Lithium battery internal temperature and heating power online detection system and method based on heat flow and surface temperature

Abstract

The invention discloses a lithium battery internal temperature and heating power online detection system and method based on heat flow and surface temperature. The method comprises the following steps: step 1, placing a lithium battery attached with a high-precision surface temperature sensor and a flexible heat flow sensor layer in an isothermal environment chamber; 2, the temperature of the isothermal environment chamber is controlled to be stabilized to a set value; 3, applying an electric load to the lithium battery; 4, synchronously collecting a heat flux density signal and a temperature signal on the surface of the lithium battery in real time; 5, calculating the instantaneous total heating power of the battery according to the heat flux density signal; and 6, combining the temperature signal, the environment temperature and the instantaneous total heating power, and calculating the internal temperature of the battery based on thermal model inversion. By directly measuring the heat flux density and temperature of the surface of the battery and combining the heat conduction model, the instantaneous heating power of the battery is calculated in a real-time, high-precision and non-invasive manner, the internal temperature of the battery is inverted, and the method is suitable for thermal behavior analysis, state evaluation and safety early warning of the lithium battery.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of lithium battery thermal management test, and particularly relates to a lithium battery internal temperature and heating power online detection system and method based on heat flow and surface temperature. BACKGROUND

[0002] The thermal safety of a lithium battery is a key restrictive factor for its wide application in the fields of electric vehicles, energy storage systems and the like. The heating power and internal temperature of the battery are core parameters for evaluating the thermal behavior, state (SOH, SOF) and safety risk (such as thermal runaway) of the battery.

[0003] At present, the main equipment for measuring the thermal characteristics of a battery is an isothermal calorimeter (such as ARC). The basic principle is to place the battery in an adiabatic or isothermal environment, and indirectly calculate the heat generation by measuring the energy required to maintain the constant temperature of the environment. However, the traditional method has the following limitations: 1. Indirect measurement and thermal inertia delay, which is difficult to capture transient thermal changes; 2. Unable to distinguish local heat sources, only the overall heat generation can be obtained; 3. Difficult to estimate the internal temperature, the implanted sensor will damage the structure, and the estimation model of the surface temperature has limited precision.

[0004] Therefore, there is an urgent need for a method and device that can directly, quickly and non-invasively measure the heating power of the battery and accurately calculate the internal temperature. SUMMARY

[0005] In order to solve the problems of thermal inertia delay, inability to locate local heat sources and low estimation accuracy of internal temperature of the traditional isothermal calorimeter, the application provides a lithium battery internal temperature and heating power online detection system and method based on heat flow and surface temperature. The system integrates a heat flow sensor and a surface temperature sensor, directly measures the heat flow density and temperature of the battery surface, combines a heat conduction model, and realizes real-time, high-precision and non-invasive calculation of the instantaneous heating power of the battery and inversion of the internal temperature. The system is suitable for lithium battery thermal behavior analysis, state evaluation and safety warning.

[0006] To achieve the above purpose, the application provides the following scheme: A lithium battery internal temperature and heating power online detection system based on heat flow and surface temperature, comprising: an isothermal environment cabin, a battery clamp, at least one heat flow sensing module, a data acquisition card, a main control system, a heating / cooling unit, a lithium battery, a high-precision surface temperature sensor, a flexible heat flow sensor layer, an electrical load module and a flexible heat-conducting gasket. The isothermal environment cabin is used to provide a constant temperature environment, and the inner wall is provided with a heating / cooling unit controlled by the main control system to maintain the set temperature. The battery clamp is used to fix the lithium battery. The lithium battery is the test object, and a high-precision surface temperature sensor and a flexible heat flow sensor layer are attached to the surface respectively; The high-precision surface temperature sensor adopts a thin-film platinum resistance or an ultrathin thermocouple structure and is used for measuring the battery surface temperature in real time; The flexible heat flow sensor layer is a thermopile array based on the Seebeck effect and is used for converting the heat flow density vertically through the battery surface into an electric signal output; The electric load module is connected with the lithium battery through wires and is used for applying a charging and discharging current in the experiment process to simulate the battery heating behavior under different working conditions; The flexible heat-conducting gasket is arranged between the battery surface and the sensor layer and is used for filling micro gaps; The data acquisition card is used for synchronously collecting the heat flow density and temperature signals in real time; The main control system is used for calculating the instantaneous total heating power of the battery according to the heat flow density signal; and the battery internal temperature is calculated based on a thermal model inversion in combination with the temperature signal, the ambient temperature and the instantaneous total heating power.

