A Wide-Temperature-Range Online Fast Charging Method for Lithium Batteries Based on an Improved Electro-Thermal Coupling Model

Abstract

This invention discloses a wide-temperature-range online fast charging method for lithium batteries based on an improved electro-thermal coupling model. The method includes: constructing an improved electro-thermal coupling model and identifying its parameter vectors; real-time acquisition of measured state information of the lithium battery, obtaining predicted state information based on the improved electro-thermal coupling model, and updating the improved electro-thermal coupling model online; determining safety constraint boundaries based on the predicted state information, and finding the optimal charging current that maximizes charging efficiency while satisfying the safety constraints; and generating an optimal charging current command based on a temperature-based differentiated control strategy using the optimal charging current. This invention is applied to the field of battery thermal management. By constructing a high-precision improved electro-thermal coupling model that integrates electrochemical equations, it accurately describes the dynamic characteristics of the battery under wide temperature range conditions. Combined with real-time optimization control technology, it achieves safe and efficient adaptive fast charging within a wide temperature range.

Description

Technical Field

[0001] This invention relates to the field of battery thermal management technology, specifically a wide-temperature-range online fast charging method for lithium batteries based on an improved electro-thermal coupling model. Background Technology

[0002] Lithium-ion batteries, as the core of modern energy storage technology, play a crucial role in electric vehicles, smart grids, and portable electronic devices. With the continuous expansion of application scenarios, especially the increasing demand for applications in extreme conditions such as polar scientific expeditions, high-altitude operations, and desert environments, the technical requirement for fast charging of lithium-ion batteries across a wide temperature range is becoming increasingly urgent. However, existing fast-charging technologies have revealed many limitations when facing wide-temperature applications.

[0003] Traditional fast charging methods mainly include constant current / constant voltage charging, multi-stage charging, and pulse charging strategies. These methods achieve fast charging through preset charging curves or fixed parameter configurations, and can achieve certain results in room temperature environments. However, when the ambient temperature changes significantly, especially under low or high temperature conditions, the limitations of these traditional methods become very apparent. In low-temperature environments, the electrochemical reaction kinetics of lithium-ion batteries slow down significantly, the ion diffusion rate decreases sharply, and the electrolyte conductivity decreases. These factors together lead to a sharp increase in the battery's internal resistance. More seriously, fast charging under low-temperature conditions easily triggers lithium deposition, where lithium ions directly deposit metallic lithium on the negative electrode surface. This not only permanently damages the battery capacity but may also cause serious safety accidents. Traditional fast charging methods lack real-time monitoring of the battery's internal electrochemical state and cannot accurately assess the risk of lithium deposition. Therefore, they often adopt overly conservative charging strategies under low-temperature conditions, resulting in extremely low charging efficiency. In high-temperature environments, although the battery's electrochemical activity is enhanced, further increases in temperature accelerate the battery aging process and may even lead to safety issues such as thermal runaway. Traditional fast charging methods lack dynamic response to temperature changes and cannot adjust charging parameters according to real-time temperature conditions, easily leading to battery overheating or decreased charging efficiency. Another significant drawback of existing fast charging technologies lies in their design philosophy based on fixed charging curves. These preset charging curves are typically based on battery characteristic test data under standard operating conditions and cannot adapt to changes in battery performance at different stages of use and under different environmental conditions. In reality, during long-term use, key parameters such as battery capacity and internal resistance change, and traditional methods lack the ability to adaptively adjust these parameters, resulting in a deviation between the charging strategy and the actual state of the battery.

[0004] Furthermore, existing technologies lack the ability to accurately model the internal states of batteries. Although some advanced charging methods have begun to use battery models for charging optimization, most of these models are overly simplified and fail to fully consider the complex physicochemical processes inside the battery. In particular, under wide temperature range conditions, the open-circuit voltage characteristics, internal resistance characteristics, and thermal characteristics of the battery will change significantly, and existing models often cannot accurately describe these changes, resulting in insufficient accuracy of model-based control strategies. Summary of the Invention

[0005] To address the shortcomings of the existing technologies, this invention provides a wide-temperature-range online fast charging method for lithium batteries based on an improved electro-thermal coupling model. By constructing a high-precision improved electro-thermal coupling model that integrates electrochemical equations, the dynamic characteristics of the battery under wide temperature range conditions can be accurately described. At the same time, combined with real-time optimization control technology, safe and efficient adaptive fast charging is achieved within a wide temperature range.

[0006] To achieve the above objectives, this invention provides a wide-temperature-range online fast charging method for lithium batteries based on an improved electro-thermal coupling model, comprising the following steps:

[0007] Step 1: Construct an improved electro-thermal coupling model, including an electrical model consisting of an open-circuit voltage model considering the solid-phase diffusion process, an ohmic internal resistance model with a quadratic correction term, and a polarization internal resistance model with a quadratic nonlinear term, and a thermal model consisting of a battery heat generation model and a battery heat transfer model, and identify the parameter vector of the improved electro-thermal coupling model.

[0008] Step 2: Collect the measured state information of the lithium battery in real time, obtain the predicted state information of the lithium battery based on the state information and the improved electro-thermal coupling model, and update the improved electro-thermal coupling model online based on the error between the predicted state information and the measured state information.

