Lithium iron phosphate power system health state estimation method

Abstract

The invention provides a lithium iron phosphate power system health state estimation method comprising the following steps: collecting parameter data of a battery, a motor and an electric control system in a power system, and preprocessing the parameter data to obtain a battery data set, a motor data set and an electric control data set; combining every two of the battery data set, the motor data set and the electric control data set to form a state estimation matrix, and calculating a characteristic value of the state estimation matrix; the correlation degree between every two of the battery, the motor and the electric control system is obtained, and corresponding weights are given to the corresponding characteristic values according to the correlation degree; the weighted characteristic values are summed, the health state of the power system is estimated according to the summing result, and the parameter data of the battery, the motor and the electric control system are preprocessed to form the state estimation matrix, so that the interaction among the components of the power system can be fully considered, and a more comprehensive evaluation view angle is provided; therefore, all-around repair and maintenance can be carried out in time.

Description

Technical Field

[0001] The present invention relates to the technical field of lithium battery detection and evaluation, and in particular to a method for estimating the health status of a lithium iron phosphate power system. Background Art

[0002] With the rapid development of the new energy vehicle industry, lithium iron phosphate (LFP) batteries have become one of the mainstream choices for electric vehicle power batteries due to their high safety, long life and environmental protection characteristics. However, with the widespread application of lithium iron phosphate batteries, accurate assessment of their health status has become particularly important. The health status of the lithium iron phosphate power system directly affects the range, safety and service life of electric vehicles.

[0003] The power system of new energy vehicles mainly includes batteries, motors and electronic control systems. Batteries are the energy source of motors. The state of batteries directly affects the working efficiency of motors. The operation of motors is controlled by electronic control systems, which send instructions to motors according to the needs of drivers, such as acceleration and deceleration. The health status of the electronic control system directly affects whether the motor can respond to these instructions correctly. The electronic control system is responsible for the distribution and management of battery energy, including current control during battery charging and discharging. If there is a problem with the electronic control system, it may cause the battery to be over-discharged or under-charged, thereby damaging the health of the battery. It can be seen that the three electrics of the power system are highly correlated. When evaluating the health status of the power system, the current health status estimation method mostly estimates the battery in a single direction, without taking the motor and electronic control system into consideration. The estimated health status cannot fully reflect the operating status and current status of the power system. Summary of the invention

[0004] In view of this, the present invention proposes a method for estimating the health status of a lithium iron phosphate power system, which can perform an all-round health status estimation on the power system, so as to facilitate timely and comprehensive repair and maintenance of the power system.

[0005] The technical solution of the present invention is achieved in this way:

[0006] A method for estimating the health status of a lithium iron phosphate power system comprises the following steps:

[0007] Step S1, collecting parameter data of the battery, motor and electronic control system in the power system, and pre-processing the parameter data to obtain a battery data group, a motor data group and an electronic control data group;

[0008] Step S2, combining the battery data group, the motor data group and the electric control data group in pairs to form a state estimation matrix, and calculating the eigenvalue of the state estimation matrix;

[0009] Step S3, obtaining the correlation between the battery, the motor and the electronic control system, and assigning corresponding weights to the corresponding feature values ​​according to the correlation;

[0010] Step S4: sum the weighted eigenvalues ​​and estimate the health status of the power system based on the summation result.

[0011] Preferably, the specific steps of step S1 include:

[0012] Step S11, collecting the voltage, current, temperature, internal resistance, remaining power, rated power, current actual capacity of the battery, designed capacity of the battery, number of charge and discharge times, and usage time of the battery in the power system;

[0013] Step S12: The SOC value of the battery is calculated based on the ratio of the remaining power to the rated power, and the SOH value of the battery is calculated based on the ratio of the current actual capacity of the battery to the designed capacity of the battery;

[0014] Step S13: The collected parameter data, SOC value and SOH value are combined into a battery data group.

[0015] Preferably, the specific steps of step S1 also include:

[0016] Step S14: collecting the motor's speed, current, voltage, temperature, torque, power, efficiency, vibration and noise decibel to form a motor data group.

