A battery temperature estimation method based on extended Kalman filter

Abstract

The application discloses a battery temperature estimation method based on an extended Kalman filter, and relates to the technical field of battery management. The method comprises the following steps: constructing an electrical model and a thermal model of a battery under a preset operating condition; constructing an electro-thermal coupling model of the battery under the preset operating condition according to the electrical model and the thermal model; the electro-thermal coupling model is used for describing the change process of electricity and heat in the battery; and based on the electro-thermal coupling model and electrical measurement data of the battery in the charging and discharging process, an extended Kalman filter algorithm is used to estimate the internal temperature and the surface temperature of the battery under the preset operating condition. Compared with a traditional monitoring method relying on a temperature sensor, the method can more accurately reflect the internal and surface temperature changes of the battery of an electric bicycle, dynamically adapt to the change of thermal characteristics under complex operating conditions, reduces the system complexity and cost, and effectively improves the safety and reliability of the battery management system.

Description

Technical Field

[0001] This invention relates to the field of battery management technology, and in particular to a battery temperature estimation method based on extended Kalman filtering. Background Technology

[0002] In electric bicycles using lithium-ion batteries, temperature has a significant impact on battery safety, performance, and lifespan. The electrochemical reaction rate and material properties of lithium-ion batteries are significantly affected by temperature changes under different operating conditions. High temperatures may lead to thermal runaway and aging of the battery, while low temperatures may slow down internal reactions and affect battery performance. To mitigate the adverse effects of temperature on batteries, temperature monitoring and management are essential.

[0003] Currently, battery temperature monitoring primarily relies on temperature sensors placed on or inside the battery casing. This method has several limitations: First, the use of temperature sensors increases the complexity and manufacturing cost of the battery management system, especially in multi-cell battery packs where a large number of temperature sensors are needed to comprehensively monitor temperature distribution. Second, the accuracy of temperature sensors is easily affected by environmental interference, noise, and aging effects, leading to the accumulation of measurement errors. Furthermore, sensor malfunctions can result in missing or erroneous monitoring data, reducing system reliability. Summary of the Invention

[0004] This invention provides a battery temperature estimation method based on extended Kalman filtering to overcome the limitations of methods that rely on temperature sensors to monitor battery temperature in terms of accuracy, reliability, and economy.

[0005] In a first aspect, embodiments of the present invention provide a battery temperature estimation method, including...

[0006] Construct electrical and thermal models of the battery under preset operating conditions;

[0007] An electrothermal coupling model of the battery under preset operating conditions is constructed based on the electrical and thermal models; the electrothermal coupling model is used to describe the electrical and thermal changes inside the battery.

[0008] Based on the electrothermal coupling model and the electrical measurement data of the battery during the charging and discharging process, the extended Kalman filter algorithm is used to estimate the internal temperature and surface temperature of the battery under preset operating conditions.

[0009] Secondly, embodiments of the present invention provide an electric bicycle, the electric bicycle comprising:

[0010] At least one processor; and

[0011] A memory communicatively connected to the at least one processor; wherein,

[0012] The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the battery temperature estimation method according to any embodiment of the present invention.

[0013] The technical solution of this invention involves constructing an electrical and thermal model of the battery under preset operating conditions; constructing an electrothermal coupling model of the battery under preset operating conditions based on the electrical and thermal models; the electrothermal coupling model describes the electrical and thermal changes within the battery; and using an extended Kalman filter algorithm to estimate the internal and surface temperatures of the battery under preset operating conditions based on the electrothermal coupling model and electrical measurement data of the battery during charging and discharging. By using battery temperature parameter estimation instead of traditional temperature sensors, and through feedback from electrical measurement data, dynamic estimation of the internal and external temperatures of the electric bicycle battery is achieved. Compared to traditional monitoring methods relying on temperature sensors, this method can more accurately reflect changes in the internal and surface temperatures of the electric bicycle battery, dynamically adapt to changes in thermal characteristics under complex operating conditions, reduce system complexity and cost, effectively improve the safety and reliability of the battery management system, and solve the limitations in accuracy, reliability, and economy of methods relying on temperature sensors to monitor battery temperature.

[0014] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 This is a flowchart of a battery temperature estimation method provided in Embodiment 1 of the present invention;

[0017] Figure 2 This is a schematic diagram of a battery temperature estimation device provided in Embodiment 2 of the present invention;

[0018] Figure 3 A schematic diagram of the structure of an electric bicycle for implementing the battery temperature estimation method of this invention. Detailed Implementation

[0019] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0020] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0021] Example 1

[0022] Figure 1 This is a flowchart of a battery temperature estimation method provided in Embodiment 1 of the present invention. This embodiment is applicable to estimating the battery temperature of an electric bicycle. The method can be executed by a battery temperature estimation device, which can be implemented in hardware and / or software and can be configured in the electric bicycle. Figure 1 As shown, the method includes:

[0023] S110. Construct electrical and thermal models of the battery under preset operating conditions.

[0024] The electrical model can be understood as a model describing the electrical behavior of the battery, which can include the changes in the battery's resistance and capacitance with temperature. The electrical model can be represented using an equivalent circuit model. The thermal model can be understood as a model representing the temperature changes of the battery. Battery temperature can include the battery's internal temperature and surface temperature. The preset operating conditions can be understood as the operating conditions of the electric bicycle, such as high-speed operation, acceleration, and hill climbing. Under these preset operating conditions, the battery is prone to generating a large amount of heat, causing the battery temperature to rise and posing a threat to the safety of the electric bicycle.