[0007] The application further provides a lithium battery internal temperature and heating power online detection method based on heat flow and surface temperature. Step 1: placing the lithium battery attached with the high-precision surface temperature sensor and the flexible heat flow sensor layer in an isothermal environment cabin; Step 2: controlling the isothermal environment cabin temperature to be stable to a set value; Step 3: applying an electric load to the lithium battery; Step 4: synchronously collecting the heat flow density signal and the temperature signal of the lithium battery surface in real time; Step 5: calculating the instantaneous total heating power of the battery according to the heat flow density signal; Step 6: calculating the battery internal temperature based on a thermal model inversion in combination with the temperature signal, the ambient temperature and the instantaneous total heating power.

[0008] Preferably, the method for calculating the instantaneous total heating power of the battery according to the heat flow density signal is as follows: ; wherein, is the heat flow density measured by the heat flow sensor; and A is the effective heat dissipation area of the battery in contact with the sensor.

[0009] Preferably, the construction method of the thermal model is as follows: According to the actual size of the lithium battery to be measured, a three-dimensional entity geometric model of the lithium battery is created, and the battery geometric body is discretized into a plurality of volume units, i.e., mesh units, by using a finite element analysis method; Assigning material thermophysical properties to all grid cells; Setting control equations, i.e. transient heat conduction equations, on each grid cell; Setting boundary conditions and initial conditions and source term loading on each grid cell, and finally completing the construction of the thermal model.

[0010] Preferably, the transient heat conduction equation is: ; Wherein, is the density of the battery material; is the specific heat capacity of the battery material; T is the temperature; t is the time; k is the thermal conductivity of the battery material; is the internal heat source generation rate, is the gradient operator.

[0011] Preferably, the method for calculating the internal temperature of the battery based on the thermal model inversion comprises: Using the finite element analysis method to perform weighted residual approximation of the control equation on each grid cell, and converting the continuous partial differential equation into a system of ordinary differential equations about all node temperatures : ; Wherein, is the heat capacity matrix, determined by and c p ; is the heat conduction matrix, determined by the thermal conductivity; is the heat load vector; Discretizing the time derivative term, for time step n+1, the control equation becomes a linear equation system: ; Wherein, is the time step; is the heat load vector at time n+1, containing the volume heat source term q and the influence of the boundary condition; is the temperature node vector at time n; At each time step, the linear equation system is solved to obtain the temperature of all grid nodes at the corresponding time; the battery core temperature is the temperature value at the geometric center node of the battery.

[0012] Compared with the prior art, the present application has the following beneficial effects: 1. The present application does not need to implant sensors in the battery, directly measures the surface heat flow by multi-point arrangement of heat flow sensors, eliminates the thermal inertia of traditional compensation method, can respond to the transient change of battery heating power more quickly and accurately, ensures the safety of test and the originality of battery state, is suitable for the test of commercial battery, can preliminarily judge the heating uniformity of the battery, provides data support for three-dimensional temperature field reconstruction, and helps to locate the local overheating point.

[0013] 2. The present application combines the directly measured heat flow (reflecting the internal heat generation intensity) and the surface temperature, provides extremely critical and accurate input conditions for the heat model, and makes the internal temperature calculated by inversion much more accurate than the estimated model based on the surface temperature. BRIEF DESCRIPTION OF DRAWINGS

[0014] In order to more clearly illustrate the technical solutions of the present application, the drawings needed in the embodiments are briefly introduced as follows. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0015] Figure 1 The structure diagram of a lithium battery internal temperature and heating power online detection system based on heat flow and surface temperature in an embodiment of the present application; Figure 2 The battery clamp and heat flow sensor module schematic diagram (showing the flexible heat conduction gasket design) in an embodiment of the present application; Figure 3 The flow chart of a lithium battery internal temperature and heating power online detection method based on heat flow and surface temperature in an embodiment of the present application; Figure 4 The measured battery surface temperature, current and heating power curve diagram in an embodiment of the present application; Figure 5 The internal temperature diagram of the battery obtained by finite element simulation in an embodiment of the present application; 1 - isothermal environment chamber; 2 - battery clamp; 3 - data acquisition card; 4 - main control system; 5 - heating / cooling unit; 6 - high-precision surface temperature sensor; 7 - flexible heat flow sensor layer; 8 - lithium battery; 9 - electrical load module; 10 - flexible heat conduction gasket; 11 - protective and insulating outer layer; 12 - heat conduction and interface filling layer; 13 - flexible substrate. DETAILED DESCRIPTION

[0016] With reference to the drawings of the embodiments of the present application, the technical solutions in the embodiments of the present application will be clearly and completely described, obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without creative work belong to the protection scope of the present application.

