Lithium battery control optimization method and device adopting parameter identification method

Abstract

The invention discloses a lithium battery control optimization method and device adopting a parameter identification method, and the method comprises the steps: collecting the real-time voltage, current, temperature and other data of a lithium battery, and accurately extracting health state parameters through employing an identification algorithm; an optimization algorithm is designed, the capacity and voltage of the super capacitor and the health state of the lithium battery are comprehensively considered, a mathematical model is established, and the service life attenuation relation between the lithium battery and the super capacitor is described; the model is trained through an optimization algorithm, and the optimal super capacitor access opportunity and capacity distribution scheme is determined. According to the invention, a service life optimization control strategy is provided, the access opportunity and the capacity of the super capacitor are specified for the energy storage equipment, optimal power control is realized, and the service life of the lithium battery is prolonged.

Description

Technical Field

[0001] This patent involves the field of battery management systems, involving knowledge and technologies in multiple fields such as battery management, control algorithms, data acquisition and processing, parameter identification and optimization algorithms. Background Art

[0002] With the rapid development of renewable energy and electric vehicles, the demand for energy storage systems is growing. However, the healthy life of lithium batteries limits their application scope and reliability. In order to extend the healthy life of lithium batteries, researchers have proposed a technology that connects multiple groups of supercapacitors to lithium batteries to provide better energy management and protect the use of batteries. At present, the technology of connecting multiple groups of supercapacitors to lithium batteries has attracted widespread attention and research. The main purpose of this technology is to reduce the load of lithium batteries through the assistance of supercapacitors, thereby extending their healthy life. The following is the current status of this technology:

[0003] Health status assessment: By monitoring and analyzing the current, voltage, temperature and other parameters of the lithium battery, the health status of the lithium battery is evaluated. Mathematical models and algorithms are used to estimate and predict the health status of the lithium battery in order to better control the use of supercapacitors.

[0004] Connection method of supercapacitors: Multiple groups of supercapacitors and lithium batteries can be connected in series, parallel or mixed. Series connection can increase the total voltage, provide a higher voltage range and energy density. Parallel connection can increase the total capacity, provide greater instantaneous power and energy storage capacity.

[0005] Control strategy and energy management: Based on the health status of lithium batteries and the characteristics of supercapacitors, control strategies and energy management algorithms are developed to achieve optimal energy distribution and use. By reasonably controlling the charging and discharging process of supercapacitors, the load on lithium batteries can be reduced and their life can be extended.

[0006] Experimental verification and application: Conduct verification in the laboratory and actual environment to evaluate the impact of multiple sets of supercapacitors and lithium battery connection technology on the healthy life of lithium batteries. Apply it in fields such as energy storage systems and electric vehicles to verify the feasibility and effectiveness of this technology in practical applications. In short, by connecting multiple sets of supercapacitors to lithium batteries, combined with health status assessment and optimized energy management, the healthy life of lithium batteries can be extended. At present, the relevant technology has been verified in laboratories and actual applications, providing new solutions for the development of fields such as energy storage systems and electric vehicles. However, further research and practice are still needed to improve this technology and promote its widespread application in practical applications. Summary of the invention

[0007] Purpose of the invention: In response to the above problems, this invention aims to improve system performance, extend battery life, and improve system stability by integrating supercapacitors and lithium batteries and utilizing parameter identification and optimization technology, so as to promote the development and application of clean energy technology.

[0008] Technical solution: A lithium battery control optimization method using a parameter identification method, comprising the following steps:

[0009] (1) establishing a lithium battery control optimization model, wherein the lithium battery control optimization model includes a circuit second-order RC model and a battery internal resistance change model over time; collecting real-time data, using the lithium battery control optimization model to solve the lithium battery health state parameters, bringing the lithium battery health state parameters into the battery attenuation minimum objective function, judging the battery health state and predicting the battery life;

[0010] (2) Using the whale optimization algorithm that introduces the Levy flight mechanism to train the lithium battery control optimization model, the optimal lithium battery control optimization model is obtained, and the optimal access timing and capacity allocation plan of supercapacitors and lithium batteries are obtained;

[0011] (3) The optimal access timing and capacity allocation plan of lithium batteries and supercapacitors are sent to the energy distribution module and battery components to implement the optimal charging and discharging strategy.

