Multi-state joint estimation method of energy storage system considering electro-thermal coupling characteristics

Abstract

The application provides a kind of energy storage system multi-state joint estimation method considering electro-thermal coupling characteristics, relating to battery management technical field.Establish the electro-thermal coupling model of battery module; offline identification of the battery parameters under different temperatures and state of charge SOC of the constructed battery equivalent circuit model and lumped parameter thermal model; the end voltage U 0,i (t) of each monomer battery is obtained by using the electro-thermal coupling model of each monomer battery, the core temperature T c,i (t) and the simulation value of surface temperature T s,i (t) of each monomer battery; the state of charge of each battery monomer is estimated by using extended Kalman filtering algorithm; the state of charge S OC,modules (t) of monomer battery representing battery module is obtained; the sustained peak current estimation under multi-parameter constraint condition is carried out; the sustained peak power estimation under multi-parameter constraint condition is carried out.The method provided by the application is suitable for energy storage system under complex working condition, improves the accuracy of state estimation of energy storage system, and ensures the safe operation of energy storage system.

Description

Technical Field

[0001] This invention relates to the field of battery management technology, and in particular to a multi-state joint estimation method for energy storage systems that takes into account electrothermal coupling characteristics. Background Technology

[0002] In complex application environments, changes in battery temperature can affect performance and parameters. These changing parameters, in turn, alter battery heat generation, further influencing temperature. This demonstrates a coupling relationship between the battery's electrical and thermal characteristics. Based on this coupling, an electro-thermal coupling model is established to continuously update battery parameters, thereby more accurately describing the battery's dynamic characteristics. The electro-thermal coupling model offers advantages such as high accuracy and low complexity, making it more suitable for practical applications.

[0003] Although the different state parameters of a battery energy storage system represent different indicators, they are correlated. The sustained peak power of a battery is constrained by multiple factors, including the battery's terminal voltage, state of charge (SOC), temperature, and its own safe current. Based on this correlation, the battery's state of charge (SOC) and state of power (SOP) can be jointly estimated. Furthermore, when the battery operates in complex application environments, it can provide state information that closely reflects the actual environment, reducing safety and improving energy utilization efficiency and extending battery life. In the event of an accident, it can also take timely and appropriate intervention measures for the battery pack, ensuring the safe and reliable operation of the battery energy storage system and preventing major accidents.

[0004] Most current research on state estimation for energy storage systems focuses on only one battery state, neglecting the interactions between different battery states. A battery is a complex electrochemical system constrained by multiple factors, and its various states are interconnected. Existing battery state-of-the-art (SOP) estimation methods rely on battery testing, which requires extensive experimental data, cannot estimate sustained SOP, and only considers terminal voltage. In practice, SOP estimation is limited by factors such as current state of charge (SOC), temperature, and terminal voltage, making this method unsuitable for energy storage systems operating under complex conditions. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention proposes a multi-state joint estimation method for energy storage systems that considers electrothermal coupling characteristics. Taking into account battery circuit and thermal characteristics, it combines a second-order RC equivalent circuit model with a lumped-parameter thermal model to construct an electrothermal coupling model for battery modules applicable to a wide temperature range. It establishes multi-parameter constraints such as battery terminal voltage, SOC, temperature, and the battery's own safe current, and proposes a sustained peak power estimation under these constraints. Furthermore, it analyzes the correlation between the multiple states of the battery energy storage system, incorporating battery temperature and state of charge into the sustained peak power state estimation, thereby achieving multi-state joint estimation of the battery energy storage system.

[0006] A multi-state joint estimation method for energy storage systems considering electrothermal coupling characteristics includes the following steps:

[0007] Step 1: For each individual cell in the battery module, establish an electro-thermal coupling model for each individual cell; the electro-thermal coupling model includes a second-order RC equivalent circuit model, a lumped parameter thermal model, a parameter update module, an ampere-hour integration module, and a heat generation module;

[0008] Step 1-1: Based on the battery's electrical characteristics, construct a second-order RC equivalent circuit model; the second-order RC equivalent circuit model is used to simulate the resistive and capacitive characteristics of the battery terminal voltage.

[0009] The expression for the second-order RC equivalent circuit model is:

[0010]

[0011] In the formula, U 1,i (t), U 2,i (t) represents the voltages of the two RC loops in the i-th battery; I(t) is the operating current; R 0,i R is the ohmic internal resistance of the i-th battery; 1,i C 1,i These are the electrochemical polarization resistance and electrochemical polarization capacitance of the i-th cell, respectively; R 2,i C 2,i These are the concentration difference polarization resistance and concentration difference polarization capacitance of the i-th cell, respectively; U 0,i (t) represents the terminal voltage of the i-th battery; U OCV,i (t) represents the open-circuit voltage of the i-th battery; t represents time; and i represents the cell number, which is determined by the series connection order of the cells in the battery module.

[0012] Step 1-2: Construct a lumped parameter thermal model based on the battery's thermal characteristics; the lumped parameter thermal model is used to simulate the thermal characteristics of the battery's surface temperature and core temperature;

[0013] The expression for the lumped parameter thermal model is:

[0014]

[0015] In the formula, C c,i C represents the core heat capacity of the i-th battery; s,i R is the surface heat capacity of the i-th cell; c,i R is the thermal resistance between the i-th battery core and the battery surface; u,i R is the thermal resistance between the surface of the i-th battery and the air; e It is the equivalent thermal resistance between batteries; Q i (t) represents the heat generated by the i-th battery; T c,i (t) represents the core temperature of the i-th battery; T s,i (t) represents the surface temperature of the i-th battery; T f (t) represents the ambient temperature; T s,i-1 (t) represents the surface temperature of the (i-1)th cell; T s,i+1 (t) represents the surface temperature of the (i+1)th battery.

