Charging control method and device without lithium precipitation of power battery and vehicle

Abstract

The invention provides a charging control method and device for a power battery without lithium precipitation and a vehicle, and the charging control method comprises the steps: correspondingly obtaining a battery parameter according to a to-be-estimated parameter in an equivalent circuit model based on the equivalent circuit model corresponding to the target power battery when the target power battery is charged; updating the battery parameters by using the parameter identification network model to quantify the aging characteristics of the target power battery to obtain target battery parameters corresponding to the battery parameters; and based on the target battery parameters, determining a state-of-charge estimated value and a polarization voltage predicted value of the target power battery, performing rolling optimization by using the prediction model, and determining a terminal voltage predicted value of the target power battery so as to control the cathode potential of the target power battery, so that the target power battery has no lithium precipitation phenomenon in the charging process. By means of the method, the effectiveness and safety of lithium precipitation-free charging control of the power battery are improved, and then the cost of charging control is reduced.

Description

Technical Field

[0001] The present application relates to the technical field of power batteries, and in particular to a charging control method, device, and vehicle for power batteries without lithium plating. Background Art

[0002] When the power battery is at low temperature or is being charged quickly, lithium plating side reaction is prone to occur on the negative electrode surface of the power battery, which will cause the capacity of the power battery to decay and form dendrites that pierce the diaphragm, thereby inducing thermal runaway of the power battery and seriously affecting the safety performance of the power battery.

[0003] At present, in the optimized lithium-free charging control method based on the battery model, the battery model mainly includes the electrochemical model and the equivalent circuit model. The electrochemical model can reflect the real electrochemical state of the power battery during operation, but the calculation cost of the model is high and the parameter identification is difficult. The ordinary equivalent circuit model cannot obtain the electrode state information inside the battery, which reduces the effectiveness of the lithium-free charging control. In addition, it also includes a fast charging control method based on PID control. In this method, the anode potential and charging current of the power battery both oscillate, which weakens the dynamic characteristics of the power battery. Summary of the Invention

[0004] In view of this, the purpose of the present application is to provide a charging control method, device and vehicle for power batteries without lithium plating. The method obtains the battery parameters of the power battery based on the equivalent circuit model corresponding to the power battery during the charging process, and updates the battery parameters using a parameter identification network model to quantify the aging characteristics of the target power battery. Based on the updated battery parameters, the state of charge estimation value and polarization voltage prediction value of the power battery are estimated, and then the prediction model is used for rolling optimization to determine the terminal voltage prediction value of the power battery to control the negative electrode potential of the power battery, so that the power battery does not plating lithium during the charging process, solves the problems of oscillation, poor dynamic characteristics and noise disturbance in the power battery during the charging process, improves the effectiveness and safety of the power battery charging control without lithium plating, and thus reduces the cost of charging control.

[0005] The present application provides a method for controlling charging of a power battery without lithium deposition, the method comprising:

[0006] During charging of a target power battery, based on an equivalent circuit model corresponding to the target power battery, battery parameters of the target power battery are obtained according to parameters to be estimated in the equivalent circuit model;

[0007] Updating the battery parameters using a preset parameter identification network model to quantify the aging characteristics of the target power battery and obtain target battery parameters corresponding to the battery parameters;

[0008] Based on the target battery parameters, determining the estimated state of charge value and the predicted polarization voltage value of the target power battery using a preset estimation method;

[0009] Based on the state of charge estimation value and the polarization voltage prediction value, a preset prediction model is used to perform rolling optimization to determine a terminal voltage prediction value of the target power battery, and based on the terminal voltage prediction value, the negative electrode potential of the target power battery is controlled so that the target power battery does not undergo lithium deposition during charging.

[0010] Furthermore, the acquiring of battery parameters of the target power battery according to the estimated parameters preset in the equivalent circuit model based on the equivalent circuit model corresponding to the target power battery includes:

[0011] Determining parameters to be estimated in the equivalent circuit model based on an equivalent circuit model corresponding to the target power battery;

[0012] Obtaining a terminal current value, a terminal voltage value, and a charging temperature value of the target power battery, and using the parameters to be estimated as a reference, and based on the terminal current value and the terminal voltage value, using a preset conversion formula to determine a resistance value, an available capacity value, an open circuit voltage value, and a polarization battery parameter corresponding to the target power battery;

[0013] Based on the open circuit voltage value, determining the state of charge value corresponding to the target power battery using a preset state of charge fitting function;

[0014] The terminal current value, the terminal voltage value, the charging temperature value, the resistance value, the available capacity value, the open circuit voltage value, the polarization battery parameter and the state of charge value are determined as battery parameters of the target power battery corresponding to the parameters to be estimated.

[0015] Furthermore, the battery parameters are updated using a preset parameter identification network model to quantify the aging characteristics of the target power battery and obtain target battery parameters corresponding to the battery parameters, including:

[0016] Based on the preset initial model parameters, the preset tuna optimization model is used to perform parameter optimization to obtain the target model parameters;

[0017] Based on the target model parameters, the battery parameters are updated using a preset parameter identification network model to obtain target battery parameters corresponding to the battery parameters, so as to quantify the aging characteristics of the target power battery.

[0018] Furthermore, the method of determining the state of charge estimation value and polarization voltage prediction value of the target power battery based on the target battery parameters using a preset estimation method includes:

[0019] In each recursive cycle, based on the state transfer matrix, state prediction matrix, and current prediction value corresponding to the previous recursive cycle, a preset state equation is used to determine the state prediction matrix corresponding to the current recursive cycle; wherein the initial state matrix corresponding to the first recursive cycle includes an expectation matrix composed of the state of charge values and polarization voltage values in the target battery parameters, and the initial error covariance matrix corresponding to the first recursive cycle includes a variance matrix composed of the state of charge values and the polarization voltage values;

[0020] Calculating a state transition matrix corresponding to a current recursive cycle based on the target battery parameters and a state prediction matrix corresponding to a previous recursive cycle, and determining an error covariance matrix corresponding to the current recursive cycle based on the state transition matrix corresponding to the current recursive cycle and an error covariance matrix corresponding to the previous recursive cycle;

[0021] Based on the state prediction matrix corresponding to the current recursive period, the observation value and observation matrix corresponding to the current recursive period are determined respectively using the preset observation equation;

[0022] Determining a gain matrix corresponding to the current recursion period based on the error covariance matrix corresponding to the current recursion period and the measurement matrix;

[0023] Determine the difference between the target battery parameter and the observation value corresponding to the current recursive period as the residual value corresponding to the current recursive period, and determine the updated state prediction matrix corresponding to the current recursive period based on the residual value corresponding to the current recursive period, the gain matrix, and the state prediction matrix;

[0024] Determine an updated error covariance matrix corresponding to the current recursive period based on the error covariance matrix, the observation matrix, and the gain matrix corresponding to the current recursive period;

[0025] Repeat the prediction and update of multiple recursive cycles until the number of executions of the recursive cycles reaches a preset value, and output the updated state prediction matrix corresponding to the final recursive cycle to determine the estimated state of charge value and the polarization voltage predicted value of the target power battery in the updated state prediction matrix.

[0026] Furthermore, the step of performing rolling optimization based on the state of charge estimation value and the polarization voltage prediction value using a preset prediction model to determine the terminal voltage prediction value of the target power battery includes:

[0027] Based on the state of charge estimate and the polarization voltage prediction value, a preset prediction model is used to determine a state of charge prediction value corresponding to the target power battery in the current prediction period; wherein a preset safety boundary threshold is used to limit the intermediate parameters corresponding to the prediction model in the current prediction period;

[0028] subtracting the predicted state of charge value from a preset state of charge reference value to obtain a target difference, and determining whether the target difference shows convergence and whether the predicted state of charge value is less than the preset state of charge reference value;

[0029] When the target difference shows divergence and / or the state of charge prediction value is greater than or equal to a preset state of charge reference value, using the prediction model to perform prediction optimization for the next prediction cycle;

[0030] When the target difference shows convergence and the state of charge prediction value is less than a preset state of charge reference value, the target power battery terminal voltage prediction value is determined based on the state of charge prediction value and the polarization voltage prediction value.

