Lithium iron phosphate battery monitoring system

Abstract

The invention discloses a lithium iron phosphate battery monitoring system, and the system comprises a data collection module which is used for collecting the related data of the charging and discharging process of a battery in real time, and carrying out the preprocessing of the related data; the model building module is used for building an equivalent circuit model of the battery based on the chemical characteristics of the battery, and the equivalent circuit model associates the key parameters of the battery with the state parameters of the battery and the environmental information; the parameter estimation module is used for updating key parameters in real time according to an extended Kalman filtering algorithm in combination with the equivalent circuit model and currently acquired related data; and the monitoring module is used for monitoring the state of the battery in real time based on the key parameters. According to the lithium iron phosphate battery state monitoring system provided by the embodiment of the invention, the real-time and accurate monitoring of the charging and discharging states of the battery is realized through the improved algorithm, and the overall safety performance of the system is improved.

Description

Technical Field

[0001] The present application relates to a lithium iron phosphate battery, and in particular to a lithium iron phosphate battery monitoring system. Background Art

[0002] Lithium iron phosphate batteries are widely used in new energy vehicles, energy storage systems, and starting power supply industries due to their high safety and long cycle life. However, existing battery management systems have insufficient accuracy in battery status assessment, which results in the battery performance not being fully utilized and may even cause safety issues due to monitoring errors. Summary of the invention

[0003] The technical problem to be solved by the present application is to provide a lithium iron phosphate battery monitoring system in view of the deficiencies in the prior art.

[0004] The technical problem to be solved by this application is solved by the following technical solution: A lithium iron phosphate battery monitoring system, comprising: A data acquisition module, used for real-time acquisition of relevant data of the battery charging and discharging process, and preprocessing the relevant data, wherein the relevant data includes operating parameter data and environmental information, wherein the operating parameters include battery voltage and battery current, and the environmental information includes battery temperature; A model building module, for building an equivalent circuit model of the battery based on the chemical characteristics of the battery, wherein the equivalent circuit model associates key parameters of the battery with state parameters and environmental information of the battery, wherein the key parameters include open circuit voltage and internal resistance, and the state parameters include state of charge of the battery and aging state of the battery; A parameter estimation module, used to update the key parameters in real time according to an extended Kalman filter algorithm in combination with the equivalent circuit model and the currently collected relevant data; A monitoring module is used to monitor the state of the battery in real time based on the key parameters.

[0005] The above system, the data acquisition module, comprises: A data acquisition unit, used to measure and collect the relevant data of the battery charging and discharging process in real time through an analog front-end circuit, wherein the circuit includes signal amplification, filtering and linearization processing to achieve accurate measurement of the battery voltage and the battery current; The preprocessing unit is used to synchronize the collected related data through timestamps, remove abnormal data, and then eliminate noise through filters using the RLS algorithm, wherein the RLS algorithm introduces a forgetting factor.

[0006] In the above system, the measured battery voltage and battery current are respectively expressed by the following formulas:

[0007]

[0008] in, and are the measured voltage and current, and is the gain and offset of the amplifier and bias circuit, is the conversion factor of the current sensor, is the input voltage, is the current sensed by the current sensor.

[0009] For the above system, the forgetting factor is calculated by the following formula:

[0010] in, represents the forgetting factor, It is The error signal at time It is a small positive constant used to control the range of the forgetting factor.

[0011] In the above system, the preprocessing unit is further used to optimize the filter weights by the following formula:

[0012] in, It is The weight vector at time, is the learning rate, is the transpose of the input signal.

[0013] The above system further comprises: The health assessment module is used to extract assessment features from the continuously collected relevant data, train an assessment model, assess the health status of the battery through the assessment model, and predict the remaining service life of the battery.

[0014] In the above system, the health assessment module comprises: A feature extraction unit, used to extract evaluation features from the continuously collected relevant data, wherein the evaluation features include cycle capacity decay, charging efficiency change and temperature fluctuation; A model training unit, used to train the health assessment model using a machine learning algorithm and evaluate the accuracy of the model using a cross-validation method; The health assessment unit is used to score the current health status of the battery.

[0015] The above system, the health assessment unit, is also used to select a long short-term memory network as a life prediction model, and predict the future health status and remaining service life of the battery by learning the time dependency in the data through the LSTM network.

