Battery temperature estimation method and electric bicycle

Abstract

The invention discloses a battery temperature estimation method and an electric bicycle, 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 operation condition; constructing an electrothermal coupling model of the battery under a preset operation condition according to the electrical model and the thermal model; the electrothermal coupling model is used for describing the electrical and heat change process in the battery; and based on the electrothermal coupling model and the electrical measurement data of the battery in the charging and discharging process, estimating the internal temperature and the surface temperature of the battery under the preset operation condition by adopting an extended Kalman filtering algorithm. Compared with a traditional monitoring method depending on a temperature sensor, the internal and surface temperature changes of the electric bicycle battery can be reflected more accurately, thermal characteristic changes under complex working conditions can be dynamically adapted, the complexity and the cost of the system are reduced, and the safety and the reliability of the battery management system are effectively improved.

Description

Technical Field

[0001] The present invention relates to the technical field of battery management, and in particular, to a method for estimating the temperature of a battery for an electric bicycle. Background Art

[0002] In electric bicycles using lithium-ion batteries, temperature has an important impact on the safety, performance, and lifespan of the batteries. The electrochemical reaction rate and material properties of lithium-ion batteries under different operating conditions are significantly affected by temperature changes. High-temperature environments may lead to thermal runaway and aging of the batteries, while low temperatures may slow down the internal reactions of the batteries and affect their performance. In order to mitigate the adverse effects of temperature on the batteries, it is necessary to detect and manage the temperature of the batteries.

[0003] Currently, the monitoring method of battery temperature mainly relies on temperature sensors arranged on the surface or inside the battery casing. This method has various limitations: First, the use of temperature sensors increases the complexity and manufacturing cost of the battery management system. Especially in multi-cell battery packs, a large number of temperature sensors need to be arranged to comprehensively monitor the temperature distribution. Second, the accuracy of temperature sensors is easily affected by environmental interference, noise, and aging effects, resulting in the accumulation of measurement errors. In addition, sensor failures may lead to missing or incorrect monitoring data, reducing the reliability of the system. Summary of the Invention

[0004] The present invention provides a method for estimating the temperature of a battery and an electric bicycle to solve the limitations of the method of relying on temperature sensors to monitor the battery temperature in terms of accuracy, reliability, and economy.

[0005] In a first aspect, an embodiment of the present invention provides a method for estimating the temperature of a battery, including

[0006] Constructing an electrical model and a thermal model of the battery under a preset operating condition;

[0007] Constructing an electrothermal coupling model of the battery under a preset operating condition according to the electrical model and the thermal model; the electrothermal coupling model is used to describe the change process of electricity and heat inside the battery;

[0008] Based on the electrothermal coupling model and the electrical measurement data of the battery during the charge and discharge process, using the extended Kalman filter algorithm to estimate the internal temperature and surface temperature of the battery under a preset operating condition.

[0009] In a second aspect, an embodiment of the present invention provides an electric bicycle, which includes:

[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 executable by the at least one processor. The computer program is executed by the at least one processor to enable the at least one processor to execute the battery temperature estimation method according to any embodiment of the present invention.

[0013] The technical solution of the embodiment of the present invention is as follows: an electrical model and a thermal model of the battery under a preset operating condition are constructed; an electro-thermal coupling model of the battery under the preset operating condition is constructed according to the electrical model and the thermal model; the electro-thermal coupling model is used to describe the change process of electricity and heat inside the battery; based on the electro-thermal coupling model and the electrical measurement data during the charge and discharge process of the battery, the extended Kalman filter algorithm is used to estimate the internal temperature and surface temperature of the battery under the preset operating condition. By using the estimation of the battery temperature parameter instead of the traditional temperature sensor and realizing the dynamic estimation of the internal temperature and external temperature of the electric bicycle battery through the feedback of the electrical measurement data, compared with the traditional monitoring method relying on the temperature sensor, it can more accurately reflect the internal and surface temperature changes of the electric bicycle battery, dynamically adapt to the thermal characteristic changes under complex working conditions, reduce the system complexity and cost, effectively improve the safety and reliability of the battery management system, and solve the problem that the method of relying on the temperature sensor to monitor the battery temperature has limitations in terms of accuracy, reliability and economy.

[0014] It should be understood that the content described in this part is not intended to identify the key or important features of the embodiments of the present invention, nor is it used to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

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

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

[0018] Figure 3 It is a schematic structural diagram of an electric bicycle for implementing the battery temperature estimation method of the embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0019] To enable those skilled in the art to better understand the solution of the present invention, the following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without making creative efforts shall fall within the scope of protection of the present invention.

[0020] It should be noted that the terms "first", "second", etc. in the description and claims of the present invention and the above-mentioned drawings 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 under appropriate circumstances so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device that includes a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0021] Embodiment 1

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

[0023] S110. Construct an electrical model and a thermal model of the battery under a preset operating condition.

[0024] Among them, the electrical model can be understood as a model used to describe the electrical behavior of the battery, and the electrical behavior can include the changes in the resistance and capacitance of the battery with temperature. The electrical model can be represented by an equivalent circuit model. The thermal model can be understood as a model used to represent the temperature change of the battery. The battery temperature can include the internal temperature and the surface temperature of the battery. The preset operating condition can be understood as the operating condition of the electric bicycle, such as a high-speed operating condition, an acceleration condition, and a climbing condition, etc. Under the preset operating condition, the battery is likely to generate a large amount of heat, resulting in an increase in the battery temperature and posing a threat to the safety of the electric bicycle.

