WO2023201781A1 - 基于锂离子电池电化学模型功率特性的荷电状态更新方法 - Google Patents

基于锂离子电池电化学模型功率特性的荷电状态更新方法 Download PDF

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WO2023201781A1
WO2023201781A1 PCT/CN2022/091037 CN2022091037W WO2023201781A1 WO 2023201781 A1 WO2023201781 A1 WO 2023201781A1 CN 2022091037 W CN2022091037 W CN 2022091037W WO 2023201781 A1 WO2023201781 A1 WO 2023201781A1
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current
battery
charge
state
moment
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French (fr)
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陈启鑫
陈远博
顾宇轩
郭鸿业
郑可迪
吕睿可
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清华大学
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
    • G01R31/396—Acquisition or processing of data for testing or for monitoring individual cells or groups of cells within a battery
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
    • G01R31/367—Software therefor, e.g. for battery testing using modelling or look-up tables
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
    • G01R31/378—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC] specially adapted for the type of battery or accumulator
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
    • G01R31/385—Arrangements for measuring battery or accumulator variables
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16C—COMPUTATIONAL CHEMISTRY; CHEMOINFORMATICS; COMPUTATIONAL MATERIALS SCIENCE
    • G16C20/00—Chemoinformatics, i.e. ICT specially adapted for the handling of physicochemical or structural data of chemical particles, elements, compounds or mixtures
    • G16C20/30—Prediction of properties of chemical compounds, compositions or mixtures
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E60/00—Enabling technologies; Technologies with a potential or indirect contribution to GHG emissions mitigation
    • Y02E60/10—Energy storage using batteries

Definitions

  • the present application relates to the technical field of lithium-ion battery energy management operation, and in particular to a state-of-charge update method and device based on the power characteristics of the lithium-ion battery electrochemical model.
  • lithium-ion batteries are widely used in power, transportation and other business fields.
  • the battery state of charge is one of the key information that managers pay attention to.
  • lithium-ion battery models can be mainly divided into four categories in engineering applications: water tank models, equivalent circuit models, data-driven black box models, and electrochemical models based on chemical reaction mechanisms.
  • the water tank model and the equivalent circuit model are the most widely used in engineering.
  • the battery voltage is determined or measured by the manufacturer and treated as a constant, such as those from MIT and Argonne National Laboratory.
  • Scholars use an equivalent circuit model when studying the arbitrage ability of batteries in microgrids, treating the battery voltage as a constant value and using the port power to directly update the state of charge.
  • state estimation methods are generally used to obtain the state of charge in continuous time series simulations.
  • the present application aims to solve, at least to a certain extent, one of the technical problems in the related art.
  • the purpose of this application is to propose a state-of-charge update method based on the power characteristics of the electrochemical model of lithium-ion batteries, which solves the technical problem of mutually exclusive update accuracy and efficiency of the state-of-charge of lithium-ion batteries in the existing method, and utilizes
  • the lithium-ion battery electrochemical model has the characteristics of high external characteristic simulation accuracy. It considers the impact of the battery's time-varying port voltage on the state of charge update without increasing the computational complexity, and realizes the situation where the battery power and state of charge are known. Under this method, there is no need to perform continuous sequential simulation to complete the update of the battery state of charge, which improves the calculation efficiency of the electrochemical model in power application scenarios and expands the application scenarios of the electrochemical model in engineering.
  • the first embodiment of the present application proposes a state-of-charge update method based on the power characteristics of a lithium-ion battery electrochemical model, including: S1: Obtaining the initial state-of-charge of the battery and a constant-amplitude current sequence.
  • S2 Obtain the battery's initial state information, and obtain the battery port voltage at each moment within the preset time period based on the battery's initial state information and lithium-ion battery electrochemical model simulation;
  • S3 Calculate based on the battery port voltage and current sequence amplitude Obtain the port power corresponding to the initial state of charge;
  • S4 Adjust the initial state of charge and current sequence amplitude, repeat steps S1-S3, and obtain the port power corresponding to multiple different initial states of charge and current sequence amplitudes.
  • the initial state information of the battery includes: the surface lithium concentration of the electrode active material, the average lithium concentration of the electrode active material, the electrode electrolyte lithium concentration, and the initial value of the battery temperature.
  • the battery port voltage at each moment within the preset time period is obtained, including:
  • the battery environment temperature sequence includes the battery environment temperature at each moment within the preset time period.
  • the parameter vector at the current moment is updated based on the lithium concentration of the electrode electrolyte, the average lithium concentration of the electrode active material and the battery temperature at the previous moment:
  • ⁇ (k+1) is the parameter vector at the current moment
  • f ⁇ is the parameter update function
  • c e (k) is the electrode electrolyte lithium concentration at the previous moment
  • c s is the average electrode active material at the previous moment Lithium concentration
  • T b (k) is the battery temperature at the last moment;
  • the reaction current intensity at the current moment is updated:
  • j n (k+1) is the reaction current intensity at the current moment
  • f j is the reaction current update function
  • c s is the reaction current update function
  • surf (k) is the lithium concentration on the surface of the electrode active material at the previous moment
  • I(k) is the previous moment port current
  • ⁇ se (k+1) is the electrode surface potential difference at the current moment
  • f ⁇ is the electrode surface potential difference update function
  • c s,surf (k+1) f surf (c s,av (k),c s,surf (k),j n (k+1), ⁇ (k+1), ⁇ t)
  • c s,av (k+1) is the average lithium concentration of the electrode active material at the current moment
  • f av is the average lithium concentration update function of the electrode active material
  • ⁇ t is the sampling interval
  • c s,surf (k+1) is the current moment
  • f surf is the update function of the lithium concentration on the surface of the electrode active material
  • c e (k+1) is the electrode electrolyte lithium concentration at the current moment
  • f e is the electrode electrolyte lithium concentration update function
  • the lithium concentration of the electrode electrolyte at the current moment the lithium concentration on the surface of the electrode active material, the reaction current intensity, the parameter vector, the battery temperature and the port current at the previous moment, the battery port voltage V and the internal potential difference U of the battery are obtained at the current moment:
  • V(k+1) f V (c e (k+1),c s,surf (k+1),j n (k+1),T b (k),I(k), ⁇ (k +1))
  • V(k+1) is the battery port voltage at the current moment
  • f V is the battery port voltage update function
  • U(k+1) is the potential difference within the battery at the current moment
  • f U is the potential difference update function within the battery
  • the battery port voltage at the current moment the potential difference within the battery, the reaction current intensity, the parameter vector, the battery temperature at the previous moment, the ambient temperature, the port current and the sampling interval, the battery temperature at the current moment is obtained:
  • T b (k+1) f T (V(k+1),U(k+1),j n (k+1),T b (k),T amb (k),I(k), ⁇ (k+1), ⁇ t)
  • T b (k+1) is the battery temperature at the current moment
  • f T is the battery temperature update function
  • T amb (k) is the ambient temperature at the previous moment
  • the battery port voltage at each moment within the preset time period can be expressed as
  • V [V 1 V 2 ...V k ...V N ]
  • V is the battery port voltage at each moment in the preset time period
  • V k is the battery port voltage value at the kth moment
  • N is the total number of moments in the preset time period, where N is an integer greater than or equal to 2.
  • the port power corresponding to the initial state of charge is calculated based on the battery port voltage and current sequence amplitude, including:
  • the average port voltage within the preset time period is obtained. Based on the average port voltage and current sequence amplitude, the port power corresponding to the initial state of charge and current sequence amplitude is calculated.
  • the average port voltage is expressed as:
  • the port power is expressed as:
  • P is the port power
  • SOC 0 is the initial state of charge
  • T amb is the set battery ambient temperature value
  • I is the current sequence amplitude.
  • the initial state of charge and current sequence amplitude are adjusted, and steps S1-S3 are repeated to obtain port powers corresponding to multiple different initial states of charge and current sequence amplitudes.
  • the port power Obtain the current amplitude-state-of-charge-port power surface, including:
  • the current amplitude-state-of-charge-port power surface is fitted to a plane equation, expressed as:
  • A, B, and C are the linear fitting coefficients of the plane
  • D is the constant coefficient of the plane fitting.
  • the plane equation is used to obtain the current amplitude corresponding to the port power and state of charge, and the state of charge is updated according to the current amplitude and the preset time period, including :
  • the current amplitude is expressed as:
  • I is the current amplitude
  • f I is the current amplitude function
  • a 0 is the constant coefficient of the current amplitude function
  • a 1 is the linear coefficient corresponding to SOC 0 in the current amplitude function
  • a 2 is P in the current amplitude function The corresponding linear coefficient
  • a 0 , a 1 , a 2 can be derived from the plane fitting coefficients A, B, C, D:
  • the state of charge is updated according to the current amplitude and the preset time period, which is expressed as:
  • ⁇ SOC is the change in state of charge in adjacent time periods
  • C 0 is the total battery capacity in ampere hours
  • ⁇ T is the length of the preset time period
  • SOC T+1 is the battery charge at the beginning of the next time period.
  • SOC T is the battery state of charge at the beginning of this time period.
  • the second embodiment of the present application proposes a state-of-charge updating device based on the power characteristics of the lithium-ion battery electrochemical model, including:
  • the acquisition module is used to acquire the initial state of charge of the battery and the current sequence of constant amplitude
  • the processing module is used to obtain the battery initial state information, and obtain the battery port voltage at each moment within the preset time period based on the battery initial state information and lithium-ion battery electrochemical model simulation;
  • the calculation module is used to calculate the port power corresponding to the initial state of charge based on the battery port voltage and current sequence amplitude;
  • the cycle module is used to adjust the initial state of charge and current sequence amplitude. It repeatedly calls the acquisition module, processing module and calculation module to obtain the port power corresponding to multiple different initial states of charge and current sequence amplitudes. According to the port Power, obtain the current amplitude-state of charge-port power surface;
  • Fitting module used to fit the current amplitude-state-of-charge-port power surface into a plane equation
  • the update module is used to obtain the current amplitude corresponding to the port power and the state of charge by using plane equations based on the port power and the state of charge, and update the state of charge based on the current amplitude and the preset time period.
  • the third embodiment of the present application provides an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor.
  • the processor executes the program Implement the state-of-charge update method based on the power characteristics of the lithium-ion battery electrochemical model as described above.
  • the fourth embodiment of the present application proposes a non-transitory computer-readable storage medium on which a computer program is stored.
  • the computer program is executed by a processor, the above-mentioned lithium-ion battery-based storage medium is implemented.
  • the fifth embodiment of the present application proposes a computer program product, including a computer program.
  • the computer program When executed by a processor, the computer program implements the above-mentioned charge based on the electrochemical model power characteristics of lithium ion batteries. Power status update method.