[0016] Steps 1-3: Based on the process of the interaction between electricity and heat, construct a parameter update module; the parameter update module is used to correct the parameter data of the next moment in the simulation of the second-order RC equivalent circuit model;

[0017] The expression for the parameter update module is:

[0018] R 0,i ,R 1,i C 1,i ,R 2,i C 2,i =f interp2 (I(t),S OC,i (t),T i (t)) (3)

[0019] In the formula, f interp2 (I(t),S OC,i (t),T i (t) is a three-dimensional interpolation function; S OC,i (t) represents the state of charge of the i-th battery at time t; T i (t) represents the average temperature of the i-th battery.

[0020] Steps 1-4: Construct an ampere-hour integration module based on the definition of state of charge; the ampere-hour integration module is used to calculate the current state of charge of the battery.

[0021] The expression for the ampere-hour integration module is:

[0022]

[0023] SOC,i (t0) represents the initial state of charge of the i-th battery, where t0 is the initial time; C N,i Let η be the rated capacity of the i-th battery; η is the coulombic efficiency of the battery.

[0024] Steps 1-5: Construct a heat generation module based on the Bernadi heat generation formula; the heat generation module is used to calculate the heat generation of the battery at the current moment;

[0025] The expression for the heat generation module is:

[0026]

[0027] in, Let be the temperature entropy coefficient of the i-th battery.

[0028] Steps 1-6: A single cell's electro-thermal coupling model is constructed from a second-order RC equivalent circuit model, a lumped parameter thermal model, a parameter update module, an ampere-hour integration module, and a heat generation module.

[0029] Step 2: Obtain hybrid pulse power characteristic operating condition data at different temperatures and identify the battery parameters under different temperatures and SOC of the constructed second-order RC equivalent circuit model and lumped parameter thermal model offline based on this operating condition data, thereby constructing an offline database of electro-thermal coupling models; the hybrid pulse power characteristic operating condition data includes terminal voltage, operating current, surface temperature and core temperature;

[0030] The battery parameters of the identified second-order RC equivalent circuit model at different temperatures and states of charge (SOC) are as follows:

[0031]

[0032] Where, θ e This is a set of battery parameters for a second-order RC equivalent circuit model at different temperatures and under the state of charge (SOC).

[0033] The identified lumped-parameter thermal model provides the following battery parameters at different temperatures and under state of charge (SOC):

[0034] θ t =[R c,i C c,i ,R u,i C s,i ,R e (7)

[0035] Where, θ t This is a collection of battery parameters at different temperatures and states of charge (SOC) for a lumped-parameter thermal model. Since the hybrid pulse power characteristic operation is performed in a constant-temperature chamber, θ... t The parameters within can be identified as constants.

[0036] The offline database of the electro-thermal coupling model includes the ohmic internal resistance R at different temperatures and under different states of charge. 0,i Electrochemical polarization resistance R 1,i Concentration difference polarization resistance R 2,i Electrochemical polarization capacitance C 1,i and concentration difference polarization capacitance C 2,i .

[0037] Step 3: Obtain the operating current I(t-1) and ambient temperature T at the previous moment. f (t-1), and the current terminal voltage U of each individual cell is obtained using the electro-thermal coupling model of each individual cell. 0,i (t), core temperature T c,i (t) and surface temperature T s,i (t);

[0038] Step 3-1: Obtain the operating current I(t-1) and ambient temperature T at the previous moment. f (t-1);

[0039] Step 3-2: Assign the battery parameters identified in Step 2 at different temperatures and under the State of Charge (SOC) to the second-order RC equivalent circuit model. Input the operating current I(t-1) from the previous moment into the second-order RC equivalent circuit model to obtain the terminal voltage U from the previous moment. 0,i (t-1);

[0040] Step 3-3: Convert the terminal voltage U from the previous moment... 0,i (t-1) Input the heat generation module and use the heat generation module to obtain the heat generated by the battery at the previous moment, Q. i (t-1);

[0041] Steps 3-4: Assign the battery parameters at different temperatures and under the state of charge identified in Step 2 to the lumped parameter thermal model, and assign the heat generated by the battery at the previous moment, Q. i (t-1) Input the lumped parameter thermal model to obtain the battery core temperature T at the previous moment. c,i (t-1) and surface temperature T s,i (t-1), further yielding the average temperature T of the battery at the previous moment. i (t-1);

[0042] The average temperature of the battery at the previous moment was:

[0043] T i (t-1)=(T s,i (t-1)+T c,i (t-1)) / 2 (8)

[0044] Steps 3-5: Input the battery operating current I(t-1) from the previous moment into the ampere-hour integrator to obtain the state of charge S from the previous moment. OC,i (t-1);

[0045] Steps 3-6: Based on the obtained average battery temperature T from the previous moment i (t-1) and state of charge S OC,i (t-1), the parameter update module finds the battery parameters under the average temperature and state of charge of the battery from the offline database of the electro-thermal coupling model, and updates the battery parameters to the second-order RC equivalent circuit model;

[0046] Steps 3-7: Obtain the current operating current I(t), and use the second-order RC equivalent circuit model with updated battery parameters to obtain the current terminal voltage U of each individual cell. 0,i (t);

[0047] Step 3-8: Following the methods in steps 3-4 and 3-5, based on the current terminal voltage U of each individual cell... 0,i (t) yields the current battery core temperature T. c,i (t) and surface temperature T s,i (t), further obtaining the current average temperature T of the battery. i (t);

[0048] Steps 3-9: Compare the current battery terminal voltage, surface temperature, and core temperature with their actual values ​​to determine if the accuracy meets the requirements. If it does, output the values; otherwise, return to step 2 to re-identify the model parameters.