[0012] Furthermore, the lithium battery control optimization model is specifically as follows:

[0013] The second-order RC model of the circuit is specifically:

[0014]

[0015] Where R0 is the battery ohmic internal resistance, C1 is the charge transfer capacitor, C2 is the concentration difference capacitor, I(t) is the battery current, V(t) is the battery voltage, j is the imaginary unit, and ω is the angular frequency;

[0016] The battery internal resistance variation model over time is expressed as:

[0017]

[0018] Where k is an aging rate function that depends on the battery temperature T(t), the battery state ε(t) and the battery current I(t), S(t) is a source term representing the external aging effect; the battery ohmic internal resistance is R0, and the time is t;

[0019] Formula (2) is expressed by the integral equation:

[0020]

[0021] τ is the integral variable, R 0,new is the ohmic internal resistance of the battery at the initial moment.

[0022] Furthermore, the lithium battery control optimization model is used to solve the health status parameters of the lithium battery, wherein the health status parameters of the lithium battery include: charge transfer resistance R1, concentration difference impedance R2, charge transfer capacitor C1, concentration difference capacitor C2;

[0023]

[0024] Among them, ρ1, ρ2 are time constants, and the coefficients are:

[0025]

[0026] Among them, R0 is the battery ohmic internal resistance, a, b, c, d are coefficients,

[0027] The circuit second-order RC model is discretized by combining the coefficients to solve the lithium battery health status parameters:

[0028]

[0029] Where k0 = T 2 +bT+a, T is the sampling time, V[k] is the discretized voltage, and I[k] is the discretized current.

[0030] Furthermore, the battery attenuation minimum objective function is:

[0031]

[0032] V meas (t i ) is the time point t i The actual measured voltage, V calc (t i ) is the time point t i The voltage calculated by the lithium battery control optimization model;

[0033] Optimizing the objective function using the least squares method have to:

[0034]

[0035] Among them, V meas I[k] is the voltage actually measured at time point k, I[k] is the current measured at time point k, I[i] is the current measured at time point i, and N is a positive integer.

[0036] Furthermore, the determination of the battery health status and prediction of the battery life are specifically:

[0037] Characterize the battery health status SOH by the change of internal resistance:

[0038]

[0039] By estimating the SOH based on the second-order RC model of the equivalent circuit, and considering the internal resistance factor after the resistance is consumed under different conditions, an accelerated aging test is performed on the battery throughout its life cycle to determine the battery health status and predict the battery life. aged is the internal resistance of the battery after aging, R now is the internal resistance of the battery at the current moment, R new Indicates the internal resistance of the battery in its new state.

[0040] A lithium battery control optimization device using a parameter identification method, comprising a load management module, a charge and discharge control module, a battery management module, a system monitoring and data analysis module, an energy distribution module and a battery assembly;

[0041] The load management module is connected to the battery assembly to monitor the load status of the battery in real time, and communicates with the charge and discharge control module to send load status information and receive energy allocation instructions;

[0042] The charge and discharge control module is connected to the battery assembly to control the charging and discharging process of the battery, and communicates with the battery management module to receive battery status information and send charge and discharge control instructions;

[0043] The battery management module is connected to the battery assembly to monitor the battery status and performance, and communicates with the system monitoring and data analysis module to send battery status information and receive data analysis results;

[0044] The system monitoring and data analysis module is connected to the battery assembly to collect battery operation data and system status information, and communicates with the load management module, charge and discharge control module, and battery management module to send control instructions to optimize system performance;

[0045] The energy distribution module is connected to the battery assembly and the supercapacitor to adjust the energy distribution, and communicates with the load management module to receive load information and optimize the energy distribution strategy;

[0046] Establishing a lithium battery control optimization model in the system monitoring and data analysis module, the lithium battery control optimization model includes a circuit second-order RC model and a battery internal resistance change model over time; collecting real-time data, using the lithium battery control optimization model to solve the lithium battery health state parameters, bringing the lithium battery health state parameters into the battery attenuation minimum objective function, judging the battery health state and predicting the battery life;

[0047] The whale optimization algorithm that introduces the Levy flight mechanism is used to train the lithium battery control optimization model to obtain the optimal lithium battery control optimization model, and the optimal access time and capacity allocation plan for supercapacitors and lithium batteries are obtained;

[0048] The optimal access timing and capacity allocation plan for lithium batteries and supercapacitors are sent to the energy distribution module and battery components to implement the optimal charging and discharging strategy.