  • the state-of-charge update method based on the power characteristics of the lithium-ion battery electrochemical model, the state-of-charge update device based on the power characteristics of the lithium-ion battery electrochemical model, and the non-transitory computer storage medium of the embodiments of the present application solve the problem of existing methods.
  • the technical problem of lithium-ion battery state-of-charge update accuracy and efficiency being mutually exclusive is to take advantage of the high accuracy of external characteristic simulation of the lithium-ion battery electrochemical model to consider the impact of the battery's time-varying port voltage on charge without increasing computational complexity.
  • the impact of state update enables the update of the battery state of charge without the need for continuous sequential simulation when the battery power and state of charge are known, which improves the calculation efficiency of the electrochemical model in power application scenarios and expands The application scenarios of electrochemical models in engineering are discussed.
  • Figure 1 is a flow chart of a state-of-charge update method based on the power characteristics of a lithium-ion battery electrochemical model provided in Embodiment 1 of the present application;
  • Figure 2 is a schematic structural diagram of a lithium-ion battery cell based on the state-of-charge update method based on the power characteristics of the lithium-ion battery electrochemical model according to the embodiment of the present application;
  • Figure 3 is another flow chart of a state-of-charge update method based on the power characteristics of a lithium-ion battery electrochemical model according to an embodiment of the present application;
  • FIG. 4 is a schematic structural diagram of a state-of-charge updating device based on the power characteristics of a lithium-ion battery electrochemical model provided in Embodiment 2 of the present application.
  • This application proposes a state-of-charge update method based on the power characteristics of the lithium-ion battery electrochemical model. It takes advantage of the high accuracy of simulation of the external characteristics of the lithium-ion battery electrochemical model to consider the time-varying battery without increasing computational complexity.
  • the impact of the port voltage on the update of the state of charge enables the update of the battery state of charge to be completed without the need for continuous sequential simulation when the battery power and state of charge are known, improving the performance of the electrochemical model in power application scenarios.
  • the computational efficiency expands the application scenarios of electrochemical models in engineering.
  • the lithium-ion electrochemical model consists of a set of nonlinear high-order differential state equations, which provides a more accurate internal state by accurately describing the internal chemical reactions of the battery. information and external characteristic information.
  • Multivariate equation fitting technology Multivariate equation fitting uses a smooth surface to connect a series of sample points in a three-dimensional or high-dimensional space, so that the surface can approximate the distribution of sample points. In this application, plane fitting in three-dimensional space is utilized, involving sample point fitting of three variables, thereby giving the fitting coefficient to be obtained.
  • Figure 1 is a flow chart of a state-of-charge updating method based on the power characteristics of a lithium-ion battery electrochemical model provided in Embodiment 1 of the present application.
  • the state-of-charge update method based on the power characteristics of the lithium-ion battery electrochemical model includes the following steps:
  • S2 Obtain the battery's initial state information, and obtain the battery port voltage at each moment within the preset time period based on the battery's initial state information and lithium-ion battery electrochemical model simulation;
  • the state-of-charge update method based on the power characteristics of the lithium-ion battery electrochemical model according to the embodiment of the present application, it can solve the technical problem of mutually exclusive update accuracy and efficiency of the lithium-ion battery state-of-charge update method in the existing method, and utilize the electrochemistry of the lithium-ion battery.
  • the external characteristics of the model have high simulation accuracy.
  • the impact of the battery's time-varying port voltage on the state-of-charge update is considered without increasing computational complexity. This eliminates the need for continuous operation when the battery power and state-of-charge are known. Sequential simulation completes the update of the battery state of charge, improves the calculation efficiency of the electrochemical model in power application scenarios, and expands the application scenarios of the electrochemical model in engineering.
  • This application proposes a state-of-charge update method based on the power characteristics of the lithium-ion battery electrochemical model.
  • the electrochemical model is used to describe the time-varying characteristics of the lithium-ion battery port voltage that changes with the change of the state of charge.
  • the current amplitude is constructed.
  • the power-current characteristic description method of state-of-charge-port power surface and plane fitting can avoid long-term continuous sequential simulation of electrochemical models in battery energy management while improving the accuracy of state update. This method is used to test the battery in a short period of time, and the fitting plane is used to describe the power-current characteristics based on the electrochemical model. In the long period of time, the above characteristic plane equation is used to perform calculations related to battery energy operation management.
  • This application retains the time-varying characteristics of the port voltage during the operation of the lithium-ion battery without increasing the computational complexity of battery energy operation management.
  • This application can apply the lithium-ion battery electrochemical model to long-term planning, joint scheduling, etc. Optimize scenarios without the need for continuous sequential simulation of electrochemical models with high computational complexity, provide battery energy operation management with state-of-charge update information that takes into account accuracy and efficiency, and support efficient and economical operation of lithium-ion batteries in different scenarios. It has important practical significance and good application prospects.
  • T amb the battery ambient temperature
  • I is the current sequence amplitude
  • N is the total number of moments in the preset time period
  • the current action period at each moment is t k ⁇ t ⁇ t k + 1
  • the initial state information of the battery includes: the lithium concentration on the surface of the electrode active material, the average lithium concentration of the electrode active material, the lithium concentration of the electrode electrolyte, and the initial value of the battery temperature.
  • the initial state information of the battery including: obtaining the type of electrode active material used in the electrode to be analyzed, querying the average lithium concentration of the electrode active material corresponding to the maximum and minimum state of charge of the electrode active material, and based on the relationship between the state of charge and the average lithium concentration. The proportional relationship is used to obtain the initial value of the average lithium concentration of the electrode active material.
  • the lithium concentration of the electrode active material is initially uniformly distributed.
  • the lithium concentration on the surface of the electrode active material is equal to the initial value of the average lithium concentration.
  • the initial value of the electrode electrolyte lithium concentration is obtained according to the parameter settings. value, the initial value of the battery temperature is set to the ambient temperature;
  • the average lithium concentration of the electrode active material is:
  • the lithium concentration on the surface of the electrode active material is:
  • the lithium concentration of the electrode electrolyte is:
  • the initial value of battery temperature is:
  • f init,c is the initial value setting function of the average lithium concentration of the electrode active material, is the theoretical minimum value of the average lithium concentration of the positive and negative electrode active materials of the battery, is the theoretical maximum value of the average lithium concentration of the positive and negative electrode active materials of the battery
  • SOC 0 is the initial state of charge
  • c e (0) is the initial value of the electrode electrolyte lithium concentration
  • f init is the initial value setting function of the electrode electrolyte lithium concentration
  • c e0 is the electrode electrolyte lithium concentration material parameter
  • T b (0) is the initial value of battery temperature
  • T amb is the battery ambient temperature.
  • the battery port voltage at each moment within the preset time period is obtained, including:
  • the battery environment temperature sequence includes the battery environment temperature at each moment within the preset time period, where the environment temperature sequence can be recorded as:
  • T amb is the battery ambient temperature
  • N is the total number of moments in the preset time period
  • the battery ambient temperature action period at each moment is t k ⁇ t ⁇ t k + 1
  • the parameter vector at the current moment is updated based on the lithium concentration of the electrode electrolyte, the average lithium concentration of the electrode active material and the battery temperature at the previous moment:
  • ⁇ (k+1) is the parameter vector at the current moment
  • f ⁇ is the parameter update function
  • c e (k) is the electrode electrolyte lithium concentration at the previous moment
  • c s is the average electrode active material at the previous moment Lithium concentration
  • T b (k) is the battery temperature at the last moment;
  • the reaction current intensity at the current moment is updated:
  • j n (k+1) is the reaction current intensity at the current moment
  • f j is the reaction current update function
  • c e (k) is the electrode electrolyte lithium concentration at the previous moment
  • c s, surf (k) is the electrode at the previous moment Lithium concentration on the surface of the active material
  • T b (k) is the battery temperature at the previous moment
  • I (k) is the port current at the previous moment
  • ⁇ (k+1) is the parameter vector at the current moment
  • ⁇ se (k+1) is the electrode surface potential difference at the current moment
  • f ⁇ is the electrode surface potential difference update function
  • j n (k+1) is the reaction current intensity at the current moment
  • ⁇ (k+1) is the parameter vector at the current moment.
  • c s,surf (k+1) f surf (c s,av (k),c s,surf (k),j n (k+1), ⁇ (k+1), ⁇ t)
  • c s,av (k+1) is the average lithium concentration of the electrode active material at the current moment
  • f av is the average lithium concentration update function of the electrode active material
  • c s,av (k) is the average lithium concentration of the electrode active material at the previous moment.
  • c s,surf (k) is the lithium concentration on the surface of the electrode active material at the previous moment
  • j n (k+1) is the reaction current intensity at the current moment
  • ⁇ (k+1) is the parameter vector at the current moment
  • ⁇ t is the sampling interval
  • c s,surf (k+1) is the lithium concentration on the surface of the electrode active material at the current moment
  • f surf is the lithium concentration update function on the surface of the electrode active material
  • c s,av (k) is the average lithium concentration of the electrode active material at the previous moment
  • c s,surf (k) is the lithium concentration on the surface of the electrode active material at the previous moment
  • j n (k+1) is the reaction current intensity at the current moment
  • ⁇ (k+1) is the parameter vector at the current moment
  • ⁇ t is the sampling interval
  • c e (k+1) is the electrode electrolyte lithium concentration at the current moment
  • f e is the electrode electrolyte lithium concentration update function
  • c e (k) is the electrode electrolyte lithium concentration at the previous moment
  • I (k) is the port at the previous moment.