[0049] Step 4: Based on the current terminal voltage U of each individual cell... 0,i (t), using the extended Kalman filter algorithm to estimate the current state of charge S of each individual cell. OC,i (t);

[0050] Step 5: Characterize the state of charge (SOC) of the battery module by individual cells, and based on the current SOC of each individual cell... OC,i (t) yields the current state of charge S of the battery module. OC,modules (t) compares the current state of charge of the battery module with its true value to determine whether its accuracy meets the requirements. If it does, output the result; otherwise, return to step 4 to adjust the calibration parameters in the extended Kalman filter algorithm and re-estimate them.

[0051] The state of charge S of the battery module OC,modules for:

[0052]

[0053] In the formula, S OC,modules (t) represents the current state of charge of the battery module; C N,i S represents the rated capacity of the i-th battery; OC,i (t) represents the current state of charge of the i-th battery.

[0054] Step 6: Establish multi-parameter constraints and estimate the continuous peak current under the multi-parameter constraints to obtain the continuous peak charging current and continuous peak discharging current under the multi-parameter constraints; the multi-parameter constraints include the battery's terminal voltage, state of charge, core temperature, and the battery's own safe current;

[0055] Step 6.1: Perform continuous peak current estimation based on state of charge constraints; the peak current includes continuous peak charging current and continuous peak discharging current;

[0056]

[0057] In the formula, Let be the sustained peak charging current and sustained peak discharging current of the i-th battery under state-of-charge constraints, respectively; k is the discrete time; Δt is the sampling period; L is the number of sampling periods; and the duration is L×Δt; S OC,min S OC,max These are the minimum and maximum limits for the state of charge (S) during battery charging and discharging, respectively; OC,i,k The state of charge of the i-th battery at time k;

[0058] Step 6.2: Perform continuous peak current estimation based on terminal voltage constraints;

[0059]

[0060] In the formula, ξ 1,i =exp{-Δt / (R)} 1,i *C 1,i )};ξ 2,i =exp{-Δt / (R)} 2,i *C 2,i )}; These represent the sustained peak charging current and sustained peak discharging current of the i-th battery under terminal voltage constraints, respectively; U 0,max U 0,min These represent the minimum and maximum terminal voltage limits during battery charging and discharging, respectively. OCV,i,k U is the open-circuit voltage of the i-th battery at time k; 1,i,k U 2,i,k Let be the voltages of the two RC loops at time k for the i-th battery; j is the summation variable, and j = 1, 2, 3…L-1.

[0061] Step 6.3: Perform continuous peak current estimation based on core temperature constraints;

[0062]

[0063] In the formula, These are the sustained peak charging current and sustained peak discharging current of the i-th battery under core temperature constraints, respectively; R a,i T is the sum of the ohmic internal resistance and polarization internal resistance of the i-th cell; i,k Q represents the average temperature of the i-th battery at time k; max,i The maximum heat generation of the i-th battery is given by the following formula:

[0064]

[0065] In the formula, T c,max The maximum permissible operating temperature of the battery; T c,i,k T represents the core temperature of the i-th battery at time k; s,i,k Let be the surface temperature of the i-th battery at time k;

[0066] Step 6.4: Based on the estimated continuous peak current under the above constraints, perform continuous peak current estimation based on multi-parameter constraints to obtain the continuous peak current of charging and discharging under multi-parameter constraints.

[0067]

[0068] In the formula, These represent the continuous peak charging current and continuous peak discharging current under multi-parameter constraints for the i-th battery; I min I max These are the battery's safe charging current and discharging current, respectively.

[0069] Step 7: Combine the battery terminal voltage over the duration to estimate the continuous peak power under multi-parameter constraints, and obtain the rated charging power and rated discharging power of the battery module;

[0070] Step 7.1: Define the battery terminal voltage over the duration;

[0071] The battery terminal voltage during the specified duration is:

[0072]

[0073] In the formula, U 0,i,k+L The terminal voltage of the i-th battery during the duration; I k Let be the current at time k.

[0074] Step 7.2: Perform continuous peak power estimation under multi-parameter constraints to obtain the rated charging power and rated discharging power of the battery module;

[0075]

[0076] In the formula, These represent the peak charging power state and peak discharging power state of the battery module under multiple constraints, respectively; n is the number of individual cells connected in series within the battery module. These are the rated charging power and rated discharging power of the battery module, respectively.

[0077] Step 7.3: Compare the obtained rated charging power and rated discharging power of the battery module with their actual values ​​to determine whether their accuracy meets the requirements. If it does, output the result; otherwise, return to step 6 to adjust the parameters within the constraints and re-estimate them.

