Method and system for constructing service life decline model of agricultural tractor battery system and method and system for predicting service life of agricultural tractor battery system

Abstract

The invention discloses a service life decline model construction and service life prediction method and system of an agricultural tractor battery system, and the method comprises the steps: firstly, constructing a circulation aging model and a calendar aging model of the agricultural tractor battery system; the cyclic aging model can capture the influence of temperature on battery recession and the effect of different aging mechanisms in the recycling process of the agricultural tractor battery system. The calendar aging model may capture capacity degradation of the agricultural tractor battery system in a rest state. And respectively fitting corresponding test data to obtain model parameters of the cyclic aging model and the calendar aging model, and constructing the cyclic aging model and the calendar aging model. And then, based on a calendar aging model, fusing the cyclic aging model and the calendar aging model to construct a life decline model capable of accurately predicting the agricultural tractor battery system. The life decline amount of the agricultural tractor battery system can be accurately predicted through the life decline model.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of determining battery aging or degradation, and particularly relates to a life decline model construction method and system for a battery system of an agricultural tractor. BACKGROUND

[0002] The related technologies of new energy passenger cars, commercial vehicles and other vehicles have gradually matured, and there are many technical means for predicting the life of the battery system. However, the characteristics of passenger cars and commercial vehicles are basically used every day, while the operation mode of large agricultural tractors is special, which is generally divided into operation season and non-operation season. The operation season refers to the long-time operation of conventional spring plowing and autumn harvesting. The non-operation season refers to the long-time (up to several months) storage of tractors during the slack season. And some agricultural vehicles or machinery have a very short working time and a long storage time in a year, such as cotton picking machines which work only for about one month in a year. Therefore, the technical means for predicting the life of the battery system of passenger cars and commercial vehicles are not suitable for predicting the life of the battery system of agricultural tractors. SUMMARY

[0003] In view of the deficiencies in the prior art, the present application provides a life decline model construction method and system for a battery system of an agricultural tractor, which can improve the life prediction accuracy of the battery system of the agricultural tractor. The specific technical solutions are as follows: In a first aspect, a life decline model construction method for a battery system of an agricultural tractor is provided. In a first implementation manner of the first aspect, the method comprises: constructing a cycle aging model and a calendar aging model of the battery system of the agricultural tractor, and obtaining model parameters of the cycle aging model and the calendar aging model through experimental data fitting; constructing a life decline model by fusing the cycle aging model and the calendar aging model.

[0004] In a second implementation manner of the first aspect, the cycle aging model of the battery system of the agricultural tractor is constructed in combination with the first implementation manner. In the second implementation manner, the cycle aging model is constructed in combination with an Arrhenius equation and a double exponential model.

[0005] In a third implementation manner of the first aspect, the model parameters of the cycle aging model are obtained through experimental data fitting in combination with the first implementation manner. In the third implementation manner, the model parameters of the cycle aging model are obtained through experimental data fitting, comprising: performing a charge-discharge cycle test on the battery system of the agricultural tractor to obtain charge-discharge cycle test data of the battery system of the agricultural tractor; obtaining the model parameters of the cycle aging model by adopting a staged fitting strategy based on the charge-discharge cycle test data.

[0006] ​In a fourth implementable manner of the first aspect, in the third implementable manner of the first aspect, the model parameters of the cycle aging model are fitted by using a staged fitting strategy, including: Based on the charge-discharge cycle test data, the initial decay rate constant and the long-term decay rate constant of the cycle aging model are obtained by fitting through a double exponential model; The initial decay rate constant and the long-term decay rate constant obtained by fitting are substituted into the cycle aging model, and the initial capacity decay amplitude coefficient, the long-term capacity decay amplitude coefficient and the cycle activation energy of the cycle aging model are obtained by fitting through a fitting algorithm based on the charge-discharge cycle test data.

[0007] In a fifth implementable manner of the first aspect, in the first implementable manner of the first aspect, the model parameters of the calendar aging model are obtained by fitting through test data, including: The agricultural tractor battery system is subjected to quantitative testing to obtain quantitative test data of the agricultural tractor battery system, which includes the battery capacity of the agricultural tractor battery system at different time points under different states of charge and temperatures; Based on the quantitative test data, the aging rate pre-exponential factor, the calendar activation energy, the time index, the SOC positive power index and the SOC reverse power index of the calendar aging model are obtained by fitting through a fitting algorithm.