[0009] Step 3: Determine the safety constraint boundary based on the predicted state information, and find the optimal charging current that maximizes charging efficiency while satisfying the safety constraints.

[0010] Step 4: Based on the optimal charging current, an optimal charging current command is generated using a temperature-based differential control strategy.

[0011] Compared with the prior art, the present invention has the following beneficial technical effects:

[0012] 1. This invention integrates electrochemical equations and equivalent circuit models, starting from the internal physicochemical processes of the battery, and constructs a high-precision improved electro-thermal coupling model that can accurately reflect the dynamic characteristics of the battery. Combined with real-time optimization control technology, it can achieve stable fast charging in a wide temperature range of -20°C to 50°C, significantly expanding the operating temperature range of lithium-ion batteries.

[0013] 2. This invention fundamentally prevents lithium deposition by real-time monitoring of key electrochemical parameters such as the negative electrode potential, and the dynamic safety constraint mechanism ensures that the charging process is always in a safe state.

[0014] 3. The online optimization strategy of the present invention makes full use of the charging capacity of the battery in its current state, avoids the conservatism of traditional methods, and significantly improves charging efficiency while ensuring safety;

[0015] 4. In practical applications, this invention has moderate computational complexity, can run in real time on conventional embedded controllers, and its online adaptive update mechanism for model parameters ensures control accuracy for long-term use. It is also compatible with existing charging protocols and can be adapted to different types of lithium-ion batteries by adjusting model parameters. It provides a safe and efficient solution for lithium-ion battery fast charging technology, and has important theoretical value and broad application prospects. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the structures shown in these drawings without creative effort.

[0017] Figure 1 This is a flowchart of the online fast charging method for lithium batteries with a wide temperature range based on an improved electro-thermal coupling model in an embodiment of the present invention.

[0018] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0019] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0020] Furthermore, the technical solutions of the various embodiments of the present invention can be combined with each other, but only if they are feasible for those skilled in the art. If the combination of technical solutions is contradictory or cannot be implemented, it should be considered that such combination of technical solutions does not exist and is not within the scope of protection claimed by the present invention.

[0021] This embodiment discloses a wide-temperature-range online fast charging method for lithium batteries based on an improved electro-thermal coupling model (hereinafter referred to as the "fast charging method"). By constructing a high-precision improved electro-thermal coupling model that integrates electrochemical equations, parameterizing the model, and then combining it with real-time optimization control technology, safe and efficient adaptive fast charging is achieved over a wide temperature range. Reference Figure 1 The lithium battery wide-temperature-range online fast charging method based on the improved electro-thermal coupling model in this embodiment specifically includes the following steps:

[0022] Step 1: Construct an improved electro-thermal coupling model, which includes an electrical model consisting of an open-circuit voltage model considering the solid-phase diffusion process, an ohmic internal resistance model with a quadratic correction term, and a polarization internal resistance model with a quadratic nonlinear term, as well as a thermal model consisting of a battery heat generation model and a battery heat transfer model, and identify the parameter vector of the improved electro-thermal coupling model.

[0023] Step 2: Collect the measured state information of the lithium battery in real time, obtain the predicted state information of the lithium battery based on the state information and the improved electro-thermal coupling model, and update the improved electro-thermal coupling model online based on the error between the predicted state information and the measured state information.

[0024] Step 3: Determine the safety constraint boundary based on the predicted state information, and find the optimal charging current that maximizes charging efficiency while satisfying the safety constraints.

[0025] Step 4: Based on the optimal charging current, a temperature-based differential control strategy is used to generate the optimal charging current command.

[0026] Depend on Figure 1 As shown in the steps, the fast charging method in this embodiment includes two parts:

[0027] One aspect involves constructing an improved electro-thermal coupling model to achieve accurate modeling over a wide temperature range. Traditional battery models lack accuracy for wide-temperature applications, primarily because they fail to adequately consider the complex influence of temperature on the internal electrochemical processes of the battery. This embodiment constructs an improved electro-thermal coupling model that accurately describes the dynamic characteristics of the battery under wide temperature conditions by integrating fundamental electrochemical equations into an equivalent circuit model. This model introduces a solid-phase diffusion process into the open-circuit voltage model to describe the ion diffusion confinement effect under low-temperature conditions. In the internal resistance model, a modified Arrhenius equation is used to characterize the influence of temperature on ohmic and polarization resistance. Finally, a bidirectional coupling mechanism between the electrical and thermal models is established in the thermal model. This improvement not only maintains high prediction accuracy over a wide temperature range of -20°C to 50°C but also addresses model complexity, providing a foundation for real-time control.

[0028] Secondly, an online adaptive control method based on an improved electro-thermal coupling model is employed to achieve real-time optimization. Traditional fast-charging control methods use preset charging curves and cannot dynamically adjust according to the actual battery state. This embodiment develops a measurement-prediction-adjustment closed-loop adaptive control strategy based on an improved electro-thermal coupling model. This strategy achieves adaptive control of the charging process by real-time monitoring of battery state, online updating of model parameters, dynamic calculation of safety constraints, and real-time optimization of charging current. In particular, real-time monitoring of key safety parameters such as the negative electrode potential effectively prevents safety issues such as lithium deposition.