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

[0026] S120. Construct an electro-thermal coupling model of the battery under the preset operating conditions based on the electrical model and the thermal model; the electro-thermal coupling model is used to describe the change process of electricity and heat inside the battery.

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

[0028] Specifically, the operating state of the battery is affected by both electrical behavior and heat conduction behavior, and there is a close coupling relationship between the two. Therefore, the dynamic characteristics of the electrical model and the thermal model are coupled to construct an electro-thermal coupling model to comprehensively describe the change process of electricity and heat inside the battery.

[0029] S130. Based on the electro-thermal coupling model and the electrical measurement data of the battery during the charge and discharge process, use the extended Kalman filter algorithm to estimate the internal temperature and surface temperature of the battery under the preset operating conditions.

[0030] Among them, the electrical measurement data of the battery during the charge and discharge process can include the current and terminal voltage of the battery during the charge and discharge process. The Extended Kalman Filter (EKF) is an extended form of the Kalman filter algorithm and is used to estimate the state of a nonlinear system.

[0031] Specifically, the electro-thermal coupling model and the electrical measurement data of the battery during the charge and discharge process are combined, and the extended Kalman filter algorithm is used to dynamically correct the internal temperature and surface temperature of the battery based on the electrical measurement data on the basis of prediction, thereby significantly improving the estimation accuracy.

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

[0033] The technical solution of the embodiment of the present invention is to construct an electrical model and a thermal model of the battery under a preset operating condition; construct 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 to describe the change process of electricity and heat inside the battery; based on the electro-thermal coupling model and the electrical measurement data of the battery during charge and discharge, the extended Kalman filter algorithm is used to estimate the internal temperature and surface temperature of the battery under the preset operating condition. Using the estimation of the battery temperature parameters instead of the traditional temperature sensor, the dynamic estimation of the internal temperature and external temperature of the battery is realized through the feedback of electrical measurement data. Compared with the traditional sensor-dependent monitoring method, it can more accurately reflect the internal and surface temperature changes of the electric bicycle battery, dynamically adapt to the thermal characteristic changes under complex working conditions, reduce the system complexity and cost, and effectively improve the safety and reliability of the battery management system.

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

[0035] Describing the electrical parameter characteristics of the battery under the preset operating condition based on the second-order equivalent circuit model to obtain an electrical model; the electrical model includes:

[0036]

[0037] U TOV =U OCV (T)-U1-U2-IR0(T);

[0038] Wherein, U1 is the first voltage on the first resistor-capacitor circuit branch in the electrical model, U2 is the second voltage on the second resistor-capacitor circuit branch in the electrical model; R1(T) is the electrochemical polarization resistance under the preset operating condition, R2(T) is the concentration polarization resistance under the preset operating condition, C1(T) is the electrochemical polarization capacitance under the preset operating condition, C2(T) is the concentration polarization capacitance under the preset operating condition, and the electrochemical polarization resistance, the concentration polarization resistance, the electrochemical polarization capacitance and the concentration polarization capacitance are related to the temperature T; R0(T) is the internal resistance of the battery; U OCV (T) is the open-circuit voltage of the battery; U TOV is the terminal voltage of the battery, and I is the current.

[0039] Specifically, the first voltage U1 and the second voltage U2 of the battery reflect the electrochemical behavior of the battery. The electrochemical polarization resistance R1(T) refers to the internal resistance caused by the resistance of the charge transfer process when the electrode undergoes an electrochemical reaction, which mainly affects the overpotential of the electrode reaction and thus affects the heat generation of the battery. The concentration polarization resistance R2(T) refers to the resistance caused by the deviation of the potential value due to the difference between the concentration of the reactants on the electrode surface and the bulk solution, which mainly affects the diffusion rate of lithium ions in the electrode material and further affects the charge-discharge performance and thermal behavior of the battery.

[0040] The electrochemical polarization capacitance C1(T) refers to the capacitance formed due to the uneven distribution of charges during the electrochemical reaction process on the electrode surface, which reflects the ability of the electrode surface to accumulate and release charges. The concentration polarization capacitance C2(T) refers to the capacitance formed due to the change in the concentration of reactants or products near the electrode surface, which reflects the change in the concentration gradient near the electrode surface.

[0041] As an optional embodiment of the embodiment of the present application, when the preset operating condition is an acceleration condition or a climbing condition, the electrochemical polarization resistance, the concentration polarization resistance, the electrochemical polarization capacitance, and the concentration polarization capacitance are in turn:

[0042] R1(T) = R 1,0 (1 + α(T - T ref )) + δ|I|);

[0043] R2(T) = R 2,0 (1 + β(T - T ref )) + κ|I|);

[0044] C1(T) = C 1,0 (1 - γ(T - T ref )) - ηN cycle ));

[0045] C2(T) = C 2,0 (1 - λ(T - T ref )) - ζN cycle ));

[0046] Wherein, T ref is the reference temperature; R 1，0 is the electrochemical polarization resistance at the reference temperature, R 2，0 is the concentration polarization resistance at the reference temperature; C 1，0 is the electrochemical polarization capacitance at the reference temperature, C 2，0The concentration polarization capacitance at the reference temperature; α is the temperature sensitivity coefficient of the electrochemical polarization resistance, and β is the temperature sensitivity coefficient of the concentration polarization resistance; δ is the current sensitivity coefficient of the electrochemical polarization resistance, and κ is the current sensitivity coefficient of the concentration polarization resistance; γ is the temperature decay coefficient of the electrochemical polarization capacitance, and λ is the temperature decay coefficient of the concentration polarization capacitance; η is the aging decay coefficient of the electrochemical polarization capacitance, and ζ is the aging decay coefficient of the concentration polarization capacitance, N cycle is the number of charge-discharge cycles.