[0016] The above system, the health assessment module, also includes a decision suggestion unit, which is used to make suggestions for maintaining or replacing the battery based on the future health status and remaining service life of the battery.

[0017] Due to the adoption of the above technical solution, the beneficial effects of this application are: The lithium iron phosphate battery monitoring system disclosed in the embodiment of the present application includes: a data acquisition module for real-time acquisition of relevant data of the battery charging and discharging process and preprocessing of the relevant data; a model building module for constructing an equivalent circuit model of the battery based on the chemical characteristics of the battery, the equivalent circuit model associates the key parameters of the battery with the state parameters and environmental information of the battery; a parameter estimation module for real-time update of key parameters based on the extended Kalman filter algorithm, in combination with the equivalent circuit model and the currently collected relevant data; a monitoring module for real-time monitoring of the battery state based on the key parameters. The embodiment of the present application provides a lithium iron phosphate battery state monitoring system, which realizes real-time and accurate monitoring of the battery charging and discharging state by improving the algorithm, thereby improving the overall safety performance of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 A schematic diagram of the structure of a monitoring system provided in an embodiment of the present application in one implementation manner; Figure 2 A schematic diagram of the structure of a data acquisition module provided in an embodiment of the present application in one implementation manner; Figure 3 A schematic diagram of the structure of a monitoring system provided in an embodiment of the present application in another implementation manner; Figure 4 A schematic diagram of the structure of a health assessment module in one embodiment of the present application. DETAILED DESCRIPTION

[0019] The present invention is further described in detail below by specific embodiments in conjunction with the accompanying drawings. Wherein similar elements in different embodiments adopt associated similar element numbers. In the following embodiments, many detailed descriptions are for making the present application better understood. However, those skilled in the art can easily recognize that some features can be omitted in different situations, or can be replaced by other elements, materials, methods. In some cases, some operations related to the present application are not shown or described in the specification, this is to avoid the core part of the present application being overwhelmed by too much description, and for those skilled in the art, it is not necessary to describe these related operations in detail, and they can fully understand the related operations according to the description in the specification and the general technical knowledge in the art.

[0020] In addition, the features, operations or characteristics described in the specification can be combined in any appropriate manner to form various implementations. At the same time, the steps or actions in the method description can also be interchanged or adjusted in a manner that is obvious to those skilled in the art. Therefore, the various sequences in the specification and the drawings are only for the purpose of clearly describing a certain embodiment and are not meant to be a required sequence, unless otherwise specified that a certain sequence must be followed.

[0021] The serial numbers assigned to the components in this article, such as "first", "second", etc., are only used to distinguish the objects described and do not have any order or technical meaning.

[0022] In addition, the technical features and technical solutions described herein may be combined in any suitable manner in one or more embodiments. It is easy for a person skilled in the art to understand that the steps or operation sequence of the methods related to the embodiments provided herein may also be changed. Therefore, any sequence in the drawings and embodiments is only for illustrative purposes and does not imply a requirement to follow a certain sequence unless it is explicitly stated that a certain sequence is required.

[0023] like Figure 1 As shown, the lithium iron phosphate battery monitoring system provided in the embodiment of the present application, in one implementation manner, includes a data acquisition module 110 , a model building module 120 , a parameter estimation module 130 and a monitoring module 140 .

[0024] The data acquisition module 110 is used to collect relevant data of the battery charging and discharging process in real time and pre-process the relevant data. The relevant data includes operating parameter data and environmental information. The operating parameters include battery voltage and battery current, and the environmental information includes battery temperature.

[0025] The model building module 120 is used to construct an equivalent circuit model of the battery based on the chemical characteristics of the battery. The equivalent circuit model associates the key parameters of the battery with the state parameters and environmental information of the battery. The key parameters include open circuit voltage (OCV for short) and internal resistance (Ri for short). The state parameters include the state of charge (SOC for short) and the aging state (SOH for short) of the battery.

[0026] The parameter estimation module 130 is used to update key parameters in real time according to an extended Kalman filter algorithm (Extended Kalman Filter, EKF for short) in combination with an equivalent circuit model and currently collected relevant data.

[0027] The algorithm is implemented on the microcontroller (MCU) to process the collected data in real time and calculate the current OCV and internal resistance of the battery.

[0028] The EKF algorithm is used to estimate the parameters of the battery model. The EKF algorithm can handle nonlinear systems and provide real-time, high-precision estimation of the battery OCV and internal resistance.

