used to distinguish different objects, not to describe a specific order of objects. For example, "first target object" and "second target object," etc., are used to distinguish different target objects, not to describe a specific order of target objects.

[0026] In the embodiments of this application, the terms "exemplary" or "for example" are used to indicate that something is an example, illustration, or description. Any embodiment or design that is described as "exemplary" or "for example" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design. Specifically, the use of the terms "exemplary" or "for example" is intended to present the relevant concepts in a specific manner.

[0027] It should be understood that the specific embodiments described herein are merely illustrative of this application and are not intended to limit this application.

[0028] State of power (SOP) has multi-dimensional significance in a battery management system (BMS), and its value permeates core aspects such as battery safety, performance optimization, lifespan management, and system coordination. The SOP limit is particularly important. Details are as follows:

[0029] Ensuring safe battery operation: Preventing overcharging and over-discharging, SOP (Start of Production) estimates the maximum charge and discharge power the battery can withstand in real time, avoiding current / voltage exceeding safety thresholds and preventing safety risks such as thermal runaway and electrolyte decomposition due to overpower operation. Safety constraints under dynamic operating conditions: In high-current demand scenarios such as vehicle acceleration and hill climbing, SOP can dynamically adjust power output based on the battery's current state (e.g., temperature, internal resistance) to prevent sudden voltage drops or excessive temperature rises caused by instantaneous high current.

[0030] Optimizing battery performance and energy utilization: Maximizing available energy, SOP (State of Charge) can fully release battery energy within a safe range by accurately estimating the power boundary. For example, it avoids conservative power limiting at low state of charge (SOC) (SOC < 0.2), ensuring maximum driving range for electric vehicles. At high SOC (SOC > 0.8), it rationally utilizes remaining capacity, improving energy recovery efficiency. Improving system response speed: Predictive calculations of SOP can allocate energy to the load in advance, avoiding voltage fluctuations caused by sudden power changes, and improving the dynamic response performance of electric vehicle drive systems or energy storage converters.

[0031] Extending battery life: Balancing power demand and aging losses, Standard Operating Procedures (SOPs) reduce aging factors such as battery polarization and active material degradation by limiting harmful high-power operating conditions. For example, it avoids negative electrode breakdown caused by prolonged high-current discharge or positive electrode structural collapse due to overcharging. This is achieved through intelligent adjustment based on aging models.

[0032] However, the current assessment of SOP power limits mainly uses one of the following three methods.

[0033] Static voltage limiting method: Using the battery terminal voltage as the sole hard constraint, by limiting the voltage range v min ≤u≤v max Inversely determine the power limit. Clamp the terminal voltage to v. min The discharge power is given by the following formula (1):

[0034]

[0035] Among them, T k (i) This was determined through laboratory pulse testing, reflecting the cumulative voltage changes under long-term pulses. This represents the discharge power limit at the k-th state point in the i-th charge-discharge cycle. OCV (open circuit voltage) represents the battery's open circuit voltage. k (i) This represents the state of charge during the i-th charge-discharge cycle and the k-th state.

[0036] Power limiting method based on State of Charge (SOC): SOC directly affects the battery's usable energy and maximum power capability. Power output is limited by using real-time SOC values ​​and empirically looked up tables or curves. Maximum charge / discharge current curves at different SOCs are calibrated (usually through fitting experimental data). Power limiting is dynamically adjusted by incorporating a temperature compensation factor. However, this method exhibits significant static characteristics and struggles to cope with dynamic operating conditions and the effects of aging.

[0037] A dynamic voltage limiting method based on a simple model: This method directly predicts whether the voltage will exceed the safe range (such as charging cut-off voltage or discharging cut-off voltage) within a time interval Δt based on the real-time voltage and current of the battery. A simplified first-order RC model, v = OCV, is used. (z) —iR estimates power p using a fixed pulse resistance max,k+ΔT For details, please refer to formulas (2) and (3) below:

[0038]

[0039] The model ignores internal dynamic processes within the battery (such as diffusion voltage and hysteresis), and cannot capture voltage recovery characteristics after strong discharge. Experimental data shows that this method overestimates discharge power by 9.8% at high SOC and underestimates it by 10%-15% at low SOC.

[0040] Traditional models have limited ability to describe battery dynamic response, lacking accurate descriptions of battery diffusion and hysteresis effects, and struggle to adapt to real-time changing operating conditions, especially exhibiting significant errors when predicting continuous power over a future period. Their poor adaptability to operating conditions stems from the fact that simplified equivalent circuit models often assume the battery is in equilibrium before the current pulse, an assumption that is unrealistic in practice. They also cannot adapt to special scenarios such as continuous dynamic loads (e.g., vehicle acceleration) and rapid high-current charging. For example, in the high SOC region, traditional methods overestimate discharge power by up to 9.8%. This is because their fixed resistor R0 fails to capture the cumulative decrease in diffusion voltage caused by strong discharge.

[0041] To address the aforementioned issues, this application constructs a discrete-time spatial state battery model based on parameters describing the battery's dynamic characteristics. This ensures that the constructed battery model fully considers the battery's dynamic response under various states, tracks the battery's internal dynamics, and can adapt to various complex operating conditions (such as the battery's state during vehicle acceleration and hill climbing). The state transition equations included in the battery model are used to construct state transition equations for multiple time steps. These equations are then used to update the voltage prediction equations included in the battery model, resulting in an updated voltage prediction equation. A limit current is determined based on the updated voltage prediction equation. Finally, the battery's usable power limit is calculated based on the updated voltage prediction equation and the limit current. In other words, by using a battery model that fully considers the battery's dynamic changes for construction, updating, and determination, an updated voltage prediction equation and a limit current are obtained. These are used to calculate the battery's usable power limit, which accurately reflects the battery's actual behavior under different operating conditions, making the estimated usable power limit more closely match reality.

[0042] The following is combined Figure 1 The embodiments of this application will be described in detail below.

[0043] Figure 1 This is a schematic flowchart of a battery available power state estimation method provided in an embodiment of this application, in order to solve the above-mentioned problems.