[0049] Furthermore, the lithium battery control optimization model is specifically as follows:

[0050] The second-order RC model of the circuit is specifically:

[0051]

[0052] Where R0 is the battery ohmic internal resistance, C1 is the charge transfer capacitor, C2 is the concentration difference capacitor, I(t) is the battery current, V(t) is the battery voltage, j is the imaginary unit, and ω is the angular frequency;

[0053] The battery internal resistance variation model over time is expressed as:

[0054]

[0055] Where k is an aging rate function that depends on the battery temperature T(t), the battery state ε(t) and the battery current I(t), S(t) is a source term representing the external aging effect; the battery ohmic internal resistance is R0, and the time is t;

[0056] Formula (2) is expressed by the integral equation:

[0057]

[0058] τ is the integral variable, R 0,new is the ohmic internal resistance of the battery at the initial moment.

[0059] Furthermore, the lithium battery control optimization model is used to solve the health status parameters of the lithium battery, wherein the health status parameters of the lithium battery include: charge transfer resistance R1, concentration difference impedance R2, charge transfer capacitor C1, concentration difference capacitor C2;

[0060]

[0061] Among them, ρ1, ρ2 are time constants, and the coefficients are:

[0062]

[0063] Among them, R0 is the battery ohmic internal resistance, a, b, c, d are coefficients,

[0064] The circuit second-order RC model is discretized by combining the coefficients to solve the lithium battery health status parameters:

[0065]

[0066] Where k0 = T 2+bT+a, T is the sampling time, V[k] is the discretized voltage, and I[k] is the discretized current.

[0067] Furthermore, the battery attenuation minimum objective function is:

[0068]

[0069] V meas (t i ) is the time point t i The actual measured voltage, V calc (t i ) is the time point t i The voltage calculated by the lithium battery control optimization model;

[0070] Optimizing the objective function using the least squares method have to:

[0071]

[0072] Among them, V meas I[k] is the voltage actually measured at time point k, I[k] is the current measured at time point k, I[i] is the current measured at time point i, and N is a positive integer.

[0073] Furthermore, the determination of the battery health status and prediction of the battery life are specifically:

[0074] Characterize the battery health status SOH by the change of internal resistance:

[0075]

[0076] By estimating the SOH based on the second-order RC model of the equivalent circuit, and considering the internal resistance factor after the resistance is consumed under different conditions, an accelerated aging test is performed on the battery throughout its life cycle to determine the battery health status and predict the battery life. aged is the internal resistance of the battery after aging, R now is the internal resistance of the battery at the current moment, R new Indicates the internal resistance of the battery in its new state.

[0077] Beneficial effects: SOH connected supercapacitor application integrates software and hardware technologies to achieve efficient energy utilization and battery management. It includes modules such as intelligent load management, precise charge and discharge control, real-time monitoring of battery status, system data analysis, etc., and uses high-quality lithium batteries and supercapacitor components to improve energy utilization efficiency, extend battery life and enhance system stability. Compared with traditional systems, this application optimizes lithium battery parameter identification and significantly improves overall performance.

[0078] The present invention improves the reliability and performance of the energy storage system. By accurately evaluating the health status of the lithium battery and optimally controlling the use of the supercapacitor, the life of the lithium battery can be extended, thereby improving the reliability and performance of the energy storage system. BRIEF DESCRIPTION OF THE DRAWINGS

[0079] Figure 1 Schematic diagram of the system structure of the present invention. DETAILED DESCRIPTION

[0080] The technical solution of the present invention is further described below in conjunction with the accompanying drawings.

[0081] like Figure 1 As shown, a lithium battery control optimization method using a parameter identification method includes the following steps:

[0082] (1) establishing a lithium battery control optimization model, wherein the lithium battery control optimization model includes a circuit second-order RC model and a battery internal resistance change model over time; collecting real-time data, using the lithium battery control optimization model to solve the lithium battery health state parameters, bringing the lithium battery health state parameters into the battery attenuation minimum objective function, judging the battery health state and predicting the battery life;

[0083] (2) Using the whale optimization algorithm that introduces the Levy flight mechanism to train the lithium battery control optimization model, the optimal lithium battery control optimization model is obtained, and the optimal access timing and capacity allocation plan of supercapacitors and lithium batteries are obtained;

[0084] (3) The optimal access timing and capacity allocation plan of lithium batteries and supercapacitors are sent to the energy distribution module and battery components to implement the optimal charging and discharging strategy.