[0031] Furthermore, the safety boundary threshold is determined by the following steps:

[0032] Placing the target power battery in a test incubator, charging the target power battery, and detecting the surface temperature, negative electrode potential, and terminal voltage of the target power battery in real time;

[0033] When the surface temperature reaches a preset temperature value and becomes stable, adjusting the charging current value of the target power battery so that the negative electrode potential approaches a preset threshold value;

[0034] When the terminal voltage value reaches the cut-off voltage value, determining a safety voltage threshold and a safety current threshold of the target power battery based on the terminal voltage values respectively corresponding to the plurality of charging current values during the adjustment process;

[0035] The safety voltage threshold, the safety current threshold, and the preset temperature value are determined as the safety boundary threshold.

[0036] The present application also provides a charging control device for a power battery without lithium deposition, the charging control device comprising:

[0037] a parameter acquisition module, configured to acquire, during charging of a target power battery, battery parameters of the target power battery based on an equivalent circuit model corresponding to the target power battery and corresponding to parameters to be estimated in the equivalent circuit model;

[0038] a parameter updating module, configured to update the battery parameters using a preset parameter identification network model to quantify the aging characteristics of the target power battery and obtain target battery parameters corresponding to the battery parameters;

[0039] a parameter estimation module, configured to determine, based on the target battery parameters, an estimated state of charge value and a predicted polarization voltage value of the target power battery using a preset estimation method;

[0040] a prediction control module, configured to perform rolling optimization using a preset prediction model based on the estimated state of charge value and the predicted polarization voltage value, determine a predicted terminal voltage value of the target power battery, and control the negative electrode potential of the target power battery based on the predicted terminal voltage value so that the target power battery does not undergo lithium plating during charging.

[0041] Furthermore, when the parameter acquisition module is used to acquire the battery parameters of the target power battery according to the estimated parameters preset in the equivalent circuit model based on the equivalent circuit model corresponding to the target power battery, the parameter acquisition module is used to:

[0042] Determining parameters to be estimated in the equivalent circuit model based on an equivalent circuit model corresponding to the target power battery;

[0043] Obtaining a terminal current value, a terminal voltage value, and a charging temperature value of the target power battery, and using the parameters to be estimated as a reference, and based on the terminal current value and the terminal voltage value, using a preset conversion formula to determine a resistance value, an available capacity value, an open circuit voltage value, and a polarization battery parameter corresponding to the target power battery;

[0044] Based on the open circuit voltage value, determining the state of charge value corresponding to the target power battery using a preset state of charge fitting function;

[0045] The terminal current value, the terminal voltage value, the charging temperature value, the resistance value, the available capacity value, the open circuit voltage value, the polarization battery parameter and the state of charge value are determined as battery parameters of the target power battery corresponding to the parameters to be estimated.

[0046] Furthermore, when the parameter updating module is used to update the battery parameters using a preset parameter identification network model to quantify the aging characteristics of the target power battery and obtain target battery parameters corresponding to the battery parameters, the parameter updating module is used to:

[0047] Based on the preset initial model parameters, the preset tuna optimization model is used to perform parameter optimization to obtain the target model parameters;

[0048] Based on the target model parameters, the battery parameters are updated using a preset parameter identification network model to obtain target battery parameters corresponding to the battery parameters, so as to quantify the aging characteristics of the target power battery.

[0049] Furthermore, when the parameter estimation module is used to determine the state of charge estimation value and polarization voltage prediction value of the target power battery based on the target battery parameters using a preset estimation method, the parameter estimation module is used to:

[0050] In each recursive cycle, based on the state transfer matrix, state prediction matrix, and current prediction value corresponding to the previous recursive cycle, a preset state equation is used to determine the state prediction matrix corresponding to the current recursive cycle; wherein the initial state matrix corresponding to the first recursive cycle includes an expectation matrix composed of the state of charge values and polarization voltage values in the target battery parameters, and the initial error covariance matrix corresponding to the first recursive cycle includes a variance matrix composed of the state of charge values and the polarization voltage values;

[0051] Calculating a state transition matrix corresponding to a current recursive cycle based on the target battery parameters and a state prediction matrix corresponding to a previous recursive cycle, and determining an error covariance matrix corresponding to the current recursive cycle based on the state transition matrix corresponding to the current recursive cycle and an error covariance matrix corresponding to the previous recursive cycle;

[0052] Based on the state prediction matrix corresponding to the current recursive period, the observation value and observation matrix corresponding to the current recursive period are determined respectively using the preset observation equation;

[0053] Determining a gain matrix corresponding to the current recursion period based on the error covariance matrix corresponding to the current recursion period and the measurement matrix;

[0054] Determine the difference between the target battery parameter and the observation value corresponding to the current recursive period as the residual value corresponding to the current recursive period, and determine the updated state prediction matrix corresponding to the current recursive period based on the residual value corresponding to the current recursive period, the gain matrix, and the state prediction matrix;

[0055] Determine an updated error covariance matrix corresponding to the current recursive period based on the error covariance matrix, the observation matrix, and the gain matrix corresponding to the current recursive period;

[0056] Repeat the prediction and update of multiple recursive cycles until the number of executions of the recursive cycles reaches a preset value, and output the updated state prediction matrix corresponding to the final recursive cycle to determine the estimated state of charge value and the polarization voltage predicted value of the target power battery in the updated state prediction matrix.

[0057] Furthermore, when the prediction control module is used to perform rolling optimization based on the state of charge estimate and the polarization voltage prediction value using a preset prediction model to determine the terminal voltage prediction value of the target power battery, the prediction control module is used to:

[0058] Based on the state of charge estimate and the polarization voltage prediction value, a preset prediction model is used to determine a state of charge prediction value corresponding to the target power battery in the current prediction period; wherein a preset safety boundary threshold is used to limit the intermediate parameters corresponding to the prediction model in the current prediction period;

[0059] subtracting the predicted state of charge value from a preset state of charge reference value to obtain a target difference, and determining whether the target difference shows convergence and whether the predicted state of charge value is less than the preset state of charge reference value;

[0060] When the target difference shows divergence and / or the state of charge prediction value is greater than or equal to a preset state of charge reference value, using the prediction model to perform prediction optimization for the next prediction cycle;

[0061] When the target difference shows convergence and the state of charge prediction value is less than a preset state of charge reference value, the target power battery terminal voltage prediction value is determined based on the state of charge prediction value and the polarization voltage prediction value.

[0062] Furthermore, when the prediction control module is used to determine the safety margin threshold, the prediction control module is used to:

[0063] Placing the target power battery in a test incubator, charging the target power battery, and detecting the surface temperature, negative electrode potential, and terminal voltage of the target power battery in real time;

[0064] When the surface temperature reaches a preset temperature value and becomes stable, adjusting the charging current value of the target power battery so that the negative electrode potential approaches a preset threshold value;

[0065] When the terminal voltage value reaches the cut-off voltage value, determining a safety voltage threshold and a safety current threshold of the target power battery based on the terminal voltage values respectively corresponding to the plurality of charging current values during the adjustment process;

[0066] The safety voltage threshold, the safety current threshold, and the preset temperature value are determined as the safety boundary threshold.