[0044] Figure 1 The battery available power state estimation method shown includes steps S110 to S150.

[0045] Step S110: Based on the parameters describing the dynamic characteristics of the battery, construct a discrete-time spatial state battery model, which includes state transition equations and voltage prediction equations.

[0046] In some embodiments, parameters of the battery dynamic characteristics are used to describe how the battery dynamically changes under real-time varying operating conditions. These real-time varying operating conditions may include continuous dynamic loads (e.g., vehicle acceleration), rapid high-current charging, etc.

[0047] In some embodiments, a discrete-time spatial state battery model typically includes two parts. The first part may include a state transition equation. The state transition equation can be used to represent the relationship between states at different times. These different times can be adjacent times. The second part may include a voltage prediction equation. The voltage prediction equation can be used to represent the relationship between the state at a certain time and the output result. This certain time can be the current time. The output result can be determined based on the required output parameter. For example, if the required output parameter is voltage, then the output result is voltage. The voltage prediction equation can then be used to represent the relationship between the state and the voltage at a certain time.

[0048] Based on the parameters describing the dynamic characteristics of the battery, the constructed discrete-time spatial state battery model can track the internal dynamics of the battery and adapt to complex, real-time changing operating conditions (such as acceleration and hill climbing).

[0049] This application provides an optional embodiment in which the parameters describing the dynamic characteristics of the battery include diffusion effect parameters and hysteresis effect parameters.

[0050] The diffusion effect refers to the directional migration of active materials (such as lithium ions and electrons) within a battery due to concentration gradients or chemical potential differences in electrode materials or electrolytes. It is one of the main limiting factors in battery reaction kinetics.

[0051] In some embodiments, diffusion effect parameters include parameters related to the dynamic changes of the battery during the diffusion effect. For example, diffusion effect parameters may include one or more of the following: polarization internal resistance, RC time constant of polarization phenomenon.

[0052] Hysteresis refers to the phenomenon where the voltage curves of a battery do not coincide during charging and discharging at the same state of charge, manifesting as a voltage "lag". Hysteresis is commonly found in lithium-ion batteries and lithium-sulfur batteries.

[0053] In some embodiments, hysteresis parameters include parameters related to the dynamic changes of the battery during hysteresis. For example, hysteresis parameters may include one or more of the following: hysteresis parameter, hysteresis rate constant.

[0054] By utilizing parameters describing the diffusion and hysteresis effects of a battery, a discrete-time spatial state battery model is constructed. This model enables accurate tracking of the battery's dynamic changes under diffusion and hysteresis effects, thereby improving the accuracy of calculating the battery's usable power limit using the discrete-time spatial state battery model.

[0055] In some embodiments, constructing a discrete-time spatial state battery model is based not only on parameters describing the battery's dynamic characteristics but also on parameters describing the battery's static characteristics. That is, constructing a discrete-time spatial state battery model based on parameters describing the battery's dynamic characteristics can include: constructing a discrete-time spatial state battery model based on both parameters describing the battery's dynamic characteristics and parameters describing the battery's static characteristics.

[0056] In some embodiments, parameters describing the static characteristics of a battery may include parameters describing the current state of the battery and parameters that assist in describing the dynamic characteristics of the battery. For example, parameters describing the static characteristics of a battery may include the current at the current moment, battery capacity, series internal resistance, etc.

[0057] In the embodiments of this application, not only parameters describing the dynamic characteristics of the battery are used, but also parameters describing the static characteristics of the battery are used to construct a discrete-time spatial state battery model. This allows the constructed battery model to describe the state of the battery under various static and dynamic conditions, ensuring the comprehensiveness of the description of the battery state and thus ensuring the accuracy of the calculated battery usable power limit.

[0058] In some embodiments, the state transition equation includes the relationship equation between the state vectors at time k and time k+1, where the state vectors include the battery's state of charge, diffusion state current, and hysteresis state.

[0059] State transition equations are a core mathematical tool for describing the evolution of a system's state over time. For example, state transition equations are a core mathematical tool for describing the change of a system's state from time k to time k+1.

[0060] The state vector is the core variable in the state transition equation. The state vector can include the battery's state of charge, diffusion current, and hysteresis state. The state vector is a set, or more simply, a list. It can be represented by x = [battery state of charge, diffusion current, hysteresis state].

[0061] In some embodiments, an equation expression for the state transition equation is provided, as shown in the following formula (4):

[0062]

[0063] Simplifying the above formula (4), we get the following formula (5):

[0064] x k+1 =f(x) k i k (5)

[0065] Among them, i k Let represent the current at time k. Q represents the battery capacity, in Ah. RC represents the RC time constant of polarization, in seconds. Γ represents the hysteresis rate constant. diag(n) represents the diagonal matrix. (n) This represents an exponential function with base e. k Let x represent the state vector at time k. k+1 This represents the state vector at time k+1. `sign(n)` represents the sign function. For example, if `n` inside the parentheses of `sign` is positive, then the output of `sign(n)` is 1. As another example, if `n` inside the parentheses of `sign` is negative, then the output of `sign(n)` is -1. And as yet another example, if `n` inside the parentheses of `sign` is 0, then the output of `sign(n)` is 0.

[0066] In some embodiments, the voltage prediction equation includes a relationship equation between the output predicted voltage and the state vector at time k.

[0067] In some embodiments, an equation expression for a voltage prediction equation is provided, as shown in the following formula (6):

[0068]

[0069] Simplifying the above formula (6), we get the following formula (7):

[0070] v k =h(x k i k (7)

[0071] Where M represents the hysteresis parameter, in volts (v). Q represents the polarization resistance, in ohms. R0 represents the series resistance, in ohms. k This represents the output voltage at time k.

[0072] In some embodiments, based on the above formula (7), the voltage prediction equation can also be expressed as the following formula (8):

[0073] v pred =h(x k i k (8)

[0074] Among them, v pred The output predicted voltage v of the battery model can be referred to as pred .

