Lithium battery energy state and core temperature estimation method and related device

Abstract

The invention provides a lithium battery energy state and core temperature estimation method and a related device, and relates to the technical field of lithium battery state estimation. The method comprises the following steps: establishing a lithium battery electrothermal coupling model, including establishing a battery electrical characteristic and thermal characteristic coupling model, and identifying electrothermal model parameters and an open-circuit voltage-residual available energy curve; the method comprises the following steps: firstly, carrying out battery electrothermal model parameter identification through a particle swarm optimization algorithm according to lithium battery voltage, current and temperature data measured offline; and secondly, designing an active-disturbance-rejection battery power optimization controller for electrothermal coupling calibration, regulating and controlling the voltage and the surface temperature of the lithium battery electrothermal coupling model on line through the active-disturbance-rejection battery power optimization controller to always track the actual battery state, and finally obtaining the battery energy state and the core temperature. Accurate estimation of the battery energy state and the core temperature is achieved by continuously calibrating the battery model state tracking actual measurement value, the hardware cost is reduced, and the method has the advantages of being high in estimation precision and easy to achieve.

Description

Technical Field

[0001] The present application relates to the technical field of lithium battery state estimation, and in particular to a lithium battery energy state and core temperature estimation method and related devices. Background Art

[0002] Lithium batteries are important energy storage and conversion carriers for current energy storage systems, electric vehicles and consumer electronics, and their operating parameters directly affect the overall performance of the system. Accurate and efficient acquisition of battery status is conducive to improving the performance of battery management systems, improving energy utilization efficiency and alleviating performance degradation, thereby achieving efficient utilization of lithium battery systems. At the same time, accurate state estimation will also reduce consumers' anxiety about driving range, failure risk and charging time. Lithium battery state estimation methods can be divided into data-driven and model-based estimation. Among them, model-based estimation methods have the advantages of high accuracy, strong reliability and good interpretability and are widely studied. Existing model-based estimation methods usually combine equivalent circuit models or electrochemical models with various filters or observers to predict lithium battery status. Its working process is: according to real-time data such as battery voltage, current and temperature, combined with prediction models and state observers, the battery status can be estimated. Most of the current research only focuses on the battery state of charge, that is, the ratio of the current available capacity to the rated capacity, while the state of charge cannot fully reflect the remaining available energy of the battery, which in turn causes certain difficulties in realizing the prediction of battery available energy. The battery energy state combines factors such as capacity, internal resistance and state of charge, and is more suitable for battery management systems. Furthermore, the hardware complexity and control cost of the above methods are high, which is not conducive to widespread application in the industrial field.

[0003] Therefore, how to achieve accurate estimation of the energy state and core temperature of lithium batteries has become a technical problem that needs to be solved urgently. Summary of the invention

[0004] In order to achieve accurate estimation of the energy state and core temperature of a lithium battery, the present application provides a method for estimating the energy state and core temperature of a lithium battery and a related device.

[0005] In the first aspect, the present application provides a lithium battery energy state and core temperature estimation method using the following technical solutions:

[0006] A lithium battery energy state and core temperature estimation method, comprising:

[0007] Establishing an electrothermal coupling model for lithium batteries, including building a coupling model of battery electrical and thermal characteristics, identifying electrothermal model parameters and open circuit voltage-remaining available energy curve;

[0008] An auto-disturbance rejection battery power optimization controller with electrothermal coupling calibration is designed. The voltage and surface temperature of the lithium battery electrothermal coupling model are regulated online by the auto-disturbance rejection battery power optimization controller to always track the actual battery state, and finally the battery energy state and core temperature are obtained.

[0009] Optionally, the battery electrical characteristic model adopts a first-order RC equivalent circuit model. The parameters of the lithium battery identification electrothermal model include internal resistance Re, polarization resistance Rp and polarization capacitance Cp. The battery open circuit voltage-remaining available energy curve is obtained by fitting according to the number of pulse discharges.

[0010] V ocv =677.74392·SOE 8 -2730.08797 SOE 7 +4492.55473 SOE 6 -3875.47578 SOE 5 +1862.83645 SOE 4 -483.01806 SOE 3 +56.06453 SOE 2 +0.67403·SOE+2.91103, V ocv is the open circuit voltage of the battery, and SOE is the battery energy state.