  • Current, ⁇ (k+1) is the parameter vector at the current moment, ⁇ t is the sampling interval;
  • the lithium concentration of the electrode electrolyte at the current moment the lithium concentration on the surface of the electrode active material, the reaction current intensity, the parameter vector, the battery temperature and the port current at the previous moment, the battery port voltage V and the internal potential difference U of the battery are obtained at the current moment:
  • V(k+1) f V (c e (k+1),c s,surf (k+1),j n (k+1),T b (k),I(k), ⁇ (k +1))
  • V(k+1) is the battery port voltage at the current moment
  • f V is the battery port voltage update function
  • c e (k+1) is the electrode electrolyte lithium concentration at the current moment
  • c s, surf (k+1) is the current The lithium concentration on the surface of the electrode active material at the moment
  • j n (k+1) is the reaction current intensity at the current moment
  • T b (k) is the battery temperature at the previous moment
  • I (k) is the port current at the previous moment
  • ⁇ (k+1) ) is the parameter vector at the current moment
  • U(k+1) is the potential difference within the battery at the current moment
  • f U is the potential difference update function within the battery
  • c e (k+1) is the electrode electrolyte lithium concentration at the current moment
  • c s,surf (k +1) is the lithium concentration on the surface of the electrode active material at the current moment
  • T b (k) is the battery temperature at the previous
  • the battery port voltage at the current moment the potential difference within the battery, the reaction current intensity, the parameter vector, the battery temperature at the previous moment, the ambient temperature, the port current and the sampling interval, the battery temperature at the current moment is obtained:
  • T b (k+1) f T (V(k+1),U(k+1),j n (k+1),T b (k),T amb (k),I(k), ⁇ (k+1), ⁇ t)
  • T b (k+1) is the battery temperature at the current moment
  • f T is the battery temperature update function
  • V (k+1) is the battery port voltage at the current moment
  • U (k+1) is the potential difference within the battery at the current moment
  • j n (k+1) is the reaction current intensity at the current moment
  • T b (k) is the battery temperature at the previous moment
  • T amb (k) is the ambient temperature at the previous moment
  • I (k) is the port current at the previous moment
  • ⁇ ( k+1) is the parameter vector at the current moment
  • ⁇ t is the sampling interval
  • the battery port voltage at each moment within the preset time period can be expressed as
  • V [V 1 V 2 ...V k ...V N ]
  • V is the battery port voltage at each moment in the preset time period
  • V k is the battery port voltage value at the k-th moment
  • V N is the battery port voltage value at the N-th moment
  • the above simulation iterative update can be recorded as:
  • V f bat (SOC 0 ,T amb ,I)
  • V is the battery port voltage at each moment in the preset time period
  • f bat is the state update function set
  • SOC 0 is the initial state of charge
  • T amb is the battery ambient temperature
  • I is the current sequence amplitude.
  • reaction current intensity ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇
  • Reaction current intensity, electrode surface potential difference, electrode electrolyte lithium concentration, electrode active material surface lithium concentration, and electrode active material average lithium concentration were measured at 4 sampling points along the increasing direction of the electrode thickness at the positive and negative electrodes of the battery, as shown in Figure 2. Referred to as:
  • j n (k) is the reaction current intensity at the current moment, is the reaction current intensity at sampling point 1 at the current moment, is the reaction current intensity at sampling point 4 at the current moment
  • ⁇ se (k) is the electrode surface potential difference at the current moment
  • c e (k) is the electrode electrolyte lithium concentration at the current moment
  • c s, av (k) is the average lithium concentration of the electrode active material at the current moment, is the average lithium concentration of the electrode active material at coordinate sampling point 1 at the current moment, is the average lithium concentration of the electrode active material at coordinate sampling point 4 at the current time
  • c s,surf (k) is the lithium concentration on the surface of the electrode active material at the current time, is the lithium concentration on the
  • the port power corresponding to the initial state of charge is calculated based on the battery port voltage and current sequence amplitude, including:
  • the average port voltage within the preset time period is obtained. Based on the average port voltage and current sequence amplitude, the port power corresponding to the initial state of charge and current sequence amplitude is calculated.
  • the average port voltage is expressed as:
  • V k is the battery port voltage value at the k-th moment
  • V N is the battery port voltage value at the N-th moment
  • N is the total number of moments in the preset time period
  • the port power is expressed as:
  • P is the port power
  • SOC 0 is the initial state of charge
  • T amb is the set battery ambient temperature value
  • I is the current sequence amplitude
  • I is the average port voltage.
  • the initial state of charge and current sequence amplitude are adjusted, and steps S1-S3 are repeated to obtain port powers corresponding to multiple different initial states of charge and current sequence amplitudes.
  • the port power obtain the current amplitude-state-of-charge-port power surface, including:
  • the current amplitude-state-of-charge-port power surface is fitted to a plane equation, expressed as:
  • A, B, and C are the linear coefficients of plane fitting, and D is the constant coefficient of plane fitting.
  • the plane equation is used to obtain the current amplitude corresponding to the port power and the state of charge, and the state of charge is updated according to the current amplitude and the preset time period, include:
  • the current amplitude is expressed as:
  • I is the current amplitude
  • f I is the current amplitude function
  • SOC 0 is the initial state of charge
  • T amb is the set ambient temperature value
  • T is the actual ambient temperature of the battery
  • a 0 is the constant coefficient of the current amplitude function
  • a 1 is the first-order coefficient corresponding to SOC 0 in the current amplitude function
  • a 2 is the first-order coefficient corresponding to P in the current amplitude function
  • P is the port power
  • a 0 , a 1 , a 2 can be derived from the plane fitting coefficients A, B, C, D:
  • A, B, C are the first-order coefficients of plane fitting
  • D is the constant coefficient of plane fitting
  • a 0 is the constant coefficient of the current amplitude function
  • a 1 is the first-order coefficient corresponding to SOC 0 in the current amplitude function
  • a 2 is The linear coefficient corresponding to P in the current amplitude function
  • the state of charge is updated according to the current amplitude and the preset time period, which is expressed as:
  • ⁇ SOC is the change in state of charge in adjacent time periods
  • I is the current amplitude
  • C 0 is the total battery capacity in ampere hours
  • ⁇ T is the length of the preset time period
  • N is the total number of moments in the preset time period.
  • ⁇ t is the sampling interval
  • SOC T+1 is the battery state of charge at the beginning of the next time period
  • SOC T is the battery state of charge at the beginning of this time period.
  • FIG. 3 is another flow chart of a state-of-charge updating method based on the power characteristics of a lithium-ion battery electrochemical model according to an embodiment of the present application.
  • the battery ambient temperature and initial state of charge are preset to obtain a constant amplitude current sequence.
  • Conduct electrochemical model simulation based on preset parameters and constant amplitude current sequence, update parameter vector, reaction current intensity, electrode surface potential difference, electrode active material lithium concentration, electrode electrolyte lithium concentration, and calculate battery port voltage and temperature.
  • plane equations are used to fit the above surface.
  • the current sequence amplitude can be derived from the above plane equation based on the known battery port power and initial state of charge, and the resulting amplitude can be used to update the state of charge.
  • FIG. 4 is a schematic structural diagram of a state-of-charge updating device based on the power characteristics of a lithium-ion battery electrochemical model provided in Embodiment 2 of the present application.
  • the state-of-charge update device based on the power characteristics of the lithium-ion battery electrochemical model includes:
  • the acquisition module 10 is used to acquire the initial state of charge of the battery and the current sequence of constant amplitude
  • the processing module 20 is used to obtain the battery initial state information, and obtain the battery port voltage at each moment within the preset time period based on the battery initial state information and lithium-ion battery electrochemical model simulation;
  • the calculation module 30 is used to calculate the port power corresponding to the initial state of charge based on the battery port voltage and current sequence amplitude;
  • the loop module 40 is used to adjust the initial state of charge and current sequence amplitude, and repeatedly calls the acquisition module, processing module and calculation module to obtain port powers corresponding to multiple different initial states of charge and current sequence amplitudes, according to Port power, obtain the current amplitude-state of charge-port power surface;
  • the fitting module 50 is used to fit the current amplitude-state-of-charge-port power surface into a plane equation
  • the update module 60 is configured to use plane equations to obtain the current amplitude corresponding to the port power and the state of charge according to the port power and the state of charge, and update the state of charge according to the current amplitude and the preset time period.
  • the state-of-charge updating device based on the power characteristics of the lithium-ion battery electrochemical model in the embodiment of the present application includes an acquisition module for acquiring the initial state of charge of the battery and a constant amplitude current sequence; and a processing module for acquiring the initial state of charge of the battery.
  • Status information based on the initial battery status information and lithium-ion battery electrochemical model simulation, obtains the battery port voltage at each moment within the preset time period; the calculation module is used to calculate the initial charge based on the battery port voltage and current sequence amplitude.
  • the port power corresponding to the electrical state; the circulation module is used to adjust the initial state of charge and current sequence amplitude. It repeatedly calls the acquisition module, processing module and calculation module to obtain multiple different initial charge states and current sequence amplitudes.
  • the current amplitude-state of charge-port power surface is obtained; the fitting module is used to fit the current amplitude-state of charge-port power surface into a plane equation; the update module is used Based on the port power and state of charge, the plane equation is used to obtain the current amplitude corresponding to the port power and state of charge, and the state of charge is updated according to the current amplitude and the preset time period.
  • the impact of the battery's time-varying port voltage on the state of charge update enables the update of the battery state of charge without the need for continuous timing simulation when the battery power and state of charge are known, which improves the performance of the electrochemical model in power
  • the computational efficiency in application scenarios expands the application scenarios of electrochemical models in engineering.
  • this application also proposes an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor.
  • the processor executes the program, the above is implemented.
  • the state of charge update method based on the power characteristics of the lithium-ion battery electrochemical model is described.
  • this application also proposes a non-transitory computer-readable storage medium on which a computer program is stored.
  • the computer program is executed by the processor, the power characteristics based on the lithium-ion battery electrochemical model of the above embodiments are realized. State of charge update method.
  • the present application also proposes a computer program product, including a computer program that, when executed by a processor, implements the state-of-charge update based on the electrochemical model power characteristics of a lithium-ion battery as described above. method.
  • references to the terms “one embodiment,” “some embodiments,” “an example,” “specific examples,” or “some examples” or the like means that specific features are described in connection with the embodiment or example. , structures, materials or features are included in at least one embodiment or example of the present application. In this specification, the schematic expressions of the above terms are not necessarily directed to the same embodiment or example. Furthermore, the specific features, structures, materials or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, those skilled in the art may combine and combine different embodiments or examples and features of different embodiments or examples described in this specification unless they are inconsistent with each other.
  • first and second are used for descriptive purposes only and cannot be understood as indicating or implying relative importance or implicitly indicating the quantity of indicated technical features. Therefore, features defined as “first” and “second” may explicitly or implicitly include at least one of these features.
  • “plurality” means at least two, such as two, three, etc., unless otherwise expressly and specifically limited.
  • a "computer-readable medium” may be any device that can contain, store, communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
  • Non-exhaustive list of computer readable media include the following: electrical connections with one or more wires (electronic device), portable computer disk cartridges (magnetic device), random access memory (RAM), Read-only memory (ROM), erasable and programmable read-only memory (EPROM or flash memory), fiber optic devices, and portable compact disc read-only memory (CDROM).
  • the computer-readable medium may even be paper or other suitable medium on which the program may be printed, as the paper or other medium may be optically scanned, for example, and subsequently edited, interpreted, or otherwise suitable as necessary. process to obtain the program electronically and then store it in computer memory.
  • the program can be stored in a computer-readable storage medium.
  • the program can be stored in a computer-readable storage medium.