[0017] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the present application will be further described in detail below with reference to the drawings and specific embodiments.

[0018] Embodiment one The present application provides a kind of lithium battery internal temperature and heat generation power online detection system based on heat flow and surface temperature, comprising: isothermal environment cabin 1, battery fixture 2, data acquisition card 3, main control system 4, heating / cooling unit 5, high-precision surface temperature sensor 6, flexible heat flow sensor layer 7, lithium battery 8, electrical load module 9 and flexible heat-conducting gasket 10.

[0019] The isothermal environment cabin 1 is a double-layer insulation structure airtight test cabin, for providing stable and uniform constant temperature environment. The inner wall of cabin is equipped with heating / cooling unit 5, which is controlled by main control system 4, and the temperature in the cabin is maintained at the set value (T_set) by closed-loop regulation.

[0020] The battery fixture 2 is arranged inside the isothermal environment cabin 1, for fixing the lithium battery 8, to ensure that the battery position is stable and maintains good thermal contact during the test.

[0021] The lithium battery 8 is the test object, and the main surface thereof is attached with high-precision surface temperature sensor 6 and flexible heat flow sensor layer 7 respectively. The high-precision surface temperature sensor 6 adopts thin film platinum resistance or ultra-thin thermocouple structure, for real-time measurement of battery surface temperature; the flexible heat flow sensor layer 7 is a thermopile array based on Seebeck effect, which can convert the heat flux q''_surface vertically through the battery surface into electrical signal output, to realize high-sensitivity and fast-response heat flow measurement.

[0022] The flexible heat-conducting gasket 10 is arranged between the battery surface and the sensor layer, for filling micro-gap, reducing contact thermal resistance, improving measurement stability and heat conduction efficiency.

[0023] The electrical load module 9 is connected with the lithium battery 8 through wire, for applying charge and discharge current during the experiment, to simulate the battery heating behavior under different working conditions.

[0024] Data acquisition card 3 is used to synchronously collect signals from high-precision surface temperature sensor 6 and flexible heat flow sensor layer 7, and transmit the collected data to the main control system 4 in real time. The main control system 4 performs data processing and calculation, including: (1) Calculate the instantaneous total heat generation power of the battery based on the heat flux signal; (2) Combine the battery surface temperature, ambient temperature and heat generation power data, and calculate the internal temperature distribution and core temperature of the battery based on the thermal model inversion. The specific process is as follows: Direct calculation of heat generation power: Instantaneous total heat generation power of the battery (W) is obtained by integrating the surface heat flux: ; Where q''_surface(W / m²) is the heat flux density measured by the heat flow sensor; A(m²) is the effective heat dissipation area of the battery in contact with the sensor. For the case of arranging sensors on n discrete surfaces, the formula calculation can be simplified as: ; Where is the battery surface heat flux measured by the i-th heat flow sensor, W / m²; is the effective heat dissipation area of the i-th heat flow sensor in close contact with the battery surface, m²; n is the number of sensors; is the instantaneous total heat generation power of the battery, W.

[0025] This method eliminates the thermal inertia of the traditional compensation method by directly measuring the heat flow, and can quickly respond to the transient changes of the battery heat generation power.

[0026] Internal temperature inversion: To achieve high-precision internal temperature inversion, we first establish a three-dimensional thermal model of the battery to capture the real heat distribution. One-dimensional model can be used as a simplification in specific scenarios.

[0027] A. Three-dimensional thermal model construction using finite element analysis method: 1) When creating a three-dimensional solid geometric model, it needs to be constructed according to the actual internal physical structure of the lithium battery 8 to be measured. The model specifically includes: the battery core as the main heat generation and conduction area, the tab as an important heat conduction path and potential local heat source, and the shell or packaging layer with different thermal physical properties than the battery core material. By accurately reproducing the spatial form, relative position and assembly relationship of the above components, it is ensured that the heat conduction path in the model is highly consistent with the physical heat path of the real battery.

