Agricultural tractor battery system life degradation model construction, life prediction method and system

Abstract

This invention discloses a method and system for constructing a lifespan degradation model and predicting the lifespan of an agricultural tractor battery system. The method includes: First, constructing a cyclic aging model and a calendar aging model for the agricultural tractor battery system. The cyclic aging model captures the influence of temperature on battery degradation and the role of different aging mechanisms during the cyclic use of the agricultural tractor battery system. The calendar aging model captures the capacity degradation of the agricultural tractor battery system during storage. The model parameters of the cyclic aging model and the calendar aging model are obtained by fitting corresponding experimental data, thus constructing the cyclic aging model and the calendar aging model. Then, using the calendar aging model as the primary model, the cyclic aging model and the calendar aging model are fused to construct a lifespan degradation model that can accurately predict the lifespan degradation of the agricultural tractor battery system. The lifespan degradation model can accurately predict the amount of lifespan degradation of the agricultural tractor battery system.

Description

Technical Field

[0001] This invention relates to the field of determining battery aging or degradation, specifically to the construction of a life degradation model, life prediction method, and system for an agricultural tractor battery system. Background Technology

[0002] The technologies related to new energy passenger vehicles and commercial vehicles have gradually matured, and numerous technical means exist for predicting their battery system lifespan. However, passenger vehicles and commercial vehicles are used almost daily, while the operating methods of large agricultural tractors are more unique, generally divided into working seasons and non-working seasons. The working season refers to the long-term operation during regular spring plowing and autumn harvesting. The non-working season refers to the off-season, when tractors are parked for extended periods (up to several months). Furthermore, some agricultural vehicles or machinery have a very short working time and a long storage period throughout the year; for example, cotton harvesters are only used for about one month a year. Therefore, the technical means currently used to predict the battery system lifespan of passenger vehicles and commercial vehicles are not suitable for predicting the battery system lifespan of agricultural tractors. Summary of the Invention

[0003] To address the shortcomings of existing technologies, this invention proposes a life degradation model construction, life prediction method, and system for agricultural tractor battery systems, which can improve the accuracy of life prediction for agricultural tractor battery systems. The specific technical solution is as follows:

[0004] In a first aspect, a method for constructing a life degradation model for an agricultural tractor battery system is provided. In a first implementable manner of the first aspect, the method includes:

[0005] 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.

[0006] A lifespan decline model is constructed by combining the cyclic aging model and the calendar aging model.

[0007] In conjunction with the first feasible approach of the first aspect, in the second feasible approach of the first aspect, a cycle aging model for an agricultural tractor battery system is constructed, including:

[0008] The cyclic aging model is constructed by combining the Arrhenius equation and the double exponential model.

[0009] In conjunction with the first feasible method of the first aspect, in the third feasible method of the first aspect, the model parameters of the cyclic aging model are obtained by fitting experimental data, including:

[0010] 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;

[0011] Based on charge-discharge cycle test data, a staged fitting strategy was adopted to fit the model parameters of the cyclic aging model.

[0012] In conjunction with the third feasible approach of the first aspect, the fourth feasible approach of the first aspect employs a staged fitting strategy to fit the model parameters of the cyclic aging model, including:

[0013] 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.

[0014] 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.

[0015] In conjunction with the first feasible method of the first aspect, in the fifth feasible method of the first aspect, the model parameters of the calendar aging model are obtained by fitting experimental data, including:

[0016] 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.

[0017] 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 the model using a fitting algorithm.

[0018] In conjunction with the first feasible method of the first aspect, in the sixth feasible method of the first aspect, the least squares method is used to fit the model parameters of the cyclic aging model and the calendar aging model.

[0019] Secondly, a method for predicting the lifespan of an agricultural tractor battery system is provided, including:

[0020] A life degradation model for an agricultural tractor battery system is constructed using any of the first to sixth feasible methods described in the first aspect.

[0021] 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.

[0022] Thirdly, a system for constructing a life degradation model for agricultural tractor battery systems is provided, including:

[0023] 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.

[0024] The decay model construction module is configured to integrate the cyclic aging model and the calendar aging model to construct a lifespan decay model.

[0025] Fourthly, a life prediction system for an agricultural tractor battery system is provided, comprising:

[0026] 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.

[0027] 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.

[0028] 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

[0029] 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.

[0030] 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;

[0031] Figure 2 A flowchart illustrating a lifespan prediction method for an agricultural tractor battery system according to an embodiment of the present invention;

[0032] 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;

[0033] 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

[0034] 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.