[0011] Optionally, the battery thermal characteristic model adopts a Cauer thermal lumped model, and the thermal characteristic model parameters include the lithium battery surface heat capacity CC, core heat capacity CS, thermal conduction impedance RC, and convection impedance Ra.

[0012] Optionally, the battery electrothermal dynamics discrete equation designed for model identification is expressed as

[0013] Among them, V p is the battery polarization voltage, Δt s is the sampling interval, the time constant τ p =R p ×C p , I b

[0014] is the battery current, V b is the battery terminal voltage; the battery open circuit voltage is obtained by a nonlinear function with the energy state SOE as input; η b is the battery energy efficiency, E b Indicates the rated energy of the battery; T a Indicates the ambient temperature, H b,k is external heat transfer, T c is the core temperature, T sis the surface temperature, dV ocv / dT c is the entropy thermal coefficient.

[0015] Optionally, the self-disturbance rejection battery power optimization controller includes: a tracking differentiator, a nonlinear error feedback controller and an extended state observer.

[0016] In summary, the present application includes the following beneficial technical effects:

[0017] This application establishes a lithium battery electrothermal coupling model, including constructing a battery electrical and thermal characteristics coupling model, identifying electrothermal model parameters and open circuit voltage-remaining available energy curve; designing an electrothermal coupling calibrated self-disturbance rejection battery power optimization controller, and online regulating the voltage and surface temperature of the lithium battery electrothermal coupling model through the self-disturbance rejection battery power optimization controller to always track the actual battery state, and finally obtain the battery energy state and core temperature. By continuously calibrating the battery energy state and core temperature, accurate estimation of the battery energy state and core temperature is achieved, hardware cost is reduced, and it has the advantages of high estimation accuracy and easy implementation. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 It is a flowchart of the first embodiment of the lithium battery energy state and core temperature estimation method of the present application;

[0019] Figure 2 This is a control logic block diagram of the first embodiment of the lithium battery energy state and core temperature estimation method of the present application;

[0020] Figure 3 This is a flow chart of the principle of self-disturbance rejection battery power optimization control of the first embodiment of the lithium battery energy state and core temperature estimation method of the present application;

[0021] Figure 4 It is a real-time tracking curve diagram of the battery terminal voltage under the DST current condition of the first embodiment of the lithium battery energy state and core temperature estimation method of the present application;

[0022] Figure 5 It is a real-time tracking curve diagram of the battery surface temperature under the DST current condition of the first embodiment of the lithium battery energy state and core temperature estimation method of the present application;

[0023] Figure 6 It is a battery energy state estimation curve diagram under the DST current condition of the first embodiment of the lithium battery energy state and core temperature estimation method of the present application;

[0024] Figure 7 This is a battery core temperature estimation curve diagram under the DST current condition of the first embodiment of the lithium battery energy state and core temperature estimation method of the present application. DETAILED DESCRIPTION

[0025] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below through the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0026] The present application embodiment provides a lithium battery energy state and core temperature estimation method, referring to Figure 1 , Figure 1 This is a flow chart of the first embodiment of the lithium battery energy state and core temperature estimation method of the present application.

[0027] In this embodiment, the lithium battery energy state and core temperature estimation method includes the following steps:

[0028] Step S10: Establishing an electrothermal coupling model of a lithium battery, including constructing a coupling model of battery electrical and thermal characteristics, identifying electrothermal model parameters and an open circuit voltage-remaining available energy curve.

[0029] It should be noted that the battery electrical characteristic model adopts a first-order RC equivalent circuit model. The parameters of the lithium battery identification electrothermal model include internal resistance Re, polarization resistance Rp and polarization capacitance Cp. The battery open circuit voltage-remaining available energy curve is obtained by fitting according to the number of pulse discharges.

[0030] V ocv =677.74392·SOE 8 -2730.08797 SOE 7 +4492.55473 SOE 6 -3875.47578 SOE 5 +1862.83645 SOE 4 -483.01806 SOE 3 +56.06453 SOE 2 +0.67403·SOE+2.91103, V ocv is the open circuit voltage of the battery, and SOE is the battery energy state.

[0031] It is understandable that the battery thermal characteristic model adopts the Cauer thermal lumped model, and the thermal characteristic model parameters include the lithium battery surface heat capacity CC, core heat capacity CS, thermal conduction impedance RC

[0032] And the convection resistance Ra.