[0028] 2) Discretize the battery geometry The discretization process is realized by finite element analysis method, which aims to transform the continuous partial differential governing equations into a calculable discrete algebraic system, and specifically includes the following steps: ① Spatial discretization: the weighted residual method is adopted to discretize the transient heat conduction equation on each mesh element. First, the shape function of the element is used to construct the approximate expression of the element temperature field; then, the approximate solution is substituted into the governing equation and the weighted integral over the element domain is set to zero, thereby transforming the continuous partial differential equation into a matrix equation at the element level, i.e., ; where, is the element heat capacity matrix, is the element heat conduction matrix, is the element heat load vector, is the element temperature vector.

[0029] ② System assembly: according to the node connection relationship, the matrix equations of all mesh elements are assembled into the global system equation describing the entire battery temperature field: ; where is the global heat capacity matrix, is the global heat conduction matrix, is the global heat load vector, is the temperature vector of all nodes.

[0030] ③ Time discretization: the time derivative term in the global system equation is discretized. The implicit integration method is adopted to transform the equation into a linear algebraic equation group that needs to be solved at each time step: ; where, is the time step.

[0031] The equation can be expressed in the form of .

[0032] ④ Real-time solution and inversion: at each time step, the real-time collected surface temperature measurement value is taken as the boundary condition, and the instantaneous total heat generation power is taken as the internal heat source, to update the right-hand side term of the linear equation group , and the temperature of all mesh nodes at this moment is obtained by solving the equation group . The battery core temperature is determined by the node temperature value located at the geometric center of the battery.

[0033] 3) Assign material thermal physical properties to all mesh elements.

[0034] Functions / parameters: ① Density ρ (kg / m 3).

[0035] cp (J / kg K) is the constant pressure specific heat capacity.

[0036] k (W / m K) is the thermal conductivity. For anisotropic materials, k is a tensor.

[0037] 4) Governing equations: On each element, the law of conservation of energy is followed, which is mathematically expressed as the transient heat conduction equation: ; where is the density of the battery material, kg / m 3 ; is the specific heat capacity of the battery material, J / (kg K); T(x, y, z, t) is the temperature, representing a function with respect to the spatial coordinates (x, y, z) and time t; t is the time, s; k is the thermal conductivity of the battery material, W / m K; is the internal heat source generation rate, W / m 3 , that is, the heat generation power per unit volume, is the gradient operator.

[0038] The transient heat conduction equation is directly imposed on each grid element through the weighted residual principle of the finite element analysis method, which is specifically manifested as establishing a local governing equation at the element level and forming a global system through assembly. On each grid element, the transient heat conduction equation is applied in a weak form (integral form). Through the element shape function and the weighted residual method, the partial differential equation is converted into an algebraic relationship. For example, for element (e), the governing equation is rewritten as: ; where is the area; The diffusion term is handled by partial integration, and the boundary conditions are introduced, and finally the element matrix equation is obtained.

[0039] 5) Boundary condition and initial condition setting: Boundary condition: On all external surfaces of the model, a first-type boundary condition (Dirichlet condition) is applied, that is, the measured surface temperature Tsurface(x, t) at spatial coordinate position x and time t is directly assigned. This ensures strong coupling between the model and the experimental measurement on the boundary.

[0040] ; where is the temperature distribution at the outer boundary of the model, K; is the measured battery surface temperature through the surface temperature sensor, K.

[0041] Initial condition: Set the temperature of the whole model at the start of time, usually the set temperature of the isothermal environment cabin 1: ; Where is the temperature field distribution of the model at the initial moment t=0, K; is the set temperature of the isothermal environment cabin 1, K.

[0042] Source term loading: The calculated instantaneous total heat generation power P heat is introduced into the model to convert it into a volumetric heat generation rate . Under the assumption of uniform heat generation, there is the equation: ; Where V is the battery volume. This value is loaded as a source term to all (or specified region) volume elements.

[0043] B. Equation solving and internal temperature calculation process: 1) Use the finite element analysis method to perform weighted residual approximation on the control equation at each grid element, convert the continuous partial differential equation into a large ordinary differential equation system about all node temperatures : ; Where is the heat capacity matrix, determined by and c p ; is the heat conduction matrix, determined by the thermal conductivity coefficient; is the heat load vector, which contains the information of the source term and the boundary condition.

[0044] Discretize the time derivative term (such as using the backward Euler method, Crank-Nicolson method, etc.). For time step n+1, the control equation becomes: ; Where is the time step, s; is the heat load vector at time n+1, containing the volumetric heat source term q and the influence of the boundary condition, W; is the temperature node vector of the previous step (time n), K.

[0045] This is a linear equation system .