[0075] Step S120: Construct state transition equations for multiple time steps based on the state transition equations.

[0076] In some embodiments, the multiple time steps can be pre-set. The multiple time steps can be represented by a time period. For example, the multiple time steps can be represented as k. ΔT Each time step.

[0077] In some embodiments, the state transition equations for multiple time steps are constructed based on the state transition equations included in the battery model for multiple time steps. The state transition equations for multiple time steps are the state transition equations for predicting multiple time steps backward from the current time. For example, the multiple time steps are k. ΔT If there are multiple time steps, and the current time is k, then the state transition equation for multiple time steps includes the state vector at time k and k+k. ΔT The relationship between the state vectors at different times.

[0078] In some embodiments, the multiple time steps are k ΔT One time step. k ΔT The state transition equation for each time step is given in the following formula (9):

[0079]

[0080] Step S130: Based on the state transition equations of the multiple time steps, update the voltage prediction equation to obtain the updated voltage prediction equation.

[0081] In some embodiments, the voltage prediction equation is updated based on the state transition equations at multiple time steps, such that the updated voltage prediction equation corresponds to the state transition equations at multiple time steps. For example, the updated voltage prediction equation includes the relationship equation between the output predicted voltage at multiple time steps and the state vector at multiple time steps.

[0082] In some embodiments, updating the voltage prediction equation utilizes k ΔT The state transition equations at multiple time steps are used to update the state vector at time k in the voltage prediction equation. That is, based on the state transition equations at multiple time steps, the voltage prediction equation is updated to obtain the updated voltage prediction equation. This can include using k... ΔT The state transition equations at time step k are used to update the state vector at time k, thus obtaining the updated voltage prediction equation.

[0083] Using k ΔT The state transition equation at each time step updates the state vector at time k. This can be achieved by replacing the state vector at time k in the voltage prediction equation with k... ΔT The state vector at each time step.

[0084] In some embodiments, an example of an updated voltage prediction equation is provided below, specifically referring to the following formula (10):

[0085]

[0086] Step S140: Determine the limit current based on the updated voltage prediction equation.

[0087] In some embodiments, determining the limiting current is used to calculate the available power limit of the battery. The determined limiting current can be any of the limiting current, zero current, or current determined using the secant method. The limiting current may include the maximum discharge current of the battery during discharge and / or the minimum charging current during charging.

[0088] In some embodiments, the limit current can be i limit To express.

[0089] In some embodiments, determining the limit current based on the updated voltage prediction equation includes:

[0090] Based on the updated voltage prediction equation, a first predicted voltage at the limiting current is calculated; if the first predicted voltage is within a first voltage range, then the limiting current is determined as a limit current, wherein the limiting current is the current of the battery in the charging / discharging state; or,

[0091] According to the updated voltage prediction equation, the second predicted voltage at zero current is calculated; if the second predicted voltage is not within the first voltage range, the zero current is determined as the limit current, wherein the zero current is the current of the battery in the quiescent state.

[0092] In some embodiments, the first predicted voltage under the limiting current can be calculated using the above formula (10) according to the updated voltage prediction equation. For example, substituting the limiting current into formula (10), i k =i 极限 The result of formula (10) is obtained and the output result is determined as the first predicted voltage.

[0093] The first voltage range is a preset voltage range. It is used to determine whether the battery is in a specified state based on a first predicted voltage and a second predicted voltage. For example, the first voltage range is used to determine whether the limiting current meets constraints during charging / discharging based on the first predicted voltage. As another example, the first voltage range is used to determine whether the battery is in an unsafe state when at rest based on the second predicted voltage.

[0094] In some embodiments, the first voltage range may be determined by the discharge cutoff voltage v. minand charging cutoff voltage v max Composition. For example, the first voltage range can be represented by [v min v max ]express.

[0095] In some embodiments, the limiting current includes the maximum discharge current of the battery during discharge and / or the minimum charging current during charging.

[0096] In some embodiments, the maximum discharge current is a positive value, which can be represented by i. max This indicates that the minimum charging current is negative, which can be represented by i. min The minimum charging current, regardless of direction, is also called the maximum charging current. The maximum discharge current and minimum charging current can be the current values ​​specified by the battery at the factory, which can be commonly referred to as theoretical values.

[0097] In some embodiments, a first sub-predicted voltage at the maximum discharge current and / or a second sub-predicted voltage at the minimum charging current are calculated according to the updated voltage prediction equation.

[0098] Accordingly, the step of determining whether to set the limiting current as the limiting current using the first voltage range may include: if the first sub-predicted voltage, and / or the second sub-predicted voltage is within the first voltage range, then the maximum discharge current, and / or the minimum charging current is set as the limiting current.

[0099] For example, if the first sub-predicted voltage is within the first voltage range, the maximum discharge current is determined as the limit current.

[0100] For example, if the second sub-predicted voltage is within the first voltage range, then the minimum charging current is determined as the limit current.

[0101] For example, if both the first sub-predicted voltage and the second sub-predicted voltage are within the first voltage range, then either the maximum discharge current or the minimum charging current can be determined as the limit current. That is, if the maximum discharge current i max and minimum charging current i min The corresponding first sub-predicted voltage and second sub-predicted voltage All in [v min ,v max If the current is within the specified range, it means that the current maximum current has met the constraint, and the maximum discharge current i can be used. max and minimum charging current i min As a limit current. For example, i limit =i max Or i limit =i min .

[0102] In practical applications, if the first predicted voltage corresponding to the limiting current is within the first voltage range, and the second predicted voltage corresponding to the zero current is also within the first voltage range, then the limiting current is determined as the limit current.

[0103] In some embodiments, a second predicted voltage at zero current is calculated according to an updated voltage prediction equation; if the second predicted voltage is not within the first voltage range, then zero current is determined as a limit current. That is, if the voltage at rest... Exceeding the first voltage range indicates that the battery is in an unsafe state, and the default current limit is set to zero. For example, i limit =Zero current.

[0104] Zero current refers to the current of a battery when it is at rest. Zero current can be represented as 0.