[0078] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0079] This invention provides a multi-state joint estimation method for energy storage systems that considers electrothermal coupling characteristics. By leveraging the coupling relationship between the electrical and thermal characteristics of the battery in the energy storage system, an electro-thermal coupling model is established to update battery parameters in real time, thereby providing a more accurate description of the battery's dynamic characteristics. Furthermore, by utilizing the correlation between different states of the energy storage system, multiple states are jointly estimated. The method proposed in this invention is applicable to energy storage systems under complex operating conditions, improving the accuracy of energy storage system state estimation and ensuring the safe operation of the energy storage system. Attached Figure Description

[0080] Figure 1 This is a flowchart illustrating the multi-state joint estimation of the electrothermal coupling characteristics of a battery energy storage system in an embodiment of the present invention.

[0081] Figure 2 This is a diagram illustrating the electro-thermal coupling process of the battery in an embodiment of the present invention;

[0082] Figure 3 This is a graph showing the estimated core temperature and surface temperature of the battery in an embodiment of the present invention.

[0083] Figure 4 This is a peak power state estimation diagram of the battery module in an embodiment of the present invention. Detailed Implementation

[0084] The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0085] Multi-state joint estimation methods for energy storage systems considering electrothermal coupling characteristics, such as... Figure 1 As shown, it includes the following steps:

[0086] Step 1: For each individual cell in the battery module, establish an electro-thermal coupling model for each individual cell; the electro-thermal coupling model includes a second-order RC equivalent circuit model, a lumped parameter thermal model, a parameter update module, an ampere-hour integration module, and a heat generation module;

[0087] Step 1-1: Based on the battery's electrical characteristics, construct a second-order RC equivalent circuit model; the second-order RC equivalent circuit model is used to simulate the resistive and capacitive characteristics of the battery terminal voltage.

[0088] The expression for the second-order RC equivalent circuit model is:

[0089]

[0090] In the formula, U 1,i (t), U 2,i (t) represents the voltages of the two RC loops in the i-th battery; I(t) is the operating current; R 0,i R is the ohmic internal resistance of the i-th battery; 1,i C 1,i These are the electrochemical polarization resistance and electrochemical polarization capacitance of the i-th cell, respectively; R 2,i C 2,i These are the concentration difference polarization resistance and concentration difference polarization capacitance of the i-th cell, respectively; U 0,i (t) represents the terminal voltage of the i-th battery; U OCV,i (t) represents the open-circuit voltage of the i-th battery; t represents time; and i represents the cell number, which is determined by the series connection order of the cells in the battery module.

[0091] Step 1-2: Construct a lumped parameter thermal model based on the battery's thermal characteristics; the lumped parameter thermal model is used to simulate the thermal characteristics of the battery's surface temperature and core temperature;

[0092] The expression for the lumped parameter thermal model is:

[0093]

[0094] In the formula, C c,i C represents the core heat capacity of the i-th battery; s,i R is the surface heat capacity of the i-th cell; c,i R is the thermal resistance between the i-th battery core and the battery surface; u,i R is the thermal resistance between the surface of the i-th battery and the air; e It is the equivalent thermal resistance between batteries; Q i (t) represents the heat generated by the i-th battery; T c,i (t) represents the core temperature of the i-th battery; T s,i (t) represents the surface temperature of the i-th battery; Tf (t) represents the ambient temperature; T s,i-1 (t) represents the surface temperature of the (i-1)th cell; T s,i+1 (t) represents the surface temperature of the (i+1)th battery.

[0095] Steps 1-3: Based on the process of the interaction between electricity and heat, construct a parameter update module; the parameter update module is used to correct the parameter data of the next moment in the simulation of the second-order RC equivalent circuit model;

[0096] The expression for the parameter update module is:

[0097] R 0,i ,R 1,i C 1,i ,R 2,i C 2,i =f interp2 (I(t),S OC,i (t),T i (t)) (3)

[0098] In the formula, f interp2 (I(t),S OC,i (t),T i (t) is a three-dimensional interpolation function; S OC,i (t) represents the state of charge of the i-th battery at time t; T i (t) represents the average temperature of the i-th battery.

[0099] Steps 1-4: Construct an ampere-hour integration module based on the definition of state of charge; the ampere-hour integration module is used to calculate the current state of charge of the battery.

[0100] The expression for the ampere-hour integration module is:

[0101]

[0102] S OC,i (t0) represents the initial state of charge of the i-th battery, where t0 is the initial time; C N,i Let η be the rated capacity of the i-th battery; η is the coulombic efficiency of the battery.

[0103] Steps 1-5: Construct a heat generation module based on the Bernadi heat generation formula; the heat generation module is used to calculate the heat generation of the battery at the current moment;

[0104] The expression for the heat generation module is:

[0105]

[0106] in, Let be the temperature entropy coefficient of the i-th battery.

[0107] Steps 1-6: A single cell's electro-thermal coupling model is constructed from a second-order RC equivalent circuit model, a lumped parameter thermal model, a parameter update module, an ampere-hour integration module, and a heat generation module.

[0108] Step 2: Obtain hybrid pulse power characteristic operating condition data at different temperatures and identify the battery parameters under different temperatures and SOC of the constructed second-order RC equivalent circuit model and lumped parameter thermal model offline based on this operating condition data, thereby constructing an offline database of electro-thermal coupling models; the hybrid pulse power characteristic operating condition data includes terminal voltage, operating current, surface temperature and core temperature;

[0109] The battery parameters of the identified second-order RC equivalent circuit model at different temperatures and states of charge (SOC) are as follows:

[0110]

[0111] Where, θ e This is a set of battery parameters for a second-order RC equivalent circuit model at different temperatures and under the state of charge (SOC).