[0067] An embodiment of the present application also provides a vehicle, which is used to execute the steps of the above-mentioned power battery charging control method without lithium plating.

[0068] An embodiment of the present application also provides an electronic device, comprising: a processor, a memory, and a bus, wherein the memory stores machine-readable instructions executable by the processor. When the electronic device is running, the processor and the memory communicate through the bus, and when the machine-readable instructions are executed by the processor, the steps of the above-mentioned power battery charging control method without lithium deposition are performed.

[0069] An embodiment of the present application further provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the steps of the above-mentioned method for controlling charging of a power battery without lithium deposition are executed.

[0070] The embodiments of the present application provide a power battery charging control method, device, and vehicle without lithium plating. The charging control method includes: during charging of a target power battery, based on an equivalent circuit model corresponding to the target power battery, obtaining battery parameters of the target power battery according to the parameters to be estimated in the equivalent circuit model; updating the battery parameters using a preset parameter identification network model to quantify the aging characteristics of the target power battery and obtain target battery parameters corresponding to the battery parameters; based on the target battery parameters, determining a state of charge estimate and a polarization voltage prediction value of the target power battery using a preset estimation method; based on the state of charge estimate and the polarization voltage prediction value, performing rolling optimization using a preset prediction model to determine a terminal voltage prediction value of the target power battery, and controlling the negative electrode potential of the target power battery based on the terminal voltage prediction value to prevent lithium plating during charging of the target power battery.

[0071] Compared with the prior art's optimized lithium-free charging control method based on electrochemical models or equivalent circuit models and the fast charging control method based on PID control, the battery parameters of the power battery are obtained based on the equivalent circuit model corresponding to the power battery during the charging process, and the battery parameters are updated using a parameter identification network model to quantify the aging characteristics of the target power battery. Based on the updated battery parameters, the state of charge estimate and polarization voltage prediction value of the power battery are estimated, and then rolling optimization is performed using the prediction model to determine the terminal voltage prediction value of the power battery to control the negative electrode potential of the power battery, so that the power battery does not undergo lithium plating during the charging process. This solves the problems of oscillation, poor dynamic characteristics and noise disturbance in the power battery during the charging process, improves the effectiveness and safety of the power battery's lithium-free charging control, and thus reduces the cost of charging control.

[0072] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0073] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following is a brief introduction to the drawings required for use in the embodiments. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other relevant drawings can be obtained based on these drawings without creative work.

[0074] Figure 1 A flowchart of a power battery charging control method without lithium plating provided in an embodiment of the present application;

[0075] Figure 2 A schematic diagram of the circuit structure of an equivalent circuit model provided in an embodiment of the present application;

[0076] Figure 3 A schematic structural diagram of a power battery charging control device without lithium plating provided in an embodiment of the present application;

[0077] Figure 4 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0078] In order to make the purpose, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments. The components of the embodiments of the present application generally described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the application for which protection is claimed, but merely represents the selected embodiments of the present application. Based on the embodiments of the present application, each other embodiment obtained by those skilled in the art without making creative work falls within the scope of protection of the present application.

[0079] Research has found that at present, in the optimized lithium-free charging control method based on the battery model, the battery model mainly includes the electrochemical model and the equivalent circuit model. The electrochemical model can reflect the real electrochemical state of the power battery during operation, but the calculation cost of the model is high and the parameter identification is difficult. The ordinary equivalent circuit model cannot obtain the electrode state information inside the battery, which reduces the effectiveness of the lithium-free charging control; in addition, it also includes a fast charging control method based on PID control. The anode potential and charging current of the power battery in this method both oscillate, which weakens the dynamic characteristics of the power battery.

[0080] Based on this, an embodiment of the present application provides a charging control method for a power battery without lithium plating. The method obtains the battery parameters of the power battery based on the equivalent circuit model corresponding to the power battery during the charging process, and updates the battery parameters using a parameter identification network model to quantify the aging characteristics of the target power battery. Based on the updated battery parameters, the state of charge estimation value and polarization voltage prediction value of the power battery are estimated, and then the prediction model is used for rolling optimization to determine the terminal voltage prediction value of the power battery to control the negative electrode potential of the power battery, so that the power battery does not have lithium plating during the charging process. The method solves the problems of oscillation, poor dynamic characteristics and noise disturbance in the power battery during the charging process, improves the effectiveness and safety of the power battery charging control without lithium plating, and thus reduces the cost of charging control.

[0081] See also Figure 1 , Figure 1 This is a flow chart of a method for controlling charging of a power battery without lithium deposition provided in an embodiment of the present application. Figure 1 As shown in , the charging control method for a power battery without lithium plating provided in an embodiment of the present application includes:

[0082] S101 . During charging of a target power battery, based on an equivalent circuit model corresponding to the target power battery, obtain battery parameters of the target power battery according to parameters to be estimated in the equivalent circuit model.

[0083] It should be noted that power batteries refer to batteries used to power electric vehicles (such as electric vehicles and hybrid vehicles), power tools and energy storage systems. Compared with ordinary batteries, power batteries usually have higher energy density, power density and longer cycle life to meet the needs of high load and frequent charging and discharging.

[0084] In an embodiment of the present application, due to the differences between battery materials and working environments, the various equivalent circuit models corresponding to the power battery may exhibit different effects in terms of applicability and accuracy. By balancing the relationship between battery model accuracy and computational complexity, the equivalent circuit model corresponding to the target power battery described in the embodiment of the present application may include a Thevenin model.

[0085] Here, the Thevenin model is a method widely used for battery equivalent circuit modeling, which simplifies the complex internal structure of the actual battery and uses an ideal voltage source in series with an internal resistor and an RC parallel circuit to represent the behavior of the battery.

[0086] For details, please refer to Figure 2 , Figure 2 This is a schematic diagram of the circuit structure of an equivalent circuit model provided in an embodiment of the present application. Figure 2 As shown in , V represents the terminal voltage of the battery model (target power battery); I represents the charging current for charging the target power battery; R0 represents the internal resistance of the target power battery; R s Represents the polarization resistance and is related to the polarization capacitance C s Together they form an RC loop to simulate the polarization phenomenon of the target power battery, V s Indicates the polarization voltage of the target power battery to simulate the change in charge during the battery charging process.

[0087] In one embodiment of the present application, during specific implementation, step S101 may include:

[0088] S1011 . Determine parameters to be estimated in an equivalent circuit model corresponding to the target power battery based on the equivalent circuit model.

[0089] In this step, during specific implementation, first, based on Ohm's law, an expression for calculating the polarization voltage of the target power battery is determined; then, the expression for the polarization voltage is differentiated, and then based on Kirchhoff's voltage law, an expression for calculating the terminal voltage of the target power battery is determined; thereafter, based on the above expression, a state space expression for the target power battery is determined, and the state space expression is discretized to determine the state equation of the equivalent circuit model; finally, by performing parameter analysis on the state equation, the parameters to be estimated of the target power battery in the equivalent circuit model are determined.

[0090] Specifically, the expression for calculating the polarization voltage of the target power battery is as follows.

[0091]

[0092] Among them, V s represents the polarization voltage of the target power battery; I represents the terminal current of the power battery; R s Represents polarization resistance; C s Represents polarized capacitance.

[0093] Furthermore, the expression of the polarization voltage of the target power battery is differentiated, and the obtained expression is as follows.

[0094]

[0095] Furthermore, the expression for calculating the terminal voltage of the target power battery is as follows.

[0096] V=U ocv (SOC)+V s +I·R0

[0097] Where, V represents the terminal voltage of the target power battery; U ocvIndicates the open circuit voltage of the target power battery; SOC indicates the state of charge value of the target power battery; V s represents the polarization voltage of the target power battery; I represents the terminal current of the power battery; R s Represents polarization resistance; C s Represents polarized capacitance.