[0047] Among them, the reference temperature can be a preset temperature used to define the initial parameters of the battery. For example, it can be the temperature value under standard test conditions (such as 25 °C). The temperature sensitivity coefficients α and β are used to describe the proportion of the resistance change with temperature. The current sensitivity coefficients δ and κ are used to describe the influence of current change on the resistance. The temperature decay coefficients γ and λ are used to describe the decay effect of the capacitance with the increase in temperature. The aging decay coefficients η and ζ are used to describe the influence of the battery on the capacitance during use.

[0048] Specifically, under accelerating conditions or climbing conditions, a greater output power is required, resulting in a significant increase in the current I. The polarization reaction caused by the high current will cause dynamic changes in the electrochemical polarization resistance R1(T) and the concentration polarization resistance R2(T). The heat generated during acceleration causes the temperature T to rise, further affecting the changes in the electrochemical polarization capacitance C1(T) and the concentration polarization capacitance C2(T).

[0049] In this embodiment, when constructing the electrical model under accelerating conditions or climbing conditions, the influence of current and temperature on the electrochemical polarization resistance, as well as the influence of temperature on the electrochemical polarization capacitance and the concentration polarization capacitance, are fully considered, improving the accuracy of the electrical model in describing electrical behavior under different conditions, thereby improving the accuracy of temperature estimation under different conditions.

[0050] As an optional embodiment of the present application, when the preset operating condition is an accelerating condition or a climbing condition, the open-circuit voltage of the battery is:

[0051]

[0052] Among them, U OCV，0 is the reference open-circuit voltage at the reference temperature, and γ·In(T) is the temperature correction value; is the current correction value.

[0053] Among them, the reference open-circuit voltage U OCV，0 is the open-circuit voltage measured at the reference temperature T ref .

[0054] Specifically, in the case of an acceleration condition or a climbing condition, the battery needs to provide a large power output, resulting in a significant increase in the current I. In the high-current state, the internal voltage of the battery fluctuates due to the polarization effect and the electrochemical kinetic process. The current correction value is determined according to the current I, the electrochemical polarization capacitance C1(T), and the aging attenuation coefficient η of the electrochemical polarization capacitance, reflecting the influence of the dynamic response of the battery on the open-circuit voltage; the temperature correction value is determined according to the temperature T and the temperature attenuation coefficient γ of the electrochemical polarization capacitance, reflecting the influence of the temperature change of the battery on the open-circuit voltage. Through the temperature correction term and the current correction term, the temperature estimation of the battery can be dynamically adjusted, more accurately reflecting the actual working state of the battery, and ensuring the safety and performance stability of the battery under high load or environmental changes.

[0055] It should be noted that since the open-circuit voltage reflects the static chemical equilibrium state, and the dynamic behavior of the electrochemical polarization capacitance C1(T) can reflect the transient influence of the chemical reaction on the voltage, the open-circuit voltage is mainly related to the electrochemical polarization capacitance C1(T), and the influence of the concentration polarization capacitance C2(T) on the open-circuit voltage can be ignored.

[0056] In this embodiment, when constructing the electrical model under the acceleration condition or the climbing condition, the influence of the current, temperature, and electrochemical polarization capacitance on the reference open-circuit voltage is fully considered, further improving the accuracy of the electrical model in describing the electrical behavior under different conditions, and thus improving the accuracy of the temperature estimation under different conditions.

[0057] As an optional embodiment of the present application, the thermal model of the battery under the preset operating condition includes: the thermal model of the internal temperature and the thermal model of the surface temperature;

[0058] The thermal model of the internal temperature is:

[0059]

[0060] The thermal model of the surface temperature is:

[0061]

[0062] where, T in is the internal temperature of the battery, T s is the surface temperature of the battery, T a is the external temperature of the battery, T amb is the ambient temperature; C c is the internal heat capacity of the battery, C s is the surface heat capacity of the battery; R i is the internal thermal resistance of the battery, R o is the surface thermal resistance of the battery; h core is the internal heat conduction coefficient of the battery, h surfis the surface heat transfer coefficient of the battery; Q is the heat generated by the battery.

[0063] Among them, the surface temperature T of the battery s is used to represent the heat exchange between the battery and the external environment. The external temperature T of the battery a mainly refers to the external environment temperature near the battery. The ambient temperature T amb is the temperature of the environment where the battery is located, which can be the temperature of the atmospheric environment or the temperature of the cooling system outside the battery. The internal heat capacity C of the battery c and the surface heat capacity C s represent the heat storage capacity of the battery.

[0064] As an optional embodiment of the embodiment of the present application, when the preset operating condition is a climbing condition, the internal thermal resistance of the battery is:

[0065] R i = R i,0 ·exp(-δ·I);

[0066] When the preset operating condition is an acceleration condition, the surface thermal resistance of the battery is:

[0067] R o = R o，0 ·exp(1 + ∈·v wind) ;

[0068] Among them, R i，0 is the initial thermal resistance between the inside and the surface of the battery, and R o，0 is the initial thermal resistance between the surface of the battery and the environment; ∈ is the wind speed sensitivity coefficient, v wind is the wind speed caused by the vehicle speed; δ is the current sensitivity coefficient of the electrochemical polarization resistance, and I is the current.