[0025] Specifically, under the preset operating conditions of the electric bicycle, an electrical model is constructed based on the electrical characteristics of the battery to describe the changes in the battery's resistance and capacitance with temperature, and a thermal model is constructed based on the battery's thermal conduction characteristics to describe the temperature changes inside and on the surface of the battery.

[0026] S120. Construct an electrothermal coupling model of the battery under preset operating conditions based on the electrical and thermal models; the electrothermal coupling model is used to describe the electrical and thermal changes inside the battery.

[0027] Among them, the electrothermal coupling model can be understood as a physical model used to describe the interaction process between electrical energy and thermal energy.

[0028] Specifically, the operating state of a battery is influenced by both electrical and thermal conduction behaviors, which are closely coupled. Therefore, by coupling the dynamic characteristics of the electrical and thermal models, an electrothermal coupled model is constructed to comprehensively describe the electrical and thermal changes within the battery.

[0029] S130. Based on the electrothermal coupling model and the electrical measurement data of the battery during the charging and discharging process, the extended Kalman filter algorithm is used to estimate the internal temperature and surface temperature of the battery under the preset operating conditions.

[0030] The electrical measurement data of the battery during charging and discharging can include the battery's current and terminal voltage during the charging and discharging process. The Extended Kalman Filter (EKF) is an extension of the Kalman filter algorithm used to estimate the state of nonlinear systems.

[0031] Specifically, by combining the electrothermal coupling model with electrical measurement data of the battery during charging and discharging, and using the extended Kalman filter algorithm, the internal and surface temperatures of the battery are dynamically corrected based on the predictions and electrical measurement data, thereby significantly improving the estimation accuracy.

[0032] In some embodiments, safety mechanisms, such as reducing charging power or stopping charging at high temperatures, can be triggered based on the estimated internal and surface temperatures of the battery to prevent thermal runaway caused by excessive temperature. In other embodiments, the real-time detected dynamic temperature change trend can be used as the basis for real-time monitoring and decision-making by the battery management system.

[0033] The technical solution of this invention involves constructing an electrical and thermal model of the battery under preset operating conditions; constructing an electrothermal coupling model of the battery under preset operating conditions based on the electrical and thermal models; the electrothermal coupling model describes the electrical and thermal changes within the battery; and using an extended Kalman filter algorithm to estimate the internal and surface temperatures of the battery under preset operating conditions based on the electrothermal coupling model and electrical measurement data during charging and discharging. By using battery temperature parameter estimation instead of traditional temperature sensors, and through feedback from electrical measurement data, dynamic estimation of the battery's internal and external temperatures is achieved. Compared to traditional sensor-dependent monitoring methods, this approach more accurately reflects changes in the internal and surface temperatures of the electric bicycle battery, dynamically adapts to changes in thermal characteristics under complex operating conditions, reduces system complexity and cost, and effectively improves the safety and reliability of the battery management system.

[0034] As an optional embodiment of this application, constructing an electrical model of the battery under preset operating conditions includes:

[0035] An electrical model is obtained by describing the electrical parameter characteristics of the battery under preset operating conditions based on a second-order equivalent circuit model; the electrical model includes:

[0036]

[0037]

[0038] U TOV =U OCV (T)-U1-U2-IR0(T);

[0039] Wherein, U1 is the first voltage on the first resistor-capacitor circuit branch in the electrical model, and U2 is the first voltage on the first resistor-capacitor circuit branch in the electrical model; R1(T) is the electrochemical polarization resistance under the preset operating conditions, R2(T) is the concentration polarization resistance under the preset operating conditions, C1(T) is the electrochemical polarization capacitor under the preset operating conditions, and C2(T) is the concentration polarization capacitor under the preset operating conditions. The electrochemical polarization resistance, the concentration polarization resistance, the electrochemical polarization capacitor, and the concentration polarization capacitor are related to temperature T; R0(T) is the internal resistance of the battery; U OCV (T) is the open-circuit voltage of the battery; U TOV I is the terminal voltage of the battery, and I is the current.

[0040] Specifically, the first voltage U1 and the first voltage U2 of the battery reflect its electrochemical behavior. Electrochemical polarization resistance R1(T) refers to the internal resistance caused by the resistance to charge transfer during the electrochemical reaction, primarily affecting the overpotential of the electrode reaction and thus the heat generation of the battery. Concentration polarization resistance R2(T) refers to the resistance caused by the potential deviation due to the difference in concentration between the reactants on the electrode surface and the bulk solution, primarily affecting the diffusion rate of lithium ions in the electrode material, and consequently affecting the battery's charge / discharge performance and thermal behavior.

[0041] Electrochemical polarization capacitance C1(T) refers to the capacitance formed due to the uneven charge distribution during the electrochemical reaction on the electrode surface, reflecting the ability of the electrode surface to accumulate and release charge. Concentration polarization capacitance C2(T) refers to the capacitance formed due to the concentration change of reactants or products near the electrode surface, reflecting the change of concentration gradient near the electrode surface.