  • each functional unit in various embodiments of the present application can be integrated into a processing module, or each unit can exist physically alone, or two or more units can be integrated into one module.
  • the above integrated modules can be implemented in the form of hardware or software function modules. If the integrated module is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.
  • the storage media mentioned above can be read-only memory, magnetic disks or optical disks, etc.

Abstract

一种基于锂离子电池电化学模型功率特性的荷电状态更新方法，包括：S1：获取初始荷电状态和恒定幅值的电流序列；S2：获取电池初始状态信息，根据锂离子电池电化学模型仿真获得预设时间周期内每一时刻的电池端口电压；S3：根据电池端口电压和电流序列幅值得到初始荷电状态对应的端口功率；S4：调整初始荷电状态和电流序列幅值，重复步骤S1-S3，获得电流幅值-荷电状态-端口功率曲面；S5：将电流幅值-荷电状态-端口功率曲面拟合为平面方程；S6：利用平面方程获取端口功率和荷电状态对应的电流幅值，根据电流幅值和预设时间周期进行荷电状态更新。

Description

基于锂离子电池电化学模型功率特性的荷电状态更新方法
相关申请的交叉引用
本申请基于申请号为202210431423.3、申请日为2022年4月22日的中国专利申请提出，并要求该中国专利申请的优先权，该中国专利申请的全部内容在此引入本申请作为参考。
技术领域
本申请涉及锂离子电池能量管理运行技术领域，尤其涉及基于锂离子电池电化学模型功率特性的荷电状态更新方法和装置。
背景技术
锂离子电池作为灵活、高效、安全的储能介质，广泛应用于电力、交通等业务领域。在锂离子电池能量管理运行过程中，电池荷电状态是管理者所关注的关键信息之一。为了获得精确的荷电状态描述，以满足锂离子电池高效运行要求，需要利用电化学模型考虑电池时变端口电压特性。而在时变端口特性表征过程中，不仅需要保证其精确度，还应在实际应用中考虑荷电状态更新的计算效率。
目前，锂离子电池模型在工程应用中主要可分为四类：水箱模型、等效电路模型、基于数据驱动的黑箱模型、基于化学反应机理的电化学模型。其中，水箱模型和等效电路模型在工程中应用最为广泛，其荷电状态更新过程中电池电压由制造商确定或测量，并视为常数，例如来自麻省理工大学和阿贡国家实验室的学者在研究电池在微网中的套利能力时采用等效电路模型，将电池电压视为恒定值，利用端口功率直接进行荷电状态更新。对于电化学模型和黑箱模型，一般在连续时序仿真中利用状态估计的方法获得荷电状态，例如来自北京理工大学和密歇根大学的学者在电化学模型中利用联合状态估计的方法确定电池当前荷电状态。然而，将电池端口电压视为恒定值，本质上是忽略了电池运行过程中端口电压关于电池荷电状态的变化，导致荷电状态更新过程中误差较大。而利用连续时序仿真中的状态估计方法确定荷电状态，则要重复求解计算电化学模型中非线性高阶微分状态方程，需要大量的计算资源，运算效率较低，难以适应较长时段内的电池能量管理运行。
发明内容
本申请旨在至少在一定程度上解决相关技术中的技术问题之一。
为此，本申请的目的在于提出一种基于锂离子电池电化学模型功率特性的荷电状态更新方法，解决了现有方法的锂离子电池荷电状态更新精度和效率互斥的技术问题，利用锂离子 电池电化学模型外特性仿真精度高的特点，在不增加计算复杂度的情况下考虑电池时变端口电压对荷电状态更新的影响，实现了在已知电池功率、荷电状态的情况下，无需进行连续时序仿真，完成对电池荷电状态的更新，提高了电化学模型在功率应用场景下的计算效率，拓展了电化学模型在工程中的应用场景。
为达上述目的，本申请第一方面实施例提出了一种基于锂离子电池电化学模型功率特性的荷电状态更新方法，包括：S1：获取电池的初始荷电状态和恒定幅值的电流序列；S2：获取电池初始状态信息，根据电池初始状态信息和锂离子电池电化学模型仿真，获得预设时间周期内每一时刻的电池端口电压；S3：根据电池端口电压和电流序列幅值，计算得到初始荷电状态对应的端口功率；S4：调整初始荷电状态和电流序列幅值，重复步骤S1-S3，分别得到多个不同的初始荷电状态和电流序列幅值所对应的端口功率，根据端口功率，获得电流幅值-荷电状态-端口功率曲面；S5：将电流幅值-荷电状态-端口功率曲面拟合为平面方程；S6：根据端口功率和荷电状态，利用平面方程获取端口功率和荷电状态对应的电流幅值，并根据电流幅值和预设时间周期进行荷电状态更新。
在本申请的一个实施例中，电池初始状态信息，包括：电极活性材料表面锂浓度、电极活性材料平均锂浓度、电极电解质锂浓度、电池温度初值。
在本申请的一个实施例中，根据电池初始状态信息和锂离子电池电化学模型仿真，获得预设时间周期内每一时刻的电池端口电压，包括：
获取恒定幅度的电池环境温度序列，电池环境温度序列包括预设时间周期内每一时刻的电池环境温度，
预设时间周期起始时刻，根据上一时刻电极电解质锂浓度、电极活性材料平均锂浓度和电池温度，更新当前时刻参数向量：
θ(k+1)＝f θ(c e(k),c s,av(k),T b(k))
其中，θ(k+1)为当前时刻参数向量，f θ为参数更新函数，c e(k)为上一时刻电极电解质锂浓度，c s,av(k)为上一时刻电极活性材料平均锂浓度，T b(k)为上一时刻电池温度；
根据上一时刻电极电解质锂浓度、电极活性材料表面锂浓度、电池温度、端口电流和当前时刻参数向量，更新当前时刻反应电流强度：
j n(k+1)＝f j(c e(k),c s,surf(k),T b(k),I(k),θ(k+1))
其中，j n(k+1)为当前时刻反应电流强度，f j为反应电流更新函数，c s,surf(k)为上一时刻电极活性材料表面锂浓度，I(k)为上一时刻端口电流；
根据当前时刻反应电流强度和参数向量，更新当前时刻电极表面电势差：
φ se(k+1)＝f φ(j n(k+1),θ(k+1))
其中，φ se(k+1)为当前时刻电极表面电势差，f φ为电极表面电势差更新函数；
根据上一时刻电极活性材料平均锂浓度、电极活性材料表面锂浓度、当前时刻反应电流强度、当前时刻参数向量和采样间隔，更新当前时刻电极活性材料锂浓度：
c s,av(k+1)＝f av(c s,av(k),c s,surf(k),j n(k+1),θ(k+1),Δt)
c s,surf(k+1)＝f surf(c s,av(k),c s,surf(k),j n(k+1),θ(k+1),Δt)
其中，c s,av(k+1)为当前时刻电极活性材料平均锂浓度，f av为电极活性材料平均锂浓度更新函数，Δt为采样间隔，c s,surf(k+1)为当前时刻电极活性材料表面锂浓度，f surf为电极活性材料表面锂浓度更新函数；
根据上一时刻电极电解质锂浓度、端口电流和当前时刻参数向量及采样间隔，更新当前时刻电极电解质锂浓度：
c e(k+1)＝f e(c e(k),I(k),θ(k+1),Δt)
其中，c e(k+1)为当前时刻电极电解质锂浓度，f e为电极电解质锂浓度更新函数；
根据当前时刻电极电解质锂浓度、电极活性材料表面锂浓度、反应电流强度、参数向量和上一时刻电池温度、端口电流，获得当前时刻电池端口电压V和电池内电势差U：
V(k+1)＝f V(c e(k+1),c s,surf(k+1),j n(k+1),T b(k),I(k),θ(k+1))
U(k+1)＝f U(c e(k+1),c s,surf(k+1),j n(k+1),T b(k),I(k),θ(k+1))
其中，V(k+1)为当前时刻电池端口电压，f V为电池端口电压更新函数，U(k+1)为当前时刻电池内电势差，f U为电池内电势差更新函数；
根据当前时刻电池端口电压、电池内电势差、反应电流强度、参数向量和上一时刻电池温度、环境温度、端口电流及采样间隔，获得当前时刻电池温度：
T b(k+1)＝f T(V(k+1),U(k+1),j n(k+1),T b(k),T amb(k),I(k),θ(k+1),Δt)
其中，T b(k+1)为当前时刻电池温度，f T为电池温度更新函数，T amb(k)为上一时刻环境温度；
重复上述仿真迭代更新步骤，由上一时刻状态值循环更新当前时刻状态值：参数向量、反应电流强度、电极表面电势差、电极活性材料锂浓度、电极电解质锂浓度，并根据状态更新结果输出电池端口电压和电池温度，直至预设时间周期结束，以得到预设时间周期内每一时刻的电池端口电压，
其中，所述预设时间周期内每一时刻的电池端口电压可表示为
V＝[V 1V 2…V k…V N]
其中，V为预设时间周期内每一时刻的电池端口电压，V k为第k时刻电池端口电压值，N为预设时间周期内时刻总数，其中N为大于等于2的整数。
在本申请的一个实施例中，根据电池端口电压和电流序列幅值，计算得到初始荷电状态对应的端口功率，包括：
根据电池端口电压得到预设时间周期内的平均端口电压，根据平均端口电压与电流序列幅值，计算得到初始荷电状态和电流序列幅值对应的端口功率，
其中，平均端口电压表示为：
Figure PCTCN2022091037-appb-000001
其中，
Figure PCTCN2022091037-appb-000002
为平均端口电压，
端口功率表示为：
Figure PCTCN2022091037-appb-000003
其中，P为端口功率，SOC 0为初始荷电状态，T amb为设定的电池环境温度值，I为电流序列幅值。
在本申请的一个实施例中，调整初始荷电状态和电流序列幅值，重复步骤S1-S3，分别得到多个不同的初始荷电状态和电流序列幅度所对应的端口功率，根据端口功率，获得电流幅值-荷电状态-端口功率曲面，包括：
在保持电池环境温度恒定的情况下，调整电池的初始荷电状态和电流序列幅值，重复S1-S3，分别得到多个不同初始荷电状态和电流序列幅值所对应的端口功率，将端口功率平滑连接，获得电流幅值-荷电状态-端口功率曲面，