[0098] Furthermore, the state space expression of the target power battery is as follows.

[0099]

[0100] Wherein, the state vector of the target power battery is x=[SOC V s ] T ;Input u=I;Output y=V;The expressions of A and B are as follows.

[0101]

[0102] Among them, Q t Indicates the available capacity value of the target power battery.

[0103] Furthermore, the state space expression is discretized to determine the state equation of the equivalent circuit model, and the expression of the state equation is as follows.

[0104]

[0105] Among them, ω k and v k Represent the process noise and measurement noise of the system respectively, which are independent and unrelated to each other; A d 、B d 、C d and D d The expression is as follows.

[0106]

[0107] In summary, the parameters to be estimated of the target power battery in the equivalent circuit model may include the resistance value, available capacity value, open circuit voltage value, state of charge value and polarization battery parameters corresponding to the target power battery; among which, the polarization battery parameters may include polarization resistance value, polarization capacitance value and polarization voltage value.

[0108] S1012: Obtain the terminal current value, terminal voltage value, and charging temperature value of the target power battery, and use the parameters to be estimated as a reference, based on the terminal current value and the terminal voltage value, and using a preset conversion formula to determine the resistance value, available capacity value, open circuit voltage value, and polarization battery parameters corresponding to the target power battery.

[0109] In this step, after determining the parameters to be estimated of the target power battery in the equivalent circuit model, the terminal current value, terminal voltage value, and charging temperature value of the target power battery are collected using the multi-state sensor of the target power battery; then, using the parameters to be estimated as a reference, based on the terminal current value and terminal voltage value, the resistance value, available capacity value, open circuit voltage value, and polarization battery parameters corresponding to the target power battery are calculated using a preset circuit physical quantity conversion formula.

[0110] The polarization battery parameters may include polarization resistance, polarization capacitance and polarization voltage.

[0111] S1013 : Based on the open circuit voltage value, determine a state of charge value corresponding to the target power battery using a preset state of charge fitting function.

[0112] In the embodiment of the present application, a relationship fitting test between the open circuit voltage (OCV) and the state of charge (SOC) of the battery is performed to determine a state of charge fitting function between the open circuit voltage value and the state of charge value. The expression of the state of charge fitting function is shown below.

[0113] U OC =l0+l1z 1 +l2z 2 +l3z 3 +l4z 4 .

[0114] Among them, U OC Indicates the open circuit voltage value; z i Indicates the state of charge value of the target power battery at different times; i represents the fitting coefficient.

[0115] S1014: Determine the terminal current value, the terminal voltage value, the charging temperature value, the resistance value, the available capacity value, the open circuit voltage value, the polarization battery parameter, and the state of charge value as battery parameters of the target power battery corresponding to the parameters to be estimated.

[0116] In this step, based on the parameters to be estimated of the target power battery in the equivalent circuit model, the terminal current value, terminal voltage value, charging temperature value, resistance value, available capacity value, open circuit voltage value, polarization battery parameters and state of charge value of the target power battery are correspondingly determined as the battery parameters of the target power battery.

[0117] S102 : Using a preset parameter identification network model to update the battery parameters to quantify the aging characteristics of the target power battery and obtain target battery parameters corresponding to the battery parameters.

[0118] It should be noted that, considering that the battery parameters of the target power battery will undergo non-negligible changes as the battery ages, which will affect the estimated results of the state of charge value, it is necessary to timely update the battery parameters of the target power battery in the current aging state.

[0119] In an embodiment of the present application, improper selection of initial model parameters (e.g., weights and thresholds) in the parameter identification network model may cause gradient explosion or gradient vanishing, thereby leading to failure in estimating the state of charge value. Parameter optimization using the preset tuna optimization model can effectively solve problems such as gradient explosion or gradient vanishing.

[0120] In one embodiment of the present application, during specific implementation, step S102 may include:

[0121] S1021. Based on the preset initial model parameters, the preset tuna optimization model is used to perform parameter optimization to obtain target model parameters.

[0122] In the embodiment of the present application, the tuna optimization model relies on simulating the activity of tuna preying on prey to search the parameter space and find the global optimal point. It has strong convergence performance and can obtain better optimization results given better initial conditions.

[0123] Among them, the initial model parameters preset by the tuna optimization model are shown in the following table.

[0124] Model parameters Initial value a 0.7 Dim 3 b 0.05 <![CDATA[t max ]]> 500

[0125] In this step, during the specific implementation, first, based on the initial model parameters, the optimization period and population size are set, and the upper and lower limits of each parameter are set; then, the fitness function is used to calculate the fitness of all particles; finally, the preset tuna optimization expression is used to update the particle position.

[0126] In the examples of the present application, the tuna optimization expression is shown below.

[0127]

[0128] in, is the i-th element of the t+1 iteration; represents a random reference individual; represents the current optimal individual; a1 and α2 are weight coefficients that affect the tendency of the current individual to move toward the optimal and previous individuals; a is a constant used to determine the degree to which the current individual follows the optimal individual and the previous individual in the initial stage; rand is a random number in the interval [0 1]; t is the current number of iterations, t max represents the maximum number of iterations, b is a random number uniformly distributed between [0,1], and τ is a random number between [-1,1].

[0129] Furthermore, the fitness function is repeatedly used to calculate the fitness of all particles and the particle positions are updated using the preset tuna optimization expression until the current number of iterations reaches the maximum number of iterations, then the update is stopped and the target model parameters at this time are output.

[0130] S1022: Based on the target model parameters, update the battery parameters using a preset parameter identification network model to obtain target battery parameters corresponding to the battery parameters, so as to quantify the aging characteristics of the target power battery.

[0131] In this step, the target model parameters are input into a preset parameter identification network model, and the battery parameters are updated using the model expression of the parameter identification network model to obtain the target battery parameters corresponding to the battery parameters to quantify the aging characteristics of the target power battery.

[0132] In the embodiment of the present application, the model expression of the parameter identification network model is as follows.

[0133]

[0134] Among them, Q (t) represents the target battery parameter; X(t) represents the battery parameter; c k represents the threshold of the k-th output layer neuron; represents the purelin activation function; ω ij represents the weight from the i-th input layer node to the j-th hidden layer node; b j represents the threshold of the hidden layer.

[0135] Here, the model expression of the parameter identification network model is obtained by training through the following steps.

[0136] First, define the input layer to have I neurons, is the input set; the output layer has K neurons, is the output set; the hidden layer has J neurons; for a given training set in, is a known set of expected outputs.

[0137] Furthermore, define O lj (j=1,…,J) is the jth hidden layer node, and the expression of the output value generated by the lth input layer node is as follows.

[0138]

[0139] Among them, ω ij is the weight from the i-th input layer node to the j-th hidden layer node; b jis the threshold of the hidden layer; f is the transfer function of the hidden layer, also known as the activation function. The expression of the transfer function of the hidden layer is shown below.

[0140]

[0141] in,

[0142] Furthermore, the activation function of the output layer is a linear function, that is, the output expression of the output layer is as follows.

[0143]

[0144] Among them, v jk is the weight of the kth output layer node and the jth hidden layer node; c k is the threshold of the kth output layer neuron; y lk is the output of the k-th output layer neuron; is the purelin activation function.

[0145] Furthermore, in order to compare the difference between the expected output and the predicted output, the mean square error and the mean absolute error are calculated, where the calculation expression of the mean square error is as follows.

[0146]

[0147] The calculation expression of mean absolute error is as follows.