[0085] Furthermore, the lithium battery control optimization model is specifically as follows:

[0086] The second-order RC model of the circuit is specifically:

[0087]

[0088] Where R0 is the battery ohmic internal resistance, C1 is the charge transfer capacitor, C2 is the concentration difference capacitor, I(t) is the battery current, V(t) is the battery voltage, j is the imaginary unit, and ω is the angular frequency;

[0089] The battery internal resistance variation model over time is expressed as:

[0090]

[0091] Where k is an aging rate function that depends on the battery temperature T(t), the battery state ε(t) and the battery current I(t), S(t) is a source term representing the external aging effect; the battery ohmic internal resistance is R0, and the time is t;

[0092] Formula (2) is expressed by the integral equation:

[0093]

[0094] τ is the integral variable, R 0,new is the ohmic internal resistance of the battery at the initial moment.

[0095] Furthermore, the lithium battery control optimization model is used to solve the health status parameters of the lithium battery, wherein the health status parameters of the lithium battery include: charge transfer resistance R1, concentration difference impedance R2, charge transfer capacitor C1, concentration difference capacitor C2;

[0096]

[0097] Among them, ρ1, ρ2 are time constants, and the coefficients are:

[0098]

[0099] Among them, R0 is the battery ohmic internal resistance, a, b, c, d are coefficients,

[0100] The circuit second-order RC model is discretized by combining the coefficients to solve the lithium battery health status parameters:

[0101]

[0102] Where k0 = T 2 +bT+a, T is the sampling time, V[k] is the discretized voltage, and I[k] is the discretized current.

[0103] Furthermore, the battery attenuation minimum objective function is:

[0104]

[0105] V meas (t i ) is the time point t i The actual measured voltage, V calc (t i ) is the time point t i The voltage calculated by the lithium battery control optimization model;

[0106] Optimizing the objective function using the least squares method have to:

[0107]

[0108] Among them, V measI[k] is the voltage actually measured at time point k, I[k] is the current measured at time point k, I[i] is the current measured at time point i, and N is a positive integer.

[0109] Furthermore, the determination of the battery health status and prediction of the battery life are specifically:

[0110] Characterize the battery health status SOH by the change of internal resistance:

[0111]

[0112] By estimating the SOH based on the second-order RC model of the equivalent circuit, and considering the internal resistance factor after the resistance is consumed under different conditions, an accelerated aging test is performed on the battery throughout its life cycle to determine the battery health status and predict the battery life. aged is the internal resistance of the battery after aging, R now is the internal resistance of the battery at the current moment, R new Indicates the internal resistance of the battery in its new state.

[0113] A lithium battery control optimization device using a parameter identification method, comprising a load management module, a charge and discharge control module, a battery management module, a system monitoring and data analysis module, an energy distribution module and a battery assembly;

[0114] The load management module is connected to the battery assembly to monitor the load status of the battery in real time, and communicates with the charge and discharge control module to send load status information and receive energy allocation instructions;

[0115] The charge and discharge control module is connected to the battery assembly to control the charging and discharging process of the battery, and communicates with the battery management module to receive battery status information and send charge and discharge control instructions;

[0116] The battery management module is connected to the battery assembly to monitor the battery status and performance, and communicates with the system monitoring and data analysis module to send battery status information and receive data analysis results;

[0117] The system monitoring and data analysis module is connected to the battery assembly to collect battery operation data and system status information, and communicates with the load management module, charge and discharge control module, and battery management module to send control instructions to optimize system performance;

[0118] The energy distribution module is connected to the battery assembly and the supercapacitor to adjust the energy distribution, and communicates with the load management module to receive load information and optimize the energy distribution strategy;

[0119] Establishing a lithium battery control optimization model in the system monitoring and data analysis module, the lithium battery control optimization model includes a circuit second-order RC model and a battery internal resistance change model over time; collecting real-time data, using the lithium battery control optimization model to solve the lithium battery health state parameters, bringing the lithium battery health state parameters into the battery attenuation minimum objective function, judging the battery health state and predicting the battery life;

[0120] The whale optimization algorithm that introduces the Levy flight mechanism is used to train the lithium battery control optimization model to obtain the optimal lithium battery control optimization model, and the optimal access time and capacity allocation plan for supercapacitors and lithium batteries are obtained;

[0121] The optimal access timing and capacity allocation plan for lithium batteries and supercapacitors are sent to the energy distribution module and battery components to implement the optimal charging and discharging strategy.