[0046] Where A is the system coefficient matrix, composed of C / Δt and K; is the equivalent load vector, determined by the previous time step temperature field and the current heat source; At each time step, solve this system of linear equations to obtain the temperature at all grid nodes at that moment. Battery core temperature = T(x=L / 2, t), K. This represents the temperature value of the node located at the geometric center of the battery. This can be obtained by iterating through all time steps. The complete curve.

[0047] In this invention, the "battery geometric center" refers to the geometric center location determined in the three-dimensional model based on the actual physical structure of the battery. When establishing the three-dimensional finite element model of the battery, this location corresponds to one or more mesh nodes. Core temperature This means taking the temperature value at that location node. For common square lithium-ion batteries, their geometric center is usually located at the spatial center of the battery body; for cylindrical batteries, it is the intersection of their axial and radial centers.

[0048] The global temperature vector is obtained by solving. Then, by querying the index of the corresponding geometric center node in the vector, the core temperature value at that moment can be directly obtained. If the geometric center is located between multiple nodes, it can be obtained by interpolating the temperatures of adjacent nodes.

[0049] The above process is implemented in the algorithm through pre-defined node numbers or coordinate matching, ensuring that each time step can be automatically extracted. .

[0050] C. One-dimensional simplified model (taking an infinitely large flat plate as an example): For a square battery with thickness L, it can be simplified to a one-dimensional model along the thickness direction (x direction).

[0051] The governing equations simplify to: ; Boundary conditions: ; in and Let K be the surface temperature boundary conditions of the battery at x=0 and x=L, respectively; Solution: Spatial and temporal discretization can be performed using the finite difference method. The solution process is similar to that of the three-dimensional finite element analysis method, but the computational load is significantly reduced, making it suitable for applications with high real-time requirements.

[0052] In this embodiment, the heat flow sensing module is a flexible thermally conductive pad, which has a heat flow sensor and a temperature sensor embedded inside.

[0053] Example 2 like Figure 3As shown, the application also provides a lithium battery internal temperature and heating power online detection method based on heat flow and surface temperature, which is realized by the system of embodiment one, and the method comprises: Step 1: Place the lithium battery 8 attached with the high-precision surface temperature sensor 6 and the flexible heat flow sensor layer 7 in the isothermal environment cabin 1; Step 2: Control the temperature of the isothermal environment cabin 1 to be stable at the set value; Step 3: Apply an electrical load to the lithium battery 8; Step 4: Collect the heat flow density signal and the temperature signal of the surface of the lithium battery 8 in real time and synchronously; Step 5: Calculate the instantaneous total heating power of the battery according to the heat flow density signal; Step 6: Combine the temperature signal, the environmental temperature and the instantaneous total heating power, and calculate the internal temperature of the battery based on the heat model inversion.

[0054] In this embodiment, the method for calculating the instantaneous total heating power of the battery according to the heat flow density signal is: ; Wherein, is the heat flow density measured by the heat flow sensor; A is the effective heat dissipation area of the battery in contact with the sensor.

[0055] In this embodiment, the construction method of the heat model is: According to the actual size of the lithium battery 8 to be measured, a three-dimensional entity geometric model is created, and the battery geometric body is discretized into a plurality of volume units, i.e. mesh units, using a finite element analysis method; Assign material thermal physical properties to all mesh units; On each mesh unit, set the control equation, i.e. the transient heat conduction equation; On each mesh unit, set the boundary conditions and initial conditions and source term loading, and finally complete the construction of the heat model.

[0056] In this embodiment, the transient heat conduction equation is: ; Wherein, is the density of the battery material; is the specific heat capacity of the battery material; T is the temperature; t is time; k is the thermal conductivity of the battery material; is the internal heat source generation rate.

[0057] In this embodiment, the method for calculating the internal temperature of the battery based on the heat model inversion comprises: The control equation is weighted residual approximation on each grid element by using finite element analysis method, and the continuous partial differential equation is converted into a set of ordinary differential equations about the temperature of all nodes: ; where, is the heat capacity matrix, determined by and c p ; is the heat conduction matrix, determined by the thermal conductivity coefficient; is the heat load vector, which contains the information of the source term and the boundary condition; The time derivative term is discretized, and for the time step n+1, the control equation becomes a linear equation set: ; At each time step, the linear equation set is solved to obtain the temperature of all grid nodes at the corresponding time ; the battery core temperature is the temperature value of the node located at the geometric center of the battery.