[0105] In some embodiments, if the first predicted voltage is not within the first voltage range and the second predicted voltage is within the first voltage range, the limit current is searched using the truncated method.

[0106] In some embodiments, if the limiting current includes the maximum discharge current of the battery during discharge, then if the first predicted voltage is not within the first voltage range and the second predicted voltage is within the first voltage range, it can be understood as the voltage at rest. Normal, but the voltage at maximum discharge current is... Exceeding limits. During discharge, i max This causes the voltage to be lower than v. min At this point, it is necessary to search for the battery's actual maximum allowable discharge current.

[0107] In some embodiments, if the limiting current includes the minimum charging current of the battery during charging, then if the first predicted voltage is not within the first voltage range and the second predicted voltage is within the first voltage range, it can be understood as the voltage at rest. Normal, but the voltage at the minimum charging current is normal. Exceeding limits. During charging... min This causes the voltage to be higher than V. max At this point, it is necessary to search for the actual minimum allowable charging current of the battery.

[0108] The method of secant sine crossings can be used to search for a limiting current. This can involve using the logic of the secant sine crossings to find a root within a specified interval, and then determining the root as the limiting current. The specified interval varies depending on the limiting current.

[0109] In some embodiments, the limiting current includes the maximum discharge current of the battery during discharge, and the specified interval includes 0 to that maximum discharge current. That is, the limiting current is searched within the interval from 0 to the maximum discharge current using the truncation method. In other words, during discharge, the maximum discharge current causes the voltage to fall below a minimum value specified by a first voltage range. Therefore, it is necessary to determine the actual maximum discharge current of the battery within the interval from 0 to the maximum discharge current.

[0110] In some embodiments, the interval from 0 to the maximum discharge current can be represented as (0, i max ).

[0111] In some embodiments, searching for a limiting current within the interval from 0 to the maximum discharge current using the secant method may include: constructing a discharge objective function, wherein the output value of the discharge objective function is less than or equal to 0 for any input value; using the secant method and the discharge objective function, receiving a first value determined within the interval from 0 to the maximum discharge current, and using the first value as the limiting current. That is, a current less than the maximum discharge current within the interval from 0 to the maximum discharge current is determined as the actual maximum discharge current of the battery during the discharge process.

[0112] In some embodiments, the constructed discharge objective function outputs a value less than or equal to 0 for any input value. This can be understood as the predicted voltage being no less than the discharge cutoff voltage V. min Furthermore, the battery's state of charge is not lower than the minimum permissible state of charge z. min .

[0113] In some embodiments, the discharge objective function can be expressed as g dis (i) is represented.

[0114] In some embodiments, constructing the discharge objective function includes: determining a first predicted state-of-charge equation based on the state transition equation; obtaining a first difference function by subtracting the discharge cutoff voltage and the updated voltage prediction equation; obtaining a second difference function by subtracting the minimum allowable state of charge of the battery and the first predicted state-of-charge equation; and generating the discharge objective function based on the maximum value of the first difference function and the second difference function.

[0115] Based on the state transition equation, the first predicted state of charge is determined. An example of the first predicted state of charge is provided below, specifically in formula (11):

[0116]

[0117] They represent x respectively k+1 The 1st, 2nd, and 3rd elements. For example, These represent the state of charge, diffusion current, and hysteresis state of the battery at time k+1, respectively.

[0118] In some embodiments, the updated voltage prediction equation can be transformed from the above formula (10) into the following formula (12):

[0119]

[0120] The first difference function obtained by subtracting the discharge cutoff voltage and the updated voltage prediction equations can be expressed as: v min ―v pred The second difference function, obtained by subtracting the minimum permissible state of charge of the battery from the first predicted state of charge equation, can be expressed as: z min ―z pred .

[0121] Based on the maximum value of the first and second difference functions, the generated discharge target function can be expressed as: g dis (i)=max(v min ―v pred ,z min ―z pred ).

[0122] In some embodiments, using the secant method and the discharge target function, receiving a first root value determined from the interval from 0 to the maximum discharge current includes: defining a secantmethod function, wherein the secantmethod function is used to implement the secant method to solve the equation, the secantmethod function includes a discharge target function, an iterative first guess value, a second guess value, and a first convergence tolerance, the first guess value and the second guess value being the two endpoints of the interval from 0 to the maximum discharge current, respectively; substituting the first guess value and the second guess value into the discharge target function to obtain a first function value and a second function value. In this process, the mathematical signs of the first function value and the second function value are opposite. Substituting the first guess, the second guess, the first function value, and the second function value into the secant method formula, a third guess value in the interval is obtained. This third guess value is then substituted into the discharge target function to obtain the third function value. If the third function value is greater than or equal to the first convergence tolerance, the interval from 0 to the maximum discharge current is updated based on the third guess value. Based on the updated interval, the steps for defining the secantmethod function are returned until the third function value is less than the first convergence tolerance. Then, the iteration stops, and the third guess value determined from the interval is identified as the first root value and returned.

[0123] In some embodiments, defining the secantmethod function includes defining an algorithm function that implements the secant method for numerically solving the roots of a non-linear equation. That is, the secantmethod function is used to implement the secant method to solve the equation, and the secantmethod function may include custom values of the algorithm function for implementing the secant method.

[0124] Exemplarily, the defined secantmethod function includes the discharge target function g dis (i), the first guess value x1, the second guess value x2, and the first convergence tolerance res of the iteration. The first guess value and the second guess value are respectively the two end points of the interval from 0 to the maximum discharge current. The secantmethod function can be represented by def secantmethod(g, x1, x2, res = 1e-4).

[0125] In some embodiments, substituting the first guess value and the second guess value into the discharge target function to obtain the first function value and the second function value. Specifically, it can be expressed as:

[0126] a = x1

[0127] b = x2

[0128] That is, assign the first guess value and the second guess value to a and b respectively.