[0029] In this embodiment, the improved electro-thermal coupling model incorporates the dynamic influence of solid-phase diffusion on the open-circuit voltage, thus completing the construction of the open-circuit voltage model. Solid-phase diffusion leads to an increase in the lithium-ion concentration on the electrode particle surface (…). ) and the average lithium-ion concentration inside the particle ( Differences exist between surface SOC (Surface OCV) and electrode potential, thus altering the electrochemical potential and OCV of the electrode. To accurately describe this phenomenon, surface SOC (Surface OCV) is introduced. ) and average SOC ( Two concepts. Among them, The average state of charge of the entire electrode particles is directly calculated by current integration. It reflects the real-time state of charge (SOC) of the electrode particles. According to electrochemical theory, there is a linear relationship between SOC and the stoichiometry of the electrode material as follows:

[0030] ;

[0031] Among them, subscript They represent the positive and negative electrodes, respectively. Indicates the positive electrode. (Indicates negative electrode) Surface stoichiometry, The average stoichiometric coefficient, This represents the stoichiometric coefficient of the electrode in its fully charged state (100% SOC), i.e., when the lithium-ion concentration on the electrode particle surface reaches its maximum value. value, This represents the stoichiometric coefficient of the electrode in a fully discharged state (0% SOC), i.e., when the lithium-ion concentration on the electrode particle surface reaches its minimum value. value;

[0032] The difference between surface SOC and average SOC can be converted into stoichiometric deviation. :

[0033] ;

[0034] The difference between the surface stoichiometry and the average stoichiometry is described by the solid-phase diffusion kinetics equation. To simplify the calculation, this embodiment uses a first-order inertial element to describe the dynamic relationship of the stoichiometric difference, namely:

[0035] ;

[0036] in, The solid-phase diffusion time constants for the positive and negative electrodes are... The solid-phase diffusion influencing factors for both positive and negative electrodes. The sampling time interval;

[0037] After obtaining the open-circuit potential curves of the positive and negative electrodes through open-circuit potential testing, the open-circuit voltage of the battery can be expressed as the difference in potential between the positive and negative electrodes, i.e., the open-circuit voltage model is:

[0038] ;

[0039] in, Open circuit voltage, , The potentials of the positive and negative electrodes are determined by the curve relationship obtained by interpolating the open-circuit potential test data of the positive and negative electrodes.

[0040] The relationship between parameters and temperature can usually be described by the Arrhenius equation, which is expressed as follows:

[0041] ;

[0042] in, X The parameter represents the Arrhenius property. A Pre-exponential factor, E a For activation energy, R g The gas constant is... T This represents the thermodynamic temperature (i.e., the battery temperature). Through further derivation of the above equation, the ohmic internal resistance can be obtained. R The relationship between 0 and temperature is:

[0043] .

[0044] As can be seen from the above relationships, within the descriptive framework of the Arrhenius equation, the logarithmic form of the ohmic internal resistance... The reciprocal of thermodynamic temperature 1 / TA linear relationship is observed. Given the significant nonlinear characteristics of the fast-charging application scenario under wide temperature range and high rate conditions involved in this embodiment, to enable the model to more accurately describe such complex characteristics, this embodiment introduces a quadratic correction term into the ohmic internal resistance model. This correction aims to fully characterize the complexity of electrolyte kinematics under high-temperature conditions and the nonlinear influence of various factors such as interface effects on the internal resistance. Therefore, the ohmic internal resistance model in this embodiment is:

[0045] ;

[0046] in , , These are the parameters to be fitted for the Ohmic internal resistance model.

[0047] In fast-charging applications under wide-temperature-range, high-rate operating conditions, the effects of temperature on polarization resistance and ohmic resistance share a consistent mechanistic description, both based on the modified Arrhenius equation to characterize the resistance's temperature dependence. This embodiment focuses on the polarization resistance model, emphasizing the mechanistic description method of the influence of current rate on polarization resistance. The polarization resistance studied here specifically refers to the resistance caused by electrochemical reactions. During battery charging and discharging, complex electrochemical reactions occur on the electrode surface, influenced by a combination of factors. The generation of polarization resistance mainly stems from the kinetic limitations of electrochemical reactions, reflecting the potential changes caused by electrode reactions during charging and discharging. To accurately describe the characteristics of polarization resistance, the BV equation is typically used. This equation quantitatively describes the relationship between the electrode surface reaction current density and overpotential, and its expression is:

[0048] ;

[0049] in, Current density reflects the magnitude of the current passing through a unit area of ​​electrode; The exchange current density represents the rate of oxidation and reduction reactions of the electrode under equilibrium conditions, reflecting the intrinsic reactivity of the electrode material. Overpotential, which is the difference between the actual potential and the equilibrium potential, measures the degree to which the electrode reaction deviates from the equilibrium state. α a and α c These are the transfer coefficients for the negative and positive electrode reactions, respectively, which are related to the activation energy of the electrode reaction and typically range from 0 to 1. F It is Faraday's constant; R The gas constant is... T It is the thermodynamic temperature.