[0069] Specifically, in the climbing condition, the battery needs to output a large current to drive the electric bicycle to climb, resulting in a significant increase in current, which affects the internal thermal resistance of the battery. The internal thermal resistance is inversely proportional to the current, indicating that when discharging at a large current, the heat transfer efficiency inside the battery increases, so the thermal resistance decreases. That is to say, the heat generated by the battery chemical reaction inside the battery shows a non-linear change through the influence of the internal resistance. By adjusting the internal thermal resistance with an exponential function, the thermal characteristics of the battery under different charge and discharge states can be more accurately simulated.

[0070] Under acceleration conditions, the surface thermal resistance of the battery is mainly affected by the wind speed. The internal thermal resistance is inversely proportional to the current, indicating that during high-current discharge, the heat transfer efficiency inside the battery increases, so the thermal resistance decreases. That is to say, the heat generated by the internal battery chemical reaction shows non-linear variation through the influence of the internal resistance. By adjusting the internal thermal resistance with an exponential function, the thermal characteristics of the battery under different charge and discharge states can be more accurately simulated. The dynamic adjustment of the internal thermal resistance model and the surface thermal resistance model enables the battery system to more accurately simulate its thermal characteristics under different working environments and operating conditions, ensuring the heat dissipation efficiency and safety of the battery under conditions such as acceleration and high-speed driving.

[0071] In this embodiment, when constructing the thermal model under acceleration conditions or climbing conditions, the influence of current on the internal thermal resistance and the influence of wind speed on the surface thermal resistance are fully considered. By introducing an external heat source compensation term, accurate description of the battery temperature rise characteristics under extreme environments (such as high temperature or low temperature) is achieved, effectively improving the reliability of the battery management system in long-term outdoor operation scenarios.

[0072] As an alternative embodiment of the embodiment of the present application, when the preset operating condition is an acceleration condition or a climbing condition, the electro-thermal coupling model is:

[0073] Q=(U OCV -U TOV )I + ξ·I 2 ;

[0074] where Q is the heat generated by the battery, U OCV is the open-circuit voltage of the battery; U TOV is the terminal voltage of the battery, I is the current, and ξ·I 2 is the non-linear heat generated by the battery.

[0075] Among them, the heat generated by the battery consists of irreversible heat and reversible heat. ξ·I 2 reflects the non-linear heat generated by the battery under acceleration conditions or climbing conditions, which is mainly caused by ohmic loss. It is the pure energy loss during the charge and discharge process of the battery and cannot be recovered, belonging to irreversible heat. (U OCV -U TOV )I is the heat generated by the release or absorption of the electrochemical reaction, which is related to the equilibrium state of the internal reaction of the battery and belongs to reversible heat.

[0076] Specifically, the voltage difference term and the current square term of the electro-thermal coupling model are used to model the heat generated by the battery, reflecting the heat generation caused by the electrochemical reaction and the current during the actual operation of the battery. The heat generated by the difference between the open-circuit voltage and the terminal voltage of the battery is direct, while the non-linear heat caused by the current reflects the influence of the battery internal resistance on the thermal effect, especially under high current or large load.

[0077] In this embodiment, an improved electro-thermal coupling model (IETM) is adopted to closely combine the electrical behavior of the battery with the heat conduction process. Compared with the temperature estimation method of the traditional single model, it can capture the dynamic voltage change and heat source characteristics of the battery, reflect the dynamic characteristics of the battery under conditions such as acceleration or climbing, and provide more accurate temperature prediction.

[0078] As an alternative embodiment of the embodiment of the present application, based on the electro-thermal coupling model and the electrical measurement data of the battery during charge and discharge, the extended Kalman filter algorithm is used to estimate the internal temperature and surface temperature of the battery under a preset operating condition, including:

[0079] Construct a state matrix and a measurement matrix, where the state matrix is: X = [T in , T s , U1, U2] T ; the measurement matrix is Y = U TOV ; T in is the internal temperature of the battery, T s is the surface temperature of the battery; U1 is the first voltage on the first resistor-capacitor circuit branch in the electrical model, U2 is the second voltage on the second resistor-capacitor circuit branch in the electrical model; U TOV is the terminal voltage of the battery;

[0080] Construct a state equation and a 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 is the state matrix at time k, X k+1 is the state matrix at time k + 1, Y k is the measurement matrix at time k; u k is the input control matrix at time k; W k is the process noise matrix at time k, V k is the measurement noise matrix at time k; f(·) is the state transition function, h(·) is the measurement function;

[0081] Based on the extended Kalman filter algorithm, estimate the internal temperature and surface temperature of the battery under the preset operating condition according to the state matrix, the measurement matrix, the state equation, the measurement equation and the electrical measurement data;

[0082] Among them, the steps of estimating the internal temperature and surface temperature of the battery according to the state matrix, the measurement matrix, the state equation and the measurement equation include:

[0083] Predict the state matrix at time k+1:

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

[0085] Calculate the Kalman gain at time k:

[0086] Update the state matrix at time k+1:

[0087] Update the covariance matrix at time k+1: P k+1|k+1 =(E-K k H k )P k+1|k ;

[0088] H k is the Jacobian matrix of the measurement equation at time k, E is the identity matrix, Q k is the process noise covariance matrix at time k, R k is the measurement noise covariance matrix at time k, F k is the state transition matrix at time k; is the prior state estimation matrix at time k+1, is the posterior state estimation matrix at time k, is the posterior state estimation matrix at time k+1; P k+1|k is the prior covariance estimation matrix at time k+1, P k|k is the posterior covariance estimation matrix at time k, P k+1|k+1 is the posterior covariance estimation matrix at time k+1; K k is the Kalman gain at time k.