[0033] It should be noted that the battery electrothermal dynamics discrete equation designed for model identification is expressed as

[0034] for

[0035]

[0036] Among them, V p is the battery polarization voltage, Δt s is the sampling interval, the time constant τ p =R p ×C p , I b is the battery current, V b is the battery terminal voltage; the battery open circuit voltage is obtained by a nonlinear function with the energy state SOE as input; η b is the battery energy efficiency, E b Indicates the rated energy of the battery; T a Indicates the ambient temperature, H b,k is external heat transfer, T c is the core temperature, T s is the surface temperature, dV ocv / dT c is the entropy thermal coefficient.

[0037] In the specific implementation, Figure 2 As shown, the control logic block diagram of the execution body of this embodiment includes three parts: a lithium battery electrothermal characteristic test platform, a lithium battery electrothermal coupling model and an anti-disturbance battery power optimization controller. Specific working process: In the lithium battery energy state and core temperature joint estimation method, first initialize the initial value of the internal state of the battery electrothermal coupling model, and then the anti-disturbance battery power optimization controller adjusts the input power of the coupling model in real time according to the difference between the terminal voltage and surface temperature output by the coupling model and the actual battery terminal voltage and surface temperature measured, and makes the coupling model output always track the actual battery state, and finally the energy state and core temperature estimation of the lithium battery can be obtained according to the internal state of the coupling model.

[0038] It should be noted that the experimental data for offline identification and accuracy testing of the electrothermal coupling model parameters of lithium batteries are obtained as follows: first, the lithium battery is placed in the fixture of the battery charge and discharge tester, and thermocouple sensors are installed on the surface and core of the battery and then placed in a temperature control box; the temperature control box is set to work at 25°C, and the pulse discharge test and dynamic operating condition test data are loaded in the battery charge and discharge tester respectively; finally, the collected lithium battery terminal voltage, current, core temperature and surface temperature are preprocessed.

[0039] Step S20: Design an electrothermal coupling calibrated self-disturbance rejection battery power optimization controller, and use the self-disturbance rejection battery power optimization controller to online regulate the voltage and surface temperature of the lithium battery electrothermal coupling model to always track the actual battery state, and finally obtain the battery energy state and core temperature.

[0040] It can be understood that the ADRC battery power optimization controller includes: a tracking differentiator, a nonlinear error feedback controller, and an extended state observer.

[0041] It can be understood that this embodiment uses the feedback of the electrothermal characteristics of lithium batteries to achieve the joint estimation of energy state and core temperature without current sensors. The battery electrothermal model parameters are identified through the particle swarm optimization algorithm based on the offline measured lithium battery voltage, current and temperature data; the input current of the lithium battery electrothermal coupling model is regulated online in combination with the self-anti-disturbance battery power optimization controller, so that the model terminal voltage and surface temperature respectively track the actual battery terminal voltage and surface temperature, thereby continuously calibrating the battery energy state and core temperature.

[0042] It should be noted that in this embodiment, based on the constructed battery joint estimation test platform, the dynamic stress test (DST) of the automobile is used to perform charge and discharge tests on the battery, and the obtained lithium battery terminal voltage, current and temperature data are applied to the proposed lithium battery energy state and core temperature estimation method based on electrothermal coupling calibration. The lithium battery energy state and core temperature curves are compared to analyze the estimation accuracy of the method proposed in the present invention.

[0043] In the specific implementation, Figure 3 The self-disturbance rejection battery power optimization control principle flow chart shown in the figure designs an electrothermal coupling calibrated self-disturbance rejection battery power optimization controller, whose purpose is to online regulate the coupling model voltage and surface temperature to always track the actual battery state, and finally obtain the battery energy state and core temperature.

[0044] In the specific implementation, a lithium battery energy state and core temperature joint estimation test bench is built, and the acquired battery data is loaded into the test platform to obtain the energy state and core temperature joint estimation results, including the estimation results based on the self-disturbance rejection battery power optimization controller.

[0045] The acquired battery terminal voltage and surface temperature data are loaded into the battery energy state and core temperature joint estimation simulation model to obtain the lithium battery SOE and SOT estimation results, and the estimated values ​​are compared with the actual measured values ​​to obtain the final comparison results.

[0046] in, Figure 4 It is the real-time tracking curve of battery terminal voltage under DST current condition;

[0047] Figure 5 It is a real-time tracking curve of battery surface temperature under DST current condition;

[0048] Figure 6 It is the battery energy state estimation curve under DST current condition;

[0049] Figure 7 It is the battery core temperature estimation curve under DST current condition.