[0122] Furthermore, the lithium battery control optimization model is specifically as follows:

[0123] The second-order RC model of the circuit is specifically:

[0124]

[0125] Where R0 is the battery ohmic internal resistance, C1 is the charge transfer capacitor, C2 is the concentration difference capacitor, I(t) is the battery current, V(t) is the battery voltage, j is the imaginary unit, and ω is the angular frequency;

[0126] The battery internal resistance variation model over time is expressed as:

[0127]

[0128] Where k is an aging rate function that depends on the battery temperature T(t), the battery state ε(t) and the battery current I(t), S(t) is a source term representing the external aging effect; the battery ohmic internal resistance is R0, and the time is t;

[0129] Formula (2) is expressed by the integral equation:

[0130]

[0131] τ is the integral variable, R 0,new is the ohmic internal resistance of the battery at the initial moment.

[0132] Furthermore, the lithium battery control optimization model is used to solve the health status parameters of the lithium battery, wherein the health status parameters of the lithium battery include: charge transfer resistance R1, concentration difference impedance R2, charge transfer capacitor C1, concentration difference capacitor C2;

[0133]

[0134] Among them, ρ 1 , ρ2 is the time constant, and the coefficient is:

[0135]

[0136] Among them, R0 is the battery ohmic internal resistance, a, b, c, d are coefficients,

[0137] The circuit second-order RC model is discretized by combining the coefficients to solve the lithium battery health status parameters:

[0138]

[0139] Where k0 = T 2 +bT+a, T is the sampling time, V[k] is the discretized voltage, and I[k] is the discretized current.

[0140] Furthermore, the battery attenuation minimum objective function is:

[0141]

[0142] V meas (t i ) is the time point t i The actual measured voltage, V calc (t i ) is the time point t i The voltage calculated by the lithium battery control optimization model;

[0143] Optimizing the objective function using the least squares method have to:

[0144]

[0145] Among them, V meas I[k] is the voltage actually measured at time point k, I[k] is the current measured at time point k, I[i] is the current measured at time point i, and N is a positive integer.

[0146] Furthermore, the determination of the battery health status and prediction of the battery life are specifically:

[0147] Characterize the battery health status SOH by the change of internal resistance:

[0148]

[0149] By estimating the SOH based on the second-order RC model of the equivalent circuit, and considering the internal resistance factor after the resistance is consumed under different conditions, an accelerated aging test is performed on the battery throughout its life cycle to determine the battery health status and predict the battery life. aged is the internal resistance of the battery after aging, Rnow is the internal resistance of the battery at the current moment, R new Indicates the internal resistance of the battery in its new state.

[0150] The whale optimization algorithm is specifically:

[0151] Construct an estimation method model based on whale algorithm optimization. In the early stage of hunting, whales update their positions by surrounding their prey. The position update is as follows:

[0152] D1=|CX * (n)-X(n)|(10)

[0153] X(n+1)=X * (n)-ZD(11)

[0154] Where n is the current iteration number; X*(n) is the position vector with the best current fitness; X(n) is the position vector of the individual whale in the current iteration number; D1 is the distance between the individual whale and the prey; Z and C are coefficient vectors used to control the swimming mode of the whale, and there is

[0155] Z=2z·r1-z(12)

[0156] C=2r2(13)

[0157]

[0158] Where r1 and r2 are random vectors in [0,1]; z is the control parameter, N max is the maximum number of iterations.

[0159] When whales are hunting, they use the spiral equation to simulate the whale's hunting method of blowing out a bubble net along a spiral line and approaching the prey. The expression of the spiral equation is as follows:

[0160] D2=|X * (n)-X(n)|(15)

[0161]

[0162] Where: D2 is the distance between the individual whale and its prey; s is a constant used to define the logarithmic spiral shape; l is a random number in [-1,1].

[0163] When the whale is spiraling around its prey, it also needs to continuously shrink the encirclement. Therefore, it is necessary to set the probability of selecting the shrinking encirclement mechanism and the spiral model to update the position. Here, the probability is 50%. The mathematical model is as follows:

[0164]

[0165] Where: a is a random number in [0,1].

[0166] In the prey search phase, the whale population conducts global exploration. When |Z|≥1, the whale population no longer updates its position based on the current optimal solution and searches for the optimal solution.

[0167] D rand =|C·X rand -X(n)| (18)

[0168] X(n+1)=X rand -Z.D rand (19)

[0169] Where X rand is the position vector of a randomly selected individual whale, D rand is the distance from a randomly selected individual whale to its prey.

[0170] The Levi flight mechanism is specifically as follows:

[0171] The Levy flight is introduced to avoid falling into the local optimum, improve the convergence speed and reduce the estimation error.