[0058] The specific implementation process is: step 1. Install the lithium battery 8 to be tested in the clamp, and ensure that the heat flow sensing module is in good contact with the surface of the battery; Step 2. Set the target temperature (T_set) of the isothermal environment chamber 1, start the system, and wait for the environment temperature to stabilize; Step 3. Perform charge and discharge test or run other working conditions on the battery; Step 4. The data acquisition system synchronously and real-timely collects the heat flux density signal (q''_surface,i) and the corresponding surface temperature signal (T_surface,i) output by each heat flow sensor; Step 5. The heat generation power calculation module calculates the total instantaneous heat generation power (P_heat) of the battery by integrating all sensor data; Step 6. The internal temperature inversion module takes P_heat, T_surface (multiple), and T_set as inputs, and runs the thermal model algorithm to real-timely estimate the internal temperature (T_core) of the battery; Step 7. Output and record the heat generation power curve and internal temperature curve.

[0059] Example Three This embodiment provides a lithium battery internal temperature and heat generation power system based on heat flow and surface temperature as Figure 1 , Figure 2 ​As shown, based on a commercial isothermal calorimeter, remove or bypass the heater near the battery clamp in the isothermal environmental cabin 1 for compensation calculation, customize a dedicated battery clamp, which has a precise mechanical structure (such as spring-loaded or air pressure device), can ensure that the flexible heat flow-temperature composite sensor is tightly attached to the two large faces of the battery with constant and uniform pressure, to ensure direct measurement of the heat flow emitted from the surface of the battery, fast response, no delay; At the same time, the weak analog signal output by the sensor (the heat flow sensor is micro-voltage, and the temperature sensor is resistance or millivolt signal) is connected to a high-speed, high-precision, multi-channel synchronous data acquisition card (NI PXIe system).

[0060] The flexible heat flow-temperature composite sensor adopts a top-down layered stack structure and has good flexibility as a whole, which can be attached to the curved surface of a square or cylindrical battery. The overall structure is composed of the following functional layer sequence: 1. Protective and insulating outer layer 11, which is made of polyimide film or other high-performance polymers.

[0061] Used to protect the internal precise sensing elements from physical scratches or impacts; prevent the sensor from making electrical contact with the external environment or the battery shell, ensuring test safety; while having moisture-proof and chemical corrosion-resistant properties, ensuring the long-term stability of the sensor under complex working conditions.

[0062] 2. High-precision temperature sensor layer, which uses high-precision platinum resistance (such as PT100) or ultra-thin T-type thermocouple made by thin film process. This layer is integrated above the heat flow sensor and as close to its measurement area as possible. Used to directly and real-time measure the real temperature (T surface ) of the battery and sensor contact interface, providing boundary conditions for subsequent thermal model inversion calculation.

[0063] 3. Flexible heat flow sensor layer 7, the core is a thermocouple structure based on the Seebeck effect, composed of dozens of pairs or even hundreds of pairs of micro thermocouples (usually copper-constantan or other materials). These thermocouple junctions are arranged on the upper and lower planes of a flexible substrate (such as polyimide). Used to sense heat flux, when heat vertically passes through the sensor, a small temperature difference (ΔT ) will be generated in the thickness direction. The thermocouple converts this temperature difference into a proportional millivolt-level voltage signal (V). The output voltage V is calibrated after leaving the factory, which can be directly converted into heat flux density value (unit: W / m²), the relationship is ; where K is the calibration coefficient of the sensor.

[0064] 4. The heat-conducting and interface filling layer 12 is composed of flexible silicone pad with high thermal conductivity, thermal paste or low-melting phase change material. It is used to fill the air gap caused by all micro-unevenness between the sensor and the battery surface, thereby greatly reducing the contact thermal resistance; ensuring that the heat generated on the battery surface can be efficiently and losslessly transmitted to the sensing unit, improving the response speed and accuracy of the measurement; and endowing the sensor with overall flexibility, enabling it to conform to the slight deformation of the battery surface and achieve close fitting.

[0065] 5. The flexible substrate 13 is composed of polyimide film. As the structural basis of the entire sensor, it carries all functional layers and ensures that the sensor is electrically insulated from the battery surface.

[0066] Set the temperature of the isothermal environmental chamber 1 to 25°C. After the temperature stabilizes, discharge the battery at 1C constant current. The flexible heat flow-temperature composite sensor directly measures the heat flux emitted through the battery surface to the isothermal environmental chamber 1. At the same time, the flexible heat flow-temperature composite sensor measures the real temperature of the battery surface, and the high-speed data acquisition card synchronously acquires the voltage signals (converted to heat flux density q'') and temperature signals (T_s1, T_s2) of the two heat flow sensors at a frequency of 1 Hz. The signals are transmitted to the central processing unit.