[0008] In a sixth implementable manner of the first aspect, in the first implementable manner of the first aspect, the model parameters of the cycle aging model and the calendar aging model are fitted by using a least squares method.

[0009] In a second aspect, a life prediction method for an agricultural tractor battery system is provided, including: The life degradation model construction method of any one of the first to sixth implementable manners of the first aspect is used to construct a life degradation model for the agricultural tractor battery system; The current detection indexes of the agricultural tractor battery system are obtained, and the battery life degradation amount of the agricultural tractor battery system is calculated through the life degradation model.

[0010] In a third aspect, a life degradation model construction system for an agricultural tractor battery system is provided, including: A single model construction module is configured to construct a cycle aging model and a calendar aging model for the agricultural tractor battery system, and to obtain the model parameters of the cycle aging model and the calendar aging model by fitting through test data; A degradation model construction module is configured to fuse the cycle aging model and the calendar aging model to construct a life degradation model.

[0011] Fourthly, a life prediction system for an agricultural tractor battery system is provided, comprising: The model building module is configured to use the life degradation model building method described in any of the first to sixth implementable methods of the first aspect to build a life degradation model for an agricultural tractor battery system. The lifespan prediction module is configured to acquire the current detection indicators of the agricultural tractor battery system and calculate the battery lifespan degradation amount of the agricultural tractor battery system through the lifespan degradation model.

[0012] Beneficial Effects: The life degradation model, life prediction method, and system for agricultural tractor battery systems of this invention can capture the impact of temperature on battery degradation and the role of different aging mechanisms during the cyclic use of agricultural tractor battery systems through the cyclic aging model. The calendar aging model can capture the capacity degradation of agricultural tractor battery systems under storage conditions. By fusing the cyclic aging model and the calendar aging model, a life degradation model suitable for predicting the life of agricultural tractor battery systems can be constructed, improving the accuracy of life prediction for agricultural tractor battery systems. Attached Figure Description

[0013] To more clearly illustrate the specific embodiments of the present invention, the accompanying drawings used in the specific embodiments will be briefly described below. In all the drawings, the elements or parts are not necessarily drawn to scale.

[0014] Figure 1 A flowchart illustrating a method for constructing a life degradation model for an agricultural tractor battery system according to an embodiment of the present invention; Figure 2 A flowchart illustrating a lifespan prediction method for an agricultural tractor battery system according to an embodiment of the present invention; Figure 3 A system block diagram of a system for constructing a life degradation model for an agricultural tractor battery system according to an embodiment of the present invention; Figure 4 This is a system block diagram of a life prediction system for an agricultural tractor battery system provided in an embodiment of the present invention. Detailed Implementation

[0015] The embodiments of the technical solution of the present invention will now be described in detail with reference to the accompanying drawings. These embodiments are merely illustrative of the technical solution of the present invention and are therefore intended to limit the scope of protection of the present invention.

[0016] like Figure 1 The flowchart shown illustrates a method for constructing a life degradation model for an agricultural tractor battery system. This method includes: Step 1, construct a cycle aging model and a calendar aging model of the agricultural tractor battery system, and obtain model parameters of the cycle aging model and the calendar aging model through test data fitting; Step 2, fuse the cycle aging model and the calendar aging model to construct a life degradation model.

[0017] Specifically, first, a cycle aging model and a calendar aging model of the agricultural tractor battery system can be constructed. The cycle aging model can capture the influence of temperature on battery degradation and the role of different aging mechanisms in the cycle use of the agricultural tractor battery system. The calendar aging model can capture the capacity degradation of the agricultural tractor battery system in the standby state. The corresponding test data can be obtained through the corresponding test, and then the model parameters of the cycle aging model and the calendar aging model can be obtained through the test data fitting, so as to construct the cycle aging model and the calendar aging model. Then, due to the special use scenario of the agricultural tractor, the cycle aging model can be fused with the calendar aging model, and the life degradation model of the agricultural tractor battery system can be constructed. Through the construction of the life degradation model, the life degradation amount of the agricultural tractor battery system can be accurately predicted, and then the remaining life of the agricultural tractor battery system can be predicted in combination with the service life of the agricultural tractor battery system.