[0050] When the current flowing through the battery is large, i.e., when the absolute value of the overpotential is large, a situation may arise where one-sided reaction dominates. In the research scenario involved in this embodiment, The value will be much smaller than Therefore, it can be ignored. At this point, the BV equation can be simplified to:

[0051] ;

[0052] Where α represents the transmission coefficient. By transforming and deriving the above equation, we can obtain the Tafel equation:

[0053] ;

[0054] The Tafel equation shows that at high current densities, the overpotential is linearly related to the natural logarithm of the current density. Therefore, this embodiment uses the Tafel equation to describe the relationship between polarization resistance and current multiplier. In practical applications, the current across the entire electrode is more relevant, as it equals the product of the current density and the effective area of ​​the electrode. Polarization resistance It can be defined as the ratio of overpotential to current, that is:

[0055] ;

[0056] Substituting the Tafel equation into the definition of polarization resistance, we get:

[0057] ;

[0058] make , Then the polarization internal resistance It can be represented as:

[0059] .

[0060] This formula shows that polarization resistance has a linear relationship with the natural logarithm of current divided by the current. Based on the above derivation, a model relating polarization resistance to current can be obtained. However, considering that some nonlinear effects exist inside the battery under high current conditions, such as concentration polarization, etc., this relationship is affected. Concentration polarization is caused by the difference in concentration of active materials between the electrode surface and the electrode interior during battery charging and discharging. This concentration difference becomes more pronounced under high current, thus having a significant impact on polarization resistance. To more comprehensively and accurately describe the behavior of polarization resistance under high current, this embodiment introduces a quadratic nonlinear term into the polarization resistance model. That is, the polarization resistance model in this embodiment is:

[0061] ;

[0062] in, For current, , and The parameters to be fitted for the polarization internal resistance model are... Used to describe the effect of current on polarization resistance; This is used to describe the effect of the second-order nonlinear term on the polarization internal resistance, mainly reflecting nonlinear effects such as concentration polarization.

[0063] In constructing the battery heat generation and heat production models in this embodiment, the following assumptions are made: the battery is simplified as a heat-generating particle, and its internal heat conduction process is ignored, thereby constructing a lumped parameter thermal model; when the battery releases heat and exchanges heat with the outside world, only the convective heat transfer between the battery and the surrounding air is considered, while other heat transfer methods are ignored.

[0064] Based on the first assumption and the simplified Bernardi equation, the battery heat generation model can be derived as follows:

[0065] ;

[0066] in, This represents the total heat generation power inside the battery. This refers to the battery terminal voltage. Irreversible heat, Reversible heat; irreversible heat term The heat power reflected is generated by the difference between the battery terminal voltage and the open-circuit voltage, which can be calculated through an electrical model; while the reversible heat term... Then subject to battery current ,temperature and entropy heat coefficient The impact;

[0067] Based on the second assumption, the battery heat transfer model can be derived as follows:

[0068] ;

[0069] in, This refers to the heat dissipation power per unit area of ​​a battery. h The convective heat transfer coefficient at the battery surface is denoted as . T a The ambient temperature.

[0070] In the improved electro-thermal coupling model of this embodiment, the coupling mechanism between the electrical model and the thermal model is mainly reflected in the following two aspects: Firstly, the parameters of the electrical model are affected by temperature; secondly, the irreversible heat in the thermal model is calculated through the electrical model, thus forming a coupling effect between the two. Based on the law of conservation of energy, and since the battery temperature is calculated through the thermal model, the temperature-affected equivalent circuit model parameters are further corrected. Specifically:

[0071] ;

[0072] in, This refers to the volumetric heat capacity of the battery.

[0073] In practical applications, the accuracy of the electro-thermal coupling model hinges on the precise identification of its parameters. These parameters can be categorized into three types: stoichiometric parameters, solid-phase diffusion parameters, and internal resistance temperature characteristic parameters. Each type of parameter employs a corresponding identification method.

[0074] The stoichiometric parameters include the stoichiometric coefficients of the positive and negative electrodes in the fully charged and fully discharged states, i.e. , , , These parameters determine the basic electrochemical characteristics of the battery and are fundamental to the accuracy of the model. The identification of stoichiometric parameters is based on the fundamental principle that the open-circuit voltage of the battery equals the potential difference between the positive and negative electrodes. Therefore, the stoichiometric coefficients of the electrodes can be considered to have the following linear relationship with the state of charge:

[0075] ;

[0076] Combining the open-circuit potential curves of the positive and negative electrodes, the open-circuit voltage of the battery can be expressed as:

[0077] ;

[0078] The identification of stoichiometric coefficients is achieved by minimizing the root mean square error between the calculated open-circuit voltage and the measured open-circuit voltage:

[0079] ;

[0080] in, Indicates the number of measured data points. This represents the open-circuit voltage calculated based on the current stoichiometric parameters. This represents the open-circuit voltage measured in the experiment. This embodiment employs the differential evolution algorithm for global optimization. The differential evolution algorithm has advantages such as strong global search capability, insensitivity to initial values, and simple parameter settings, making it particularly suitable for multi-parameter nonlinear optimization problems.