其中，电流幅值-荷电状态-端口功率曲面表示为：
Figure PCTCN2022091037-appb-000004
其中，f P为T＝T amb时的电流幅值-荷电状态-端口功率曲面函数，T为电池实际环境温度。
在本申请的一个实施例中，将电流幅值-荷电状态-端口功率曲面拟合为平面方程，表示为：
Figure PCTCN2022091037-appb-000005
其中，f P为T＝T amb时的电流幅值-荷电状态-端口功率曲面函数，A,B,C为平面拟合一次系数，D为平面拟合常系数。
在本申请的一个实施例中，根据端口功率和荷电状态，利用平面方程获取端口功率和荷电状态对应的电流幅值，并根据电流幅值和预设时间周期进行荷电状态更新，包括：
在保持电池环境温度恒定的情况下，根据已知的端口功率和荷电状态，利用平面方程得到电流幅值，
其中，电流幅值表示为：
Figure PCTCN2022091037-appb-000006
其中，I为电流幅值，f I为电流幅值函数，a 0为电流幅值函数常系数，a 1为电流幅值函数中SOC 0对应的一次系数，a 2为电流幅值函数中P对应的一次系数，
a 0,a 1,a 2可由平面拟合系数A,B,C,D导出：
Figure PCTCN2022091037-appb-000007
Figure PCTCN2022091037-appb-000008
Figure PCTCN2022091037-appb-000009
根据电流幅值和预设时间周期进行荷电状态更新，表示为：
Figure PCTCN2022091037-appb-000010
ΔT＝NΔt
SOC T+1＝SOC T+ΔSOC
其中，ΔSOC为相邻时间周期荷电状态变化量，C 0为以安时为单位的电池总容量，ΔT为预设时间周期长度，SOC T+1为下一时间周期开始时的电池荷电状态，SOC T为本时间周期开始时的电池荷电状态。
为达上述目的，本申请第二方面实施例提出了一种基于锂离子电池电化学模型功率特性的荷电状态更新装置，包括：
获取模块，用于获取电池的初始荷电状态和恒定幅值的电流序列；
处理模块，用于获取电池初始状态信息，根据电池初始状态信息和锂离子电池电化学模型仿真，获得预设时间周期内每一时刻的电池端口电压；
计算模块，用于根据电池端口电压和电流序列幅值，计算得到初始荷电状态对应的端口功率；
循环模块，用于调整初始荷电状态和电流序列幅值，重复调用获取模块、处理模块和计算模块，分别得到多个不同的初始荷电状态和电流序列幅值所对应的端口功率，根据端口功率，获得电流幅值-荷电状态-端口功率曲面；
拟合模块，用于将电流幅值-荷电状态-端口功率曲面拟合为平面方程；
更新模块，用于根据端口功率和荷电状态，利用平面方程获取端口功率和荷电状态对应的电流幅值，并根据电流幅值和预设时间周期进行荷电状态更新。
为了实现上述目的，本申请第三方面实施例提出了一种电子设备，包括：存储器、处理器及存储在存储器上并可在处理器上运行的计算机程序，所述处理器执行所述程序时实现如上所述的基于锂离子电池电化学模型功率特性的荷电状态更新方法。
为了实现上述目的，本申请第四方面实施例提出了一种非临时性计算机可读存储介质， 其上存储有计算机程序，所述计算机程序被处理器执行时实现如上所述的基于锂离子电池电化学模型功率特性的荷电状态更新方法。
为了实现上述目的，本申请第五方面实施例提出了一种计算机程序产品，包括计算机程序，所述计算机程序在被处理器执行时实现如上所述的基于锂离子电池电化学模型功率特性的荷电状态更新方法。
本申请实施例的基于锂离子电池电化学模型功率特性的荷电状态更新方法、基于锂离子电池电化学模型功率特性的荷电状态更新装置和非临时性计算机可存储介质，解决了现有方法的锂离子电池荷电状态更新精度和效率互斥的技术问题，利用锂离子电池电化学模型外特性仿真精度高的特点，在不增加计算复杂度的情况下考虑电池时变端口电压对荷电状态更新的影响，实现了在已知电池功率、荷电状态的情况下，无需进行连续时序仿真，完成对电池荷电状态的更新，提高了电化学模型在功率应用场景下的计算效率，拓展了电化学模型在工程中的应用场景。
本申请附加的方面和优点将在下面的描述中部分给出，部分将从下面的描述中变得明显，或通过本申请的实践了解到。
附图说明
本申请上述的和/或附加的方面和优点从下面结合附图对实施例的描述中将变得明显和容易理解，其中：
图1为本申请实施例一所提供的一种基于锂离子电池电化学模型功率特性的荷电状态更新方法的流程图；
图2为本申请实施例的基于锂离子电池电化学模型功率特性的荷电状态更新方法的锂离子电池单体结构示意图；
图3为本申请实施例的基于锂离子电池电化学模型功率特性的荷电状态更新方法的另一个流程图；
图4为本申请实施例二所提供的一种基于锂离子电池电化学模型功率特性的荷电状态更新装置的结构示意图。
具体实施方式
下面详细描述本申请的实施例，所述实施例的示例在附图中示出，其中自始至终相同或类似的标号表示相同或类似的元件或具有相同或类似功能的元件。下面通过参考附图描述的实施例是示例性的，旨在用于解释本申请，而不能理解为对本申请的限制。
现有关于锂离子电池能量运行管理的研究中，(1)若将端口电压视为恒定值，直接利用 端口功率确定荷电状态，难以保证荷电状态更新的精确度；(2)若在连续时序仿真中进行状态估计，运算效率较低，在长时段管理中需要大量计算资源。针对以上问题，能反映电池运行中真实功率特性的高效荷电状态更新方法尚待研究。因此，对锂离子电池电化学模型功率特性的荷电状态更新方法，既需要准确地考虑电池端口电压的时变特性，又需要满足在长时段电池能量运行管理中的计算效率。
本申请提出了一种基于锂离子电池电化学模型功率特性的荷电状态更新方法，利用锂离子电池电化学模型外特性仿真精度高的特点，在不增加计算复杂度的情况下考虑电池时变端口电压对荷电状态更新的影响，实现了在已知电池功率、荷电状态的情况下，无需进行连续时序仿真，完成对电池荷电状态的更新，提高了电化学模型在功率应用场景下的计算效率，拓展了电化学模型在工程中的应用场景。
本申请的相关技术包括：锂离子电池电化学模型构建及仿真技术：锂离子电化学模型由一组非线性高阶微分状态方程构成，其通过准确描述电池内部化学反应，提供较为准确的内部状态信息和外特性信息。多元方程拟合技术：多元方程拟合是在三维或高维空间中将一系列样本点利用光滑表面连接，使得该表面能够逼近样本点的分布。在本申请中利用到三维空间中的平面拟合，涉及3个变量的样本点拟合，从而给出待求拟合系数。
下面参考附图描述本申请实施例的基于锂离子电池电化学模型功率特性的荷电状态更新方法和装置。
图1为本申请实施例一所提供的一种基于锂离子电池电化学模型功率特性的荷电状态更新方法的流程图。
如图1所示，该基于锂离子电池电化学模型功率特性的荷电状态更新方法包括以下步骤：
S1：获取电池的初始荷电状态和恒定幅值的电流序列；
S2：获取电池初始状态信息，根据电池初始状态信息和锂离子电池电化学模型仿真，获得预设时间周期内每一时刻的电池端口电压；
S3：根据电池端口电压和电流序列幅值，计算得到初始荷电状态对应的端口功率；
S4：调整初始荷电状态和电流序列幅值，重复步骤S1-S3，分别得到多个不同的初始荷电状态和电流序列幅值所对应的端口功率，根据端口功率，获得电流幅值-荷电状态-端口功率曲面；
S5：将电流幅值-荷电状态-端口功率曲面拟合为平面方程；
S6：根据端口功率和荷电状态，利用平面方程获取端口功率和荷电状态对应的电流幅值，并根据电流幅值和预设时间周期进行荷电状态更新。
根据本申请实施例的基于锂离子电池电化学模型功率特性的荷电状态更新方法，能够解 决现有方法的锂离子电池荷电状态更新精度和效率互斥的技术问题，利用锂离子电池电化学模型外特性仿真精度高的特点，在不增加计算复杂度的情况下考虑电池时变端口电压对荷电状态更新的影响，实现了在已知电池功率、荷电状态的情况下，无需进行连续时序仿真，完成对电池荷电状态的更新，提高了电化学模型在功率应用场景下的计算效率，拓展了电化学模型在工程中的应用场景。
本申请提出了一种基于锂离子电池电化学模型功率特性的荷电状态更新方法，通过电化学模型描述锂离子电池端口电压随荷电状态的变化而改变的时变特性，同时采用构建电流幅度-荷电状态-端口功率曲面并进行平面拟合的功率-电流特性描述方法，在提高状态更新精度时，避免在电池能量管理中对电化学模型进行长时段连续时序仿真。在短时段内利用本方法对电池测试，基于电化学模型利用拟合平面描述功率-电流特性，在长时段内则利用上述特性平面方程进行电池能量运行管理的相关计算。
本申请在保留锂离子电池运行过程中端口电压时变特性的情况下，不增加电池能量运行管理的计算复杂度，本申请能够将锂离子电池电化学模型应用于日前规划、联合调度等长时段优化场景，而无需进行计算复杂度较高的电化学模型连续时序仿真，为电池能量运行管理提供兼顾精度和效率的荷电状态更新信息，支撑锂离子电池在不同场景下的高效经济运行，具有重要的现实意义和良好的应用前景。
设定电池环境温度，记作T amb，本申请中，电池环境温度保持不变。
获取电池的初始荷电状态，记作SOC 0，其中，初始荷电状态的定义域为[0,1]。
获取恒定幅值的电流序列，可记作：
Figure PCTCN2022091037-appb-000011
其中，I为电流序列幅值，N为预设时间周期内时刻总数，每一时刻电流作用时段为t k≤t<t k+1，采样间隔为Δt＝t k+1-t k。
进一步地，在本申请实施例中，电池初始状态信息，包括：电极活性材料表面锂浓度、电极活性材料平均锂浓度、电极电解质锂浓度、电池温度初值。
获取电池初始状态信息,包括：获取待分析电极所使用的电极活性材料类型，查询电极活性材料在最大、最小荷电状态所对应的电极活性材料平均锂浓度，根据荷电状态与平均锂浓度的正比关系得到电极活性材料平均锂浓度初值,初始状态下设电极活性材料锂浓度初始均匀分布，得到电极活性材料表面锂浓度与平均锂浓度初值相等,根据参数设定获得电极电解质锂浓度初值,电池温度初值设定为环境温度；
电极活性材料平均锂浓度为：
Figure PCTCN2022091037-appb-000012
电极活性材料表面锂浓度为：
Figure PCTCN2022091037-appb-000013
电极电解质锂浓度为：
c e(0)＝f init,e(c e0)
电池温度初值为：
T b(0)＝T amb
其中，
Figure PCTCN2022091037-appb-000014
是正、负极电极活性材料平均锂浓度初值，f init,c是电极活性材料平均锂浓度初值设定函数，