[0129] In some embodiments, after assigning the first guess value and the second guess value to a and b respectively, it further includes calculating the upper limit of the maximum number of iterations, based on the convergence rate of the bisection method (log2 to ensure convergence). The formula derivation is that the interval length is halved each time until |b - a| < res, so the number of iterations is log2(res∣x2 - x1∣). Specifically, it can be expressed as:

[0130] maxIter = np.ceil(np.log2(np.abs(x2 - x1) / res))

[0131] In some embodiments, it is necessary to ensure that the mathematical signs of the first function value and the second function value are different. That is, under the condition that it is ensured to hold, the step of substituting the parameters into the secant method formula to solve the third guess value can be executed. If the condition is not ensured to hold, the step of solving the third guess value is not executed, and an error is directly returned. Specifically, it can be expressed as:

[0132] if g(a)*g(b) >= 0;

[0133] raise ValueError(”The function values at the interval endpoints need to have different signs”)

[0134] In some embodiments, after determining to perform the step of solving for the third guess value, the values ​​of g(a) and g(b) can be stored to avoid redundant calculations. Specifically, this can be expressed as:

[0135] ga = g(a)

[0136] gb = g(b)

[0137] In some embodiments, the first guess value, the second guess value, the first function value, and the second function value are substituted into the secant method formula to obtain the third guess value in the interval, and the third guess value is substituted into the discharge target function to obtain the third function value. Specifically, this can be expressed as:

[0138] for iin range(1,int(maxIter)-1);

[0139] The third guess obtained from the solution can be expressed as:

[0140] x = (a*gb – b*ga) / (gb – ga)

[0141] Substituting the third guess into the discharge target function, the resulting third function value can be represented as g(x). In some embodiments, after obtaining the third function value, g(x) can be stored to avoid redundant calculations. Specifically, it can be represented as:

[0142] gx = g(x)

[0143] In some embodiments, if the absolute value of the third function is less than the first convergence tolerance, then the third guess value x is returned. Specifically, this can be expressed as:

[0144] #Check convergence conditions

[0145] if abs(gx) <res;

[0146] return x

[0147] In some embodiments, if the absolute value of the third function is greater than or equal to the first convergence tolerance, the interval from 0 to the maximum discharge current is updated based on the third guess value. Specifically, this can be expressed as:

[0148]

[0149]

[0150] In some embodiments, after updating the interval, the step of defining the secantmethod function is returned until the third guess value is less than the first convergence tolerance, then the iteration stops, and the third guess value determined in the interval is determined as the first root value and returned.

[0151] In some embodiments, the limiting current includes the minimum charging current of the battery during charging, and the specified interval includes the minimum charging current up to 0. That is, the limiting current is searched within the interval from the minimum charging current to 0 using the truncation method. In other words, during charging, the minimum charging current causes the voltage to exceed the maximum value specified in a first voltage range. Therefore, it is necessary to determine the actual minimum charging current of the battery from the interval from the minimum charging current to 0.

[0152] In some embodiments, the range from the minimum charging current to 0 can be represented as (i min ,0)

[0153] In some embodiments, searching for a limit current within the interval from the minimum charging current to 0 using the secant method may include: constructing a charging objective function, wherein the output value of the charging objective function is greater than or equal to 0 for any input value; using the secant method and the charging objective function, receiving a second root value determined from the interval from the minimum charging current to 0, and using the second root value as the limit current. That is, a current greater than the minimum charging current is determined from the interval from the minimum charging current to 0 as the actual minimum charging current of the battery during the charging process.

[0154] In some embodiments, the constructed charging objective function outputs a value greater than or equal to 0 for any input value. This can be understood as the predicted voltage not exceeding the charging cutoff voltage V. max And the battery's state of charge is not higher than the maximum permissible state of charge z. max .

[0155] In some embodiments, the discharge objective function can be expressed as g chg (i) is represented.

[0156] In some embodiments, constructing the charging objective function includes: determining a second predicted state-of-charge equation based on the state transition equation; obtaining a third difference function by subtracting the charging cutoff voltage and the updated voltage prediction equation; obtaining a fourth difference function by subtracting the battery's maximum permissible state of charge and the second predicted state-of-charge equation; and generating the charging objective function based on the minimum value of the third and fourth difference functions.

[0157] Based on the state transition equation, the second predicted state of charge is determined. The following provides an example of the second predicted state of charge, specifically referring to the above formula (11).

[0158] In some embodiments, the updated voltage prediction equation can be transformed from the above formula (10) into the above formula (12).

[0159] The third difference function, obtained by subtracting the charging cutoff voltage and the updated voltage prediction equations, can be expressed as: vmax ―v pred The fourth difference function, obtained by subtracting the battery's maximum permissible state of charge and the second predicted state of charge equation, can be expressed as: z max ―z pred .

[0160] Based on the minimum value of the third and fourth difference functions, the generated charging objective function can be expressed as: g chg (i)=min(v max ―v pred ,z max ―z pred ).

[0161] In some embodiments, using the secant method and the charging objective function, receiving a second root value determined from the interval from the minimum charging current to 0 includes: defining a secantmethod function, wherein the secantmethod function is used to implement the secant method to solve the equation, the secantmethod function includes a discharge objective function, an iterative fourth guess value, a fifth guess value, and a second convergence tolerance, the fourth guess value and the fifth guess value being the two endpoints of the interval from the minimum charging current to 0; substituting the fourth guess value and the fifth guess value into the charging objective function to obtain the fourth function value and the fifth function value, which... In the formula, the fourth and fifth function values ​​have opposite mathematical signs. Substituting the fourth, fifth, fourth, and fifth guess values ​​into the secant method formula, we obtain the sixth guess value in the interval. Substituting the sixth guess value into the charging objective function, we obtain the sixth function value. If the sixth function value is greater than or equal to the second convergence tolerance, we update the interval with the minimum charging current to 0 based on the sixth guess value. Based on the updated interval, we return to the step of defining the secantmethod function until the sixth function value is less than the second convergence tolerance. Then, we stop the iteration and return the sixth guess value determined from the interval as the second root value.