[0017] Preferably, the specific steps of step S1 also include:

[0018] Step S15, collecting input signals, output signals, fault codes, communication status, software version, temperature, working time and operating frequency of the electronic control system;

[0019] Step S16: quantify the fault code according to its severity, the frequency of communication interruption, and the newness of the software version, and combine the quantified data and the collected parameter data into an electric control data group.

[0020] Preferably, the specific steps of step S2 are:

[0021] Step S21, standardizing the battery data group, the motor data group and the electric control data group;

[0022] Step S22, selecting at least two data from the battery data group, the motor data group and the electric control data group respectively, and combining the selected data into a state estimation matrix in pairs;

[0023] Step S23: Calculate the eigenvalues ​​of each state estimation matrix.

[0024] Preferably, the step S2 further comprises:

[0025] Step S22.5: Obtain the current ambient temperature and humidity data of the power system, and add the ambient temperature and humidity data to the state estimation matrix.

[0026] Preferably, the specific steps of step S3 are:

[0027] Step S31, obtaining normal data of the battery, motor and electronic control system when the power system is operating normally;

[0028] Step S32, obtaining the synchronization follow-up time and frequency of the other two faults when one of the battery, the motor and the electronic control system fails;

[0029] Step S33, obtaining the correlation between the battery, the motor and the electronic control system based on the synchronization follow-up time and frequency;

[0030] Step S34: assign corresponding weights to the eigenvalues ​​of the corresponding state estimation matrix according to the magnitude of the association degree.

[0031] Preferably, the specific steps of step S32 are: when one of them fails, the operating data of the other two are collected and compared with the normal data. If there is a deviation between the operating data and the normal data, the time when the deviation occurs is recorded as the synchronization follow-up time, and the number of times one of the power systems fails following the other is counted as the frequency.

[0032] Preferably, the specific steps of step S4 are:

[0033] Step S41, adding the weighted eigenvalues ​​of all state estimation matrices to obtain a sum result;

[0034] Step S42: Compare the summation result with a preset state comparison table to obtain the health state of the power system.

[0035] Compared with the prior art, the present invention has the following beneficial effects:

[0036] ① When evaluating the health status of the power system, data is collected from the battery, motor and electronic control system in the power system, and parameters are obtained for evaluation based on the interrelationship between the three. This avoids the problem of incomplete evaluation caused by the traditional method of estimating the health status of the battery only, and can comprehensively evaluate the health status of the power system to facilitate timely repair and maintenance;

[0037] ② After obtaining the parameter data of the battery, motor and electronic control system, the parameter data of the battery, motor and electronic control system are respectively combined into a state estimation matrix in pairs. After calculating the eigenvalue of the state estimation matrix, a weighted summation can be performed, which takes into account the interaction between the various components of the power system, provides a more comprehensive evaluation perspective, and can more accurately evaluate the health status of the power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only preferred embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0039] Figure 1 A flow chart of a method for estimating the health status of a lithium iron phosphate power system according to the present invention;

[0040] Figure 2 This is a flow chart of step S1 of a method for estimating the health status of a lithium iron phosphate power system of the present invention;

[0041] Figure 3 This is a flow chart of step S2 of a method for estimating the health status of a lithium iron phosphate power system of the present invention;

[0042] Figure 4 This is a flow chart of step S3 of a method for estimating the health status of a lithium iron phosphate power system of the present invention;

[0043] Figure 5 This is a flow chart of step S4 of a method for estimating the health status of a lithium iron phosphate power system of the present invention. DETAILED DESCRIPTION

[0044] In order to better understand the technical content of the present invention, a specific embodiment is provided below, and the present invention is further described in conjunction with the accompanying drawings.

[0045] See also Figures 1 to 5 The present invention provides a method for estimating the health status of a lithium iron phosphate power system, comprising the following steps:

[0046] Step S1, collecting parameter data of the battery, motor and electronic control system in the power system, and pre-processing the parameter data to obtain a battery data group, a motor data group and an electronic control data group;

[0047] Step S2, combining the battery data group, the motor data group and the electric control data group in pairs to form a state estimation matrix, and calculating the eigenvalue of the state estimation matrix;

[0048] Step S3, obtaining the correlation between the battery, the motor and the electronic control system, and assigning corresponding weights to the corresponding feature values ​​according to the correlation;

[0049] Step S4: sum the weighted eigenvalues ​​and estimate the health status of the power system based on the summation result.