Figure PCTCN2022091037-appb-000015
是电池正、负极电极活性材料平均锂浓度理论最小值，
Figure PCTCN2022091037-appb-000016
是电池正、负极电极活性材料平均锂浓度理论最大值，SOC 0是初始荷电状态，
Figure PCTCN2022091037-appb-000017
是正、负极电极活性材料表面锂浓度初值，c e(0)是电极电解质锂浓度初值，f init,e是电极电解质锂浓度初值设定函数，c e0是电极电解质锂浓度材料参数，T b(0)是电池温度初值，T amb是电池环境温度。
进一步地，在本申请实施例中，根据电池初始状态信息和锂离子电池电化学模型仿真，获得预设时间周期内每一时刻的电池端口电压，包括：
获取恒定幅度的电池环境温度序列，电池环境温度序列包括预设时间周期内每一时刻的电池环境温度，其中，环境温度序列可记作：
Figure PCTCN2022091037-appb-000018
其中，T amb为电池环境温度，N为预设时间周期内时刻总数，每一时刻电池环境温度作用时段为t k≤t<t k+1，采样间隔为Δt＝t k+1-t k。
预设时间周期起始时刻，根据上一时刻电极电解质锂浓度、电极活性材料平均锂浓度和电池温度，更新当前时刻参数向量：
θ(k+1)＝f θ(c e(k),c s,av(k),T b(k))
其中，θ(k+1)为当前时刻参数向量，f θ为参数更新函数，c e(k)为上一时刻电极电解质锂浓度，c s,av(k)为上一时刻电极活性材料平均锂浓度，T b(k)为上一时刻电池温度；
根据上一时刻电极电解质锂浓度、电极活性材料表面锂浓度、电池温度、端口电流和当前时刻参数向量，更新当前时刻反应电流强度：
j n(k+1)＝f j(c e(k),c s,surf(k),T b(k),I(k),θ(k+1))
其中，j n(k+1)为当前时刻反应电流强度，f j为反应电流更新函数，c e(k)为上一时刻电极电解质锂浓度，c s,surf(k)为上一时刻电极活性材料表面锂浓度，T b(k)为上一时刻电池温度，I(k)为上一时刻端口电流，θ(k+1)为当前时刻参数向量；
根据当前时刻反应电流强度和参数向量，更新当前时刻电极表面电势差：
φ se(k+1)＝f φ(j n(k+1),θ(k+1))
其中，φ se(k+1)为当前时刻电极表面电势差，f φ为电极表面电势差更新函数，j n(k+1)为当前时刻反应电流强度，θ(k+1)为当前时刻参数向量；
根据上一时刻电极活性材料平均锂浓度、电极活性材料表面锂浓度、当前时刻反应电流强度、当前时刻参数向量和采样间隔，更新当前时刻电极活性材料锂浓度：
c s,av(k+1)＝f av(c s,av(k),c s,surf(k),j n(k+1),θ(k+1),Δt)
c s,surf(k+1)＝f surf(c s,av(k),c s,surf(k),j n(k+1),θ(k+1),Δt)
其中，c s,av(k+1)为当前时刻电极活性材料平均锂浓度，f av为电极活性材料平均锂浓度更新函数，c s,av(k)为上一时刻电极活性材料平均锂浓度，c s,surf(k)为上一时刻电极活性材料表面锂浓度，j n(k+1)为当前时刻反应电流强度，θ(k+1)为当前时刻参数向量，Δt为采样间隔，c s,surf(k+1)为当前时刻电极活性材料表面锂浓度，f surf为电极活性材料表面锂浓度更新函数，c s,av(k)为上一时刻电极活性材料平均锂浓度，c s,surf(k)为上一时刻电极活性材料表面锂浓度，j n(k+1)为当前时刻反应电流强度，θ(k+1)为当前时刻参数向量，Δt为采样间隔；
根据上一时刻电极电解质锂浓度、端口电流和当前时刻参数向量及采样间隔，更新当前时刻电极电解质锂浓度：
c e(k+1)＝f e(c e(k),I(k),θ(k+1),Δt)
其中，c e(k+1)为当前时刻电极电解质锂浓度，f e为电极电解质锂浓度更新函数，c e(k)为上一时刻电极电解质锂浓度，I(k)为上一时刻端口电流，θ(k+1)为当前时刻参数向量，Δt为采样间隔；
根据当前时刻电极电解质锂浓度、电极活性材料表面锂浓度、反应电流强度、参数向量和上一时刻电池温度、端口电流，获得当前时刻电池端口电压V和电池内电势差U：
V(k+1)＝f V(c e(k+1),c s,surf(k+1),j n(k+1),T b(k),I(k),θ(k+1))
U(k+1)＝f U(c e(k+1),c s,surf(k+1),j n(k+1),T b(k),I(k),θ(k+1))
其中，V(k+1)为当前时刻电池端口电压，f V为电池端口电压更新函数，c e(k+1)为当前时刻电极电解质锂浓度，c s,surf(k+1)为当前时刻电极活性材料表面锂浓度，j n(k+1)为当前时刻反应电流强度，T b(k)为上一时刻电池温度，I(k)为上一时刻端口电流，θ(k+1)为当前时刻参数向量，U(k+1)为当前时刻电池内电势差，f U为电池内电势差更新函数， c e(k+1)为当前时刻电极电解质锂浓度，c s,surf(k+1)为当前时刻电极活性材料表面锂浓度，j n(k+1)为当前时刻反应电流强度，T b(k)为上一时刻电池温度，I(k)为上一时刻端口电流，θ(k+1)为当前时刻参数向量；
根据当前时刻电池端口电压、电池内电势差、反应电流强度、参数向量和上一时刻电池温度、环境温度、端口电流及采样间隔，获得当前时刻电池温度：
T b(k+1)＝f T(V(k+1),U(k+1),j n(k+1),T b(k),T amb(k),I(k),θ(k+1),Δt)
其中，T b(k+1)为当前时刻电池温度，f T为电池温度更新函数，V(k+1)为当前时刻电池端口电压，U(k+1)为当前时刻电池内电势差，j n(k+1)为当前时刻反应电流强度，T b(k)为上一时刻电池温度，T amb(k)为上一时刻环境温度,I(k)为上一时刻端口电流，θ(k+1)为当前时刻参数向量，Δt为采样间隔；
重复上述仿真迭代更新步骤，由上一时刻状态值循环更新当前时刻状态值：参数向量、反应电流强度、电极表面电势差、电极活性材料锂浓度、电极电解质锂浓度，并根据状态更新结果输出电池端口电压和电池温度，直至预设时间周期结束，以得到预设时间周期内每一时刻的电池端口电压，
其中，预设时间周期内每一时刻的电池端口电压可表示为
V＝[V 1V 2…V k…V N]
其中，V为预设时间周期内每一时刻的电池端口电压，V k为第k时刻电池端口电压值，V N为第N时刻电池端口电压值，
上述仿真迭代更新，可记作：
V＝f bat(SOC 0,T amb,I)
其中，V为预设时间周期内每一时刻的电池端口电压，f bat为状态更新函数集合，SOC 0为初始荷电状态，T amb为电池环境温度，I为电流序列幅值。
本申请中，反应电流强度、电极表面电势差、电极电解质锂浓度、电极活性材料表面锂浓度、电极活性材料平均锂浓度等参数均为向量，每一时刻有8个空间上的采样点，具体示例如下：
反应电流强度、电极表面电势差、电极电解质锂浓度、电极活性材料表面锂浓度、电极活性材料平均锂浓度在电池正、负极分别沿电极厚度方向增加方向取4个采样点，如图2所示，记作：
Figure PCTCN2022091037-appb-000019
Figure PCTCN2022091037-appb-000020
Figure PCTCN2022091037-appb-000021
Figure PCTCN2022091037-appb-000022
Figure PCTCN2022091037-appb-000023
其中，j n(k)为当前时刻反应电流强度，
Figure PCTCN2022091037-appb-000024
为当前时刻采样点1处的反应电流强度，
Figure PCTCN2022091037-appb-000025
为当前时刻采样点4处的反应电流强度，φ se(k)为当前时刻电极表面电势差，
Figure PCTCN2022091037-appb-000026
为当前时刻位置采样点1处的电极表面电势差，
Figure PCTCN2022091037-appb-000027
为当前时刻位置采样点4处的电极表面电势差，c e(k)为当前时刻电极电解质锂浓度，
Figure PCTCN2022091037-appb-000028
为当前时刻坐标采样点1处的电极电解质锂浓度，
Figure PCTCN2022091037-appb-000029
为当前时刻坐标采样点4处的电极电解质锂浓度，c s,av(k)为当前时刻电极活性材料平均锂浓度，
Figure PCTCN2022091037-appb-000030
为当前时刻坐标采样点1处的电极活性材料平均锂浓度，
Figure PCTCN2022091037-appb-000031
为当前时刻坐标采样点4处的电极活性材料平均锂浓度，c s,surf(k)为当前时刻电极活性材料表面锂浓度，
Figure PCTCN2022091037-appb-000032
为当前时刻坐标采样点1处的电极活性材料表面锂浓度，
Figure PCTCN2022091037-appb-000033
为当前时刻坐标采样点4处的电极活性材料表面锂浓度。
进一步地，在本申请实施例中，根据电池端口电压和电流序列幅值，计算得到初始荷电状态对应的端口功率,包括：
根据电池端口电压得到预设时间周期内的平均端口电压，根据平均端口电压与电流序列幅值，计算得到初始荷电状态和电流序列幅值对应的端口功率，
其中，平均端口电压表示为：
Figure PCTCN2022091037-appb-000034
其中，
Figure PCTCN2022091037-appb-000035
为平均端口电压，V k为第k时刻电池端口电压值，V N为第N时刻电池端口电压值，N为预设时间周期内时刻总数，
端口功率表示为：
Figure PCTCN2022091037-appb-000036
其中，P为端口功率，SOC 0为初始荷电状态,T amb为设定的电池环境温度值，I为电流序列幅值，
Figure PCTCN2022091037-appb-000037
为平均端口电压。
进一步地，在本申请实施例中，调整初始荷电状态和电流序列幅值，重复步骤S1-S3，分别得到多个不同的初始荷电状态和电流序列幅度所对应的端口功率，根据端口功率，获得电流幅值-荷电状态-端口功率曲面，包括：