[0089] The present invention introduces the Extended Kalman Filter (EKF), which performs real-time correction by combining a non-linear state estimation method with prediction and measurement data, significantly reducing the estimation error of traditional methods. The EKF algorithm can better handle the non-linearity and complex dynamic characteristics of the battery management system, ensuring accurate temperature estimation even under extreme operating conditions of high current discharge.

[0090] As an optional embodiment of the embodiment of the present application, when the preset operating condition is an acceleration condition or a climbing condition, the process noise covariance matrix is:

[0091] Q k =Q0(1+δ·κ|I|);

[0092] The measurement noise covariance matrix is:

[0093] Rk = R0(1 + λ|T amb - T in |);

[0094] 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 resistance, κ is the current sensitivity coefficient of the concentration polarization resistance, λ is the temperature attenuation coefficient of the concentration polarization capacitance, T amb is the ambient temperature, and T in is the internal temperature of the battery.

[0095] Among them, Q0 is the covariance matrix of the process noise in the reference state without dynamic input (i.e., when the current changes). R0 is the covariance matrix of the measurement noise in the ideal state where there is no ambient temperature fluctuation or the ambient temperature is equal to the internal temperature of the battery.

[0096] Specifically, when the current is large, during the rapid charge and discharge process of the battery, the internal heat and reaction change violently, resulting in an increase in system uncertainty. Therefore, the process noise covariance will increase with the increase of the current, thereby adjusting the confidence level of the state estimation. When the temperature difference between the battery and the external environment increases, the electrical characteristics and measurement accuracy of the battery may be affected by the temperature change. To compensate for this uncertainty, the measurement noise covariance increases with the increase of the 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.

[0097] In the embodiment of the present invention, by identifying the working conditions of the electric bicycle and dynamically adjusting the process noise covariance matrix and the measurement noise covariance matrix, the adaptability of the model to complex working conditions is enhanced, and high-precision temperature estimation can be maintained under different loads and environmental conditions.

[0098] Embodiment 2

[0099] Figure 2 is a schematic structural diagram of a battery temperature estimation device provided by the second embodiment of the present invention. As Figure 2 shown, this device is applied to the battery management system of an electric bicycle, and the device includes:

[0100] The first model construction module is used to construct an electrical model and a thermal model of the battery under preset operating conditions;

[0101] The second model construction module is used to construct an electro-thermal coupling model of the battery under preset operating conditions according to the electrical model and the thermal model; the electro-thermal coupling model is used to describe the change process of electricity and heat inside the battery;

[0102] A temperature estimation module, configured to estimate the internal temperature and surface temperature of the battery under a preset operating condition by using an extended Kalman filter algorithm based on the electrothermal coupling model and the electrical measurement data of the battery during charging and discharging.

[0103] The technical solution of the embodiment of the present invention is to construct an electrical model and a thermal model of the battery under a preset operating condition; construct an electrothermal coupling model of the battery under the preset operating condition according to the electrical model and the thermal model; the electrothermal coupling model is used to describe the change process of electricity and heat inside the battery; 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 the preset operating condition. By using the estimation of the temperature parameters of the battery instead of the traditional temperature sensor, the dynamic estimation of the internal temperature and external temperature of the electric bicycle battery is realized through the feedback of the electrical measurement data. Compared with the traditional monitoring method relying on temperature sensors, it can more accurately reflect the internal and surface temperature changes of the electric bicycle battery, dynamically adapt to the thermal characteristic changes under complex working conditions, reduce the system complexity and cost, and effectively improve the safety and reliability of the battery management system.

[0104] Optionally, the first model construction module includes:

[0105] An electrical model construction unit, configured to describe the electrical parameter characteristics of the battery under a preset operating condition based on a second-order equivalent circuit model, and obtain an electrical model; the electrical model includes:

[0106]

[0107] U TOV =U OCV (T)-U1-U2-IR0(T);

[0108] Wherein, U1 is the first voltage on the first resistor-capacitor circuit branch in the electrical model, U2 is the second voltage on the second resistor-capacitor circuit branch in the electrical model; R1(T) is the electrochemical polarization resistance under the preset operating condition, R2(T) is the concentration polarization resistance under the preset operating condition, C1(T) is the electrochemical polarization capacitance under the preset operating condition, C2(T) is the concentration polarization capacitance under the preset operating condition, and the electrochemical polarization resistance, the concentration polarization resistance, the electrochemical polarization capacitance and the concentration polarization capacitance are related to the temperature T; R0(T) is the internal resistance of the battery; U OCV (T) is the open-circuit voltage of the battery; U TOV is the terminal voltage of the battery, and I is the current.