[0081] Solid-phase diffusion parameters include time constant , and impact factor , These parameters determine the model's prediction accuracy for dynamic operating conditions. Identifying these parameters requires operating condition data that can induce solid-phase diffusion effects. In this embodiment, a low-temperature, high-rate pulse discharge condition is selected as the identification data because the concentration gradient between the surface and interior of the electrode particles is most pronounced under this condition, and the solid-phase diffusion effect has the most significant impact on the battery voltage.

[0082] The objective of identifying solid-phase diffusion parameters is to minimize the root mean square error between the model-predicted voltage and the experimentally measured voltage.

[0083] ;

[0084] in, This represents the battery terminal voltage predicted by the equivalent circuit model of the integrated electrochemical equations. This indicates the battery terminal voltage measured in the experiment.

[0085] The model-predicted voltage is calculated using the following set of equations:

[0086] State of charge update:

[0087] ;

[0088] in, This indicates the battery's rated capacity.

[0089] RC network dynamic equations:

[0090] ;

[0091] in, This represents the capacitor voltage of the RC network. Indicates polarization internal resistance. This indicates a polarized capacitor.

[0092] Battery terminal voltage:

[0093] ;

[0094] The least squares identification algorithm is used to optimize the solid-phase diffusion parameters. The algorithm iteratively adjusts the parameter values, gradually reducing the model's prediction error until it converges to a local optimum. To avoid getting trapped in local optima, a strategy of multiple random initializations is employed, and the global optimum is selected as the final identification result.

[0095] The internal resistance temperature characteristic parameters include those in the ohmic internal resistance temperature model. , , Parameters in the polarization internal resistance current characteristic model , , These parameters determine the model's adaptability under different temperature and current conditions.

[0096] Identifying internal resistance parameters requires measuring the battery's impedance characteristics under different temperature and current rates. This embodiment employs a hybrid pulse power characteristic testing method, performing pulse charge-discharge tests at different SOCs, temperatures, and current rates. The ohmic internal resistance and polarization internal resistance values ​​are extracted by analyzing the voltage response curves.

[0097] Based on the ohmic resistance data measured at different temperatures, the parameters of the modified Arrhenius equation were fitted using the least squares method:

[0098] ;

[0099] in, This indicates the number of different temperature conditions. and Let represent the ohmic resistance and temperature under the i-th temperature condition, respectively.

[0100] Similarly, based on the polarization resistance data measured at different current rates, the parameters of the current-dependent model are fitted:

[0101] ;

[0102] in, This indicates the number of different current ratio conditions. and Let represent the polarization resistance and current under the j-th current condition, respectively.

[0103] Based on the aforementioned improved electro-thermal coupling model, this embodiment designs an online adaptive control method based on the improved electro-thermal coupling model. Traditional fast charging control methods are based on the open-loop control concept of preset charging curves, which cannot cope with changes in battery state and fluctuations in environmental conditions. This embodiment, however, transforms the complex wide-temperature-range fast charging problem into a constrained real-time optimization problem.

[0104] In this embodiment, the online adaptive control method based on the improved electro-thermal coupling model can be expressed as:

[0105] ;

[0106] in, This represents the battery state vector, including SOC, various internal resistance parameters, temperature, etc. This indicates the charging current control input. This represents the parameter vector of the improved electro-thermal coupling model.

[0107] The control objective is to find the optimal charging current trajectory that maximizes charging performance while satisfying safety constraints.

[0108] ;

[0109] in, The terminal temperature is a constraint condition, including temperature constraints. Current constraint and negative electrode potential constraint wait.

[0110] The primary task of online adaptive control is to accurately acquire the real-time state information of the battery. This embodiment employs a multi-source information fusion method to improve the accuracy and reliability of state estimation. Specifically, the measured state information in this embodiment includes the battery's terminal voltage. Charging current Battery surface temperature Ambient temperature These measurement information forms the basic data source for state estimation. In addition, the measured state information also includes the state of charge calculated from the measured data, i.e., the measured state of charge is estimated using a fusion strategy combining the current integration method and the open-circuit voltage method, as follows:

[0111] ;

[0112] in, This represents the SOC estimate based on the current integral. The rated capacity of the battery. This represents the SOC estimate based on the open-circuit voltage. Indicates the fusion weight coefficient. This represents a correction term based on the model prediction error, fused with weight coefficients. Dynamically adjust according to the current operating conditions: increase the weight of the open-circuit voltage method under steady-state conditions, and increase the weight of the current integral method under dynamic conditions.

[0113] Then, the negative electrode potential is a key safety parameter to prevent lithium deposition, and it needs to be calculated in real time using an electro-thermal coupling model, i.e.:

[0114] ;

[0115] in, This represents the negative electrode reference potential, which is obtained by looking up the table using the negative electrode open-circuit potential curve. This represents the average stoichiometric coefficient of the negative electrode, calculated based on SOC. This indicates the stoichiometric deviation caused by solid-phase diffusion, which is updated in real time through a first-order inertial element.

[0116] Finally, short-term temperature prediction is performed based on the thermal model:

[0117] ;

[0118] in, It represents the entropy heat coefficient.