[0042] As an optional embodiment of this application, when the preset operating condition is an acceleration condition or a climbing condition, the electrochemical polarization resistor, the concentration polarization resistor, the electrochemical polarization capacitor, and the concentration polarization capacitor are, in sequence:

[0043] R1(T)=R 1，0 (1+α(TT ref )+δ|I|);

[0044] R2(T)=R 2，0 (1+β(TT ref )+k|I|);

[0045] C1(T)=C 1，0 (1-γ(TT ref )-ηN cycle );

[0046] C2(T)=C 2，0 (1-λ(TT ref )-ζN cycle );

[0047] Among them, T ref Reference temperature; R 1，0 R is the electrochemical polarization resistance at the reference temperature. 2，0 The concentration polarization resistance at the reference temperature; C 1，0 For the electrochemical polarization capacitance at the reference temperature, C 2，0Here, α represents the concentration polarization capacitance at the reference temperature; β represents the temperature sensitivity coefficient of the electrochemical polarization resistor; δ represents the current sensitivity coefficient of the electrochemical polarization resistor; κ represents the current sensitivity coefficient of the concentration polarization resistor; γ represents the temperature decay coefficient of the electrochemical polarization capacitor; λ represents the temperature decay coefficient of the concentration polarization capacitor; η represents the aging decay coefficient of the electrochemical polarization capacitor; ζ represents the aging decay coefficient of the concentration polarization capacitor; and N represents the aging decay coefficient of the concentration polarization capacitor. cycle This represents the number of charge-discharge cycles.

[0048] The reference temperature can be a preset temperature used to define the initial parameters of the battery, such as a temperature value under standard test conditions (e.g., 25°C). Temperature sensitivity coefficients α and β are used to describe the proportion of resistance change with temperature, current sensitivity coefficients δ and K are used to describe the effect of current change on resistance, temperature decay coefficients and λ are used to describe the decay effect of capacitance as temperature increases, and aging decay coefficients η and ζ are used to describe the effect of the battery on capacitance during use.

[0049] Specifically, during acceleration or hill climbing, higher output power is required, leading to a significant increase in current I. The polarization reaction triggered by this high current causes dynamic changes in the electrochemical polarization resistance R1(T) and the concentration polarization resistance R2(T). The heat generated during acceleration causes an increase in temperature T, further affecting the changes in the electrochemical polarization capacitance C1(T) and the concentration polarization capacitance C2(T).

[0050] This embodiment improves the accuracy of the electrical model in describing electrical behavior under different operating conditions by fully considering the effects of current and temperature on electrochemical polarization resistance, as well as the effects of temperature on electrochemical polarization capacitance and concentration polarization capacitance when constructing the electrical model under acceleration or climbing conditions. This improves the accuracy of temperature estimation under different operating conditions.

[0051] As an optional embodiment of this application, when the preset operating condition is an acceleration condition or a climbing condition, the open-circuit voltage of the battery is:

[0052]

[0053] Among them, U OCV，0 γ is the reference open-circuit voltage at the reference temperature, and γ·In(T) is the temperature correction value; This is the current correction value.

[0054] Among them, the reference open-circuit voltage U OCV，0 At the reference temperature T ref The open-circuit voltage was measured below.

[0055] Specifically, under acceleration or hill-climbing conditions, the battery needs to provide a larger power output, resulting in a significant increase in current I. Under high current conditions, the battery's internal voltage fluctuates due to polarization effects and electrochemical kinetics. A current correction value is determined based on current I, electrochemical polarization capacitance C1(T), and the aging degradation coefficient η of the electrochemical polarization capacitance, reflecting the impact of the battery's dynamic response on the open-circuit voltage. A temperature correction value is determined based on temperature T and the temperature degradation coefficient γ of the electrochemical polarization capacitance, reflecting the impact of battery temperature changes on the open-circuit voltage. Through these temperature and current correction terms, the battery's temperature estimation can be dynamically adjusted, more accurately reflecting the battery's actual operating state and ensuring the battery's safety and performance stability under high loads or environmental changes.

[0056] It should be noted that since the open-circuit voltage reflects the static chemical equilibrium state, while the dynamic behavior of the electrochemical polarization capacitance C1(T) can reflect the transient effect of the chemical reaction on the voltage, the open-circuit voltage is mainly related to the electrochemical polarization capacitance C1(T), and the effect of the concentration polarization capacitance C2(T) on the open-circuit voltage can be ignored.

[0057] This embodiment further improves the accuracy of the electrical model in describing electrical behavior under different operating conditions by fully considering the effects of current, temperature, and electrochemical polarization capacitance on the reference open-circuit voltage when constructing the electrical model under acceleration or ramp conditions, thereby improving the accuracy of temperature estimation under different operating conditions.

[0058] As an optional embodiment of this application, the thermal model of the battery under preset operating conditions includes: a thermal model of internal temperature and a thermal model of surface temperature;

[0059] The thermal model for the internal temperature is:

[0060]

[0061] The thermal model for the surface temperature is:

[0062]

[0063] Among them, T in T represents the internal temperature of the battery. s T represents the surface temperature of the battery. a T represents the external temperature of the battery. amb Ambient temperature; C c For the internal heat capacity of the battery, C s R is the surface heat capacity of the battery. i R is the internal thermal resistance of the battery. o h is the surface thermal resistance of the battery. core h is the internal thermal conductivity coefficient of the battery. surfdenoted as the surface thermal conductivity coefficient of the battery; Q represents the heat generated by the battery.