[0018] In this embodiment, the cycle aging model of the agricultural tractor battery system is constructed, which comprises: The cycle aging model is constructed in combination with the Arrhenius equation and the double exponential model.

[0019] Specifically, the cycle aging model is composed of the Arrhenius equation and the double exponential model. The Arrhenius equation can capture the influence of temperature on the aging rate. The double exponential model can capture the role of different aging mechanisms in the cycle charging and discharging process of the agricultural tractor battery system. The constructed cycle aging model is specifically: ; ; Wherein, is the initial capacity attenuation amplitude coefficient, is the long-term capacity attenuation amplitude coefficient, is the initial attenuation rate constant, is the long-term attenuation rate constant, is the cycle activation energy, is the gas constant, is the temperature, is the ampere-hour throughput, is the number of charge-discharge cycles, is the discharge depth, for the electric cell capacity.

[0020] In the embodiment, the model parameters of the cycle aging model are obtained by fitting the test data, including: Performing a charge-discharge cycle test on the agricultural tractor battery system to obtain charge-discharge cycle test data of the agricultural tractor battery system; Based on the charge-discharge cycle test data, the model parameters of the cycle aging model are fitted by using a staged fitting strategy.

[0021] Specifically, first, the charge-discharge cycle test can be performed on the agricultural tractor battery system to obtain the charge-discharge cycle test data. Then, based on the charge-discharge cycle test data, the model parameters of the cycle aging model are fitted by using a staged fitting strategy. The core advantage of using the staged fitting strategy is to decouple parameters and reduce the complexity of fitting. In the first stage, the temperature effect is ignored, and the parameters inherent to the short-term and long-term aging kinetics are fitted. In the second stage, the multi-temperature data is used to fix , and focus on fitting the initial amplitude of each mechanism and the temperature sensitivity of the long-term mechanism. The aging kinetics and temperature effects are clearly separated, avoiding the difficulties and instability caused by simultaneous optimization of highly coupled parameters.

[0022] In the embodiment, the model parameters of the cycle aging model are fitted by using a staged fitting strategy, including: Based on the charge-discharge cycle test data, the initial decay rate constant and the long-term decay rate constant of the cycle aging model are fitted by using a double exponential model; The initial decay rate constant and the long-term decay rate constant fitted are substituted into the cycle aging model, and based on the charge-discharge cycle test data, the initial capacity decay amplitude coefficient, the long-term capacity decay amplitude coefficient, and the cycle activation energy of the cycle aging model are fitted by using a fitting algorithm.

[0023] Specifically, the double exponential model is first fitted, and under the premise of ignoring the temperature effect, the initial decay rate constant and the long-term decay rate constant characterizing the inherent rate of short-term and long-term aging kinetics are robustly determined from the data of ampere-hour throughput . This utilizes the model assumption independent of , the relative simplicity of the rate part and the single temperature data. Thus, first, the model parameters of the double exponential model in the cycle aging model can be fitted based on the charge-discharge cycle test data, and the double exponential model is specifically: ; The initial decay rate constant and the long-term decay rate constant in the double exponential model are fitted.

[0024] Then, the initial decay rate constant and the long-term decay rate constant are substituted into the cycle aging model. Based on the charge-discharge cycle test data, other model parameters of the cycle aging model, such as the cycle activation energy , the initial capacity decay amplitude coefficient and the long-term capacity decay amplitude coefficient , are obtained by using a fitting algorithm.

[0025] In this embodiment, the model parameters of the calendar aging model are optionally obtained by fitting the test data, including: The quantitative test of the agricultural tractor battery system is performed to obtain the quantitative test data of the agricultural tractor battery system, which includes the battery capacity of the agricultural tractor battery system at different time points under different states of charge and temperatures; Based on the quantitative test data, the aging rate pre-exponential factor, the calendar activation energy, the time index, the SOC positive power index and the SOC reverse power index of the calendar aging model are fitted by using a fitting algorithm.