[0119] Safety constraints are crucial for ensuring the safety of the charging process. Unlike traditional methods that use fixed safety boundaries, this embodiment adjusts safety constraints based on the real-time battery status and environmental conditions, thus ensuring both safety and improving charging efficiency. Specifically, the safety constraint boundaries in this embodiment include temperature boundaries. Negative electrode potential safety threshold Maximum allowable charging current .

[0120] Temperature boundary A hierarchical setting method is adopted:

[0121] ;

[0122] in, This is the upper limit of the absolute temperature for battery materials (usually set at 60°C). For temperature safety margin; when (This indicates that the heat dissipation conditions are good) ;when (This indicates that the heat dissipation conditions are generally poor.) ;when (This indicates poor heat dissipation conditions) .

[0123] Negative electrode potential safety threshold Dynamically adjust based on temperature:

[0124] ;

[0125] in, This is the standard potential of lithium metal. This indicates the safety margin of potential related to temperature;

[0126] Considering that the risk of lithium deposition increases at lower temperatures, the potential safety margin is set using a piecewise linear approach: when hour, (Extremely low temperature, high safety margin); when hour, (Low temperature, medium safety margin); when hour, (At room temperature and above, basic safety margin).

[0127] Maximum allowable charging current The main consideration is voltage limitation:

[0128] ;

[0129] in, This is the maximum allowable voltage of the battery (usually 3.65V). Indicates the total internal resistance. The current value from the previous moment can be used for estimation. Preferably, to ensure temperature safety, a temperature limit needs to be added: when hour, It needs to be multiplied by the temperature limiting factor. ,when hour, It needs to be multiplied by the temperature limiting factor. .

[0130] After obtaining accurate state information and safety constraints, the system needs to solve a real-time optimization problem to determine the optimal charging current. First, the optimization objective function comprehensively considers multiple aspects such as charging efficiency and safety. In this embodiment, the optimization objective function is:

[0131] ;

[0132] ;

[0133] ;

[0134] ;

[0135] ;

[0136] in, Let be the objective function. This is the charging power term used to maximize charging speed. To optimize the calculated charging current in real time, Open circuit voltage, This is a temperature penalty term used to control temperature rise. To improve the temperature predicted by the electro-thermal coupling model, For safe temperature threshold, This is a negative electrode potential penalty term used to prevent lithium deposition. To improve the negative electrode potential predicted by the electro-thermal coupling model, The current change rate penalty term is used to ensure the smoothness of current changes. This is the charging current from the previous control cycle; , , , As weights, for example under normal temperature conditions (10°C ≤ T ≤ 40°C), balance the weights of each item. .

[0137] Therefore, the optimal charging current needs to be determined. The optimization problem is In solving for the optimal charging current The safety constraints that need to be met during the process include: temperature constraints. Negative electrode potential constraint Current range constraints Current change rate constraint , The threshold for the rate of change of current. The specific value can be determined based on the battery type and charging system. It can usually be set to 0.1C to 0.2C to ensure a smooth transition of charging current and avoid drastic fluctuations in the internal electrochemical reaction of the battery.

[0138] To ensure the improved electro-thermal coupling model maintains high prediction accuracy during long-term use, the system needs to update key parameters online based on actual operating data. Specifically, the system obtains predicted state information from the improved electro-thermal coupling model, including predicted state of charge, predicted temperature, and predicted terminal voltage. Then, the prediction errors for state of charge, temperature, and terminal voltage are calculated in real time.

[0139] ;

[0140] ;

[0141] ;

[0142] in, , , They are respectively The prediction errors of state of charge, temperature, and terminal voltage at time points. , , They are respectively The measured state of charge, measured temperature, and measured terminal voltage at any given time. , , They are respectively Predicted state of charge, predicted temperature, and predicted terminal voltage at any given time;

[0143] When the rolling root mean square value of any one of the state-of-charge prediction error, temperature prediction error, or terminal voltage prediction error exceeds a preset threshold, the improved electro-thermal coupling model is updated online. Specifically, this is done by using a recursive least squares method to update the internal resistance parameters online, as follows:

[0144] ;

[0145] ;

[0146] in, , They are respectively Ohmic internal resistance and polarization internal resistance at time t. , They are respectively Ohmic internal resistance and polarization internal resistance at time t. for Open-circuit voltage at any given time for Current at any moment , Here is the gain matrix for the recursive least squares algorithm:

[0147] ;

[0148] ;

[0149] in, For the regression vector, It is the covariance matrix;

[0150] Finally, to prevent instability during parameter updates, a physical constraint check is added. The updated parameters must meet physical constraints, such as the internal resistance value must be positive.

[0151] Considering the significant differences in battery characteristics under different temperature conditions, this embodiment obtains the optimal charging current by solving the problem. Then, an optimal charging current command is generated using a temperature-based differential control strategy, including:

[0152] Under low-temperature conditions (battery temperature T < 10°C), a progressive fast charging strategy is adopted. Compared with the traditional constant low-current charging method, this approach can improve charging speed and efficiency while ensuring battery safety. Specifically:

[0153] When the battery temperature During this time, preheating and charging will be performed: Preheat to Then it enters fast charging mode, in which, For the battery's rated capacity, The preheating charging current is set to the battery's rated capacity. This is used for preheating batteries in low-temperature environments;

[0154] when At that time, set the maximum allowable charging current upper limit. Then, the optimal charging current is obtained through optimization. And let the charging current be .