[0172] Update equation (16) to

[0173]

[0174] Among them: γ is a random number that conforms to uniform distribution; sign[rand-1 / 2] has three values: -1, 0, or 1; ⊕ represents dot product; Levy conforms to Levy distribution, and the mathematical model of Levy distribution is

[0175]

[0176] Where: j is the random step size, τ(x) is the gamma function, τ(x) = (x+1)! .

[0177] Send the optimal access time and capacity allocation plan of lithium batteries and supercapacitors to the energy distribution module and battery components to implement the optimal charging and discharging strategy. Normalize the original data, remove singular values ​​in the data, and improve the quality and effectiveness of the data;

[0178] Update the parameters Z and C in the mathematical model of encircling the prey according to equations (12) and (13); when |Z| < 1, if a < 0.5, update the position according to equation (17); if a ≥ 0.5, update the position according to equation (20); when |Z| ≥ 1, update the position according to equation (19);

[0179] The entire whale group is evaluated to determine the global optimal whale position; when the maximum number of iterations is reached or the quality of the solution meets the preset threshold, the iteration is stopped and the global optimal solution is output.

[0180] The optimal solution is passed to the control module, which issues adjustment instructions to the hybrid system composed of lithium batteries and supercapacitors and outputs the optimal charging and discharging strategy.

[0181] The R0, C1, R2, C2, θ1, θ2 related variables are obtained by the following methods:

[0182] Ohmic internal resistance R0: The voltage change at different currents is measured by charging and discharging the battery. The ohmic internal resistance value is extracted from the voltage and current data by linear regression analysis using the least squares system model (Equation 8).

[0183] Concentration impedance R2: Perform electrochemical impedance spectroscopy (EIS) test to obtain the impedance response of the battery at different frequencies. Use the equivalent circuit model to fit the EIS data and extract the concentration impedance value.

[0184] Active power efficiency θ1: Measure the input and output voltage and current of the battery during the charging and discharging process. Calculate the actual energy charged and discharged to obtain the active power efficiency.

[0185] Reactive power efficiency θ2: Measures the reactive power of the battery during the charging and discharging process, which is usually related to the power factor of the battery. The reactive power efficiency is calculated based on the change in reactive power.

[0186] Charge transfer capacitor C1: Obtain the impedance response of the battery in the high-frequency region through EIS testing. Use the equivalent circuit model to extract the charge transfer capacitor value from the high-frequency region data.

[0187] Concentration capacitance C2: Obtain the impedance response of the battery in the low-frequency region through EIS testing. Use the equivalent circuit model to extract the concentration capacitance value from the low-frequency region data.

[0188] The optimal access time is determined by the whale optimization algorithm to determine the order in which supercapacitors are connected to the lithium battery system. The order is based on the real-time health status parameters and historical data of the lithium battery to maximize the service life of the lithium battery and the overall performance of the system.

[0189] The capacity allocation scheme is as follows: based on the current state of health (SOH) of the lithium battery, internal resistance, and power requirements during charging and discharging, the capacity that each group of supercapacitors should provide is calculated to optimize the energy allocation and efficiency of the entire energy storage system.

[0190] The real-time data collection includes collecting real-time voltage, current, temperature and other data of the lithium battery.

[0191] The objective function is optimized using the least squares method, and combined with the results of the whale optimization algorithm, the second-order RC model is solved to determine the optimal supercapacitor access timing and capacity allocation scheme. The optimal access timing and capacity allocation scheme are applied to the energy distribution module to adjust the charging and discharging strategy to achieve optimal control of lithium batteries and supercapacitors.

[0192] The battery ohmic internal resistance refers to the inherent resistance inside the battery, which will gradually increase with the use and aging of the battery.

[0193] The charge transfer capacitance is related to the charge transfer process of the battery, and indicates the ability of the battery to accumulate and release charges on the electrode surface during the charge and discharge process.

[0194] The concentration capacitance is related to the change in concentration gradient inside the battery, and indicates the charge storage capacity of the battery due to the difference in lithium ion concentration.

[0195] The aging rate function is a function that depends on the battery temperature, battery state and current and is used to describe the growth rate of the battery internal resistance over time.

[0196] The source term represents external aging influence, which may include factors such as the number of charge and discharge cycles, overcharge and overdischarge, and temperature changes.

[0197] The time constant and coefficient: In the integral equation of the cumulative change of the battery internal resistance over time, the time constant and coefficient are used to describe the dynamic process of the change of the battery internal resistance.