[0162] In some embodiments, defining the secantmethod function includes defining an algorithmic function that implements the secant method for numerically solving the roots of nonlinear equations. That is, the secantmethod function is used to implement the secant method for solving equations, and the secantmethod function may include custom values ​​for the algorithmic function used to implement the secant method.

[0163] For example, the defined secantmethod function includes the charging target function g. chg(i) The fourth guessed value x1, the fifth guessed value x2, and the second convergence tolerance res of the iteration. The fourth guessed value and the fifth guessed value are respectively the two endpoints of the interval from the minimum charging current to 0. The secantmethod function can be represented by def secantmethod(g, x1, x2, res = 1e-4).

[0164] In some embodiments, substituting the fourth guessed value and the fifth guessed value into the discharge objective function to obtain the fourth function value and the fifth function value. Specifically, it can be expressed as:

[0165] a = x1

[0166] b = x2

[0167] That is, assign the fourth guessed value and the fifth guessed value to a and b respectively.

[0168] In some embodiments, after assigning the fourth guessed value and the fifth guessed value to a and b respectively, it further includes calculating the upper limit of the maximum number of iterations, based on the convergence rate of the bisection method (log2 to ensure convergence). The formula derivation is that the interval length is halved each time until |b - a| < res, so the number of iterations is log2(res / |x2 - x1|). Specifically, it can be expressed as:

[0169] maxIter = np.ceil(np.log2(np.abs(x2 - x1) / res))

[0170] In some embodiments, it is necessary to ensure that the mathematical signs of the fourth function value and the fifth function value are different. That is, only when this is ensured can the step of substituting the parameters into the secant method formula to solve the sixth guessed value be executed. If this is not ensured, the step of solving the sixth guessed value is not executed, and a root finding error is directly returned. Specifically, it can be expressed as:

[0171] if g(a) * g(b) >= 0;

[0172] raise ValueError("The function values at the interval endpoints must have different signs")

[0173] In some embodiments, after determining to execute the step of solving the sixth guessed value, the values of g(a) and g(b) can be stored to avoid repeated calculations. Specifically, it can be expressed as:

[0174] ga = g(a)

[0175] gb = g(b)

[0176] In some embodiments, the fourth guess value, the fifth guess value, the fourth function value, and the fifth function value are substituted into the secant method formula to obtain the sixth guess value in the interval, and the sixth guess value is substituted into the discharge target function to obtain the sixth function value. Specifically, this can be expressed as:

[0177] for iin range(1,int(maxIter)-1);

[0178] The sixth guess obtained from the solution can be expressed as:

[0179] x = (a*gb – b*ga) / (gb – ga)

[0180] Substituting the sixth guess value into the discharge target function, the resulting sixth function value can be represented as g(x). In some embodiments, after obtaining the sixth function value, g(x) can be stored to avoid redundant calculations. Specifically, it can be represented as:

[0181] gx = g(x)

[0182] In some embodiments, if the absolute value of the sixth function value is less than the second convergence tolerance, then the sixth guess value x is returned. Specifically, this can be expressed as:

[0183] #Check convergence conditions

[0184] if abs(gx) <res;

[0185] return x

[0186] In some embodiments, if the absolute value of the sixth function is greater than or equal to the second convergence tolerance, the interval from 0 to the maximum discharge current is updated based on the sixth guess value. Specifically, this can be expressed as:

[0187]

[0188]

[0189] In some embodiments, after updating the interval, the step of defining the secantmethod function is returned until the sixth guess value is less than the second convergence tolerance, then the iteration stops, and the sixth guess value determined in the interval is determined as the second root value and returned.

[0190] In some embodiments, during the calculation of the first to sixth function values, k in the voltage prediction equation is updated. ΔT The value can be selected according to actual needs, and the embodiments of this application are not limited herein.

[0191] Step S150: Calculate the battery's available power limit based on the updated voltage prediction equation and the limit current.

[0192] In some embodiments, the limit current is substituted into the updated voltage prediction equation to obtain the predicted voltage value. Based on the predicted voltage value and the limit current, the battery's usable power limit is calculated.

[0193] In some embodiments, the updated voltage prediction equation is provided by equation (12).

[0194] In some embodiments, calculating the battery's available power limit based on the predicted voltage value and the limit current may include multiplying the predicted voltage value and the limit current to obtain the battery's available power limit.

[0195] In some embodiments, the battery comprises multiple batteries connected in parallel and / or in series; calculating the battery's usable power limit based on the predicted output voltage and the limit current may include: substituting the limit current into the updated voltage prediction equation to obtain the predicted voltage value; multiplying the predicted voltage value, the limit current, the number of batteries connected in series, and / or the number of batteries connected in parallel to obtain the battery's usable power limit. See formula (13) below for details:

[0196] P SOP =N s ·N p ·v pred ·i limit (13)

[0197] Where, N s N is the number of battery cells connected in series. p It is the number of battery cells connected in parallel.

[0198] In some embodiments, during the calculation of the predicted voltage value, k in the voltage prediction equation is updated. ΔT The value can be selected according to actual needs, and the embodiments of this application are not limited herein.

[0199] The following is in conjunction with the appendix Figure 2 Regarding the above Figure 1 The provided battery available power state estimation method will be further explained. Among other things, Figure 2 This is a flowchart of a battery available power state estimation method provided in an embodiment of this application, including the following steps S210 to S290.

[0200] Step S210: Construct a battery model with discrete time and space states.

[0201] Construct a battery model as shown in formulas (4)-(7) above.

[0202] Based on the battery model, the output predicted voltage v of the battery model predThis is expressed as the equation shown in formula (8) above. The predicted SOC z pred It is represented as the equation shown in formula (9) above.

[0203] Step S220, construct the future k ΔT Each time step State transition equation.

[0204] Construct k as shown in formula (10) above. ΔT Each time step State transition equation.

[0205] Based on the content of step S210, and assuming that in k ΔT The current remains constant within each time step, thus allowing us to predict the future k. ΔT The voltage prediction equation for each time step is given in formula (11) above.