[0155] Under high temperature conditions (T>40°C), an adaptive control fast charging strategy is adopted to prioritize temperature rise control and prevent overheating. Specifically:

[0156] when At that time, set the maximum allowable charging current upper limit. Then, the optimal charging current is obtained through optimization. And let the charging current be ;

[0157] when At that time, set the maximum allowable charging current upper limit. Then, the optimal charging current is obtained through optimization. And let the charging current be ;

[0158] when At that time, set the maximum allowable charging current upper limit. Then, the optimal charging current is obtained through optimization. And let the charging current be ;

[0159] when If charging continues, it may cause an internal short circuit in the battery, posing a risk of fire or explosion. Therefore, charging should be stopped immediately and the safety protection should be activated.

[0160] During the adaptive control fast charging strategy implementation, the temperature rise rate is also monitored in real time. When the rate of temperature rise exceeds the safety threshold set according to the battery's thermal characteristics... At that time, according to the preset ratio (Value range 0.3-0.8) Reduce charging current to ,in The charging current before triggering temperature rise protection, The value is determined based on factors such as battery type and heat dissipation conditions, and typically ranges from [value range missing]. When the rate of temperature rise is below the safe threshold The charging current will then be restored to the charging current under the adaptive control fast charging strategy.

[0161] Under normal temperature conditions of 10°C ≤ T ≤ 40°C, the battery performance is relatively stable. A balance optimization strategy is adopted, and fine-tuning is performed according to the charging stage. Specifically:

[0162] Set the maximum allowable charging current limit Then, the optimal charging current is obtained through optimization. ;

[0163] When the battery At that time, and let the charging current be ;

[0164] When the battery At that time, let the charging current be .

[0165] It is worth noting that, although this embodiment Figure 1 The steps are shown sequentially as indicated by the arrows, but they are not necessarily executed in the order indicated by the arrows. Unless otherwise specified in this document, there is no strict order in which these steps are performed; they can be executed in other orders. Figure 1 At least some of the steps in the process may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least some of the sub-steps or stages of other steps.

[0166] The above description is only a preferred embodiment of the present invention and does not limit the scope of protection of the present invention. All equivalent structural transformations made under the inventive concept of the present invention using the contents of the present invention specification and drawings, or direct / indirect applications in other related technical fields, are included within the scope of protection of the present invention.

Claims

1. A method for wide-temperature-range online fast charging of lithium batteries based on an improved electro-thermal coupling model, characterized in that, Includes the following steps: Step 1: Construct an improved electro-thermal coupling model, including an electrical model consisting of an open-circuit voltage model considering the solid-phase diffusion process, an ohmic internal resistance model with a quadratic correction term, and a polarization internal resistance model with a quadratic nonlinear term, and a thermal model consisting of a battery heat generation model and a battery heat transfer model, and identify the parameter vector of the improved electro-thermal coupling model. Step 2: Collect the measured state information of the lithium battery in real time, obtain the predicted state information of the lithium battery based on the state information and the improved electro-thermal coupling model, and update the improved electro-thermal coupling model online based on the error between the predicted state information and the measured state information. Step 3: Determine the safety constraint boundary based on the measured state information, and find the optimal charging current that maximizes charging efficiency while satisfying the safety constraints; Step 4: Based on the optimal charging current, an optimal charging current command is generated using a temperature-based differentiated control strategy. The open-circuit voltage model is as follows: in, Open circuit voltage, , These are the potentials of the positive and negative electrodes, respectively. , These are the average stoichiometric coefficients for the positive and negative electrodes, respectively. , The stoichiometric deviations for the positive and negative electrodes are respectively, with subscripts. These represent the positive and negative electrodes, respectively. The solid-phase diffusion time constants for the positive and negative electrodes are... The solid-phase diffusion influencing factors for both positive and negative electrodes. The sampling time interval; The Ohmic internal resistance model is as follows: in, For ohmic internal resistance, For battery temperature, , , These are the parameters to be fitted to the Ohmic internal resistance model; The polarization internal resistance model is as follows: in, For current density, For polarization internal resistance, For current, , and These are the parameters to be fitted for the polarization internal resistance model; The battery heat generation model is as follows: in, This represents the total heat generation power inside the battery. This refers to the battery terminal voltage. Irreversible heat, It is a reversible heat; The battery heat transfer model is as follows: in, This refers to the heat dissipation power per unit area of ​​a battery. h The convective heat transfer coefficient at the battery surface is denoted as . T a The ambient temperature; In the improved electro-thermal coupling model, irreversible heat in the thermal model is calculated using the electrical model. The parameters of the electrical model are affected by the battery temperature, while the battery temperature is calculated using the thermal model, thus creating a coupling effect between the electrical and thermal models. The specific process for calculating the battery temperature is as follows: in, This refers to the volumetric heat capacity of the battery.