在保持电池环境温度恒定的情况下，调整电池的初始荷电状态和电流序列幅值，重复S1-S3，分别得到多个不同初始荷电状态和电流序列幅值所对应的端口功率，将端口功率平滑连接，获得电流幅值-荷电状态-端口功率曲面，
其中，电流幅值-荷电状态-端口功率曲面表示为：
Figure PCTCN2022091037-appb-000038
其中，f P为T＝T amb时的电流幅值-荷电状态-端口功率曲面函数，SOC 0为初始荷电状态,P为端口功率，I为电流序列幅值，T amb为设定的电池环境温度值，T为电池实际环境温度。
进一步地，在本申请实施例中，将电流幅值-荷电状态-端口功率曲面拟合为平面方程，表示为：
Figure PCTCN2022091037-appb-000039
其中，f P为T＝T amb时的电流幅值-荷电状态-端口功率曲面函数，SOC 0为初始荷电状态,P为端口功率，I为电流序列幅值，T amb为设定的电池环境温度值，T为电池实际环境温度。A,B,C为平面拟合一次系数，D为平面拟合常系数。
进一步地，在本申请实施例中，根据端口功率和荷电状态，利用平面方程获取端口功率和荷电状态对应的电流幅值，并根据电流幅值和预设时间周期进行荷电状态更新，包括：
在保持电池环境温度恒定的情况下，根据已知的端口功率和荷电状态，利用平面方程得到电流幅值，
其中，电流幅值表示为：
Figure PCTCN2022091037-appb-000040
其中，I为电流幅值，f I为电流幅值函数，SOC 0为初始荷电状态,T amb为设定的环境温度值，T为电池实际环境温度，a 0为电流幅值函数常系数，a 1为电流幅值函数中SOC 0对应的一次系数，a 2为电流幅值函数中P对应的一次系数，P为端口功率，
a 0,a 1,a 2可由平面拟合系数A,B,C,D导出：
Figure PCTCN2022091037-appb-000041
Figure PCTCN2022091037-appb-000042
Figure PCTCN2022091037-appb-000043
其中，A,B,C为平面拟合一次系数，D为平面拟合常系数，a 0为电流幅值函数常系数，a 1为电流幅值函数中SOC 0对应的一次系数，a 2为电流幅值函数中P对应的一次系数；
根据电流幅值和预设时间周期进行荷电状态更新，表示为：
Figure PCTCN2022091037-appb-000044
ΔT＝NΔt
SOC T+1＝SOC T+ΔSOC
其中，ΔSOC为相邻时间周期荷电状态变化量，I为电流幅值，C 0为以安时为单位的电池总容量，ΔT为预设时间周期长度，N为预设时间周期内时刻总数，Δt为采样间隔，SOC T+1为下一时间周期开始时的电池荷电状态，SOC T为本时间周期开始时的电池荷电状态。
图3为本申请实施例的基于锂离子电池电化学模型功率特性的荷电状态更新方法的另一个流程图。
如图3所示，预先设定电池环境温度和初始荷电状态，获得恒定幅值电流序列。根据预先设定参数和恒定幅值电流序列进行电化学模型仿真，更新参数向量、反应电流强度、电极表面电势差、电极活性材料锂浓度、电极电解质锂浓度，并计算电池端口电压和温度。根据计算所得端口电压平均值和电流序列幅值计算当前荷电状态及电流幅值下的端口平均功率。调整初始荷电状态和电流序列幅值大小，重复上述步骤，形成电流幅值-荷电状态-端口功率曲面，工程上利用平面方程拟合上述曲面。在荷电状态更新时，可由已知电池端口功率和初始荷电状态，由上述平面方程导出电流序列幅值，并利用所得幅值大小进行荷电状态更新。
图4为本申请实施例二所提供的一种基于锂离子电池电化学模型功率特性的荷电状态更新装置的结构示意图。
如图4所示，该基于锂离子电池电化学模型功率特性的荷电状态更新装置，包括：
获取模块10，用于获取电池的初始荷电状态和恒定幅值的电流序列；
处理模块20，用于获取电池初始状态信息，根据电池初始状态信息和锂离子电池电化学模型仿真，获得预设时间周期内每一时刻的电池端口电压；
计算模块30，用于根据电池端口电压和电流序列幅值，计算得到初始荷电状态对应的端口功率；
循环模块40，用于调整初始荷电状态和电流序列幅值，重复调用获取模块、处理模块和计算模块，分别得到多个不同的初始荷电状态和电流序列幅值所对应的端口功率，根据端口功率，获得电流幅值-荷电状态-端口功率曲面；
拟合模块50，用于将电流幅值-荷电状态-端口功率曲面拟合为平面方程；
更新模块60，用于根据端口功率和荷电状态，利用平面方程获取端口功率和荷电状态对应的电流幅值，并根据电流幅值和预设时间周期进行荷电状态更新。
本申请实施例的基于锂离子电池电化学模型功率特性的荷电状态更新装置，包括获取模 块，用于获取电池的初始荷电状态和恒定幅值的电流序列；处理模块，用于获取电池初始状态信息，根据电池初始状态信息和锂离子电池电化学模型仿真，获得预设时间周期内每一时刻的电池端口电压；计算模块，用于根据电池端口电压和电流序列幅值，计算得到初始荷电状态对应的端口功率；循环模块，用于调整初始荷电状态和电流序列幅值，重复调用获取模块、处理模块和计算模块，分别得到多个不同的初始荷电状态和电流序列幅值所对应的端口功率，根据端口功率，获得电流幅值-荷电状态-端口功率曲面；拟合模块，用于将电流幅值-荷电状态-端口功率曲面拟合为平面方程；更新模块，用于根据端口功率和荷电状态，利用平面方程获取端口功率和荷电状态对应的电流幅值，并根据电流幅值和预设时间周期进行荷电状态更新。由此，能够解决现有方法的锂离子电池荷电状态更新精度和效率互斥的技术问题，利用锂离子电池电化学模型外特性仿真精度高的特点，在不增加计算复杂度的情况下考虑电池时变端口电压对荷电状态更新的影响，实现了在已知电池功率、荷电状态的情况下，无需进行连续时序仿真，完成对电池荷电状态的更新，提高了电化学模型在功率应用场景下的计算效率，拓展了电化学模型在工程中的应用场景。
为了实现上述实施例，本申请还提出了一种电子设备，包括：存储器、处理器及存储在存储器上并可在处理器上运行的计算机程序，所述处理器执行所述程序时实现如上所述的基于锂离子电池电化学模型功率特性的荷电状态更新方法。
为了实现上述实施例，本申请还提出了一种非临时性计算机可读存储介质，其上存储有计算机程序，计算机程序被处理器执行时实现上述实施例的基于锂离子电池电化学模型功率特性的荷电状态更新方法。
为了实现上述实施例，本申请还提出了一种计算机程序产品，包括计算机程序，所述计算机程序在被处理器执行时实现如上所述的基于锂离子电池电化学模型功率特性的荷电状态更新方法。
在本说明书的描述中，参考术语“一个实施例”、“一些实施例”、“示例”、“具体示例”、或“一些示例”等的描述意指结合该实施例或示例描述的具体特征、结构、材料或者特点包含于本申请的至少一个实施例或示例中。在本说明书中，对上述术语的示意性表述不必须针对的是相同的实施例或示例。而且，描述的具体特征、结构、材料或者特点可以在任一个或多个实施例或示例中以合适的方式结合。此外，在不相互矛盾的情况下，本领域的技术人员可以将本说明书中描述的不同实施例或示例以及不同实施例或示例的特征进行结合和组合。
此外，术语“第一”、“第二”仅用于描述目的，而不能理解为指示或暗示相对重要性或者隐含指明所指示的技术特征的数量。由此，限定有“第一”、“第二”的特征可以明示或者隐含地包括至少一个该特征。在本申请的描述中，“多个”的含义是至少两个，例如两个，三个等，除非另有明确具体的限定。
流程图中或在此以其他方式描述的任何过程或方法描述可以被理解为，表示包括一个或更多个用于实现定制逻辑功能或过程的步骤的可执行指令的代码的模块、片段或部分，并且本申请的优选实施方式的范围包括另外的实现，其中可以不按所示出或讨论的顺序，包括根据所涉及的功能按基本同时的方式或按相反的顺序，来执行功能，这应被本申请的实施例所属技术领域的技术人员所理解。
在流程图中表示或在此以其他方式描述的逻辑和/或步骤，例如，可以被认为是用于实现逻辑功能的可执行指令的定序列表，可以具体实现在任何计算机可读介质中，以供指令执行系统、装置或设备(如基于计算机的系统、包括处理器的系统或其他可以从指令执行系统、装置或设备取指令并执行指令的系统)使用，或结合这些指令执行系统、装置或设备而使用。就本说明书而言，"计算机可读介质"可以是任何可以包含、存储、通信、传播或传输程序以供指令执行系统、装置或设备或结合这些指令执行系统、装置或设备而使用的装置。计算机可读介质的更具体的示例(非穷尽性列表)包括以下：具有一个或多个布线的电连接部(电子装置)，便携式计算机盘盒(磁装置)，随机存取存储器(RAM)，只读存储器(ROM)，可擦除可编辑只读存储器(EPROM或闪速存储器)，光纤装置，以及便携式光盘只读存储器(CDROM)。另外，计算机可读介质甚至可以是可在其上打印所述程序的纸或其他合适的介质，因为可以例如通过对纸或其他介质进行光学扫描，接着进行编辑、解译或必要时以其他合适方式进行处理来以电子方式获得所述程序，然后将其存储在计算机存储器中。
应当理解，本申请的各部分可以用硬件、软件、固件或它们的组合来实现。在上述实施方式中，多个步骤或方法可以用存储在存储器中且由合适的指令执行系统执行的软件或固件来实现。如，如果用硬件来实现和在另一实施方式中一样，可用本领域公知的下列技术中的任一项或他们的组合来实现：具有用于对数据信号实现逻辑功能的逻辑门电路的离散逻辑电路，具有合适的组合逻辑门电路的专用集成电路，可编程门阵列(PGA)，现场可编程门阵列(FPGA)等。
本技术领域的普通技术人员可以理解实现上述实施例方法携带的全部或部分步骤是可以通过程序来指令相关的硬件完成，所述的程序可以存储于一种计算机可读存储介质中，该程序在执行时，包括方法实施例的步骤之一或其组合。
此外，在本申请各个实施例中的各功能单元可以集成在一个处理模块中，也可以是各个单元单独物理存在，也可以两个或两个以上单元集成在一个模块中。上述集成的模块既可以采用硬件的形式实现，也可以采用软件功能模块的形式实现。所述集成的模块如果以软件功能模块的形式实现并作为独立的产品销售或使用时，也可以存储在一个计算机可读取存储介质中。
上述提到的存储介质可以是只读存储器，磁盘或光盘等。
尽管上面已经示出和描述了本申请的实施例，可以理解的是，上述实施例是示例性的，不能理解为对本申请的限制，本领域的普通技术人员在本申请的范围内可以对上述实施例进行变化、修改、替换和变型。

Claims (11)

  1. 一种基于锂离子电池电化学模型功率特性的荷电状态更新方法，包括：
    S1：获取电池的初始荷电状态和恒定幅值的电流序列；