[0067] The heat generation power calculation module and the internal temperature inversion module cooperatively calculate the instantaneous total heat generation power and the internal core temperature of the battery through a joint algorithm based on real-time parameter identification and adaptive Kalman filtering.

[0068] The specific process of the joint algorithm is as follows: 1. Heat generation power calculation module: Input: heat flux density of each sensor and its effective area covered .

[0069] Process: Perform operation: This calculation is performed in real time at each sampling period (e.g., 1 Hz).

[0070] Output: Instantaneous total heat generation power of the battery .

[0071] 2. Internal temperature inversion module - based on real-time parameter identification and adaptive unscented Kalman filter (AUKF): This module is the core of the innovation, aiming to dynamically correct model errors and optimally estimate internal states. It is composed of two coupled sub-modules: A. Real-time identification of model parameters sub-module Function: Online identification of the equivalent thermal conductivity of the battery kto solve the model mismatch problem caused by the change of battery state (such as temperature, SOC).

[0072] Input: Real-time measured surface temperature , instantaneous total heating power and heat flux density .

[0073] Process: 1) Construct the forward model: Establish a one-dimensional plate heat conduction forward model , which is based on the current assumed thermal conductivity k and heating power , can predict the surface temperature , and surface heat flux .

[0074] 2) Define the loss function: Construct a multi-objective loss function that combines temperature and heat flux fitting errors: ; Where is the multi-objective loss function, which represents the error between the model prediction and the measured value; is the temperature error weight coefficient; is the heat flux error weight coefficient; and are the measured surface temperatures on both sides, K; is the heat flux density calculated by the model, W / m²; is the heat flux density measured by the heat flux sensor, W / m².

[0075] 3) Optimization solution: In each time window (such as every 10 seconds), use gradient descent method or particle swarm optimization algorithm to solve the optimal thermal conductivity : ; Output: Real-time updated equivalent thermal conductivity that best reflects the current state of the battery .

[0076] B. Adaptive Unscented Kalman Filter (AUKF) State Estimation Submodule Function: Using the accurate model provided by the parameter identification submodule , and in the presence of measurement noise and process noise, optimally estimate the temperature field inside the battery, and finally output the core temperature.

[0077] Input: from the parameter identification submodule .

[0078] Real-time measured surface temperature (As an observation).

[0079] Instantaneous total heating power (As a control input).

[0080] Process: 1) State-space modeling: A one-dimensional thermal model of the battery (control equation: ) is discretized by finite difference method.

[0081] State equation: ; where, is the state vector (temperatures of all discrete nodes), is the input vector , is the nonlinear state transition function defined by the discretized heat conduction equation, is the process noise, and V is the total volume of the battery, m³.

[0082] Observation equation: ; where, is the observation vector (surface temperature measurements), H is the observation matrix, is the observation noise.

[0083] 2) AUKF iterative estimation: Prediction step: Based on the state estimation at the last time and the current model (using ), the predicted value of the state at the current time and its covariance are calculated through the unscented transformation.

[0084] Update step: After obtaining the new surface temperature measurements , the Kalman gain is calculated, and the state estimation is updated to obtain the optimal internal temperature field estimation .

[0085] Adaptive mechanism: The algorithm estimates and adjusts the process noise covariance matrix Q and the observation noise covariance matrix R online through the covariance matching technique, so that it can automatically adapt to the noise level changes under different working conditions, ensuring the stability and estimation accuracy of the filter.

[0086] Output: Calculated temperature at the center of the battery thickness (i.e., the temperature value of the corresponding center node in the state vector ).

[0087] Measured battery surface temperature, current and heating power as shown in Figure 4 , the battery internal temperature obtained by finite element simulation as shown in Figure 5 ​The heat generation power curve measured by the method of the application can capture the transient heat generation peak at the initial stage of discharge more than the traditional compensation power curve. More importantly, the center temperature calculated by real-time parameter identification and AUKF algorithm is highly consistent with the result simulated by high-precision finite element software even in complex working conditions of battery internal resistance change and heat conduction performance decline, proving that the method can still maintain high accuracy in dynamically changing environment and additionally provides the equivalent heat conduction coefficient change curve as a new dimension information reflecting the state of health (SOH) of the battery.