[0064] Among them, the surface temperature T of the battery s Used to represent the heat exchange between the battery and the external environment. The external temperature T of the battery. a This mainly refers to the ambient temperature near the battery. Ambient temperature T amb The temperature of the environment in which the battery is located can be the ambient temperature or the temperature of the external cooling system. The battery's internal heat capacity C... c and surface heat capacity C s This indicates the battery's ability to store heat.

[0065] As an optional embodiment of this application, when the preset operating condition is a climbing condition, the internal thermal resistance of the battery is:

[0066] R i =R i,0 ·exp(-δ·I);

[0067] When the preset operating condition is acceleration, the surface thermal resistance of the battery is:

[0068] R o =R o，0 ·exp(1+∈·v wind );

[0069] Among them, R i,0 R is the initial thermal resistance between the battery's interior and surface. o，0 The initial thermal resistance between the battery surface and the environment; ∈ is the wind speed sensitivity coefficient, v wind δ represents the wind speed caused by the vehicle speed; δ is the current sensitivity coefficient of the electrochemical polarization resistor; and I is the current.

[0070] Specifically, during hill climbing, the battery needs to output a large current to drive the electric bicycle uphill, resulting in a significant increase in current and thus affecting the battery's internal thermal resistance. Internal thermal resistance is inversely proportional to current, meaning that during high-current discharge, the heat transfer efficiency inside the battery increases, thus reducing thermal resistance. In other words, the heat generated by the chemical reactions within the battery exhibits a non-linear change due to the influence of internal resistance. By adjusting the internal thermal resistance using an exponential function, the thermal characteristics of the battery under different charge and discharge states can be simulated more accurately.

[0071] Under acceleration conditions, the surface thermal resistance of a battery is primarily affected by wind speed. Internal thermal resistance is inversely proportional to current, indicating that during high-current discharge, the internal heat transfer efficiency of the battery increases, thus reducing thermal resistance. In other words, the heat generated by the chemical reactions within the battery exhibits a non-linear change due to the influence of internal resistance. Adjusting the internal thermal resistance using an exponential function allows for a more accurate simulation of the battery's thermal characteristics under different charge and discharge states. Dynamic adjustment of the internal and surface thermal resistance models enables more precise simulation of the battery system's thermal characteristics under various operating environments and conditions, ensuring efficient heat dissipation and safety during acceleration, high-speed driving, and other conditions.

[0072] This embodiment fully considers the influence of current on internal thermal resistance and wind speed on surface thermal resistance when constructing the thermal model under acceleration or hill-climbing conditions. By introducing an external heat source compensation term, it achieves an accurate description of the battery temperature rise characteristics under extreme environments (such as high or low temperatures), effectively improving the reliability of the battery management system in long-term outdoor operation scenarios.

[0073] As an optional embodiment of this application, when the preset operating condition is an acceleration condition or a climbing condition, the electrothermal coupling model is as follows:

[0074] Q = (U OCV -U TOV )l+ξ·I 2 ;

[0075] Where Q represents the heat generated by the battery, and U... OCV U is the open-circuit voltage of the battery. TOV Let ξ be the battery's terminal voltage, I be the current, and ξ·I be the current. 2 This refers to the nonlinear heat generated by the battery.

[0076] The heat generated by the battery consists of irreversible heat and reversible heat. 2 This reflects the nonlinear heat generated by the battery under acceleration or hill-climbing conditions. It is mainly caused by ohmic losses and represents pure energy loss during charging and discharging; it cannot be recovered and is considered irreversible heat. (U) OCV -U TOV I is the heat released or absorbed by the electrochemical reaction, which is related to the equilibrium state of the reaction inside the battery and is a reversible heat.

[0077] Specifically, the voltage difference term and current square term in the electrothermal coupling model model the heat generated by the battery, reflecting the heat generation caused by electrochemical reactions and current during actual operation. The heat generated by the difference between the battery's open-circuit voltage and terminal voltage is direct, while the nonlinear heat caused by current reflects the influence of the battery's internal resistance on the thermal effect, especially under high current or heavy load.

[0078] This embodiment employs an improved electrothermal coupling model (IETM) that tightly integrates the battery's electrical behavior with its heat conduction process. Compared to traditional single-model temperature estimation methods, it can capture dynamic voltage changes and heat source characteristics of the battery, reflecting the battery's dynamic characteristics under conditions such as acceleration or hill climbing, and providing more accurate temperature predictions.

[0079] As an optional embodiment of this application, based on the electrothermal coupling model and the electrical measurement data of the battery during charging and discharging, an extended Kalman filter algorithm is used to estimate the internal temperature and surface temperature of the battery under preset operating conditions, including:

[0080] Construct a state matrix and a measurement matrix, wherein the state matrix is: X = [T] in T s [U1, U2] T The measurement matrix is ​​Y = U TOV ;T in T represents the internal temperature of the battery. s U1 represents the surface temperature of the battery; U2 represents the first voltage on the first resistor-capacitor circuit branch in the electrical model; U... TOV This refers to the battery's terminal voltage.

[0081] Construct the state equation and the measurement equation; the state equation is X. k+1 =f(X) k u k )+W k The measurement equation is Y k =h(X) k )+V k ;X k Let X be the state matrix at time k. k+1 Let Y be the state matrix at time k+1. k The measurement matrix at time k; u k W is the input control matrix at time k; k Let V be the process noise matrix at time k. k Let f(·) be the measurement noise matrix at time k; f(·) is the state transition function, and h(·) is the measurement function.