[0050] This embodiment establishes a lithium battery electrothermal coupling model, including constructing a battery electrical and thermal characteristics coupling model, identifying electrothermal model parameters and an open circuit voltage-remaining available energy curve; designing an electrothermal coupling calibrated self-disturbance rejection battery power optimization controller, and online-regulating the voltage and surface temperature of the lithium battery electrothermal coupling model through the self-disturbance rejection battery power optimization controller to always track the actual battery state, and finally obtain the battery energy state and core temperature. By continuously calibrating the battery energy state and core temperature, accurate estimation of the battery energy state and core temperature is achieved, hardware costs are reduced, and it has the advantages of high estimation accuracy and easy implementation.

[0051] It should be understood that the above is only an example and does not constitute any limitation on the technical solution of the present application. In specific applications, technicians in this field can make settings as needed, and the present application does not impose any limitation on this.

[0052] It should be noted that the workflow described above is merely illustrative and does not limit the scope of protection of the present application. In practical applications, technicians in this field can select part or all of it according to actual needs to achieve the purpose of the present embodiment, and no limitation is made here.

[0053] In addition, for technical details not fully described in this embodiment, please refer to the method for estimating the energy state and core temperature of the lithium battery provided in any embodiment of the present application, which will not be repeated here.

[0054] In addition, it should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or system including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, method, article or system. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the existence of other identical elements in the process, method, article or system including the element.

[0055] The serial numbers of the above-mentioned embodiments of the present application are for description only and do not represent the advantages or disadvantages of the embodiments.

[0056] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as a read-only memory (ROM) / RAM, a magnetic disk, or an optical disk), and includes a number of instructions for a terminal device (which can be a mobile phone, a computer, a server, or a network device, etc.) to execute the methods of each embodiment of the present application.

[0057] The above are only preferred embodiments of the present application, and are not intended to limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made using the contents of the present application specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present application.

Claims

1. A method for estimating energy state and core temperature of a lithium battery, characterized in that: include: Establishing an electrothermal coupling model for lithium batteries, including building a coupling model of battery electrical and thermal characteristics, identifying electrothermal model parameters and open circuit voltage-remaining available energy curve; An auto-disturbance rejection battery power optimization controller with electrothermal coupling calibration is designed. The voltage and surface temperature of the lithium battery electrothermal coupling model are regulated online by the auto-disturbance rejection battery power optimization controller to always track the actual battery state, and finally the battery energy state and core temperature are obtained.

2. The lithium battery energy state and core temperature estimation method according to claim 1, characterized in that: The battery electrical characteristic model adopts the first-order RC equivalent circuit model. The parameters of the lithium battery identification electrothermal model include internal resistance Re, polarization resistance R p and polarization capacitance C p. The battery open circuit voltage-remaining available energy curve is obtained by fitting according to the number of pulse discharges. V ocv =677.74392·SOE 8 -2730.08797 SOE 7 +4492.55473 SOE 6 -3875.47578 SOE 5 +1862.83645 SOE 4 -483.01806 SOE 3 +56.06453 SOE 2 +0.67403·SOE+2.91103, V ocv is the open circuit voltage of the battery, and SOE is the battery energy state.

3. The lithium battery energy state and core temperature estimation method according to claim 1, characterized in that: The battery thermal characteristic model adopts the Cauer thermal lumped model. The thermal characteristic model parameters include the lithium battery surface heat capacity CC, core heat capacity CS, thermal conduction impedance RC and convection impedance Ra.

4. The lithium battery energy state and core temperature estimation method according to claim 1, characterized in that: The battery electrothermal dynamics discrete equation designed by model identification is expressed as Among them, V p is the battery polarization voltage, Δt s is the sampling interval, the time constant τ p =R p ×C p , I b is the battery current, V b is the battery terminal voltage; the battery open circuit voltage is obtained by a nonlinear function with the energy state SOE as input; η b is the battery energy efficiency, E b Indicates the rated energy of the battery; T a Indicates the ambient temperature, H b,k is external heat transfer, T c is the core temperature, T s is the surface temperature, dV ocv / dT c is the entropy thermal coefficient.

5. The lithium battery energy state and core temperature estimation method according to claim 1, characterized in that: The self-disturbance rejection battery power optimization controller includes: a tracking differentiator, a nonlinear error feedback controller and an extended state observer.

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