[0088] The above-described embodiments are merely intended to describe the preferred modes of the application, and are not intended to limit the scope of the application. Various modifications and improvements to the technical solutions of the application made by those of ordinary skill in the art without departing from the design spirit of the application shall fall within the protection scope of the application as defined by the claims.

Claims

1. An online detection system for the internal temperature and heating power of a lithium battery based on heat flow and surface temperature, characterized in that, The system includes: an isothermal environment chamber, a battery clamp, at least one heat flow sensor module, a data acquisition card, a main control system, a heating / cooling unit, a lithium battery, a high-precision surface temperature sensor, a flexible heat flow sensor layer, an electrical load module, and a flexible thermal conductive pad. The isothermal environment chamber is used to provide a constant temperature environment. Its inner wall is equipped with a heating / cooling unit, which is controlled by the main control system to maintain the set temperature. The battery clamp is used to secure the lithium battery; The lithium battery is the test object, and a high-precision surface temperature sensor and a flexible heat flow sensor layer are respectively attached to its surface. The high-precision surface temperature sensor adopts a thin-film platinum resistance or ultra-thin thermocouple structure for real-time measurement of battery surface temperature. The flexible heat flux sensor layer is a thermopile array based on the Seebeck effect, used to convert the heat flux density passing vertically through the battery surface into an electrical signal output. The electrical load module is connected to the lithium battery via wires and is used to apply charging and discharging current during the experiment to simulate the battery heating behavior under different working conditions. The flexible thermally conductive pad is disposed between the battery surface and the sensor layer to fill the microscopic gaps. The data acquisition card is used to synchronously acquire heat flux density and temperature signals in real time; The main control system is used to calculate the instantaneous total heat generation power of the battery based on the heat flux density signal; and to calculate the internal temperature of the battery based on the thermal model by combining the temperature signal, ambient temperature and instantaneous total heat generation power.

2. A method for online detection of internal temperature and heating power of a lithium battery based on heat flow and surface temperature, wherein the method is implemented by the system described in claim 1, characterized in that, The method includes: Step 1: Place the lithium battery with the attached high-precision surface temperature sensor and flexible heat flow sensor layer in an isothermal environment chamber; Step 2: Control the temperature of the isothermal environment chamber to stabilize to the set value; Step 3: Apply an electrical load to the lithium battery; Step 4: Real-time synchronous acquisition of heat flux density and temperature signals on the surface of the lithium battery; Step 5: Calculate the instantaneous total heat generation power of the battery based on the heat flux density signal; Step 6: Combine the temperature signal, ambient temperature, and instantaneous total heat generation power to calculate the internal temperature of the battery based on the thermal model inversion.

3. The method according to claim 2, characterized in that, The method for calculating the instantaneous total heat generation power of a battery based on the heat flux density signal is as follows: ； in, A is the heat flux density measured by the heat flux sensor; A is the effective heat dissipation area in contact between the battery and the sensor.

4. The method according to claim 3, characterized in that, The method for constructing the thermal model is as follows: Based on the actual dimensions of the lithium battery to be tested, a three-dimensional solid geometric model is created. Using the finite element analysis method, the battery geometry is discretized into several volume elements, i.e., mesh elements. Assign material thermophysical properties to all mesh elements; For each grid cell, the governing equation, namely the transient heat conduction equation, is set; Boundary conditions, initial conditions, and source terms are set for each mesh cell to complete the construction of the thermal model.

5. The method according to claim 4, characterized in that, The transient heat conduction equation is: ； in, It refers to the density of the battery material; t is the specific heat capacity of the battery material; T is the temperature; t is the time; k is the thermal conductivity of the battery material. It is the internal heat source generation rate. This is the gradient operator.

6. The method according to claim 5, characterized in that, Methods for calculating the internal temperature of a battery based on thermal model inversion include: By using the finite element method, the governing equations are approximated by weighted residuals on each mesh element, transforming the continuous partial differential equations into a single equation relating to the temperature at all nodes. The system of ordinary differential equations: ； in, The heat capacity matrix is ​​formed by... and c p Decide; It is the heat conduction matrix, determined by the thermal conductivity. It is the thermal load vector; Discretizing the time derivative term, for time step n+1, the governing equations become a system of linear equations: ； in, It is the time step; It is the heat load vector at time n+1, which includes the volumetric heat source term q and the influence of boundary conditions; It is the temperature node vector at time n; At each time step, the system of linear equations is solved to obtain the temperature at all grid nodes at the corresponding time. Battery core temperature This refers to the temperature value located at the geometric center node of the battery.

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