[0082] Based on the extended Kalman filter algorithm, the internal temperature and surface temperature of the battery under preset operating conditions are estimated according to the state matrix, the measurement matrix, the state equation, the measurement equation, and the electrical measurement data.

[0083] The steps of estimating the internal temperature and surface temperature of the battery based on the state matrix, the measurement matrix, the state equation, and the measurement equation include:

[0084] Predict the state matrix at time k+1:

[0085] Predict the covariance matrix at time k+1:

[0086] Calculate the Kalman gain at time k:

[0087] Update the state matrix at time k+1:

[0088] Update the covariance matrix at time k+1: P k+1|k+1 =(EK) k H k )P k+1|k ;

[0089] H k Let E be the Jacobian matrix of the measurement equation at time k, and let Q be the identity matrix. k Let R be the process noise covariance matrix at time k. k Let F be the measurement noise covariance matrix at time k. k Let k be the state transition matrix at time k; Let be the prior state estimation matrix at time k+1. Let be the posterior state estimation matrix at time k. P is the posterior state estimation matrix at time k+1; k+1|k Let P be the prior covariance estimation matrix at time k+1. k|k Let P be the posterior covariance estimation matrix at time k. k+1|k+ 1 represents the posterior covariance estimation matrix at time k+1; K k Let K be the Kalman gain at time k.

[0090] This invention introduces an extended Kalman filter (EKF), which combines a nonlinear state estimation method with predicted and measured data for real-time correction, significantly reducing the estimation error of traditional methods. The EKF algorithm can better handle the nonlinear and complex dynamic characteristics of the battery management system, ensuring accurate temperature estimation even under extreme conditions of high-current discharge.

[0091] As an optional embodiment of this application, when the preset operating condition is an acceleration condition or a climbing condition, the process noise covariance matrix is:

[0092] Q k =Q0(1+δ·k|I|);

[0093] The measurement noise covariance matrix is:

[0094] Rk =R0(1+λ||T) amb -T in |);

[0095] Q0 is the initial process noise covariance matrix, R0 is the initial measurement noise covariance matrix, δ is the current sensitivity coefficient of the electrochemical polarization resistor, κ is the current sensitivity coefficient of the concentration polarization resistor, λ is the temperature decay coefficient of the concentration polarization capacitor, and T amb For ambient temperature, T in This refers to the internal temperature of the battery.

[0096] Where Q0 is the covariance matrix of the process noise under a reference state with no dynamic input (i.e., current change). R0 is the covariance matrix of the measurement noise under ideal conditions with no ambient temperature fluctuations or when the ambient temperature is equal to the internal temperature of the battery.

[0097] Specifically, when the current is high, the battery experiences drastic changes in internal heat and reaction during rapid charging and discharging, leading to increased system uncertainty. Therefore, the process noise covariance increases with increasing current, thus adjusting the confidence level of the state estimation. When the temperature difference between the battery and the external environment increases, the battery's electrical characteristics and measurement accuracy may be affected by temperature changes. To compensate for this uncertainty, the measurement noise covariance increases with increasing temperature difference. By dynamically adjusting the process noise and measurement noise covariance, the accuracy and robustness of the model under different current and temperature conditions are ensured.

[0098] This invention enhances the model's adaptability to complex operating conditions by identifying the operating conditions of electric bicycles and dynamically adjusting the process noise covariance matrix and measurement noise covariance matrix, ensuring high-precision temperature estimation under different loads and environmental conditions.

[0099] Example 2

[0100] Figure 2 This is a schematic diagram of a battery temperature estimation device provided in Embodiment 2 of the present invention. Figure 2 As shown, this device is used in the battery management system of an electric bicycle, and the device includes:

[0101] The first model building module is used to build the electrical and thermal models of the battery under preset operating conditions.

[0102] The second model construction module is used to construct an electrothermal coupling model of the battery under preset operating conditions based on the electrical model and the thermal model; the electrothermal coupling model is used to describe the electrical and thermal changes inside the battery.

[0103] The temperature estimation module is used to estimate the internal and surface temperatures of the battery under preset operating conditions based on the electrothermal coupling model and the electrical measurement data of the battery during charging and discharging, using an extended Kalman filter algorithm.

[0104] The technical solution of this invention involves constructing an electrical and thermal model of the battery under preset operating conditions; constructing an electrothermal coupling model of the battery under preset operating conditions based on the electrical and thermal models; the electrothermal coupling model describes the electrical and thermal changes within the battery; and using an extended Kalman filter algorithm to estimate the internal and surface temperatures of the battery under preset operating conditions based on the electrothermal coupling model and electrical measurement data during charging and discharging. By using battery temperature parameter estimation instead of traditional temperature sensors, and through feedback from electrical measurement data, dynamic estimation of the internal and external temperatures of the electric bicycle battery is achieved. Compared to traditional monitoring methods relying on temperature sensors, this method can more accurately reflect changes in the internal and surface temperatures of the electric bicycle battery, dynamically adapt to changes in thermal characteristics under complex operating conditions, reduce system complexity and cost, and effectively improve the safety and reliability of the battery management system.