[0148]

[0149] Furthermore, based on the gradient descent method, the parameter identification network model adjusts the parameters in the target negative gradient direction through the following expression.

[0150]

[0151] in, It is expressed as the loss function; (n) is the iteration step size; η is the learning rate.

[0152] Furthermore, before starting the learning process of the parameter identification network model, first, an initial vector and a learning rate are set, and the weights and thresholds are updated through continuous iterations to minimize the error function output by the system. The iteration ends when the convergence condition is reached.

[0153] The expression of the initial vector is as follows.

[0154]

[0155] Furthermore, the updating process of the hidden layer and input layer weights is as follows.

[0156]

[0157] The updating process of the connection weights between the output layer and the hidden layer is as follows.

[0158]

[0159] The updating process of the output layer and hidden layer thresholds is as follows.

[0160]

[0161] The hidden layer and input layer threshold update process is as follows.

[0162]

[0163] S103 . Determine a state of charge estimation value and a polarization voltage prediction value of the target power battery based on the target battery parameters using a preset estimation method.

[0164] In the embodiment of the present application, since the linearization processing of the nonlinear system generally adopts the Taylor expansion method and removes the second-order and higher-order terms to estimate the state variables, the preset estimation method may include an extended Kalman filter method (Extended Kalman Filter, EKF).

[0165] Among them, the expressions of the state equation and observation equation of the extended Kalman filter are as follows.

[0166]

[0167] Among them, W k represents system noise; V k represents the observation noise, and both the system noise and the observation noise are set to Gaussian white noise; Q k represents the system noise variance matrix, R k represents the observation noise variance matrix; f(X k ,U k ) represents the nonlinear state function, g(X k ,U k ) represents the nonlinear observation function; X k+1 and Y k They represent the state equation and observation equation of the extended Kalman filter respectively.

[0168] Furthermore, under the premise that the sampling point is differentiable, f(X k ,U k )、g(X k ,U k ) The two functions are estimated in the state Linearization is performed at the point, that is, through the first-order Tate expansion, the second-order and above terms are discarded, and the estimated expressions of the state function and observation function are obtained as follows.

[0169]

[0170] Furthermore, based on the estimated expressions of the state function and the observation function, simplified equations for linearizing the nonlinear model can be obtained, that is, another expression of the state equation and the observation equation of the extended Kalman filter is shown below.

[0171]

[0172] Furthermore, the estimated state of charge value and the predicted polarization voltage value of the target power battery are used as the state vector, that is, the expression of the state vector is as follows.

[0173]

[0174] Among them, X k Represents the state vector; SOC k Indicates the estimated state of charge of the target power battery; U P,k Indicates the predicted polarization voltage of the target power battery.

[0175] Furthermore, based on the state equation of the equivalent circuit model and combined with the ampere-hour integration method, the state equation of the target power battery in the estimation method can be obtained. The expression of the state equation is shown below.

[0176]

[0177] Among them, T s Indicates the target charging temperature value; R p Indicates the target polarization resistance value; C p Indicates the target polarization capacitance value; I k Indicates the target terminal current value.

[0178] Furthermore, the state equation remains unchanged when Taylor expansion is performed on the state equation, and the expression of the initial observation equation is as follows.

[0179]

[0180] Furthermore, the initial observation equation and the estimated expression of the observation function are combined to obtain the expression of the updated observation equation as shown below.

[0181]

[0182] Among them, R p and U ocare functions of the estimated state of charge.

[0183] Furthermore, after Taylor expansion and linearization of the updated observation equation, the observation equation of the target power battery in the estimation method is obtained. The expression of the observation equation is shown below.

[0184]

[0185] Among them, C k represents the observation matrix, C k and D k The matrix expression of is shown below.

[0186]

[0187] D k =[R o ].

[0188] In one embodiment of the present application, during specific implementation, step S103 may include:

[0189] S1031. In each recursive cycle, based on the state transfer matrix, state prediction matrix and current prediction value corresponding to the previous recursive cycle, the state prediction matrix corresponding to the current recursive cycle is determined using a preset state equation; wherein, the initial state matrix corresponding to the first recursive cycle includes the expectation matrix composed of the state of charge value and the polarization voltage value in the target battery parameters, and the initial error covariance matrix corresponding to the first recursive cycle includes the variance matrix composed of the state of charge value and the polarization voltage value.

[0190] In the embodiment of the present application, the expressions of the initial state matrix corresponding to the first recursive cycle and the initial error covariance matrix corresponding to the first recursive cycle are as follows.

[0191]

[0192] Furthermore, the expression of the state prediction matrix corresponding to the current recursive cycle is as follows.

[0193]

[0194] in, Represents the state prediction matrix corresponding to the current recursive cycle; A k-1 and B k-1 Respectively represent the state transfer matrix corresponding to the previous recursive cycle; Represents the state prediction matrix corresponding to the previous recursive cycle; I k-1 Indicates the current prediction value corresponding to the previous recursive cycle.

[0195] S1032. Calculate the state transition matrix corresponding to the current recursive cycle based on the target battery parameters and the state prediction matrix corresponding to the previous recursive cycle, and determine the error covariance matrix corresponding to the current recursive cycle based on the state transition matrix corresponding to the current recursive cycle and the error covariance matrix corresponding to the previous recursive cycle.

[0196] In the embodiment of the present application, the calculation expression of the error covariance matrix corresponding to the current recursive period is as follows.

[0197]

[0198] Among them, P k,k-1 represents the error covariance matrix corresponding to the current recursive cycle; P k-1,k-1 represents the error covariance matrix corresponding to the previous recursive cycle; A k Represents the state transfer matrix corresponding to the current recursive cycle; Q k-1 represents the system noise variance matrix.

[0199] S1033. Based on the state prediction matrix corresponding to the current recursive period, the observation value and observation matrix corresponding to the current recursive period are determined using a preset observation equation.

[0200] In this step, since the estimated state of charge value and polarization voltage predicted value of the target power battery are used as the state vector, the state prediction matrix corresponding to the current recursive cycle is input into the preset observation equation to determine the observation value and observation matrix corresponding to the current recursive cycle.

[0201] S1034: Determine a gain matrix corresponding to the current recursion period based on the error covariance matrix corresponding to the current recursion period and the measurement matrix.

[0202] In an embodiment of the present application, the expression for calculating the gain matrix corresponding to the current recursive cycle is as follows.

[0203]

[0204] Among them, K k Represents the gain matrix corresponding to the current recursive cycle; P k,k-1 represents the error covariance matrix corresponding to the current recursive cycle; C k represents the observation matrix; R k represents the observation noise variance matrix.

[0205] S1035. Determine the difference between the target battery parameter and the observation value corresponding to the current recursive cycle as the residual value corresponding to the current recursive cycle, and determine the updated state prediction matrix corresponding to the current recursive cycle based on the residual value, gain matrix and state prediction matrix corresponding to the current recursive cycle.

[0206] In an embodiment of the present application, the expression for calculating the update state prediction matrix corresponding to the current recursive cycle is as follows.

[0207]

[0208] in, Represents the updated state prediction matrix corresponding to the current recursive cycle; Represents the state prediction matrix corresponding to the current recursive cycle; K k Represents the gain matrix corresponding to the current recursive cycle; Represents the residual value.

[0209] S1036 : Determine an updated error covariance matrix corresponding to the current recursion period based on the error covariance matrix, the observation matrix, and the gain matrix corresponding to the current recursion period.

[0210] In an embodiment of the present application, the expression for calculating the updated error covariance matrix corresponding to the current recursive cycle is as follows.

[0211] P k,k =[IK k C k ]P k,k-1 .