[0112] The identified lumped-parameter thermal model provides the following battery parameters at different temperatures and under state of charge (SOC):

[0113] θ t =[R c,i C c,i ,R u,i C s,i ,R e (7)

[0114] Where, θ t This is a collection of battery parameters at different temperatures and states of charge (SOC) for a lumped-parameter thermal model. Since the hybrid pulse power characteristic operation is performed in a constant-temperature chamber, θ... t The parameters within can be identified as constants.

[0115] The offline database of the electro-thermal coupling model includes the ohmic internal resistance R at different temperatures and under different states of charge. 0,i Electrochemical polarization resistance R 1,i Concentration difference polarization resistance R 2,i Electrochemical polarization capacitance C 1,i and concentration difference polarization capacitance C 2,i .

[0116] Step 3: As Figure 2 As shown, the operating current I(t-1) and ambient temperature T at the previous moment are obtained. f (t-1), and the current terminal voltage U of each individual cell is obtained using the electro-thermal coupling model of each individual cell. 0,i(t), core temperature T c,i (t) and surface temperature T s,i (t);

[0117] Step 3-1: Obtain the operating current I(t-1) and ambient temperature T at the previous moment. f (t-1);

[0118] Step 3-2: Assign the battery parameters identified in Step 2 at different temperatures and under the State of Charge (SOC) to the second-order RC equivalent circuit model. Input the operating current I(t-1) from the previous moment into the second-order RC equivalent circuit model to obtain the terminal voltage U from the previous moment. 0,i (t-1);

[0119] Step 3-3: Convert the terminal voltage U from the previous moment... 0,i (t-1) Input the heat generation module and use the heat generation module to obtain the heat generated by the battery at the previous moment, Q. i (t-1);

[0120] Steps 3-4: Assign the battery parameters at different temperatures and under the state of charge identified in Step 2 to the lumped parameter thermal model, and assign the heat generated by the battery at the previous moment, Q. i (t-1) Input the lumped parameter thermal model to obtain the battery core temperature T at the previous moment. c,i (t-1) and surface temperature T s,i (t-1), further yielding the average temperature T of the battery at the previous moment. i (t-1);

[0121] The average temperature of the battery at the previous moment was:

[0122] T i (t-1)=(T s,i (t-1)+T c,i (t-1)) / 2 (8)

[0123] Steps 3-5: Input the battery operating current I(t-1) from the previous moment into the ampere-hour integrator to obtain the state of charge S from the previous moment. OC,i (t-1);

[0124] Steps 3-6: Based on the obtained average battery temperature T from the previous moment i (t-1) and state of charge S OC,i (t-1), the parameter update module finds the battery parameters under the average temperature and state of charge of the battery from the offline database of the electro-thermal coupling model, and updates the battery parameters to the second-order RC equivalent circuit model;

[0125] Steps 3-7: Obtain the current operating current I(t), and use the second-order RC equivalent circuit model with updated battery parameters to obtain the current terminal voltage U of each individual cell. 0,i (t);

[0126] Step 3-8: Following the methods in steps 3-4 and 3-5, based on the current terminal voltage U of each individual cell... 0,i (t) yields the current battery core temperature T. c,i (t) and surface temperature T s,i (t), further obtaining the current average temperature T of the battery. i (t);

[0127] Steps 3-9: Compare the current battery terminal voltage, surface temperature, and core temperature with their actual values ​​to determine if the accuracy meets the requirements. If it does, output the values; otherwise, return to step 2 to re-identify the model parameters.

[0128] Step 4: Based on the current terminal voltage U of each individual cell... 0,i (t), using the extended Kalman filter algorithm to estimate the current state of charge S of each individual cell. OC,i (t);

[0129] Step 5: Characterize the state of charge (SOC) of the battery module by individual cells, and based on the current SOC of each individual cell... OC,i (t) yields the current state of charge S of the battery module. OC,modules (t) compares the current state of charge of the battery module with its true value to determine whether its accuracy meets the requirements. If it does, output the result; otherwise, return to step 4 to adjust the calibration parameters in the extended Kalman filter algorithm and re-estimate them.

[0130] The state of charge S of the battery module OC,modules for:

[0131]

[0132] In the formula, S OC,modules (t) represents the current state of charge of the battery module; C N,i S represents the rated capacity of the i-th battery; OC,i (t) represents the current state of charge of the i-th battery.