[0050] A method for estimating the health status of a lithium iron phosphate power system of the present invention is used to perform an all-round state assessment of the power system, wherein the power system mainly includes a battery, a motor and an electronic control system, referred to as the three electrics, which are interrelated. The battery is the energy source of the motor, and the operation of the motor is controlled by the electronic control system. The electronic control system is responsible for the distribution and management of the battery energy. When one of the systems is abnormal, it will affect the other two systems. In the current power system health status estimation, most of them only evaluate the SOC (state of charge) and SOH (state of health) of the battery, and do not combine the data of the motor and the electronic control system for evaluation, resulting in inaccurate health status estimation of the power system, unable to correctly reflect the operation process of the power system, and thus unable to effectively perform repairs and maintenance, and there are certain safety hazards in the power system. When performing state estimation, the present invention, in addition to considering the battery, also takes the motor and the electronic control system into consideration, and at the same time, the health status estimation is performed based on the relationship between the three electrics, which can more comprehensively reflect the health status of the power system, so as to accurately perform fault status analysis and carry out targeted repairs and maintenance.

[0051] When estimating the health state, the parameter data of the three electric systems are first collected. After the collection is completed, the parameter data are divided according to the source of the collection, and a battery data group, a motor data group and an electronic control data group can be obtained respectively. Each data group will contain more parameter data, and then the three data groups are combined in pairs to form three state estimation matrices. Each state estimation matrix contains parameter data of two systems of the three electric systems. Finally, the eigenvalues ​​of the state estimation matrix are calculated, and the interaction of the three electric systems is characterized by the eigenvalues. After obtaining the eigenvalues, weights are assigned based on the correlation between the three electric systems, that is, the eigenvalues ​​of each state estimation matrix are assigned different weights. Finally, the weighted eigenvalues ​​are added, and the summed result is the health state value of the power system. The health state of the power system can be estimated according to the summed result. The present invention takes into account the interaction between the various components of the power system and provides a more comprehensive evaluation perspective. Through the calculation of eigenvalues ​​and weights, the health state evaluation has a certain quantitative basis, which is convenient for subsequent analysis and decision-making. Other weights can be flexibly adjusted to meet the needs of different application scenarios.

[0052] Preferably, the specific steps of step S1 include:

[0053] Step S11, collecting the voltage, current, temperature, internal resistance, remaining power, rated power, current actual capacity of the battery, designed capacity of the battery, number of charge and discharge times, and usage time of the battery in the power system;

[0054] Step S12: The SOC value of the battery is calculated based on the ratio of the remaining power to the rated power, and the SOH value of the battery is calculated based on the ratio of the current actual capacity of the battery to the designed capacity of the battery;

[0055] Step S13, forming a battery data group with the collected parameter data, SOC value and SOH value;

[0056] Step S14: collecting the motor's speed, current, voltage, temperature, torque, power, efficiency, vibration and noise decibel to form a motor data group.

[0057] Step S15, collecting input signals, output signals, fault codes, communication status, software version, temperature, working time and operating frequency of the electronic control system;

[0058] Step S16: quantify the fault code according to its severity, the frequency of communication interruption, and the newness of the software version, and combine the quantified data and the collected parameter data into an electric control data group.

[0059] For batteries, the data during operation include voltage, current, temperature, etc. When the battery is operating normally, its voltage, current and temperature are mostly in a normal state. If the battery is abnormal, the voltage and current will fluctuate, and the temperature will also change. In addition, the battery will age during long-term operation, resulting in changes in capacity and an increase in internal resistance. The change in capacity will shorten its use time, increase the number of charge and discharge cycles, shorten the charge and discharge time, etc. The remaining power of the battery can be calculated based on the basic data of the battery. The SOC value can be obtained based on the ratio of the remaining power to the rated power, which can be used to reflect the current remaining power of the battery and is an important indicator for measuring the health status of the battery. The SOH value of the battery can be calculated based on the ratio of the current actual capacity of the battery to the designed capacity of the battery, which can reflect the capacity loss of the battery relative to the brand new state and is a key parameter for evaluating the degree of battery aging. The collected parameter data as well as the SOC value and SOH value are used as the battery data group.