[0105] Optional, the first model building module includes:

[0106] An electrical model construction unit is used to describe the electrical parameter characteristics of the battery under preset operating conditions based on a second-order equivalent circuit model, and to obtain an electrical model; the electrical model includes:

[0107]

[0108] U TOV =U OCV (T)-U1-U2-IR0(T);

[0109] Wherein, U1 is the first voltage on the first resistor-capacitor circuit branch in the electrical model, and U2 is the first voltage on the first resistor-capacitor circuit branch in the electrical model; R1(T) is the electrochemical polarization resistance under the preset operating conditions, R2(T) is the concentration polarization resistance under the preset operating conditions, C1(T) is the electrochemical polarization capacitor under the preset operating conditions, and C2(T) is the concentration polarization capacitor under the preset operating conditions. The electrochemical polarization resistance, the concentration polarization resistance, the electrochemical polarization capacitor, and the concentration polarization capacitor are related to temperature T; R0(T) is the internal resistance of the battery; U OCV (T) is the open-circuit voltage of the battery; U TOV I is the terminal voltage of the battery, and I is the current.

[0110] Optionally, when the preset operating condition is an acceleration condition or a climbing condition, the electrochemical polarization resistor, the concentration polarization resistor, the electrochemical polarization capacitor, and the concentration polarization capacitor are, in sequence:

[0111] R1(T)=R 1，0 (1+α(TT ref )+δ|I|);

[0112] R2(T)=R 2，0 (1+β(TT ref )+κ|I|);

[0113] C1(T)=C 1，0 (1-γ(TT ref )-ηN cycle );

[0114] C2(T)=C 2，0 (1-λ(TT ref )-ζN cycle );

[0115] Among them, T ref Reference temperature; R 1，0 R is the electrochemical polarization resistance at the reference temperature. 2，0 The concentration polarization resistance at the reference temperature; C 1，0 For the electrochemical polarization capacitance at the reference temperature, C 2，0 Here, α represents the concentration polarization capacitance at the reference temperature; β represents the temperature sensitivity coefficient of the electrochemical polarization resistor; δ represents the current sensitivity coefficient of the electrochemical polarization resistor; κ represents the current sensitivity coefficient of the concentration polarization resistor; γ represents the temperature decay coefficient of the electrochemical polarization capacitor; λ represents the temperature decay coefficient of the concentration polarization capacitor; η represents the aging decay coefficient of the electrochemical polarization capacitor; ζ represents the aging decay coefficient of the concentration polarization capacitor; and N represents the aging decay coefficient of the concentration polarization capacitor. cycle This represents the number of charge-discharge cycles.

[0116] Optionally, when the preset operating condition is an acceleration condition or a hill-climbing condition, the open-circuit voltage of the battery is:

[0117]

[0118] Among them, U OCV，0 γ is the reference open-circuit voltage at the reference temperature, and γ·In(T) is the temperature correction value; This is the current correction value.

[0119] Optionally, the thermal model of the battery under preset operating conditions includes: a thermal model of internal temperature and a thermal model of surface temperature;

[0120] The thermal model for the internal temperature is:

[0121]

[0122] The thermal model for the surface temperature is:

[0123]

[0124] Among them, T in T represents the internal temperature of the battery. s T represents the surface temperature of the battery. a T represents the external temperature of the battery. amb Ambient temperature; C c For the internal heat capacity of the battery, C s R is the surface heat capacity of the battery. i R is the internal thermal resistance of the battery. o h is the surface thermal resistance of the battery. core h is the internal thermal conductivity coefficient of the battery. surf denoted as the surface thermal conductivity coefficient of the battery; Q represents the heat generated by the battery.

[0125] Optionally, when the preset operating condition is a hill-climbing condition, the internal thermal resistance of the battery is:

[0126] R i =R i，0 ·exp(-δ·I);

[0127] When the preset operating condition is acceleration, the surface thermal resistance of the battery is:

[0128] R o =R o,0 ·exp(1+∈·v wind );

[0129] Among them, R i，0 R is the initial thermal resistance between the battery's interior and surface. o，0 The initial thermal resistance between the battery surface and the environment; ∈ is the wind speed sensitivity coefficient, v wind δ represents the wind speed caused by the vehicle speed; δ is the current sensitivity coefficient of the electrochemical polarization resistor; and I is the current.

[0130] Optionally, when the preset operating condition is an acceleration condition or a climbing condition, the electrothermal coupling model is:

[0131] Q = (U OCV -U TOV )I+ξ·I 2 ;

[0132] Where Q represents the heat generated by the battery, and U... OCVU is the open-circuit voltage of the battery. TOV Let ξ be the battery's terminal voltage, I be the current, and ξ·I be the current. 2 This refers to the nonlinear heat generated by the battery.

[0133] Optionally, the temperature estimation module is specifically used for:

[0134] Construct a state matrix and a measurement matrix, wherein the state matrix is: X = [T] in T s [U1, U2] T The measurement matrix is ​​Y = U TOV ;T in T represents the internal temperature of the battery. s U1 represents the surface temperature of the battery; U2 represents the first voltage on the first resistor-capacitor circuit branch in the electrical model; U... TOV This refers to the battery's terminal voltage.