[0198] Discretized voltage and current: After discretizing the continuous model of the cumulative change of the battery internal resistance over time, it is used to represent the voltage and current values ​​at a specific sampling time. Least squares objective function: The model parameters obtained by least squares optimization are used to describe the relationship between the voltage and current of the battery during the actual charging and discharging process.

[0199] The present invention identifies the health status parameters of lithium batteries, such as remaining capacity, internal resistance, battery life, etc., through parameter identification technology. Then, the optimal supercapacitor access time and capacity allocation plan are determined through the optimization algorithm to achieve the optimal control goal, that is, to extend the comprehensive life of the lithium battery. It is divided into several modules: load management module, charge and discharge control module, battery management module, system monitoring and data analysis module, energy distribution module, and battery assembly. The data acquisition and preprocessing module transmits the collected data to the parameter identification module. The parameter identification module transmits the identified health status parameters to the health status assessment module. The health status assessment module transmits the assessment results to the optimization algorithm module. The optimization algorithm module transmits the optimal access time and capacity allocation plan to the optimal control strategy implementation module. The optimal control strategy implementation module controls the state of the controller and capacitor access switch and implements the optimal control strategy. The central control module transmits data and issues instructions with each module through the communication interface to coordinate the operation of the entire system.

Claims

1. A lithium battery control optimization method using a parameter identification method, characterized in that: The following steps are involved: (1) establishing a lithium battery control optimization model, wherein the lithium battery control optimization model includes a circuit second-order RC model and a battery internal resistance change model over time; collecting real-time data, using the lithium battery control optimization model to solve the lithium battery health state parameters, bringing the lithium battery health state parameters into the battery attenuation minimum objective function, judging the battery health state and predicting the battery life; (2) Using the whale optimization algorithm that introduces the Levy flight mechanism to train the lithium battery control optimization model, the optimal lithium battery control optimization model is obtained, and the optimal access timing and capacity allocation plan of supercapacitors and lithium batteries are obtained; (3) The optimal access timing and capacity allocation plan of lithium batteries and supercapacitors are sent to the energy distribution module and battery components to implement the optimal charging and discharging strategy.

2. A lithium battery control optimization method using a parameter identification method according to claim 1, characterized in that: The lithium battery control optimization model is specifically: The second-order RC model of the circuit is specifically: Where R0 is the battery ohmic internal resistance, C1 is the charge transfer capacitor, C2 is the concentration difference capacitor, I(t) is the battery current, V(t) is the battery voltage, j is the imaginary unit, and ω is the angular frequency; The battery internal resistance variation model over time is expressed as: Where k is an aging rate function that depends on the battery temperature T(t), the battery state ε(t) and the battery current I(t), S(t) is a source term representing the external aging effect; the battery ohmic internal resistance is R0, and the time is t; Formula (2) is expressed by the integral equation: t is the integral variable, R 0,new is the ohmic internal resistance of the battery at the initial moment.

3. A lithium battery control optimization method using a parameter identification method according to claim 2, characterized in that: The lithium battery control optimization model is used to solve the health status parameters of the lithium battery, where the health status parameters of the lithium battery include: charge transfer resistance R1, concentration difference impedance R2, charge transfer capacitor C1, and concentration difference capacitor C2; Among them, ρ1, ρ2 are time constants, and the coefficients are: Among them, R0 is the battery ohmic internal resistance, a, b, c, d are coefficients, The circuit second-order RC model is discretized by combining the coefficients to solve the lithium battery health status parameters: Where k0 = T 2 +bT+a, T is the sampling time, V[k] is the discretized voltage, and I[k] is the discretized current.

4. The lithium battery control optimization method using a parameter identification method according to claim 3, characterized in that: The battery attenuation minimum objective function is: Vmeas(ti) is the time point t i The actual measured voltage, Vcalc(ti) is the voltage at time t i The voltage calculated by the lithium battery control optimization model; Optimize the objective function J using the least squares method LS (J), we get: Among them, V meas I[k] is the voltage actually measured at time point k, I[k] is the current measured at time point k, I[i] is the current measured at time point i, and N is a positive integer.

5. The lithium battery control optimization method using parameter identification method according to claim 4, characterized in that: The determination of the battery health status and prediction of the battery life are as follows: Characterize the battery health status SOH by the change of internal resistance: By estimating the SOH based on the second-order RC model of the equivalent circuit, and considering the internal resistance factor after the resistance is consumed under different conditions, an accelerated aging test is performed on the battery throughout its life cycle to determine the battery health status and predict the battery life. aged is the internal resistance of the battery after aging, R now is the internal resistance of the battery at the current moment, R new Indicates the internal resistance of the battery in its new state.