[0026] Specifically, in this embodiment, the calendar aging model used is specifically: ; Wherein, is the aging rate pre-exponential factor, is the time, is the time index, is the calendar activation energy, is the reference temperature, is the state of charge, is the SOC positive power index, is the SOC reverse power index. The SOC positive power index can describe the amplification effect of high state of charge on the capacity decay of the agricultural tractor battery system, and the SOC reverse power index can describe the influence of low state of charge on the capacity decay of the agricultural tractor battery system.

[0027] By fitting the obtained quantitative experimental data, the model parameters in the calendar aging model can be obtained. Specifically, firstly, the agricultural tractor battery system can be stored statically according to a set state of charge and temperature, and the capacity of the agricultural tractor battery system can be collected periodically. This obtains the battery capacity of the agricultural tractor battery system at different time points under a specified state of charge and temperature. By changing the state of charge and temperature and continuing the experiment, the battery capacity of the agricultural tractor battery system at different time points under different states of charge and temperatures can be obtained. Then, based on the quantitative experimental data, existing fitting algorithms can be used to obtain the aging rate pre-exponential factor, activation energy, time exponent, SOC positive power exponent, and SOC negative power exponent of the calendar aging model.

[0028] In this embodiment, optionally, the least squares method is used to fit the model parameters of the cyclic aging model and the calendar aging model. Specifically, the least squares method has advantages such as computational efficiency, good statistical properties, and strong adaptability when fitting empirical model parameters, especially in handling large-scale data and linear or weakly nonlinear problems. The optimal parameter estimate is obtained by minimizing the sum of squared residuals between the predicted values ​​and the actual observed values. Under the assumptions that the errors are independent and identically distributed, the least squares estimation has excellent statistical properties such as unbiasedness, consistency, and minimum variance, ensuring the stability and reliability of the parameter estimate.

[0029] After obtaining the model parameters of the cyclic aging model and the calendar aging model using the least squares method, the fitted model parameters can be substituted into the cyclic aging model and the calendar aging model. Then, by fusing the constructed cyclic aging model and the calendar aging model, the life degradation model of the agricultural tractor battery system can be obtained. The life degradation model is as follows: .

[0030] in, This is the calendar aging index.

[0031] like Figure 2 The flowchart shown illustrates a lifespan prediction method for an agricultural tractor battery system. This prediction method includes: Step S1: Using the above-mentioned life degradation model construction method, a life degradation model for the agricultural tractor battery system is constructed. Step S2: Obtain the current detection indicators of the agricultural tractor battery system, and calculate the battery life degradation amount of the agricultural tractor battery system through the life degradation model.

[0032] Specifically, firstly, a lifespan degradation model for agricultural tractor battery systems can be constructed using the aforementioned method. Then, various current monitoring indicators of the agricultural tractor battery system can be collected, such as temperature, charge / discharge cycle count, cell capacity, and state of charge. These collected indicators are then input into the lifespan degradation model, which can accurately predict the amount of battery lifespan degradation in the agricultural tractor battery system, thereby predicting the remaining lifespan of the system.

[0033] like Figure 3 The diagram shown is a system block diagram of a life degradation model construction system for agricultural tractor batteries. This construction system includes: The single-item model building module is configured to build a cycle aging model and a calendar aging model for an agricultural tractor battery system, and obtain the model parameters of the cycle aging model and the calendar aging model by fitting experimental data. The decay model construction module is configured to integrate the cyclic aging model and the calendar aging model to construct a lifespan decay model.

[0034] Specifically, the system construction includes a single-item model construction module and a degradation model construction module. The single-item model construction module can fit the model parameters of the cyclic aging model and the calendar degradation model based on relevant experimental data, thereby constructing the cyclic aging model and the calendar degradation model. The degradation model construction module can merge the constructed cyclic aging model and the calendar degradation model to construct a life degradation model for predicting the battery life degradation of agricultural tractor battery systems.

[0035] like Figure 4 The diagram shown is a system block diagram of a life prediction system for an agricultural tractor battery system. The prediction system includes: The model building module is configured to use the above-mentioned life degradation model building method to build a life degradation model for agricultural tractor battery systems. The lifespan prediction module is configured to acquire the current detection indicators of the agricultural tractor battery system and calculate the battery lifespan degradation amount of the agricultural tractor battery system through the lifespan degradation model.