[0212] Among them, P k,k represents the updated error covariance matrix corresponding to the current recursive cycle; P k,k-1 represents the error covariance matrix corresponding to the current recursive cycle; C k represents the observation matrix; K k Represents the gain matrix corresponding to the current recursive cycle.

[0213] S1037. Repeat the prediction and update of multiple recursive cycles until the number of executions of the recursive cycles reaches a preset value, and output the updated state prediction matrix corresponding to the final recursive cycle to determine the estimated state of charge value and the polarization voltage predicted value of the target power battery in the updated state prediction matrix.

[0214] In this step, the above-mentioned estimation method is used to repeat the prediction and update of multiple recursive cycles to obtain the updated state prediction matrix and updated error covariance matrix corresponding to each recursive cycle, until the number of recursive cycles executed reaches a preset value, and the updated state prediction matrix corresponding to the final recursive cycle is output; then, the state of charge estimation value and polarization voltage prediction value of the target power battery are determined in the updated state prediction matrix corresponding to the final recursive cycle.

[0215] S104. Based on the state of charge estimate and the polarization voltage prediction value, a preset prediction model is used to perform rolling optimization to determine a terminal voltage prediction value of the target power battery, and based on the terminal voltage prediction value, the negative electrode potential of the target power battery is controlled to prevent lithium deposition in the target power battery during charging.

[0216] In this step, during specific implementation, first, based on the state of charge estimation value and the polarization voltage prediction value, a preset prediction model is used to perform rolling optimization to determine the terminal voltage prediction value of the target power battery; then, during the rolling optimization process, a preset safety boundary threshold is used to limit the intermediate parameters corresponding to the prediction model in the current prediction cycle; finally, based on the terminal voltage prediction value, the negative electrode potential of the target power battery is controlled to ensure that the target power battery does not undergo lithium plating during the charging process.

[0217] In one embodiment of the present application, in a specific implementation, the step of performing rolling optimization based on the state of charge estimate and the polarization voltage prediction value using a preset prediction model in step S104 to determine the terminal voltage prediction value of the target power battery may include:

[0218] S1041. Based on the state of charge estimation value and the polarization voltage prediction value, a preset prediction model is used to determine the state of charge prediction value corresponding to the target power battery in the current prediction cycle; wherein, a preset safety boundary threshold is used to limit the intermediate parameters corresponding to the prediction model in the current prediction cycle.

[0219] In an embodiment of the present application, the state of charge estimation value and the polarization voltage prediction value are used as state variables, and based on the state variables, a preset prediction model is used to determine the state of charge prediction value corresponding to the target power battery in the current prediction cycle; wherein, the expression of the state variables is as follows.

[0220] x(k)=[SOC,U p ] T .

[0221] Where x(k) represents the state variable; SOC represents the estimated state of charge; U p Represents the predicted value of polarization voltage.

[0222] In one embodiment of the present application, during specific implementation, the step of determining the security boundary threshold in step S1041 may include:

[0223] S1041A: Place the target power battery in a test incubator, charge the target power battery, and detect the surface temperature, negative electrode potential, and terminal voltage of the target power battery in real time.

[0224] S1041B: When the surface temperature reaches a preset temperature value and becomes stable, adjust the charging current of the target power battery so that the negative electrode potential approaches a preset threshold value.

[0225] S1041C: When the terminal voltage value reaches the cutoff voltage value, determine the safety voltage threshold and safety current threshold of the target power battery based on the terminal voltage values corresponding to the multiple charging current values in the adjustment process.

[0226] S1041D. Determine the safety voltage threshold, the safety current threshold, and the preset temperature value as the safety boundary threshold.

[0227] In one possible implementation of the present application, illustratively, the safety boundary threshold may include the following intervals.

[0228]

[0229] Among them, I max Indicates the upper limit of the safety current threshold, I min Indicates the lower limit of the safety current threshold; U ref Indicates the lower limit value of the safety voltage threshold; 45°C is the preset temperature value; 2.5V and 4.3V represent the first safety voltage threshold and the second safety voltage threshold respectively.

[0230] S1042: Subtract the state of charge prediction value from a preset state of charge reference value to obtain a target difference value, and determine whether the target difference value converges and whether the state of charge prediction value is less than the preset state of charge reference value.

[0231] In an embodiment of the present application, the state of charge prediction value is subtracted from the preset state of charge reference value to obtain a target difference, and the target difference is used as the optimization target. The expression of the optimization target is as follows.

[0232] J=min{soc ref -SOC k}.

[0233] Where, J represents the optimization target (target difference); SOC k Indicates the predicted value of state of charge; SOC ref Indicates the reference value of the state of charge.

[0234] S1043: When the target difference shows divergence and / or the state of charge prediction value is greater than or equal to a preset state of charge reference value, use the prediction model to perform prediction optimization for the next prediction cycle.

[0235] In this step, when the target difference shows divergence and / or the state of charge prediction value is greater than or equal to the preset state of charge reference value, it means that the rolling optimization has not reached the optimal state, and the prediction model is continued to be used for prediction optimization in the next prediction cycle.

[0236] S1044: When the target difference shows convergence and the state of charge prediction value is less than a preset state of charge reference value, determine the terminal voltage prediction value of the target power battery based on the state of charge prediction value and the polarization voltage prediction value.

[0237] In this step, when the target difference shows convergence and the state of charge prediction value is less than the preset state of charge reference value, it means that the rolling optimization has reached the optimal state. Based on the state of charge prediction value and the polarization voltage prediction value, the prediction model is used to output the terminal voltage prediction value of the target power battery.

[0238] The embodiment of the present application provides a charging control method for a power battery without lithium plating, which obtains battery parameters of the power battery based on an equivalent circuit model corresponding to the power battery during charging of the power battery, and updates the battery parameters using a parameter identification network model to quantify the aging characteristics of the target power battery. Based on the updated battery parameters, the state of charge estimation value and polarization voltage prediction value of the power battery are estimated, and then a rolling optimization is performed using the prediction model to determine the terminal voltage prediction value of the power battery to control the negative electrode potential of the power battery, so that the power battery does not exhibit lithium plating during the charging process. This solves the problems of oscillation, poor dynamic characteristics and noise disturbance in the power battery during charging, improves the effectiveness and safety of the charging control of the power battery without lithium plating, and thereby reduces the cost of charging control.

[0239] See also Figure 3 , Figure 3 This is a schematic diagram of the structure of a power battery charging control device without lithium deposition provided by an embodiment of the present application. Figure 3 As shown in FIG, the charging control device 300 includes:

[0240] The parameter acquisition module 310 is configured to acquire battery parameters of the target power battery according to the parameters to be estimated in the equivalent circuit model based on the equivalent circuit model corresponding to the target power battery during charging of the target power battery;

[0241] a parameter updating module 320 for updating the battery parameters using a preset parameter identification network model to quantify the aging characteristics of the target power battery and obtain target battery parameters corresponding to the battery parameters;

[0242] a parameter estimation module 330 for determining, based on the target battery parameters, an estimated state of charge value and a predicted polarization voltage value of the target power battery using a preset estimation method;

[0243] The prediction control module 340 is used to perform rolling optimization based on the state of charge estimation value and the polarization voltage prediction value using a preset prediction model to determine the terminal voltage prediction value of the target power battery, and control the negative electrode potential of the target power battery based on the terminal voltage prediction value so that the target power battery does not undergo lithium plating during the charging process.