6. A lithium battery control optimization device using a parameter identification method, characterized in that: It includes load management module, charge and discharge control module, battery management module, system monitoring and data analysis module, energy distribution module and battery assembly; The load management module is connected to the battery assembly to monitor the load status of the battery in real time, and communicates with the charge and discharge control module to send load status information and receive energy allocation instructions; The charge and discharge control module is connected to the battery assembly to control the charging and discharging process of the battery, and communicates with the battery management module to receive battery status information and send charge and discharge control instructions; The battery management module is connected to the battery assembly to monitor the battery status and performance, and communicates with the system monitoring and data analysis module to send battery status information and receive data analysis results; The system monitoring and data analysis module is connected to the battery assembly to collect battery operation data and system status information, and communicates with the load management module, charge and discharge control module, and battery management module to send control instructions to optimize system performance; The energy distribution module is connected to the battery assembly and the supercapacitor to adjust the energy distribution, and communicates with the load management module to receive load information and optimize the energy distribution strategy; Establishing a lithium battery control optimization model in the system monitoring and data analysis module, the lithium battery control optimization model includes a circuit second-order RC model and a battery internal resistance change model over time; collecting real-time data, using the lithium battery control optimization model to solve the lithium battery health state parameters, bringing the lithium battery health state parameters into the battery attenuation minimum objective function, judging the battery health state and predicting the battery life; The whale optimization algorithm that introduces the Levy flight mechanism is used to train the lithium battery control optimization model to obtain the optimal lithium battery control optimization model, and the optimal access time and capacity allocation plan for supercapacitors and lithium batteries are obtained; The optimal access timing and capacity allocation plan for lithium batteries and supercapacitors are sent to the energy distribution module and battery components to implement the optimal charging and discharging strategy.

7. A lithium battery control optimization device using a parameter identification method according to claim 6, characterized in that: The lithium battery control optimization model is specifically: The second-order RC model of the circuit is specifically: Where R0 is the battery ohmic internal resistance, C1 is the charge transfer capacitor, C2 is the concentration difference capacitor, I(t) is the battery current, V(t) is the battery voltage, j is the imaginary unit, and ω is the angular frequency; The battery internal resistance variation model over time is expressed as: Where k is an aging rate function that depends on the battery temperature T(t), the battery state ε(t) and the battery current I(t), S(t) is a source term representing the external aging effect; the battery ohmic internal resistance is R0, and the time is t; Formula (2) is expressed by the integral equation: t is the integral variable, R 0,new is the ohmic internal resistance of the battery at the initial moment.

8. A lithium battery control optimization device using a parameter identification method according to claim 7, characterized in that: The lithium battery control optimization model is used to solve the health status parameters of the lithium battery, where the health status parameters of the lithium battery include: charge transfer resistance R1, concentration difference impedance R2, charge transfer capacitor C1, and concentration difference capacitor C2; Among them, ρ1, ρ2 are time constants, and the coefficients are: Among them, R0 is the battery ohmic internal resistance, a, b, c, d are coefficients, The circuit second-order RC model is discretized by combining the coefficients to solve the lithium battery health status parameters: Where k0 = T 2 +bT+a, T is the sampling time, V[k] is the discretized voltage, and I[k] is the discretized current.

9. A lithium battery control optimization device using a parameter identification method according to claim 8, characterized in that: The battery attenuation minimum objective function is: Vmeas(ti) is the time point t i The actual measured voltage, Vcalc(ti) is the voltage at time t i The voltage calculated by the lithium battery control optimization model; Optimize the objective function J using the least squares method LS (J), we get: Among them, V meas I[k] is the voltage actually measured at time point k, I[k] is the current measured at time point k, I[i] is the current measured at time point i, and N is a positive integer.

10. A lithium battery control optimization device using a parameter identification method according to claim 9, characterized in that: The determination of the battery health status and prediction of the battery life are as follows: Characterize the battery health status SOH by the change of internal resistance: By estimating the SOH based on the second-order RC model of the equivalent circuit, and considering the internal resistance factor after the resistance is consumed under different conditions, an accelerated aging test is performed on the battery throughout its life cycle to determine the battery health status and predict the battery life. aged is the internal resistance of the battery after aging, R now is the internal resistance of the battery at the current moment, R new Indicates the internal resistance of the battery in its new state.

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