[0036] Specifically, the prediction system includes a model building module and a lifespan prediction module. The model building module acquires test data from charge-discharge cycle tests and quantitative tests, and based on this data, constructs a lifespan degradation model for the agricultural tractor battery system using the aforementioned construction method. The lifespan prediction module acquires current monitoring indicators of the agricultural tractor battery system and inputs these indicators into the constructed lifespan degradation model. The lifespan degradation model accurately predicts the amount of battery lifespan degradation in the agricultural tractor battery system, thereby predicting the remaining lifespan of the agricultural tractor battery system.

[0037] The above examples are only used to illustrate the technical solutions of the present application, but not to limit the present application; although the present application has been described in detail with reference to the foregoing examples, those skilled in the art should understand that the technical solutions recorded in the foregoing examples can be modified, or some or all of the technical features can be replaced equivalently; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present application, and they should be covered in the scope of the claims and the specification of the present application.

Claims

1. A method for constructing a life degradation model for an agricultural tractor battery system, characterized in that, include: A cycle aging model and a calendar aging model for an agricultural tractor battery system were constructed, and the model parameters for the cycle aging model and the calendar aging model were obtained by fitting experimental data. A lifespan decline model is constructed by combining the cyclic aging model and the calendar aging model.

2. The method for constructing a lifespan decay model according to claim 1, characterized in that, Constructing a cyclic aging model for agricultural tractor battery systems includes: The cyclic aging model is constructed by combining the Arrhenius equation and the double exponential model.

3. The method for constructing a lifespan decay model according to claim 1, characterized in that, The model parameters of the cyclic aging model were obtained by fitting experimental data, including: A charge-discharge cycle test was conducted on the agricultural tractor battery system to obtain charge-discharge cycle test data of the agricultural tractor battery system; Based on charge-discharge cycle test data, a staged fitting strategy was adopted to fit the model parameters of the cyclic aging model.

4. The method for constructing a lifespan decay model according to claim 3, characterized in that, A staged fitting strategy is used to fit the model parameters of the cyclic aging model, including: Based on the charge-discharge cycle test data, the initial decay rate constant and long-term decay rate constant of the cycle aging model are obtained by fitting a double exponential model. Substituting the initial decay rate constant and long-term decay rate constant obtained from the fitting into the cyclic aging model, and based on the charge-discharge cycle test data, the initial capacity decay amplitude coefficient, long-term capacity decay amplitude coefficient, and cycle activation energy of the cyclic aging model are obtained by fitting algorithm.

5. The method for constructing a lifespan decay model according to claim 1, characterized in that, The model parameters of the calendar aging model were obtained by fitting experimental data, including: A quantitative test was conducted on the agricultural tractor battery system to obtain quantitative test data of the agricultural tractor battery system. The quantitative test data included the battery capacity of the agricultural tractor battery system at different time points under different states of charge and temperatures. Based on the quantitative experimental data, the aging rate pre-exponential factor, calendar activation energy, time exponent, SOC positive power exponent, and SOC negative power exponent of the calendar aging model were obtained by fitting algorithm.

6. The method for constructing a lifespan decay model according to claim 1, characterized in that, The model parameters of the cyclic aging model and the calendar aging model were fitted using the least squares method.

7. A method for predicting the lifespan of an agricultural tractor battery system, characterized in that, include: A life degradation model for an agricultural tractor battery system is constructed using the life degradation model construction method described in any one of claims 1-6. Obtain the current test indicators of the agricultural tractor battery system, and calculate the battery life degradation of the agricultural tractor battery system using the life degradation model.

8. A system for constructing a life degradation model for an agricultural tractor battery system, characterized in that, include: The single-item model building module is configured to build a cycle aging model and a calendar aging model for an agricultural tractor battery system, and obtain the model parameters of the cycle aging model and the calendar aging model by fitting experimental data. The decay model construction module is configured to integrate the cyclic aging model and the calendar aging model to construct a lifespan decay model.

9. A lifespan prediction system for an agricultural tractor battery system, characterized in that, include: The model building module is configured to use the life degradation model building method as described in any one of claims 1-6 to build a life degradation model for an agricultural tractor battery system. The lifespan prediction module is configured to acquire the current detection indicators of the agricultural tractor battery system and calculate the battery lifespan degradation amount of the agricultural tractor battery system through the lifespan degradation model.

Images