[0244] Furthermore, when the parameter acquisition module 310 is used to acquire the battery parameters of the target power battery according to the estimated parameters preset in the equivalent circuit model based on the equivalent circuit model corresponding to the target power battery, the parameter acquisition module 310 is used to:

[0245] Determining parameters to be estimated in the equivalent circuit model based on an equivalent circuit model corresponding to the target power battery;

[0246] Obtaining a terminal current value, a terminal voltage value, and a charging temperature value of the target power battery, and using the parameters to be estimated as a reference, and based on the terminal current value and the terminal voltage value, using a preset conversion formula to determine a resistance value, an available capacity value, an open circuit voltage value, and a polarization battery parameter corresponding to the target power battery;

[0247] Based on the open circuit voltage value, determining the state of charge value corresponding to the target power battery using a preset state of charge fitting function;

[0248] The terminal current value, the terminal voltage value, the charging temperature value, the resistance value, the available capacity value, the open circuit voltage value, the polarization battery parameter and the state of charge value are determined as battery parameters of the target power battery corresponding to the parameters to be estimated.

[0249] Furthermore, when the parameter updating module 320 is used to update the battery parameters using a preset parameter identification network model to quantify the aging characteristics of the target power battery and obtain target battery parameters corresponding to the battery parameters, the parameter updating module 320 is used to:

[0250] Based on the preset initial model parameters, the preset tuna optimization model is used to perform parameter optimization to obtain the target model parameters;

[0251] Based on the target model parameters, the battery parameters are updated using a preset parameter identification network model to obtain target battery parameters corresponding to the battery parameters, so as to quantify the aging characteristics of the target power battery.

[0252] Furthermore, when the parameter estimation module 330 is used to determine the state of charge estimation value and polarization voltage prediction value of the target power battery based on the target battery parameters using a preset estimation method, the parameter estimation module 330 is used to:

[0253] In each recursive cycle, based on the state transfer matrix, state prediction matrix, and current prediction value corresponding to the previous recursive cycle, a preset state equation is used to determine the state prediction matrix corresponding to the current recursive cycle; wherein the initial state matrix corresponding to the first recursive cycle includes an expectation matrix composed of the state of charge values and polarization voltage values in the target battery parameters, and the initial error covariance matrix corresponding to the first recursive cycle includes a variance matrix composed of the state of charge values and the polarization voltage values;

[0254] Calculating a state transition matrix corresponding to a current recursive cycle based on the target battery parameters and a state prediction matrix corresponding to a previous recursive cycle, and determining an error covariance matrix corresponding to the current recursive cycle based on the state transition matrix corresponding to the current recursive cycle and an error covariance matrix corresponding to the previous recursive cycle;

[0255] Based on the state prediction matrix corresponding to the current recursive period, the observation value and observation matrix corresponding to the current recursive period are determined respectively using the preset observation equation;

[0256] Determining a gain matrix corresponding to the current recursion period based on the error covariance matrix corresponding to the current recursion period and the measurement matrix;

[0257] Determine the difference between the target battery parameter and the observation value corresponding to the current recursive period as the residual value corresponding to the current recursive period, and determine the updated state prediction matrix corresponding to the current recursive period based on the residual value corresponding to the current recursive period, the gain matrix, and the state prediction matrix;

[0258] Determine an updated error covariance matrix corresponding to the current recursive period based on the error covariance matrix, the observation matrix, and the gain matrix corresponding to the current recursive period;

[0259] Repeat the prediction and update of multiple recursive cycles until the number of executions of the recursive cycles reaches a preset value, and output the updated state prediction matrix corresponding to the final recursive cycle to determine the estimated state of charge value and the polarization voltage predicted value of the target power battery in the updated state prediction matrix.

[0260] Furthermore, when the prediction control module 340 is used to perform rolling optimization based on the state of charge estimation value and the polarization voltage prediction value using a preset prediction model to determine the terminal voltage prediction value of the target power battery, the prediction control module 340 is used to:

[0261] Based on the state of charge estimate and the polarization voltage prediction value, a preset prediction model is used to determine a state of charge prediction value corresponding to the target power battery in the current prediction period; wherein a preset safety boundary threshold is used to limit the intermediate parameters corresponding to the prediction model in the current prediction period;

[0262] subtracting the predicted state of charge value from a preset state of charge reference value to obtain a target difference, and determining whether the target difference shows convergence and whether the predicted state of charge value is less than the preset state of charge reference value;

[0263] When the target difference shows divergence and / or the state of charge prediction value is greater than or equal to a preset state of charge reference value, using the prediction model to perform prediction optimization for the next prediction cycle;

[0264] When the target difference shows convergence and the state of charge prediction value is less than a preset state of charge reference value, the target power battery terminal voltage prediction value is determined based on the state of charge prediction value and the polarization voltage prediction value.

[0265] Furthermore, when the prediction control module 340 is used to determine the safety boundary threshold, the prediction control module 340 is used to:

[0266] Placing the target power battery in a test incubator, charging the target power battery, and detecting the surface temperature, negative electrode potential, and terminal voltage of the target power battery in real time;

[0267] When the surface temperature reaches a preset temperature value and becomes stable, adjusting the charging current value of the target power battery so that the negative electrode potential approaches a preset threshold value;

[0268] When the terminal voltage value reaches the cut-off voltage value, determining a safety voltage threshold and a safety current threshold of the target power battery based on the terminal voltage values respectively corresponding to the plurality of charging current values during the adjustment process;

[0269] The safety voltage threshold, the safety current threshold, and the preset temperature value are determined as the safety boundary threshold.

[0270] The embodiment of the present application provides a power battery charging control device without lithium plating, which obtains battery parameters of the power battery based on an equivalent circuit model corresponding to the power battery during charging of the power battery, and updates the battery parameters using a parameter identification network model to quantify the aging characteristics of the target power battery. Based on the updated battery parameters, the state of charge estimation value and polarization voltage prediction value of the power battery are estimated, and then a rolling optimization is performed using the prediction model to determine the terminal voltage prediction value of the power battery to control the negative electrode potential of the power battery, so that the power battery does not exhibit lithium plating during the charging process, thereby solving the problems of oscillation, poor dynamic characteristics and noise disturbance in the power battery during charging, improving the effectiveness and safety of power battery charging control without lithium plating, and thereby reducing the cost of charging control.

[0271] The embodiment of the present application also provides a vehicle for performing the above Figure 1 The steps of the power battery charging control method without lithium plating in the method embodiment are shown.

[0272] See also Figure 4 , Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. Figure 4 As shown in FIG, the electronic device 400 includes a processor 410 , a memory 420 and a bus 430 .

[0273] The memory 420 stores machine-readable instructions executable by the processor 410. When the electronic device 400 is running, the processor 410 communicates with the memory 420 via the bus 430. When the machine-readable instructions are executed by the processor 410, the above-mentioned Figure 1 The specific implementation of the steps of the power battery charging control method without lithium plating in the method embodiment shown can be found in the method embodiment, which will not be repeated here.

[0274] The embodiment of the present application also provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the computer program can execute the above-mentioned Figure 1 The specific implementation of the steps of the power battery charging control method without lithium plating in the method embodiment shown can be found in the method embodiment, and will not be repeated here.

[0275] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0276] In the several embodiments provided in this application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. The device embodiments described above are merely schematic. For example, the division of the units is merely a logical function division. There may be other division methods in actual implementation. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed may be through some communication interface, indirect coupling or communication connection of devices or units, which may be electrical, mechanical or other forms.

[0277] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0278] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0279] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a non-volatile computer-readable storage medium that is executable by a processor. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0280] Finally, it should be noted that the above-described embodiments are only specific implementation methods of the present application, which are used to illustrate the technical solutions of the present application, rather than to limit them. The scope of protection of the present application is not limited thereto. Although the present application has been described in detail with reference to the above-mentioned embodiments, those skilled in the art should understand that any person skilled in the art can modify or easily conceive of changes to the technical solutions described in the above-mentioned embodiments within the technical scope disclosed in the present application, or perform equivalent replacements for some of the technical features thereof. These modifications, changes, or replacements do not deviate from the spirit and scope of the technical solutions of the embodiments of the present application, and should be included in the scope of protection of the present application. Therefore, the scope of protection of the present application should be based on the scope of protection of the claims.