[0135] Construct the state equation and the measurement equation; the state equation is X. k+1 =f(X) k u k )+W k The measurement equation is Y k =h(X) k )+V k ;X k Let X be the state matrix at time k. k+1 Let Y be the state matrix at time k+1. k The measurement matrix at time k; u k W is the input control matrix at time k; k Let V be the process noise matrix at time k. k Let f(·) be the measurement noise matrix at time k; f(·) is the state transition function, and h(·) is the measurement function.

[0136] Based on the extended Kalman filter algorithm, the internal temperature and surface temperature of the battery under preset operating conditions are estimated according to the state matrix, the measurement matrix, the state equation, the measurement equation, and the electrical measurement data.

[0137] The steps of estimating the internal temperature and surface temperature of the battery based on the state matrix, the measurement matrix, the state equation, and the measurement equation include:

[0138] Predict the state matrix at time k+1:

[0139] Predict the covariance matrix at time k+1:

[0140] Calculate the Kalman gain at time k:

[0141] Update the state matrix at time k+1:

[0142] Update the covariance matrix at time k+1: P k+1|k+1 =(EX) k H k )P k+1|k ;

[0143] H k Let E be the Jacobian matrix of the measurement equation at time k, and let Q be the identity matrix. k Let R be the process noise covariance matrix at time k. k Let F be the measurement noise covariance matrix at time k. k Let k be the state transition matrix at time k; Let be the prior state estimation matrix at time k+1. Let be the posterior state estimation matrix at time k. P is the posterior state estimation matrix at time k+1; k+1|k Let P be the prior covariance estimation matrix at time k+1. k|k Let P be the posterior covariance estimation matrix at time k. k+1|k+1 Here is the posterior covariance estimation matrix at time k+1; K k Let K be the Kalman gain at time k.

[0144] Optionally, when the preset operating condition is an acceleration condition or a climbing condition, the process noise covariance matrix is:

[0145] Q k =Q0(1+δ·κ|I|);

[0146] The measurement noise covariance matrix is:

[0147] R k =R0(1+λ|T) amb -T in |);

[0148] Q0 is the initial process noise covariance matrix, R0 is the initial measurement noise covariance matrix, δ is the current sensitivity coefficient of the electrochemical polarization resistor, κ is the current sensitivity coefficient of the concentration polarization resistor, λ is the temperature decay coefficient of the concentration polarization capacitor, and T amb For ambient temperature, T in This refers to the internal temperature of the battery.

[0149] The battery temperature estimation device provided in this embodiment of the invention can execute the battery temperature estimation method provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the method.

[0150] Example 3

[0151] Figure 3 A schematic diagram of the structure of an electric bicycle 10 that can be used to implement an embodiment of the present invention is shown. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the invention described and / or claimed herein.

[0152] like Figure 3 As shown, the electric bicycle 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 and a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 can also store various programs and data required for the operation of the electric bicycle 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0153] Multiple components in the electric bicycle 10 are connected to the I / O interface 15, including: an input unit 16; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a disk, optical disk, etc.; and a communication unit 19, such as a network card, modem, wireless transceiver, etc. The communication unit 19 allows the electric bicycle 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0154] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as the battery temperature estimation method.

[0155] In some embodiments, the battery temperature estimation method may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on the electric bicycle 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the battery temperature estimation method described above may be performed. Alternatively, in other embodiments, processor 11 may be configured to perform the battery temperature estimation method by any other suitable means (e.g., by means of firmware).

[0156] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0157] In some embodiments, the battery temperature estimation method may be implemented as a computer program, which is implicitly included in a computer program product. When executed by a processor, the computer program implements the battery temperature estimation method of the present invention. The computer program product can be understood as a software product that primarily implements its solution through a computer program. The computer program for implementing the method of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer program causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The computer program may be executed entirely on a machine, partially on a machine, partially on a remote machine as a standalone software package, or entirely on a remote machine or server.

[0158] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0159] To provide interaction with the user, the systems and techniques described herein can be implemented on an electric bicycle having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor for displaying information to the user; other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback).

[0160] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or computing systems that include middleware components (e.g., application servers), or computing systems that include frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0161] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0162] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0163] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method for estimating battery temperature, characterized in that, A battery management system for electric bicycles, the method comprising: Construct electrical and thermal models of the battery under preset operating conditions; An electrothermal coupling model of the battery under preset operating conditions is constructed based on the electrical and thermal models; the electrothermal coupling model is used to describe the electrical and thermal changes inside the battery. Based on the electrothermal coupling model and the electrical measurement data of the battery during the charging and discharging process, the extended Kalman filter algorithm is used to estimate the internal temperature and surface temperature of the battery under the preset operating conditions. The thermal model of the battery under preset operating conditions includes: a thermal model of internal temperature and a thermal model of surface temperature; The thermal model for the internal temperature is: ； The thermal model for the surface temperature is: ； in, This refers to the internal temperature of the battery. The surface temperature of the battery. The external temperature of the battery. Ambient temperature; For the internal heat capacity of the battery, The surface heat capacity of the battery; The internal thermal resistance of the battery, The surface thermal resistance of the battery; The internal thermal conductivity of the battery. The surface thermal conductivity coefficient of the battery; The heat generated by the battery; When the preset operating condition is an uphill condition, the internal thermal resistance of the battery is: ； When the preset operating condition is acceleration, the surface thermal resistance of the battery is: ； in, The initial thermal resistance between the battery's interior and surface. The initial thermal resistance between the battery surface and the environment; This is the wind speed sensitivity coefficient. Wind speed caused by vehicle speed; The current sensitivity coefficient of the electrochemical polarization resistance. It represents electric current.