[0029]

[0030]

[0031] Among them, f() and g() represent the nonlinear relationship between battery OCV and internal resistance and SOC, SOH and temperature T respectively.

[0032] In order to improve the estimation accuracy, the EKF algorithm is optimized, including adjusting the covariance matrix of process noise and measurement noise to adapt to the actual dynamic characteristics of the battery.

[0033]

[0034]

[0035] The monitoring module 140 is used to monitor the battery status in real time based on key parameters.

[0036] The algorithm is implemented on the microcontroller (MCU) to process the collected data in real time and calculate the current OCV and internal resistance of the battery.

[0037] like Figure 2 As shown, the data acquisition module 110 may include a data acquisition unit 111 and a pre-processing unit 112 .

[0038] The data acquisition unit 111 is used to measure and collect relevant data of the battery charging and discharging process in real time through an analog front-end circuit. The circuit may include signal amplification, filtering and linearization processing to achieve accurate measurement of the battery voltage and battery current; The pre-processing unit 112 is used to synchronize the collected relevant data through timestamps, remove abnormal data, and then eliminate noise through filters using the RLS algorithm, which introduces a forgetting factor.

[0039] The lithium iron phosphate battery monitoring system provided in the embodiment of the present application, the measured battery voltage and the battery current are respectively expressed by the following formulas:

[0040]

[0041] in, and are the measured voltage and current, and is the gain and offset of the amplifier and bias circuit, is the conversion factor of the current sensor, is the input voltage, is the current sensed by the current sensor.

[0042] Taking into account the noise and interference in the battery working environment, the embodiment of the present application adjusts the RLS algorithm by adjusting the forgetting factor. , to balance the convergence speed and stability of the algorithm. In one embodiment, the forgetting factor can be calculated by the following formula:

[0043] in, represents the forgetting factor, It is The error signal at time It is a small positive constant used to control the range of the forgetting factor.

[0044] The lithium iron phosphate battery monitoring system provided in the embodiment of the present application optimizes the filter weights by improving the weight update formula, thereby reducing the computational complexity and improving the noise suppression capability. The preprocessing unit 112 can also be used to optimize the filter weights by the following formula:

[0045] in, It is The weight vector at time, is the learning rate, is the transpose of the input signal.

[0046] like Figure 3 As shown, the lithium iron phosphate battery monitoring system provided in the embodiment of the present application may also include a health assessment module 150.

[0047] The health assessment module 150 is used to extract assessment features from the continuously collected relevant data, train an assessment model, assess the health status of the battery through the assessment model, and predict the remaining service life of the battery.

[0048] In one embodiment, Figure 4 As shown, the health assessment module 150 may include a feature extraction unit 151 , a model training unit 152 and a health assessment unit 153 .

[0049] The feature extraction unit 151 is used to extract evaluation features from the continuously collected relevant data, and the evaluation features include cycle capacity decay, charging efficiency change and temperature fluctuation to ensure the comprehensiveness and continuity of the data.

[0050] The model training unit 152 is used to train the health assessment model using a machine learning algorithm and evaluate the accuracy of the model using a cross-validation method. The health assessment model is trained using a machine learning algorithm, such as a support vector machine (SVM) or a random forest (RF).

[0051] The health assessment unit 153 is used to score the current health status of the battery.

[0052] The lithium iron phosphate battery monitoring system and health assessment unit 153 provided in the embodiment of the present application are also used to select a long short-term memory network as a life prediction model, and predict the future health status and remaining service life of the battery by learning the time dependency in the data through the LSTM network.

[0053] In one embodiment, the health assessment module may further include a decision suggestion unit 154, which is used to make suggestions for maintaining or replacing the battery based on the future health status and remaining service life of the battery.

[0054] The lithium iron phosphate battery monitoring system provided in the embodiment of the present application may further include a power consumption processing module 160 .

[0055] The power consumption processing module 160 is used to dynamically adjust the operating frequency and power management mode according to the power consumption processing strategy through a combination of software and hardware to reduce the overall power consumption of the system. The power consumption processing strategy includes, in terms of system state switching, dynamically adjusting the working state of the CPU and system bus according to the system load and user needs, including sleep and low power consumption modes; in terms of voltage and clock adjustment, reducing active power consumption and static power consumption by reducing voltage and clock frequency; in terms of clock gating technology, stopping the clock of unnecessary circuits to reduce active power consumption; in terms of low power consumption mode, designing idle mode, stop mode, etc. to reduce power consumption.