[0060] For the motor, its speed directly affects the vehicle's driving performance and is an important parameter for evaluating the motor's health status. The current consumption during motor operation can reflect the motor's working status and efficiency. The temperature change of the motor can reflect the motor's working status and heat dissipation. The motor's efficiency directly affects the vehicle's energy consumption and cruising range, and is a key indicator for evaluating the motor's health status. In addition, there are basic data such as current, voltage, torque and power. In addition, the vibration and noise decibels during motor operation can also be used as one of the factors for estimating the health status.

[0061] The main function of the electronic control system is to control the motor and monitor the battery. Most of the data it collects are not quantitative data, such as fault codes, input and output signals, communication status, software version, etc. Therefore, after collecting the parameter data of the electronic control system, the non-quantitative parameter data is quantified. The fault code can reflect the health status and potential problems of the system. Commonly used fault codes indicate that the fault is relatively minor and the quantified value is small, while the quantified value of serious fault codes is relatively large. The communication status can reflect the coordination and stability of the system. The ratio of the number of successful communications in a period of time to the total number of communications can be calculated as a quantitative indicator. The input and output signals can reflect the actual working status and response of the system. The number of triggering of input signals or output signals within a certain period of time can be counted as a quantitative indicator. The software version can reflect the update status and technological advancement of the system. The newness of the version number can be used as the basis for quantification, or the number of software version updates in a period of time can be counted as a quantitative indicator.

[0062] Preferably, the specific steps of step S2 are:

[0063] Step S21, standardizing the battery data group, the motor data group and the electric control data group;

[0064] Step S22, selecting at least two data from the battery data group, the motor data group and the electric control data group respectively, and combining the selected data into a state estimation matrix in pairs;

[0065] Step S22.5, obtaining the current ambient temperature and humidity data of the power system, and adding the ambient temperature and humidity data to the state estimation matrix;

[0066] Step S23: Calculate the eigenvalues ​​of each state estimation matrix.

[0067] Since the units and magnitudes of different types of sensor data may be different, standardization is required to make the data comparable on the same scale. After standardization, at least two key indicators are selected from the parameter data in the battery data group, motor data group, and electronic control data group, and then they are combined in pairs to form a state estimation matrix. At the same time, the ambient temperature and humidity data that will affect the operation of the three electrics are added to the state estimation matrix. Finally, the eigenvalues ​​of the state estimation matrix are calculated, and each state estimation matrix will obtain several eigenvalues.

[0068] Preferably, the specific steps of step S3 are:

[0069] Step S31, obtaining normal data of the battery, motor and electronic control system when the power system is operating normally;

[0070] Step S32, obtaining the synchronization follow-up time and frequency of the other two faults of the battery, motor and electronic control system when one of them fails. The specific steps are: when one of them fails, collect the operating data of the other two and compare them with the normal data. If there is a deviation between the operating data and the normal data, the time when the deviation occurs is recorded as the synchronization follow-up time, and the number of times one of the power systems fails following another is counted as the frequency.

[0071] Step S33, obtaining the correlation between the battery, the motor and the electronic control system based on the synchronization follow-up time and frequency;

[0072] Step S34: assign corresponding weights to the eigenvalues ​​of the corresponding state estimation matrix according to the magnitude of the association degree.

[0073] After calculating the eigenvalues ​​of all state estimation matrices, the eigenvalues ​​need to be weighted. The assigned weights need to take into account the relationship between the three electric systems. First, obtain the normal data of the three electric systems during operation, and then obtain the fault log based on the operation log of the power system. Determine which system of the three electric systems the fault source is through the fault log, and then determine whether to synchronize the fault with the faulty system through the transportation data of the other two systems, and record the time when the operating data deviates as the synchronization following time. In addition, the number of times the synchronization fault follows another system when there are multiple faults is recorded as the frequency. Finally, the correlation between the two systems can be calculated based on the frequency and the synchronization following time. The greater the correlation, the greater the weight assigned.

[0074] The specific steps of step S4 are:

[0075] Step S41, adding the weighted eigenvalues ​​of all state estimation matrices to obtain a sum result;

[0076] Step S42: Compare the summation result with a preset state comparison table to obtain the health state of the power system.