[0133] Step 6: Establish multi-parameter constraints and estimate the continuous peak current under the multi-parameter constraints to obtain the continuous peak charging current and continuous peak discharging current under the multi-parameter constraints; the multi-parameter constraints include the battery's terminal voltage, state of charge, core temperature, and the battery's own safe current;

[0134] Step 6.1: Perform continuous peak current estimation based on state of charge constraints; the peak current includes continuous peak charging current and continuous peak discharging current;

[0135]

[0136] In the formula, Let be the sustained peak charging current and sustained peak discharging current of the i-th battery under state-of-charge constraints, respectively; k is the discrete time; Δt is the sampling period; L is the number of sampling periods; and the duration is L×Δt; S OC,min S OC,max These are the minimum and maximum limits for the state of charge (S) during battery charging and discharging, respectively; OC,i,k The state of charge of the i-th battery at time k;

[0137] Step 6.2: Perform continuous peak current estimation based on terminal voltage constraints;

[0138]

[0139] In the formula, ξ 1,i =exp{-Δt / (R)} 1,i *C 1,i )};ξ 2,i =exp{-Δt / (R)} 2,i *C 2,i )}; These represent the sustained peak charging current and sustained peak discharging current of the i-th battery under terminal voltage constraints, respectively; U 0,max U 0,min These represent the minimum and maximum terminal voltage limits during battery charging and discharging, respectively. OCV,i,k U is the open-circuit voltage of the i-th battery at time k; 1,i,k U 2,i,k Let be the voltages of the two RC loops at time k for the i-th battery; j is the summation variable, and j = 1, 2, 3…L-1.

[0140] Step 6.3: Perform continuous peak current estimation based on core temperature constraints;

[0141]

[0142] In the formula, These are the sustained peak charging current and sustained peak discharging current of the i-th battery under core temperature constraints, respectively; R a,i T is the sum of the ohmic internal resistance and polarization internal resistance of the i-th cell; i,k Q represents the average temperature of the i-th battery at time k; max,i The maximum heat generation of the i-th battery is given by the following formula:

[0143]

[0144] In the formula, T c,max The maximum permissible operating temperature of the battery; T c,i,k T represents the core temperature of the i-th battery at time k; s,i,k Let be the surface temperature of the i-th battery at time k;

[0145] Step 6.4: Based on the estimated continuous peak current under the above constraints, perform continuous peak current estimation based on multi-parameter constraints to obtain the continuous peak current of charging and discharging under multi-parameter constraints.

[0146]

[0147] In the formula, These represent the continuous peak charging current and continuous peak discharging current under multi-parameter constraints for the i-th battery; I min I max These are the battery's safe charging current and discharging current, respectively.

[0148] Step 7: Combine the battery terminal voltage over the duration to estimate the continuous peak power under multi-parameter constraints, and obtain the rated charging power and rated discharging power of the battery module;

[0149] Step 7.1: Define the battery terminal voltage over the duration;

[0150] The battery terminal voltage during the specified duration is:

[0151]

[0152] In the formula, U 0,i,k+L The terminal voltage of the i-th battery during the duration; I k Let be the current at time k.

[0153] Step 7.2: Perform continuous peak power estimation under multi-parameter constraints to obtain the rated charging power and rated discharging power of the battery module;

[0154]

[0155] In the formula, These represent the peak charging power state and peak discharging power state of the battery module under multiple constraints, respectively; n is the number of individual cells connected in series within the battery module. These are the rated charging power and rated discharging power of the battery module, respectively.

[0156] Step 7.3: Compare the obtained rated charging power and rated discharging power of the battery module with their actual values ​​to determine whether their accuracy meets the requirements. If it does, output the result; otherwise, return to step 6 to adjust the parameters within the constraints and re-estimate them.

[0157] Finally, using operating data at an ambient temperature of 25℃, the above model and joint estimation method were modeled and simulated using the MATLAB / Simulink simulation platform. The battery core temperature and surface temperature were estimated as follows: Figure 3 As shown, the obtained results are as follows: Figure 4 As shown, the maximum error between the core temperature and the surface temperature is 0.12℃ and 0.15℃, respectively, and the maximum error at peak power is 5.16%.

Claims

1. A multi-state joint estimation method for an energy storage system considering electrothermal coupling characteristics, characterized in that, Includes the following steps: Step 1: For each individual cell in the battery module, establish an electro-thermal coupling model for each individual cell; the electro-thermal coupling model includes a second-order RC equivalent circuit model, a lumped parameter thermal model, a parameter update module, an ampere-hour integration module, and a heat generation module; Step 2: Obtain hybrid pulse power characteristic operating condition data at different temperatures and identify the battery parameters under different temperatures and SOC of the constructed second-order RC equivalent circuit model and lumped parameter thermal model offline based on this operating condition data, thereby constructing an offline database of electro-thermal coupling models; the hybrid pulse power characteristic operating condition data includes terminal voltage, operating current, surface temperature and core temperature; Step 3: Obtain the operating current I(t-1) and ambient temperature T at the previous moment. f (t-1), and the current terminal voltage U of each individual cell is obtained using the electro-thermal coupling model of each individual cell. 0,i (t), core temperature T c,i (t) and surface temperature T s,i (t); Step 4: Based on the current terminal voltage U of each individual cell... 0,i (t), using the extended Kalman filter algorithm to estimate the current state of charge S of each individual cell. OC,i (t); Step 5: Characterize the state of charge (SOC) of the battery module by individual cells, and based on the current SOC of each individual cell... oc,i (t) yields the current state of charge S of the battery module. OC,modules (t) compares the current state of charge of the battery module with its true value to determine whether its accuracy meets the requirements. If it does, output the result; otherwise, return to step 4 to adjust the calibration parameters in the extended Kalman filter algorithm and re-estimate them. Step 6: Establish multi-parameter constraints and estimate the continuous peak current under the multi-parameter constraints to obtain the continuous peak charging current and continuous peak discharging current under the multi-parameter constraints; the multi-parameter constraints include the battery's terminal voltage, state of charge, core temperature, and the battery's own safe current; Step 7: Combine the battery terminal voltage over the duration to estimate the continuous peak power under multi-parameter constraints, and obtain the simulated values ​​of the battery module's rated charging power and rated discharging power.