[0206] Step S230: Perform future k-factor analysis on the three current inputs. ΔT Voltage prediction at each time step.

[0207] i k =0 (zero current, battery at rest).

[0208] i k =i min (Minimum charging current, negative value).

[0209] i k =i max (Maximum discharge current, positive value).

[0210] Through state-space model Predict future voltage.

[0211] Step S240, the predicted voltage under the limiting current is in [v min ,v max Within this range, the limit current is used.

[0212] If i min and i max Corresponding predicted voltage All in [v min ,v max The text within the brackets indicates that the current maximum current already meets the constraints, so we can directly use i. max (Discharge) or i min (Charging), i.e., i limit =i max , or, i limit =i min No truncating method is needed.

[0213] Step S250, the resting voltage exceeds [v]min ,v max In addition, the limit current is set to 0.

[0214] If the voltage is when stationary Exceeding [v min ,v max The current limit is set to 0 by default, indicating that the battery is in an unsafe state. limit =0.

[0215] Step S260, under the scenario of normal resting voltage and exceeding the limit voltage, use the secant method to search for i. limit .

[0216] Static voltage Normal, but voltage at maximum current. Exceeding the limit:

[0217] i during discharge max This causes the voltage to be lower than v. min At this point, it is necessary to be in (0, i max Search for the maximum allowable discharge current i within the interval limit The search is performed using the formula (12) above.

[0218] Static voltage Normal, but voltage at minimum current. Exceeding the limit:

[0219] i during charging min This causes the voltage to be higher than V. max At this point, it is necessary to (i min Search for the maximum allowable charging current i within the interval ,0) limit The search is performed using the formula (12) above.

[0220] Step S270: Construct the objective function for the secant method iteration.

[0221] Discharge scenario, constructor g dis (i)=max(v min ―v pred ,z min ―z pred ), requiring g dis (i)≤0, meaning the future voltage will not be lower than v min And the SOC is not lower than z min .

[0222] Charging scenario, constructor g chg (i)=min(v max ―v pred ,z max ―z pred ), requiring gchg (i)≥0, meaning the future voltage will not exceed v. max And the SOC is not higher than z max .

[0223] Step S280: Use the secant method logic to solve the objective function within the requirements of different scenarios. limit .

[0224]

[0225]

[0226] Step S290: Calculate the available power limit based on the current value obtained from the root search and the predicted voltage value.

[0227] N s N is the number of battery cells connected in series. p This is the number of battery cells connected in parallel. The power limit calculation is given in formula (13) above.

[0228] As can be seen from the above, this application utilizes a battery model that can fully consider the dynamic changes of the battery to perform actions such as construction, updating, and determination, and obtains updated voltage prediction equations and limit currents, which are used to calculate the battery's available power limit that can accurately reflect the actual behavior of the battery under different operating conditions, so that the estimated available power limit is more in line with the actual situation.

[0229] Figure 3 This is a schematic structural diagram of a computer device provided in an embodiment of this application. Figure 3 The dashed lines in the diagram indicate that the unit or module is optional. The computer device 300 can be used to implement the methods described in the above method embodiments.

[0230] Computer device 300 may include one or more processors 310. The processor 310 can support the computer device 300 in implementing the methods described in the preceding method embodiments. The processor 310 may be a general-purpose processor or a special-purpose processor. For example, the processor may be a central processing unit (CPU). Alternatively, the processor may be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor.

[0231] The computer device 300 may also include one or more memories 320. The memories 320 store a computer program that can be executed by the processor 310, causing the processor 310 to perform the methods described in the preceding method embodiments. The memories 320 may be independent of the processor 310 or integrated within the processor 310.

[0232] The computer device 300 may also include a transceiver 330, through which the processor 310 can communicate with other devices. For example, the processor 310 can send and receive data with other devices through the transceiver 330.

[0233] In one embodiment of this application, the aforementioned components of the computer device 300 and Figure 3 Other components not shown can also be connected to each other. It should be understood that... Figure 3 The computer device structural block diagram shown is for illustrative purposes only and is not intended to limit the scope of this application. Those skilled in the art can add or replace other components as needed.

[0234] The above is an illustrative scheme of a computer device according to this embodiment. It should be noted that the technical solution of this computer device and the technical solution of the battery available power state estimation method described above belong to the same concept. For details not described in detail in the technical solution of the computer device, please refer to the description of the technical solution of the battery available power state estimation method described above.

[0235] In addition, this application also proposes a computer-readable storage medium storing a computer program. When the computer program is executed by a computer, it implements the operations in the battery available power state estimation method provided in the above embodiments. The specific steps will not be described in detail here.

[0236] This application also provides a computer program product. The computer program product includes a program / instructions. When executed by a processor, the computer program / instructions implement the steps of the above-described battery available power state estimation method.

[0237] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity / operation / object from another, and do not necessarily require or imply any such actual relationship or order between these entities / operations / objects; the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or system. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or system that includes that element.

[0238] For the device embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and relevant details can be found in the description of the method embodiments. The device embodiments described above are merely illustrative, and the units described as separate components may or may not be physically separate. Some or all of the modules can be selected according to actual needs to achieve the purpose of this application. Those skilled in the art can understand and implement this without creative effort.

[0239] The sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0240] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) as described above, and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, television, or network device, etc.) to execute the methods described in the various embodiments of this application.

[0241] The above are merely embodiments of this application and do not limit the patent scope of this application. Any equivalent structural or procedural transformations made using the content of this application's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of this application.

Claims

1. A method for estimating the available power state of a battery, characterized in that, The method includes: Based on parameters describing the dynamic characteristics of the battery, a discrete-time spatial state battery model is constructed, which includes state transition equations and voltage prediction equations. Based on the state transition equation, construct state transition equations for multiple time steps; Based on the state transition equations of the multiple time steps, the voltage prediction equation is updated to obtain the updated voltage prediction equation. The limit current is determined based on the updated voltage prediction equation; Based on the updated voltage prediction equation and the limit current, the battery's usable power limit is calculated.