Claims

1. A charging control method for a power battery without lithium deposition, characterized in that: The charging control method includes: During charging of a target power battery, based on an equivalent circuit model corresponding to the target power battery, battery parameters of the target power battery are obtained according to parameters to be estimated in the equivalent circuit model; Updating the battery parameters using a preset parameter identification network model to quantify the aging characteristics of the target power battery and obtain target battery parameters corresponding to the battery parameters; Based on the target battery parameters, determining the estimated state of charge value and the predicted polarization voltage value of the target power battery using a preset estimation method; Based on the state of charge estimation value and the polarization voltage prediction value, a preset prediction model is used to perform rolling optimization to determine a terminal voltage prediction value of the target power battery, and based on the terminal voltage prediction value, the negative electrode potential of the target power battery is controlled so that the target power battery does not undergo lithium deposition during charging.

2. The method according to claim 1, characterized in that The acquiring, based on the equivalent circuit model corresponding to the target power battery and according to the parameters to be estimated preset in the equivalent circuit model, the battery parameters of the target power battery includes: Determining parameters to be estimated in the equivalent circuit model based on an equivalent circuit model corresponding to the target power battery; Obtaining a terminal current value, a terminal voltage value, and a charging temperature value of the target power battery, and using the parameters to be estimated as a reference, and based on the terminal current value and the terminal voltage value, using a preset conversion formula to determine a resistance value, an available capacity value, an open circuit voltage value, and a polarization battery parameter corresponding to the target power battery; Based on the open circuit voltage value, determining the state of charge value corresponding to the target power battery using a preset state of charge fitting function; The terminal current value, the terminal voltage value, the charging temperature value, the resistance value, the available capacity value, the open circuit voltage value, the polarization battery parameter and the state of charge value are determined as battery parameters of the target power battery corresponding to the parameters to be estimated.

3. The method according to claim 1, characterized in that The method of updating the battery parameters using a preset parameter identification network model to quantify the aging characteristics of the target power battery and obtain target battery parameters corresponding to the battery parameters includes: Based on the preset initial model parameters, the preset tuna optimization model is used to perform parameter optimization to obtain the target model parameters; Based on the target model parameters, the battery parameters are updated using a preset parameter identification network model to obtain target battery parameters corresponding to the battery parameters, so as to quantify the aging characteristics of the target power battery.

4. The method according to claim 1, wherein The step of determining the state of charge estimation value and polarization voltage prediction value of the target power battery based on the target battery parameters by using a preset estimation method includes: In each recursive cycle, based on the state transfer matrix, state prediction matrix, and current prediction value corresponding to the previous recursive cycle, a preset state equation is used to determine the state prediction matrix corresponding to the current recursive cycle; wherein the initial state matrix corresponding to the first recursive cycle includes an expectation matrix composed of the state of charge values and polarization voltage values in the target battery parameters, and the initial error covariance matrix corresponding to the first recursive cycle includes a variance matrix composed of the state of charge values and the polarization voltage values; Calculating a state transition matrix corresponding to a current recursive cycle based on the target battery parameters and a state prediction matrix corresponding to a previous recursive cycle, and determining an error covariance matrix corresponding to the current recursive cycle based on the state transition matrix corresponding to the current recursive cycle and an error covariance matrix corresponding to the previous recursive cycle; Based on the state prediction matrix corresponding to the current recursive period, the observation value and observation matrix corresponding to the current recursive period are determined respectively using the preset observation equation; Determining a gain matrix corresponding to the current recursion period based on the error covariance matrix corresponding to the current recursion period and the measurement matrix; Determine the difference between the target battery parameter and the observation value corresponding to the current recursive period as the residual value corresponding to the current recursive period, and determine the updated state prediction matrix corresponding to the current recursive period based on the residual value corresponding to the current recursive period, the gain matrix, and the state prediction matrix; Determine an updated error covariance matrix corresponding to the current recursive period based on the error covariance matrix, the observation matrix, and the gain matrix corresponding to the current recursive period; Repeat the prediction and update of multiple recursive cycles until the number of executions of the recursive cycles reaches a preset value, and output the updated state prediction matrix corresponding to the final recursive cycle to determine the estimated state of charge value and the polarization voltage predicted value of the target power battery in the updated state prediction matrix.

5. The method according to claim 1, wherein The step of performing rolling optimization based on the state of charge estimation value and the polarization voltage prediction value using a preset prediction model to determine the terminal voltage prediction value of the target power battery includes: Based on the state of charge estimate and the polarization voltage prediction value, a preset prediction model is used to determine a state of charge prediction value corresponding to the target power battery in the current prediction period; wherein a preset safety boundary threshold is used to limit the intermediate parameters corresponding to the prediction model in the current prediction period; subtracting the predicted state of charge value from a preset state of charge reference value to obtain a target difference, and determining whether the target difference shows convergence and whether the predicted state of charge value is less than the preset state of charge reference value; When the target difference shows divergence and / or the state of charge prediction value is greater than or equal to a preset state of charge reference value, using the prediction model to perform prediction optimization for the next prediction cycle; When the target difference shows convergence and the state of charge prediction value is less than a preset state of charge reference value, the target power battery terminal voltage prediction value is determined based on the state of charge prediction value and the polarization voltage prediction value.

6. The method according to claim 5, characterized in that The safety boundary threshold is determined by the following steps: Placing the target power battery in a test incubator, charging the target power battery, and detecting the surface temperature, negative electrode potential, and terminal voltage of the target power battery in real time; When the surface temperature reaches a preset temperature value and becomes stable, adjusting the charging current value of the target power battery so that the negative electrode potential approaches a preset threshold value; When the terminal voltage value reaches the cut-off voltage value, determining a safety voltage threshold and a safety current threshold of the target power battery based on the terminal voltage values respectively corresponding to the plurality of charging current values during the adjustment process; The safety voltage threshold, the safety current threshold, and the preset temperature value are determined as the safety boundary threshold.

7. A charging control device for a power battery without lithium deposition, characterized in that: The charging control device includes: a parameter acquisition module, configured to acquire, during charging of a target power battery, battery parameters of the target power battery based on an equivalent circuit model corresponding to the target power battery and corresponding to parameters to be estimated in the equivalent circuit model; a parameter updating module, configured to update the battery parameters using a preset parameter identification network model to quantify the aging characteristics of the target power battery and obtain target battery parameters corresponding to the battery parameters; a parameter estimation module, configured to determine, based on the target battery parameters, an estimated state of charge value and a predicted polarization voltage value of the target power battery using a preset estimation method; a prediction control module, configured to perform rolling optimization using a preset prediction model based on the estimated state of charge value and the predicted polarization voltage value, determine a predicted terminal voltage value of the target power battery, and control the negative electrode potential of the target power battery based on the predicted terminal voltage value so that the target power battery does not undergo lithium plating during charging.

8. A vehicle, characterized in that: The vehicle is used to execute the steps of the power battery charging control method without lithium plating as described in any one of claims 1 to 6.

9. An electronic device, characterized in that: include: A processor, a memory and a bus, wherein the memory stores machine-readable instructions executable by the processor. When the electronic device is running, the processor and the memory communicate through the bus. When the processor is running, the machine-readable instructions execute the steps of the power battery charging control method without lithium plating as described in any one of claims 1 to 6.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the power battery charging control method without lithium deposition are executed as claimed in any one of claims 1 to 6.

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