2. The multi-state joint estimation method for energy storage systems considering electrothermal coupling characteristics according to claim 1, characterized in that, Step 1 specifically includes: Step 1-1: Based on the battery's electrical characteristics, construct a second-order RC equivalent circuit model; the second-order RC equivalent circuit model is used to simulate the resistive and capacitive characteristics of the battery terminal voltage. The expression for the second-order RC equivalent circuit model is: In the formula, U 1,i (t), U 2,i (t) represents the voltages of the two RC loops in the i-th battery; I(t) is the operating current; R 0,i R is the ohmic internal resistance of the i-th battery; 1,i C 1,i These are the electrochemical polarization resistance and electrochemical polarization capacitance of the i-th cell, respectively; R 2,i C 2,i These are the concentration difference polarization resistance and concentration difference polarization capacitance of the i-th cell, respectively; U 0,i (t) represents the terminal voltage of the i-th battery; U OCV,i (t) represents the open-circuit voltage of the i-th battery; t represents time; i represents the cell number and is numbered according to the series connection order of the cells in the battery module. Step 1-2: Construct a lumped parameter thermal model based on the battery's thermal characteristics; the lumped parameter thermal model is used to simulate the thermal characteristics of the battery's surface temperature and core temperature; The expression for the lumped parameter thermal model is: In the formula, C c,i C represents the core heat capacity of the i-th battery; s,i R is the surface heat capacity of the i-th cell; c,i R is the thermal resistance between the i-th battery core and the battery surface; u,i R is the thermal resistance between the surface of the i-th battery and the air; e It is the equivalent thermal resistance between batteries; Q i (t) represents the heat generated by the i-th battery; T c,i (t) represents the core temperature of the i-th battery; T s,i (t) represents the surface temperature of the i-th battery; T f (t) represents the ambient temperature; T s,i-1 (t) represents the surface temperature of the (i-1)th cell; T s,i+1 (t) represents the surface temperature of the (i+1)th battery; Steps 1-3: Based on the process of the interaction between electricity and heat, construct a parameter update module; the parameter update module is used to correct the parameter data of the next moment in the simulation of the second-order RC equivalent circuit model; The expression for the parameter update module is: R 0,i ,R 1,i ,C 1,i ,R 2,i ,C 2,i =f interp2 (I(t),S OC,i (t),T i (t)) (3) In the formula, f interp2 (I(t),S OC,i (t),T i (t) is a three-dimensional interpolation function; S OC,i (t) represents the state of charge of the i-th battery at time t; T i (t) represents the average temperature of the i-th battery; Steps 1-4: Construct an ampere-hour integration module based on the definition of state of charge; the ampere-hour integration module is used to calculate the current state of charge of the battery. The expression for the ampere-hour integration module is: S OC,i (t0) represents the initial state of charge of the i-th battery, where t0 is the initial time; C N,i Let be the rated capacity of the i-th battery; η be the coulombic efficiency of the battery. Steps 1-5: Construct a heat generation module based on the Bernadi heat generation formula; the heat generation module is used to calculate the heat generation of the battery at the current moment; The expression for the heat generation module is: in, Let be the temperature entropy coefficient of the i-th battery; Steps 1-6: A single cell electro-thermal coupling model is constructed from a second-order RC equivalent circuit model, a lumped parameter thermal model, a parameter update module, an ampere-hour integration module, and a heat generation module.

3. The multi-state joint estimation method for energy storage systems considering electrothermal coupling characteristics according to claim 1, characterized in that, The battery parameters of the second-order RC equivalent circuit model identified in step 2 under different temperatures and states of charge (SOC) are as follows: Where, θ e This is a set of battery parameters for a second-order RC equivalent circuit model at different temperatures and under the state of charge (SOC). The identified lumped-parameter thermal model provides the following battery parameters at different temperatures and under state of charge (SOC): θ t =[R c,i ,C c,i ,R u,i ,C s,i ,R e ] (7) Where, θ t This is a collection of battery parameters at different temperatures and states of charge (SOC) for a lumped-parameter thermal model. Since the hybrid pulse power characteristic operation is performed in a constant-temperature chamber, θ... t The parameters within can be identified as constants; The offline database of the electro-thermal coupling model includes the ohmic internal resistance R at different temperatures and under different states of charge. 0,i Electrochemical polarization resistance R 1,i Concentration difference polarization resistance R 2,i Electrochemical polarization capacitance C 1,i and concentration difference polarization capacitance C 2,i .