[0077] The weighted eigenvalues ​​are added together and then compared with the preset state comparison table. The state comparison table contains multiple numerical ranges and the health states corresponding to the numerical ranges. After comparison, the numerical range of the summation result can be obtained, so that the health state corresponding to the numerical range can be output to realize the estimation of the health state of the power system.

[0078] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for estimating the health status of a lithium iron phosphate power system, characterized in that: The following steps are involved: Step S1, collecting parameter data of the battery, motor and electronic control system in the power system, and pre-processing the parameter data to obtain a battery data group, a motor data group and an electronic control data group; Step S2, combining the battery data group, the motor data group and the electric control data group in pairs to form a state estimation matrix, and calculating the eigenvalue of the state estimation matrix; Step S3, obtaining the correlation between the battery, the motor and the electronic control system, and assigning corresponding weights to the corresponding feature values ​​according to the correlation; Step S4: sum the weighted eigenvalues ​​and estimate the health status of the power system based on the summation result.

2. A method for estimating the health status of a lithium iron phosphate power system according to claim 1, characterized in that: The specific steps of step S1 include: Step S11, collecting the voltage, current, temperature, internal resistance, remaining power, rated power, current actual capacity of the battery, designed capacity of the battery, number of charge and discharge times, and usage time of the battery in the power system; Step S12: The SOC value of the battery is calculated based on the ratio of the remaining power to the rated power, and the SOH value of the battery is calculated based on the ratio of the current actual capacity of the battery to the designed capacity of the battery; Step S13: The collected parameter data, SOC value and SOH value are combined into a battery data group.

3. A method for estimating the health status of a lithium iron phosphate power system according to claim 1, characterized in that: The specific steps of step S1 also include: Step S14: collecting the motor's speed, current, voltage, temperature, torque, power, efficiency, vibration and noise decibel to form a motor data group.

4. A method for estimating the health status of a lithium iron phosphate power system according to claim 1, characterized in that: The specific steps of step S1 also include: Step S15, collecting input signals, output signals, fault codes, communication status, software version, temperature, working time and operating frequency of the electronic control system; Step S16: quantify the fault code according to its severity, the frequency of communication interruption, and the newness of the software version, and combine the quantified data and the collected parameter data into an electric control data group.

5. A method for estimating the health status of a lithium iron phosphate power system according to claim 1, characterized in that: The specific steps of step S2 are: Step S21, standardizing the battery data group, the motor data group and the electric control data group; Step S22, selecting at least two data from the battery data group, the motor data group and the electric control data group respectively, and combining the selected data into a state estimation matrix in pairs; Step S23: Calculate the eigenvalues ​​of each state estimation matrix.

6. A method for estimating the health status of a lithium iron phosphate power system according to claim 5, characterized in that: The step S2 further comprises: Step S22.5: Obtain the current ambient temperature and humidity data of the power system, and add the ambient temperature and humidity data to the state estimation matrix.

7. A method for estimating the health status of a lithium iron phosphate power system according to claim 1, characterized in that: The specific steps of step S3 are: Step S31, obtaining normal data of the battery, motor and electronic control system when the power system is operating normally; Step S32, obtaining the synchronization follow-up time and frequency of the other two faults when one of the battery, the motor and the electronic control system fails; Step S33, obtaining the correlation between the battery, the motor and the electronic control system based on the synchronization follow-up time and frequency; Step S34: assign corresponding weights to the eigenvalues ​​of the corresponding state estimation matrix according to the magnitude of the association degree.

8. A method for estimating the health status of a lithium iron phosphate power system according to claim 7, characterized in that: The specific steps of step S32 are: when one of them fails, the operating data of the other two are collected and compared with the normal data. If there is a deviation between the operating data and the normal data, the time when the deviation occurs is recorded as the synchronization follow-up time, and the number of times one of the power systems fails following the other is counted as the frequency.

9. A method for estimating the health status of a lithium iron phosphate power system according to claim 1, characterized in that: The specific steps of step S4 are: Step S41, adding the weighted eigenvalues ​​of all state estimation matrices to obtain a sum result; Step S42: Compare the summation result with a preset state comparison table to obtain the health state of the power system.

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