[0056] The implementation of these policies requires close coordination between software and hardware. For example, the CPU needs to provide corresponding instruction set support, such as idle or stop instructions; at the same time, the operating system needs to have power management capabilities and be able to dynamically adjust power parameters according to the policy.

[0057] In embedded systems, power and power consumption management is of great significance in extending device life, reducing heat generation, and improving system stability. The research and application of dynamic power management technology can help improve the overall performance of embedded systems and meet the growing needs of mobile terminals and IoT devices.

[0058] The above contents are further detailed descriptions of the present application in combination with specific implementation methods, and it cannot be determined that the specific implementation of the present application is limited to these descriptions. For ordinary technicians in the technical field to which the present application belongs, several simple deductions or substitutions can be made without departing from the concept of the present application.

Claims

1. A lithium iron phosphate battery monitoring system, characterized in that: include: A data acquisition module, used for real-time acquisition of relevant data of the battery charging and discharging process, and preprocessing the relevant data, wherein the relevant data includes operating parameter data and environmental information, wherein the operating parameters include battery voltage and battery current, and the environmental information includes battery temperature; A model building module, for building an equivalent circuit model of the battery based on the chemical characteristics of the battery, wherein the equivalent circuit model associates key parameters of the battery with state parameters and environmental information of the battery, wherein the key parameters include open circuit voltage and internal resistance, and the state parameters include state of charge of the battery and aging state of the battery; A parameter estimation module, used to update the key parameters in real time according to an extended Kalman filter algorithm in combination with the equivalent circuit model and the currently collected relevant data; A monitoring module is used to monitor the state of the battery in real time based on the key parameters.

2. The lithium iron phosphate battery monitoring system according to claim 1, characterized in that: The data acquisition module comprises: A data acquisition unit, used to measure and collect the relevant data of the battery charging and discharging process in real time through an analog front-end circuit, wherein the circuit includes signal amplification, filtering and linearization processing to achieve accurate measurement of the battery voltage and the battery current; The preprocessing unit is used to synchronize the collected related data through timestamps, remove abnormal data, and then eliminate noise through filters using the RLS algorithm, wherein the RLS algorithm introduces a forgetting factor.

3. The lithium iron phosphate battery monitoring system according to claim 2, characterized in that: The measured battery voltage and battery current are respectively expressed by the following formulas: in, and are the measured voltage and current, and is the gain and offset of the amplifier and bias circuit, is the conversion factor of the current sensor, is the input voltage, is the current sensed by the current sensor.

4. The lithium iron phosphate battery monitoring system according to claim 2, characterized in that: The forgetting factor is calculated by the following formula: in, represents the forgetting factor, It is The error signal at time It is a small positive constant used to control the range of the forgetting factor.

5. The lithium iron phosphate battery monitoring system according to claim 4, characterized in that: The preprocessing unit is also used to optimize the filter weights by the following formula: in, It is The weight vector at time, is the learning rate, is the transpose of the input signal.

6. The lithium iron phosphate battery monitoring system according to any one of claims 1 to 5, characterized in that: Also includes: The health assessment module is used to extract assessment features from the continuously collected relevant data, train an assessment model, assess the health status of the battery through the assessment model, and predict the remaining service life of the battery.

7. The lithium iron phosphate battery monitoring system according to claim 6, characterized in that: The health assessment module includes: A feature extraction unit, used to extract evaluation features from the continuously collected relevant data, wherein the evaluation features include cycle capacity decay, charging efficiency change and temperature fluctuation; A model training unit, used to train the health assessment model using a machine learning algorithm and evaluate the accuracy of the model using a cross-validation method; The health assessment unit is used to score the current health status of the battery.

8. The lithium iron phosphate battery monitoring system according to claim 7, characterized in that: The health assessment unit is also used to select a long short-term memory network as a life prediction model, and predict the future health status and remaining service life of the battery by learning the time dependency in the data through the LSTM network.

9. The lithium iron phosphate battery monitoring system according to claim 8, characterized in that: The health assessment module also includes a decision suggestion unit, which is used to make suggestions for maintaining or replacing the battery based on the future health status and remaining service life of the battery.

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