4. The multi-state joint estimation method for energy storage systems considering electrothermal coupling characteristics according to claim 1, characterized in that, Step 3 specifically includes: Step 3-1: Obtain the operating current I(t-1) and ambient temperature T at the previous moment. f (t-1); Step 3-2: Assign the battery parameters identified in Step 2 at different temperatures and under the State of Charge (SOC) to the second-order RC equivalent circuit model. Input the operating current I(t-1) from the previous moment into the second-order RC equivalent circuit model to obtain the terminal voltage U from the previous moment. 0,i (t-1); Step 3-3: Convert the terminal voltage U from the previous moment... 0,i (t-1) Input the heat generation module and use the heat generation module to obtain the heat generated by the battery at the previous moment, Q. i (t-1); Steps 3-4: Assign the battery parameters at different temperatures and under the state of charge identified in Step 2 to the lumped parameter thermal model, and assign the heat generated by the battery at the previous moment, Q. i (t-1) Input the lumped parameter thermal model to obtain the battery core temperature T at the previous moment. c,i (t-1) and surface temperature T s,i (t-1), further yielding the average temperature T of the battery at the previous moment. i (t-1); The average temperature of the battery at the previous moment was: T i (t-1)=(T s,i (t-1)+T c,i (t-1)) / 2 (8) Steps 3-5: Input the battery operating current I(t-1) from the previous moment into the ampere-hour integrator to obtain the state of charge S from the previous moment. OC,i (t-1); Steps 3-6: Based on the obtained average battery temperature T from the previous moment i (t-1) and state of charge S OC,i (t-1), the parameter update module finds the battery parameters under the average temperature and state of charge of the battery from the offline database of the electro-thermal coupling model, and updates the battery parameters to the second-order RC equivalent circuit model; Steps 3-7: Obtain the current operating current I(t), and use the second-order RC equivalent circuit model with updated battery parameters to obtain the current terminal voltage U of each individual cell. 0,i (t); Step 3-8: Following the methods in steps 3-4 and 3-5, based on the current terminal voltage U of each individual cell... 0,i (t) yields the current battery core temperature T. c,i (t) and surface temperature T s,i (t), further obtaining the current average temperature T of the battery. i (t); Steps 3-9: Compare the current battery terminal voltage, surface temperature, and core temperature with their actual values ​​to determine if the accuracy meets the requirements. If it does, output the values; otherwise, return to step 2 to re-identify the model parameters.

5. The multi-state joint estimation method for energy storage systems considering electrothermal coupling characteristics according to claim 1, characterized in that, The state of charge S of the battery module in step 5 OC,modules for: In the formula, S OC,modules (t) represents the current state of charge of the battery module; C N,i S represents the rated capacity of the i-th battery; OC,i (t) represents the current state of charge of the i-th battery.

6. The multi-state joint estimation method for energy storage systems considering electrothermal coupling characteristics according to claim 1, characterized in that, Step 6 specifically includes: Step 6.1: Perform continuous peak current estimation based on state of charge constraints; the peak current includes continuous peak charging current and continuous peak discharging current; In the formula, Let be the sustained peak charging current and sustained peak discharging current of the i-th battery under state-of-charge constraints, respectively; k is the discrete time; Δt is the sampling period; L is the number of sampling periods; and the duration is L×Δt; S OC,min S OC,max These are the minimum and maximum limits for the state of charge (S) during battery charging and discharging, respectively; OC,i,k The state of charge of the i-th battery at time k; Step 6.2: Perform continuous peak current estimation based on terminal voltage constraints; In the formula, ξ 1,i =exp{-Δt / (R)} 1,i *C 1,i )};ξ 2,i =exp{-Δt / (R)} 2,i *C 2,i )}; These represent the sustained peak charging current and sustained peak discharging current of the i-th battery under terminal voltage constraints, respectively; U 0,max U 0,min These are the minimum and maximum terminal voltage limits during battery charging and discharging, respectively; U OCV,i,k U is the open-circuit voltage of the i-th battery at time k; 1,i,k U 2,i,k Let be the voltages of the two RC loops at time k for the i-th battery; j is the summation variable, and j = 1, 2, 3…L-1; Step 6.3: Perform continuous peak current estimation based on core temperature constraints; In the formula, These are the sustained peak charging current and sustained peak discharging current of the i-th battery under core temperature constraints, respectively; R a,i T is the sum of the ohmic internal resistance and polarization internal resistance of the i-th cell; i,k Q represents the average temperature of the i-th battery at time k; max,i The maximum heat generation of the i-th battery is given by the following formula: In the formula, T c,max The maximum permissible operating temperature of the battery; T c,i,k T represents the core temperature of the i-th battery at time k; s,i,k Let be the surface temperature of the i-th battery at time k; Step 6.4: Based on the estimated continuous peak current under the above constraints, perform continuous peak current estimation based on multi-parameter constraints to obtain the continuous peak current of charging and discharging under multi-parameter constraints. In the formula, These are the continuous peak charging current and continuous peak discharging current under multi-parameter constraints for the i-th battery, respectively; I min I max These are the battery's safe charging current and discharging current, respectively.

7. The multi-state joint estimation method for energy storage systems considering electrothermal coupling characteristics according to claim 1, characterized in that, Step 7 specifically includes: Step 7.1: Define the battery terminal voltage over the duration; The battery terminal voltage during the specified duration is: In the formula, U 0,i,k+L The terminal voltage of the i-th battery during the duration; I k Let k be the current at time k; Step 7.2: Perform continuous peak power estimation under multi-parameter constraints to obtain the rated charging power and rated discharging power of the battery module; In the formula, These represent the peak charging power state and peak discharging power state of the battery module under multiple constraints, respectively; n is the number of individual cells connected in series within the battery module. These are the rated charging power and rated discharging power of the battery module, respectively. Step 7.3: Compare the obtained rated charging power and rated discharging power of the battery module with their actual values ​​to determine whether their accuracy meets the requirements. If it does, output the result; otherwise, return to step 6 to adjust the parameters within the constraints and re-estimate them.

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