    S2：获取电池初始状态信息，根据所述电池初始状态信息和锂离子电池电化学模型仿真，获得预设时间周期内每一时刻的电池端口电压；
    S3：根据所述电池端口电压和电流序列幅值，计算得到所述初始荷电状态对应的端口功率；
    S4：调整所述初始荷电状态和所述电流序列幅值，重复步骤S1-S3，分别得到多个不同的初始荷电状态和电流序列幅值所对应的端口功率，根据所述端口功率，获得电流幅值-荷电状态-端口功率曲面；
    S5：将所述电流幅值-荷电状态-端口功率曲面拟合为平面方程；
    S6：根据端口功率和荷电状态，利用所述平面方程获取所述端口功率和荷电状态对应的电流幅值，并根据所述电流幅值和所述预设时间周期进行荷电状态更新。
  2. 如权利要求1所述的方法，其中所述电池初始状态信息，包括：电极活性材料表面锂浓度、电极活性材料平均锂浓度、电极电解质锂浓度、电池温度初值。
  3. 如权利要求2所述的方法，其中所述根据所述电池初始状态信息和锂离子电池电化学模型仿真，获得预设时间周期内每一时刻的电池端口电压，包括：
    获取恒定幅度的电池环境温度序列，电池环境温度序列包括预设时间周期内每一时刻的电池环境温度，
    预设时间周期起始时刻，根据上一时刻电极电解质锂浓度、电极活性材料平均锂浓度和电池温度，更新当前时刻参数向量：
    θ(k+1)＝f θ(c e(k),c s,av(k),T b(k))
    其中，θ(k+1)为当前时刻参数向量，f θ为参数更新函数，c e(k)为上一时刻电极电解质锂浓度，c s,av(k)为上一时刻电极活性材料平均锂浓度，T b(k)为上一时刻电池温度；
    根据上一时刻电极电解质锂浓度、电极活性材料表面锂浓度、电池温度、端口电流和当前时刻参数向量，更新当前时刻反应电流强度：
    j n(k+1)＝f j(c e(k),c s,surf(k),T b(k),I(k),θ(k+1))
    其中，j n(k+1)为当前时刻反应电流强度，f j为反应电流更新函数，c s,surf(k)为上一时刻电极活性材料表面锂浓度，I(k)为上一时刻端口电流；
    根据当前时刻反应电流强度和参数向量，更新当前时刻电极表面电势差：
    φ se(k+1)＝f φ(j n(k+1),θ(k+1))
    其中，φ se(k+1)为当前时刻电极表面电势差，f φ为电极表面电势差更新函数；
    根据上一时刻电极活性材料平均锂浓度、电极活性材料表面锂浓度、当前时刻反应电流强度、当前时刻参数向量和采样间隔，更新当前时刻电极活性材料锂浓度：
    c s,av(k+1)＝f av(c s,av(k),c s,surf(k),j n(k+1),θ(k+1),Δt)
    c s,surf(k+1)＝f surf(c s,av(k),c s,surf(k),j n(k+1),θ(k+1),Δt)
    其中，c s,av(k+1)为当前时刻电极活性材料平均锂浓度，f av为电极活性材料平均锂浓度更新函数，Δt为采样间隔，c s,surf(k+1)为当前时刻电极活性材料表面锂浓度，f surf为电极活性材料表面锂浓度更新函数；
    根据上一时刻电极电解质锂浓度、端口电流和当前时刻参数向量及采样间隔，更新当前时刻电极电解质锂浓度：
    c e(k+1)＝f e(c e(k),I(k),θ(k+1),Δt)
    其中，c e(k+1)为当前时刻电极电解质锂浓度，f e为电极电解质锂浓度更新函数；
    根据当前时刻电极电解质锂浓度、电极活性材料表面锂浓度、反应电流强度、参数向量和上一时刻电池温度、端口电流，获得当前时刻电池端口电压V和电池内电势差U：
    V(k+1)＝f V(c e(k+1),c s,surf(k+1),j n(k+1),T b(k),I(k),θ(k+1))
    U(k+1)＝f U(c e(k+1),c s,surf(k+1),j n(k+1),T b(k),I(k),θ(k+1))
    其中，V(k+1)为当前时刻电池端口电压，f V为电池端口电压更新函数，U(k+1)为当前时刻电池内电势差，f U为电池内电势差更新函数；
    根据当前时刻电池端口电压、电池内电势差、反应电流强度、参数向量和上一时刻电池温度、环境温度、端口电流及采样间隔，获得当前时刻电池温度：
    T b(k+1)＝f T(V(k+1),U(k+1),j n(k+1),T b(k),T amb(k),I(k),θ(k+1),Δt)
    其中，T b(k+1)为当前时刻电池温度，f T为电池温度更新函数，T amb(k)为上一时刻环境温度；
    重复上述仿真迭代更新步骤，由上一时刻状态值循环更新当前时刻状态值：参数向量、反应电流强度、电极表面电势差、电极活性材料锂浓度、电极电解质锂浓度，并根据状态更新结果输出电池端口电压和电池温度，直至预设时间周期结束，以得到预设时间周期内每一时刻的电池端口电压，
    其中，所述预设时间周期内每一时刻的电池端口电压可表示为
    V＝[V 1 V 2 … V k … V N]
    其中，V为预设时间周期内每一时刻的电池端口电压，V k为第k时刻电池端口电压值，N为预设时间周期内时刻总数，其中N为大于等于2的整数。
  4. 如权利要求3所述的方法，其中所述根据所述电池端口电压和电流序列幅值，计算得到所述初始荷电状态对应的端口功率，包括：
    根据所述电池端口电压得到预设时间周期内的平均端口电压，根据所述平均端口电压与电流序列幅值，计算得到所述初始荷电状态和所述电流序列幅值对应的端口功率，
    其中，平均端口电压表示为：
    Figure PCTCN2022091037-appb-100001
    其中，
    Figure PCTCN2022091037-appb-100002
    为平均端口电压，
    所述端口功率表示为：
    Figure PCTCN2022091037-appb-100003
    其中，P为端口功率，SOC 0为初始荷电状态，T amb为设定的电池环境温度值，I为电流序列幅值。
  5. 如权利要求4所述的方法，其中所述调整所述初始荷电状态和所述电流序列幅值，重复步骤S1-S3，分别得到多个不同的初始荷电状态和电流序列幅度所对应的端口功率，根据所述端口功率，获得电流幅值-荷电状态-端口功率曲面，包括：
    在保持电池环境温度恒定的情况下，调整电池的初始荷电状态和电流序列幅值，重复S1-S3，分别得到多个不同初始荷电状态和电流序列幅值所对应的端口功率，将所述端口功率平滑连接，获得电流幅值-荷电状态-端口功率曲面，
    其中，电流幅值-荷电状态-端口功率曲面表示为：
    Figure PCTCN2022091037-appb-100004
    其中，f P为T＝T amb时的电流幅值-荷电状态-端口功率曲面函数，T为电池实际环境温度。
  6. 如权利要求5所述的方法，其中所述将所述电流幅值-荷电状态-端口功率曲面拟合为平面方程，表示为：
    Figure PCTCN2022091037-appb-100005
    其中，f P为T＝T amb时的电流幅值-荷电状态-端口功率曲面函数，A,B,C为平面拟合一次系数，D为平面拟合常系数。
  7. 如权利要求6所述的方法，其中所述根据端口功率和荷电状态，利用所述平面方程获取所述端口功率和荷电状态对应的电流幅值，并根据所述电流幅值和所述预设时间周期进行荷电状态更新，包括：
    在保持电池环境温度恒定的情况下，根据已知的端口功率和荷电状态，利用平面方程得到电流幅值，
    其中，电流幅值表示为：
    I＝f I(SOC 0,P)| T＝Tamb≈a 0(T amb)+a 1(T amb)×SOC 0+a 2(T amb)×P
    其中，I为电流幅值，f I为电流幅值函数，a 0为电流幅值函数常系数，a 1为电流幅值函数中SOC 0对应的一次系数，a 2为电流幅值函数中P对应的一次系数，
    a 0,a 1,a 2可由平面拟合系数A,B,C,D导出：
    Figure PCTCN2022091037-appb-100006
    Figure PCTCN2022091037-appb-100007
    Figure PCTCN2022091037-appb-100008
    根据所述电流幅值和所述预设时间周期进行荷电状态更新，表示为：
    Figure PCTCN2022091037-appb-100009
    ΔT＝NΔt
    SOC T+1＝SOC T+ΔSOC
    其中，ΔSOC为相邻时间周期荷电状态变化量，C 0为以安时为单位的电池总容量，ΔT为预设时间周期长度，SOC T+1为下一时间周期开始时的电池荷电状态，SOC T为本时间周期开始时的电池荷电状态。
  8. 一种基于锂离子电池电化学模型功率特性的荷电状态更新装置，其特征在于，包括：
    获取模块，用于获取电池的初始荷电状态和恒定幅值的电流序列；
    处理模块，用于获取电池初始状态信息，根据所述电池初始状态信息和锂离子电池电化学模型仿真，获得预设时间周期内每一时刻的电池端口电压；
    计算模块，用于根据所述电池端口电压和电流序列幅值，计算得到所述初始荷电状态对应的端口功率；
    循环模块，用于调整所述初始荷电状态和所述电流序列幅值，重复调用获取模块、处理模块和计算模块，分别得到多个不同的初始荷电状态和电流序列幅值所对应的端口功率，根据所述端口功率，获得电流幅值-荷电状态-端口功率曲面；
    拟合模块，用于将所述电流幅值-荷电状态-端口功率曲面拟合为平面方程；
    更新模块，用于根据端口功率和荷电状态，利用所述平面方程获取所述端口功率和荷电状态对应的电流幅值，并根据所述电流幅值和所述预设时间周期进行荷电状态更新。
  9. 一种电子设备，包括：
    存储器、处理器及存储在存储器上并可在处理器上运行的计算机程序，所述处理器执行所述程序时实现如权利要求1-7中任一项所述的方法。
  10. 一种非临时性计算机可读存储介质，其上存储有计算机程序，其特征在于，所述计 算机程序被处理器执行时实现如权利要求1-7中任一所述的方法。
  11. 一种计算机程序产品，包括计算机程序，所述计算机程序在被处理器执行时实现如权利要求1-7中任一所述的方法。
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