[0109] Optionally, when the preset operating condition is an acceleration condition or a climbing condition, the electrochemical polarization resistance, the concentration polarization resistance, the electrochemical polarization capacitance, and the concentration polarization capacitance are successively as follows:

[0110] R1(T) = R 1,0 (1 + α(T - T ref )) + δ|I|);

[0111] R2(T) = R 2，0 (1 + β(T - T ref )) + κ|I|);

[0112] C1(T) = C 1,0 (1 - γ(T - T ref )) - ηN cycle ));

[0113] C2(T) = C 2，0 (1 - λ(T - T ref )) - ζN cycle ));

[0114] wherein, T ref is the reference temperature; R 1，0 is the electrochemical polarization resistance at the reference temperature, R 2，0 is the concentration polarization resistance at the reference temperature; C 1，0 is the electrochemical polarization capacitance at the reference temperature, C 2,0 is the concentration polarization capacitance at the reference temperature; α is the temperature sensitivity coefficient of the electrochemical polarization resistance, β is the temperature sensitivity coefficient of the concentration polarization resistance; δ is the current sensitivity coefficient of the electrochemical polarization resistance, κ is the current sensitivity coefficient of the concentration polarization resistance; γ is the temperature decay coefficient of the electrochemical polarization capacitance, λ is the temperature decay coefficient of the concentration polarization capacitance; η is the aging decay coefficient of the electrochemical polarization capacitance, ζ is the aging decay coefficient of the concentration polarization capacitance, N cycle is the number of charge and discharge cycles.

[0115] Optionally, when the preset operating condition is an acceleration condition or a climbing condition, the open circuit voltage of the battery is:

[0116]

[0117] wherein, U OCV，0 is the reference open circuit voltage at the reference temperature, γ·In(T) is the temperature correction value; is the current correction value.

[0118] Optionally, the thermal model of the battery under the preset operating condition includes: the thermal model of the internal temperature and the thermal model of the surface temperature;

[0119] The thermal model of the internal temperature is as follows:

[0120]

[0121] The thermal model of the surface temperature is as follows:

[0122]

[0123] Where, T in is the internal temperature of the battery, T s is the surface temperature of the battery, T a is the external temperature of the battery, T amb is the ambient temperature; C c is the internal heat capacity of the battery, C s is the surface heat capacity of the battery; R i is the internal thermal resistance of the battery, R o is the surface thermal resistance of the battery; h core is the internal heat transfer coefficient of the battery, h surf is the surface heat transfer coefficient of the battery; Q is the heat generated by the battery.

[0124] Optionally, when the preset operating condition is a climbing condition, the internal thermal resistance of the battery is:

[0125] R i = R i,0 ·exp(-δ·I);

[0126] When the preset operating condition is an acceleration condition, the surface thermal resistance of the battery is:

[0127] R o = R o,0 ·exp(1 + ∈·v wind );

[0128] Where, R i，0 is the initial thermal resistance between the inside and the surface of the battery, R o，0 is the initial thermal resistance between the surface of the battery and the environment; ∈ is the wind speed sensitivity coefficient, v wind is the wind speed caused by the vehicle speed; δ is the current sensitivity coefficient of the electrochemical polarization resistance, and I is the current.

[0129] Optionally, when the preset operating condition is an acceleration condition or a climbing condition, the electro-thermal coupling model is:

[0130] Q = (U OCV - U TOV )I + ξ·I 2 ;

[0131] Where, Q is the heat generated by the battery, U OCVis the open-circuit voltage of the battery; U TOV is the terminal voltage of the battery, I is the current, ξ·I 2 is the non-linear heat generated by the battery.

[0132] Optionally, the temperature estimation module is specifically configured to:

[0133] Construct a state matrix and a measurement matrix, where the state matrix is: X = [T in , T s , U1, U2] T ; the measurement matrix is Y = U TOV ; T in is the internal temperature of the battery, T s is the surface temperature of the battery; U1 is the first voltage on the first resistor-capacitor circuit branch in the electrical model, U2 is the second voltage on the second resistor-capacitor circuit branch in the electrical model; U TOV is the terminal voltage of the battery;

[0134] Construct a state equation and a 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 is the state matrix at time k, X k+1 is the state matrix at time k+1, Y k is the measurement matrix at time k; u k is the input control matrix at time k; W k is the process noise matrix at time k, V k is the measurement noise matrix at time k; f(·) is the state transition function, h(·) is the measurement function;

[0135] Based on the extended Kalman filter algorithm, estimate the internal temperature and surface temperature of the battery under the preset operating conditions according to the state matrix, the measurement matrix, the state equation, the measurement equation and the electrical measurement data;

[0136] Among them, the steps of estimating the internal temperature and surface temperature of the battery according to the state matrix, the measurement matrix, the state equation and the measurement equation include:

[0137] Predict the state matrix at time k+1:

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

[0139] Calculate the Kalman gain at time k:

[0140] Update the state matrix at time k+1:

[0141] Update the covariance matrix at time k+1: P k+1|k+1 =(E-K k H k )P k+1|k ;

[0142] H k is the Jacobian matrix of the measurement equation at time k, E is the identity matrix, Q k is the process noise covariance matrix at time k, R k is the measurement noise covariance matrix at time k, F k is the state transition matrix at time k; is the prior state estimation matrix at time k+1, is the posterior state estimation matrix at time k, is the posterior state estimation matrix at time k+1; P k+1|k is the prior covariance estimation matrix at time k+1, P k|k is the posterior covariance estimation matrix at time k, P k+1|k+1 is the posterior covariance estimation matrix at time k+1; K k is the Kalman gain at time k.

[0143] Optionally, when the preset operating condition is an acceleration condition or a climbing condition, the process noise covariance matrix is:

[0144] Q k =Q0(1+δ·k|I|)

[0145] The measurement noise covariance matrix is:

[0146] R k =R0(1+λ|T amb -T in |);

[0147] 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 resistance, κ is the current sensitivity coefficient of the concentration polarization resistance, λ is the temperature decay coefficient of the concentration polarization capacitance, T amb is the ambient temperature, T in is the internal temperature of the battery.