2. The method according to claim 1, characterized in that, The parameters describing the dynamic characteristics of the battery include diffusion effect parameters and hysteresis effect parameters.

3. The method according to any one of claims 1-2, characterized in that, The state transition equation includes the relationship equation between the state vectors at time k and time k+1, and the state vectors include the battery's state of charge, diffusion current, and hysteresis state. The voltage prediction equation includes the relationship between the output predicted voltage and the state vector at time k.

4. The method according to claim 3, characterized in that, The multiple time steps are k ΔT Multiple time steps; the step of updating the voltage prediction equation based on the state transition equations of the multiple time steps to obtain the updated voltage prediction equation includes: Using the k ΔT The state transition equations at time step k are used to update the state vector at time k, thereby obtaining the updated voltage prediction equation.

5. The method according to claim 1, characterized in that, The step of determining the limit current based on the updated voltage prediction equation includes: Based on the updated voltage prediction equation, a first predicted voltage at the limiting current is calculated; if the first predicted voltage is within a first voltage range, then the limiting current is determined as a limit current, wherein the limiting current is the current of the battery in the charging / discharging state; or, According to the updated voltage prediction equation, the second predicted voltage at zero current is calculated; if the second predicted voltage is not within the first voltage range, the zero current is determined as the limit current, wherein the zero current is the current of the battery in the quiescent state.

6. The method according to claim 5, characterized in that, The step of determining the limit current based on the updated voltage prediction equation includes: If the first predicted voltage is not within the first voltage range, and the second predicted voltage is within the first voltage range, then the limit current is searched using the secant method.

7. The method according to claim 6, characterized in that, The limiting current includes the maximum discharge current of the battery during the discharge process, and the left end of the first voltage range is the discharge cutoff voltage of the battery. The method of searching for the limit current using the secant method includes: Based on the state transition equation, the first predicted charge state equation is determined; The first difference function is obtained by subtracting the discharge cutoff voltage and the updated voltage prediction equation; The second difference function is obtained by subtracting the minimum permissible state of charge of the battery from the first predicted state of charge equation. A discharge target function is generated based on the maximum value of the first difference function and the second difference function, wherein the output value of the discharge target function is less than or equal to 0 for any input value; Using the secant method and the discharge target function, a first root value determined from the interval from 0 to the maximum discharge current is received, and the first root value is used as the limit current.

8. The method according to claim 7, characterized in that, The step of receiving a first root value determined from the interval from 0 to the maximum discharge current using the secant method and the discharge target function includes: Define a secantmethod function, wherein the secantmethod function is used to solve the equation using the secant method, and the secantmethod function includes a discharge objective function, a first guess value, a second guess value, and a first convergence tolerance, wherein the first guess value and the second guess value are the two endpoints of the interval from 0 to the maximum discharge current; Substituting the first guess value and the second guess value into the discharge target function, a first function value and a second function value are obtained, wherein the mathematical signs of the first function value and the second function value are opposite; Substituting the first guess value, the second guess value, the first function value, and the second function value into the secant method formula, a third guess value in the interval is obtained, and the third guess value is substituted into the discharge target function to obtain the third function value; If the third function value is greater than or equal to the first convergence tolerance, then the range from 0 to the maximum discharge current is updated based on the third guess value. Based on the updated interval, return to the step of defining the secantmethod function until the third function value is less than the first convergence tolerance, then stop iterating and return the third guess value determined from the interval as the first root value.

9. The method according to claim 6, characterized in that, The limiting current includes the minimum charging current of the battery during the charging process, and the right end of the first voltage range is the charging cut-off voltage of the battery. The method of searching for the limit current using the secant method includes: Based on the state transition equation, the second predicted charge state equation is determined; The third difference function is obtained by subtracting the charging cutoff voltage and the updated voltage prediction equation. The fourth difference function is obtained by subtracting the maximum permissible state of charge of the battery from the second predicted state of charge equation. A charging target function is generated based on the minimum value of the third and fourth difference functions, wherein the output value of the charging target function is greater than or equal to 0 for any input value; Using the secant method and the charging target function, a second root value determined from the interval from the minimum charging current to 0 is received, and the second root value is used as the limit current.

10. The method according to claim 9, characterized in that, The step of receiving a second root value determined from the interval from the minimum charging current to 0 using the secant method and the charging target function includes: Define the secantmethod function, which is used to solve the equation using the secant method. The secantmethod function includes a discharge objective function, an iterative fourth guess value, a fifth guess value, and a second convergence tolerance. The fourth guess value and the fifth guess value are the two endpoints of the interval from the minimum charging current to 0, respectively. Substituting the fourth and fifth guess values ​​into the charging target function yields the fourth and fifth function values, wherein the mathematical signs of the fourth and fifth function values ​​are opposite. Substituting the fourth guess value, the fifth guess value, the fourth function value, and the fifth function value into the secant method formula, a sixth guess value in the interval is obtained, and the sixth guess value is substituted into the charging target function to obtain a sixth function value; If the sixth function value is greater than or equal to the second convergence tolerance, then the interval of the minimum charging current to 0 is updated based on the sixth guess value; Based on the updated interval, return to the step of defining the secantmethod function until the sixth function value is less than the second convergence tolerance, then stop the iteration, and determine the sixth guess value determined from the interval as the second root value and return.

11. The method according to claim 1, characterized in that, The battery comprises multiple batteries, which are connected in parallel and / or series; the calculation of the battery's usable power limit based on the updated voltage prediction equation and the limit current includes: Substitute the limit current into the updated voltage prediction equation to obtain the predicted voltage value; The battery's usable power limit is obtained by multiplying the predicted voltage value, the limit current, the number of batteries connected in series, and / or the number of batteries connected in parallel.

12. A computer device, characterized in that, It includes a memory and a processor, the memory being used to store a computer program, and the processor being used to implement the method as described in any one of claims 1 to 11 when the computer program is executed.

13. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a program based on a battery available power state estimation method, which, when executed by a processor, implements the method as described in any one of claims 1 to 11.

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