2. The method according to claim 1, characterized in that, Construct an electrical model of the battery under preset operating conditions, including: An electrical model is obtained by describing the electrical parameter characteristics of the battery under preset operating conditions based on a second-order equivalent circuit model; the electrical model includes: ； ； ； in, This represents the first voltage on the first resistor-capacitor circuit branch in the electrical model. This refers to the second voltage on the second resistor-capacitor circuit branch in the electrical model. To preset the electrochemical polarization resistance under operating conditions, The concentration polarization resistance is preset to the operating conditions. The electrochemical polarization capacitor is set under the preset operating conditions. For the concentration polarization capacitor under preset operating conditions, the electrochemical polarization resistor, the concentration polarization resistor, the electrochemical polarization capacitor, and the concentration polarization capacitor are related to temperature. Related; the temperature Including internal temperature and surface temperature; This is the internal resistance of the battery; This is the open-circuit voltage of the battery; The terminal voltage of the battery. It represents electric current.

3. The method according to claim 2, characterized in that, When the preset operating condition is acceleration or hill-climbing, the electrochemical polarization resistor, the concentration polarization resistor, the electrochemical polarization capacitor, and the concentration polarization capacitor are, in sequence: ； ； ； ； in, Reference temperature; Electrochemical polarization resistance at the reference temperature, The concentration polarization resistance at the reference temperature; Electrochemically polarized capacitance at the reference temperature Concentration polarization capacitance at the reference temperature; The temperature sensitivity coefficient of the electrochemical polarization resistance. The temperature sensitivity coefficient of the concentration polarization resistance; The current sensitivity coefficient of the electrochemical polarization resistance. The current sensitivity coefficient of the concentration polarization resistor; The temperature decay coefficient of the electrochemically polarized capacitor. The temperature decay coefficient of the concentration polarization capacitor; This represents the aging degradation coefficient of the electrochemically polarized capacitor. This represents the aging degradation coefficient of the concentration-polarized capacitor. This represents the number of charge-discharge cycles.

4. The method according to claim 2, characterized in that, When the preset operating condition is acceleration or hill climbing, the open-circuit voltage of the battery is: ； in, The reference open-circuit voltage at the reference temperature. This is a temperature correction value; This is the current correction value; The electrochemical polarization capacitor is set under the preset operating conditions. The temperature decay coefficient of the electrochemically polarized capacitor. This is the aging degradation coefficient of the electrochemically polarized capacitor.

5. The method according to any one of claims 1-4, characterized in that, When the preset operating conditions are acceleration or hill climbing, the electrothermal coupling model is as follows: ； in, The heat generated by the battery This is the open-circuit voltage of the battery; This is the battery's terminal voltage. For current, This refers to the nonlinear heat generated by the battery.

6. The method according to any one of claims 1-4, characterized in that, Based on the electrothermal coupling model and the electrical measurement data of the battery during charging and discharging, the extended Kalman filter algorithm is used to estimate the internal and surface temperatures of the battery under preset operating conditions, including: Construct a state matrix and a measurement matrix, wherein the state matrix is: The measurement matrix is ; This refers to the internal temperature of the battery. This refers to the surface temperature of the battery. This represents the first voltage on the first resistor-capacitor circuit branch in the electrical model. This refers to the second voltage on the second resistor-capacitor circuit branch in the electrical model. This refers to the battery's terminal voltage. Construct the state equation and the measurement equation; the state equation is as follows: The measurement equation is: ; for The state matrix at time 10:

00. for The state matrix at time 10:

00. for The measurement matrix at time; for The input control matrix at time t; for The process noise matrix at time step, for The measurement noise matrix at time step; This is the state transition function. For measurement functions; Based on the extended Kalman filter algorithm, the internal temperature and surface temperature of the battery under preset operating conditions are estimated according to the state matrix, the measurement matrix, the state equation, the measurement equation, and the electrical measurement data. The steps of estimating the internal temperature and surface temperature of the battery based on the state matrix, the measurement matrix, the state equation, and the measurement equation include: predict The state matrix at each time step: ; predict Covariance matrix at time: ; calculate Kalman gain at time: ; renew The state matrix at each time step: ; renew Covariance matrix at time: ; for The Jacobian matrix of the time-measurement equation, It is the identity matrix. for The process noise covariance matrix at time step 1. for The measurement noise covariance matrix at time step. for The state transition matrix at time t; for The prior state matrix at time t, for The posterior state matrix at time t. for The posterior state matrix at time t; for The prior covariance matrix at time t, for The posterior covariance matrix at time t. for The posterior covariance matrix at time t; for Kalman gain at time step.

7. The method according to claim 6, characterized in that, When the preset operating condition is acceleration or hill climbing, the process noise covariance matrix is: ； The measurement noise covariance matrix is: ； The initial process noise covariance matrix, The initial measurement noise covariance matrix, The current sensitivity coefficient of the electrochemical polarization resistance. The current sensitivity coefficient of the concentration polarization resistor is... The temperature decay coefficient of the concentration-polarized capacitor. For ambient temperature, This refers to the internal temperature of the battery.

8. An electric bicycle, characterized in that, The electric bicycle includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor to enable the at least one processor to perform the battery temperature estimation method according to any one of claims 1-7.

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