[0148] The battery temperature estimation device provided by the embodiments of the present invention can execute the battery temperature estimation method provided by any embodiment of the present invention, and has the corresponding functional modules and beneficial effects for executing the method.

[0149] Embodiment 3

[0150] Figure 3 FIG. shows a schematic structural diagram of an electric bicycle 10 that can be used to implement an embodiment of the present invention. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the present invention described and / or claimed herein.

[0151] As Figure 3 shown, the electric bicycle 10 includes at least one processor 11 and a memory communicatively connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc. The memory stores a computer program executable by the at least one processor. The processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 into the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electric bicycle 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other via a bus 14. The input / output (I / O) interface 15 is also connected to the bus 14.

[0152] 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 magnetic disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electric bicycle 10 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0153] The processor 11 can be various general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as the battery temperature estimation method.

[0154] In some embodiments, the battery temperature estimation method may be implemented as a computer program tangibly embodied 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 onto the electric bicycle 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the battery temperature estimation method described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to execute the battery temperature estimation method by any other suitable means (e.g., by means of firmware).

[0155] The various implementations of the systems and techniques described above in this document may be implemented in digital electronic circuitry, integrated circuit systems, field programmable gate arrays (FPGA), application specific integrated circuits (ASIC), application specific standard products (ASSP), systems on a chip (SOC), complex programmable logic devices (CPLD), computer hardware, firmware, software, and / or combinations thereof. These various implementations may include: implemented in one or more computer programs that may be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a special-purpose or general-purpose programmable processor, that receives data and instructions from a storage system, at least one input device, and at least one output device, and transmits the data and instructions to the storage system, the at least one input device, and the at least one output device.

[0156] In some embodiments, the battery temperature estimation method may be implemented as a computer program invisibly embodied in a computer program product, the computer program implementing the battery temperature estimation method of the present invention when executed by a processor, and the computer program product may be understood as a software product that mainly implements its solution through the 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 the processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when the computer programs are executed by the processor, the functions / operations specified in the flowchart and / or block diagram are implemented. The computer programs may be executed entirely on the machine, partially on the machine, as a stand-alone software package partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0157] In the context of the present invention, a computer-readable storage medium can be a tangible medium that can contain or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. The computer-readable storage medium can include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. Alternatively, the computer-readable storage medium can be a machine-readable signal medium. More specific examples of the machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0158] To provide interaction with a user, the systems and techniques described herein can be implemented on an electric bicycle having: a display device for displaying information to the user (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor);. Other kinds of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback).

[0159] The systems and techniques described herein can be implemented in a computing system including backend components (e.g., as a data server), or a computing system including middleware components (e.g., an application server), or a computing system including frontend components (e.g., a user computer having a graphical user interface or a web browser through which the user can interact with an implementation of the systems and techniques described herein), or a computing system including any combination of such backend components, middleware components, or frontend components. The components of the system can be interconnected to each other by any form or medium of digital data communication (e.g., a communication network). Examples of the communication network include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0160] The computing system can include a client and a server. The client and the server are generally remote from each other and typically interact through a communication network. 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 a cloud host, which is a host product in the cloud computing service system, solving the defects of difficult management and weak business scalability existing in traditional physical hosts and VPS services.

[0161] 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 the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and no limitation is imposed herein.

[0162] The above specific embodiments do not constitute a limitation on the protection scope of the present 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 the present invention shall be included within the protection scope of the present invention.

Claims

1. A method for estimating battery temperature, characterized in that, A battery management system applied to an electric bicycle, the method comprising: Construct an electrical model and a thermal model of the battery under a preset operating condition; Construct an electrothermal coupling model of the battery under the preset operating condition according to the electrical model and the thermal model; the electrothermal coupling model is used to describe the change process of electricity and heat inside the battery; Based on the electrothermal coupling model and the electrical measurement data of the battery during charge and discharge, use the extended Kalman filter algorithm to estimate the internal temperature and surface temperature of the battery under the preset operating condition.

2. The method according to claim 1, characterized in that, Constructing an electrical model of the battery under a preset operating condition includes: Based on the second-order equivalent circuit model, describe the electrical parameter characteristics of the battery under the preset operating condition to obtain an electrical model; the electrical model includes: U TOV = U OCV (T) - U1 - U2 - IR0(T); Wherein, U1 is the first voltage on the first resistor-capacitor circuit branch in the electrical model, and U2 is the second voltage on the second 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 capacitance under the preset operating conditions, C2(T) is the concentration polarization capacitance under the preset operating conditions, and the electrochemical polarization resistance, the concentration polarization resistance, the electrochemical polarization capacitance and the concentration polarization capacitance are related to the temperature T; R0(T) is the internal resistance of the battery; U OCV (T) is the open-circuit voltage of the battery; U TOV is the terminal voltage of the battery, and I is the current.