2. The lithium battery wide-temperature-range online fast charging method based on an improved electro-thermal coupling model according to claim 1, characterized in that, The measured state information includes the measured state of charge, measured temperature, and measured terminal voltage of the battery. The predicted state information includes the predicted state of charge, predicted temperature, and predicted terminal voltage of the battery. The process of updating the improved electro-thermal coupling model online based on the error between the predicted state information and the measured state information is as follows: Real-time calculation of state of charge prediction error, temperature prediction error, and terminal voltage prediction error: in, , , They are respectively The prediction errors of state of charge, temperature, and terminal voltage at time points. , , They are respectively The measured state of charge, measured temperature, and measured terminal voltage at any given time. , , They are respectively Predicted state of charge, predicted temperature, and predicted terminal voltage at any given time; When the rolling root mean square value of any one of the state of charge prediction error, temperature prediction error, or terminal voltage prediction error exceeds a preset threshold, the improved electro-thermal coupling model is updated online.

3. The lithium battery wide-temperature-range online fast charging method based on an improved electro-thermal coupling model according to claim 2, characterized in that, The process of online updating the improved electro-thermal coupling model involves using the recursive least squares method to update the internal resistance parameters online, as follows: in, , They are respectively Ohmic internal resistance and polarization internal resistance at time t. , They are respectively Ohmic internal resistance and polarization internal resistance at time t. for Open-circuit voltage at any given time for Current at any moment , This is the gain matrix for the recursive least squares algorithm.

4. The online fast charging method for lithium batteries with a wide temperature range based on an improved electro-thermal coupling model according to claim 1, 2, or 3, characterized in that, The safety constraint boundaries include temperature boundaries. Negative electrode potential safety threshold Maximum allowable charging current , specifically: in, This represents the upper limit of the absolute temperature for battery materials. To provide a safety margin for temperature, This is the standard potential of lithium metal. This indicates the safety margin of potential related to temperature. This is the maximum allowable voltage of the battery. Open circuit voltage, This represents the total internal resistance.

5. The lithium battery wide-temperature-range online fast charging method based on an improved electro-thermal coupling model according to claim 4, characterized in that, The temperature safety margin According to ambient temperature Implement a hierarchical system, specifically: when hour, ; when hour, ; when hour, ; The potential safety margin According to battery temperature Piecewise linearity is defined specifically as follows: when hour, ; when hour, when hour, .

6. The lithium battery wide-temperature-range online fast charging method based on an improved electro-thermal coupling model according to claim 4, characterized in that, The optimal charging current is specifically: in, For optimal charging current, Let be the objective function. This is the charging power term. To optimize the calculated charging current in real time, Open circuit voltage, This is a temperature penalty item. To predict temperature, This is a penalty term for negative electrode potential. The predicted negative electrode potential. This is a penalty term for the rate of change of current. This is the charging current from the previous control cycle. , , , As weight; The safety constraints that need to be met in the process of finding the optimal charging current include: Temperature constraint ; Negative potential constraint ; Current range constraint ; Current change rate constraint , This is the threshold for the rate of change of current.

7. The lithium battery wide-temperature-range online fast charging method based on an improved electro-thermal coupling model according to claim 6, characterized in that, The method of generating the optimal charging current command using a temperature-based differential control strategy includes: Under low-temperature conditions where battery temperature T < 10°C, a gradual fast charging strategy is adopted; Under high temperature conditions (T>40°C), an adaptive control fast charging strategy is adopted; Under normal temperature conditions of 10°C≤T≤40°C, a balance optimization strategy is adopted.

8. The lithium battery wide-temperature-range online fast charging method based on an improved electro-thermal coupling model according to claim 7, characterized in that, The specific steps of generating the optimal charging current command using a temperature-based differential control strategy include: Under low-temperature conditions where battery temperature T < 10°C, a gradual fast charging strategy is adopted, specifically: When the battery temperature During this time, preheating and charging are performed: Preheat to Then it enters fast charging mode, where For preheating charging current, This refers to the battery's rated capacity. when At that time, set the maximum allowable charging current upper limit. Then, the optimal charging current is obtained through optimization. And let the charging current be ; Under high temperature conditions (T>40°C), an adaptive control fast charging strategy is adopted to prioritize temperature rise control and prevent overheating. Specifically: when At that time, set the maximum allowable charging current upper limit. Then, the optimal charging current is obtained through optimization. And let the charging current be ; when At that time, set the maximum allowable charging current upper limit. Then, the optimal charging current is obtained through optimization. And let the charging current be ; when At that time, set the maximum allowable charging current upper limit. Then, the optimal charging current is obtained through optimization. And let the charging current be ; when When charging stops, the charging process will cease. Simultaneously, monitor the temperature rise rate in real time. When the rate of temperature rise exceeds the safety threshold set according to the battery's thermal characteristics... At that time, according to the preset ratio Reduce charging current to ,in The charging current before triggering temperature rise protection, and when the temperature rise rate is below the safety threshold. The charging current will then be restored to the charging current under the adaptive control fast charging strategy. Under normal temperature conditions of 10°C ≤ T ≤ 40°C, a segmented SOC balancing optimization strategy is adopted, specifically: Set the maximum allowable charging current limit Then, the optimal charging current is obtained through optimization. ; When the battery At that time, let the charging current be ; When the battery At that time, let the charging current be .

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