3. The method according to claim 2, wherein When the preset operating condition is an acceleration condition or a climbing condition, the electrochemical polarization resistance, the concentration polarization resistance, the electrochemical polarization capacitance, and the concentration polarization capacitance are in sequence: R1(T) = R 1,0 (1 + α(T - T ref )) + δ|I|); R2(T) = R 2,0 (1 + β(T - T ref )) + k|I|); C1(T) = C 1,0 (1 - γ(T - T ref ) - ηN cycle ); C2(T) = C 2，0 (1 - λ(T - T ref ) - ζN cycle ); Among them, T ref is the reference temperature; R 1，0 is the electrochemical polarization resistance at the reference temperature, and R 2，0 is the concentration polarization resistance at the reference temperature; C 1，0 is the electrochemical polarization capacitance at the reference temperature, and C 2，0 is the concentration polarization capacitance at the reference temperature; α is the temperature sensitivity coefficient of the electrochemical polarization resistance, β is the temperature sensitivity coefficient of the concentration polarization resistance; δ is the current sensitivity coefficient of the electrochemical polarization resistance, κ is the current sensitivity coefficient of the concentration polarization resistance; γ is the temperature decay coefficient of the electrochemical polarization capacitance, λ is the temperature decay coefficient of the concentration polarization capacitance; η is the aging decay coefficient of the electrochemical polarization capacitance, ζ is the aging decay coefficient of the concentration polarization capacitance, and N cycle is the number of charge-discharge cycles.

4. The method according to claim 2, wherein When the preset operating condition is an acceleration condition or a climbing condition, the open circuit voltage of the battery is: Among them, U OCV,0 is the reference open-circuit voltage at the reference temperature, and γ·In(T) is the temperature correction value; is the current correction value.

5. The method according to claim 1, wherein The thermal model of the battery under the preset operating condition includes: a thermal model of the internal temperature and a thermal model of the surface temperature; The thermal model of the internal temperature is: The thermal model of the surface temperature is: Among them, T in is the internal temperature of the battery, T s is the surface temperature of the battery, T a is the external temperature of the battery, T amb is the ambient temperature; C c is the internal heat capacity of the battery, C s is the surface heat capacity of the battery; R i is the internal thermal resistance of the battery, R o is the surface thermal resistance of the battery; h core is the internal heat transfer coefficient of the battery, h surf is the surface heat transfer coefficient of the battery; Q is the heat generated by the battery.

6. The method according to claim 5, characterized in that When the preset operating condition is a climbing condition, the internal thermal resistance of the battery is: R i = R i，0 ·exp(-δ·I); When the preset operating condition is an acceleration condition, the surface thermal resistance of the battery is: R o = R o，0 ·exp(1 + ∈·v wind ); wherein, R i，0 is the initial thermal resistance between the interior and the surface of the battery, and R o，0 is the initial thermal resistance between the surface of the battery and the environment; ∈ is the wind speed sensitivity coefficient, v wind is the wind speed caused by the vehicle speed; δ is the current sensitivity coefficient of the electrochemical polarization resistance, and I is the current.

7. The method according to any one of claims 1-6, characterized in that, When the preset operating condition is an acceleration condition or a climbing condition, the electrothermal coupling model is: Q = (U OCV - U TOV )I + ξ·I 2 ; Among them, Q is the heat generated by the battery, and U OCV is the open-circuit voltage of the battery; U TOV is the terminal voltage of the battery, I is the current, and ξ·I 2 is the non-linear heat generated by the battery.

8. The method according to any one of claims 1 to 6, characterized in that, Based on the electrothermal coupling model and the electrical measurement data of the battery during charge and discharge, using the extended Kalman filter algorithm to estimate the internal temperature and surface temperature of the battery under the preset operating condition includes: Construct a state matrix and a measurement matrix, where the state matrix is: X = [T in , T s , U1, U2] T ; and the measurement matrix is Y = U TOV ; T in is the internal temperature of the battery, T s is the surface temperature of the battery; U1 is the first voltage on the first resistor-capacitor circuit branch in the electrical model, U2 is the second voltage on the second resistor-capacitor circuit branch in the electrical model; U TOV is the terminal voltage of the battery; 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 is the state matrix at time k, X k+1 is the state matrix at time k+1, Y k is the measurement matrix at time k; u k is the input control matrix at time k; W k is the process noise matrix at time k, V k is the measurement noise matrix at time k; f(·) is the state transition function, h(·) is the measurement function; Based on the extended Kalman filter algorithm, estimate the internal temperature and surface temperature of the battery under the preset operating condition according to the state matrix, the measurement matrix, the state equation, the measurement equation, and the electrical measurement data; Among them, the steps of estimating the internal temperature and surface temperature of the battery according to the state matrix, the measurement matrix, the state equation, and the measurement equation include: Predict the state matrix at time k+1: Covariance matrix predicted at time k+1: Calculate the Kalman gain at time k: Update the state matrix at time k+1: Update the covariance matrix at time k+1: P k+1|k+1 =(E - K k H k )P k+1|k ; H k is the Jacobian matrix of the measurement equation at time k, E is the identity matrix, Q k is the process noise covariance matrix at time k, R k is the measurement noise covariance matrix at time k, F k is the state transition matrix at time k; is the prior state estimate matrix at time k + 1, is the posterior state estimate matrix at time k, is the posterior state estimate matrix at time k + 1; P k+1|k is the prior covariance estimate matrix at time k + 1, P k|k is the posterior covariance estimate matrix at time k, P k+1|k+1 is the posterior covariance estimate matrix at time k + 1; K k is the Kalman gain at time k.

9. The method according to claim 8, wherein When the preset operating condition is an acceleration condition or a climbing condition, the process noise covariance matrix is: Q k = Q0(1 + δ·κ|I|); The measurement noise covariance matrix is: R k = R0(1 + λ|T amb - T in |); 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 resistance, κ is the current sensitivity coefficient of the concentration polarization resistance, λ is the temperature attenuation coefficient of the concentration polarization capacitance, T amb is the ambient temperature, and T in is the internal temperature of the battery.

10. 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 executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the battery temperature estimation method according to any one of claims 1-9.

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