[0035] like Figure 1 The flowchart shown illustrates a method for constructing a life degradation model for an agricultural tractor battery system. This method includes:

[0036] Step 1: Construct a cycle aging model and a calendar aging model for the agricultural tractor battery system, and obtain the model parameters of the cycle aging model and the calendar aging model by fitting experimental data;

[0037] Step 2: Combine the cyclic aging model and the calendar aging model to construct a lifespan decay model.

[0038] Specifically, firstly, a cyclic aging model and a calendar aging model can be constructed for agricultural tractor battery systems. The cyclic aging model captures the impact of temperature on battery degradation and the role of different aging mechanisms during the cyclic use of agricultural tractor battery systems. The calendar aging model captures the capacity degradation of agricultural tractor battery systems under storage conditions. Relevant experimental data can be obtained through corresponding tests, and the model parameters for the cyclic aging model and the calendar aging model can be obtained by fitting the experimental data, thus constructing the cyclic aging model and the calendar aging model. Then, due to the special usage scenarios of agricultural tractors, the calendar aging model can be used as the primary model, and the cyclic aging model and the calendar aging model can be integrated to construct a model that can accurately predict the lifespan degradation of agricultural tractor battery systems. By constructing the lifespan degradation model, the amount of lifespan degradation of agricultural tractor battery systems can be accurately predicted, and then combined with the lifespan of agricultural tractor battery systems, the remaining lifespan of agricultural tractor battery systems can be predicted.

[0039] In this embodiment, optionally, constructing a cycle aging model for an agricultural tractor battery system includes:

[0040] The cyclic aging model is constructed by combining the Arrhenius equation and the double exponential model.

[0041] Specifically, the cyclic aging model is composed of the Arrhenius equation and a double-exponential model. The Arrhenius equation captures the effect of temperature on the aging rate. The double-exponential model captures the role of different aging mechanisms in the cyclic charge-discharge process of agricultural tractor battery systems. The constructed cyclic aging model is as follows:

[0042] ;

[0043] ;

[0044] in, This is the initial capacity decay amplitude coefficient. This is the long-term capacity decay amplitude coefficient. This is the initial decay rate constant. This is the long-term decay rate constant. As the activation energy for the cycle, The gas constant is For temperature, For hourly throughput, This refers to the number of charge-discharge cycles. Depth of discharge, This refers to the cell capacity.

[0045] In this embodiment, optionally, the model parameters of the cyclic aging model are obtained by fitting experimental data, including:

[0046] 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;

[0047] Based on charge-discharge cycle test data, a staged fitting strategy was adopted to fit the model parameters of the cyclic aging model.

[0048] Specifically, firstly, charge-discharge cycle tests can be conducted on the battery system of agricultural tractors to obtain charge-discharge cycle test data. Then, based on the charge-discharge cycle test data, a staged fitting strategy can be used to fit the model parameters of the cyclic aging model. The core advantage of the staged fitting strategy lies in decoupling parameters and reducing fitting complexity. The first stage ignores the influence of temperature and focuses on fitting the short-term characteristics. and long-term The parameters of the aging kinetics' inherent rate. The second stage utilizes multi-temperature data, under a fixed... , Under the premise of [missing information], we focus on fitting the initial amplitude of each mechanism and the temperature sensitivity of the long-term mechanism. We clearly separate aging kinetics and temperature effects, avoiding the difficulties and instabilities caused by simultaneously optimizing highly coupled parameters.

[0049] In this embodiment, optionally, a staged fitting strategy is used to fit the model parameters of the cyclic aging model, including:

[0050] 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.

[0051] 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.

[0052] Specifically, the double-exponential model is first fitted, and then, ignoring the effect of temperature, the focus is on throughput in ampere-hours. Robustly determine the initial decay rate constant characterizing the intrinsic rate of short-term and long-term aging kinetics from the data. and long-term decay rate constant This utilizes model assumptions. and Irrelevant The rate is partially fixed, and the single-temperature data is relatively simplified. Therefore, firstly, the model parameters of the double-exponential model in the cyclic aging model can be fitted based on charge-discharge cycle test data. The double-exponential model is specifically as follows:

[0053] ;

[0054] The initial decay rate constant in the double exponential model was obtained by fitting. and long-term decay rate constant .

[0055] Then, the initial decay rate constant can be... and long-term decay rate constant Substitute these parameters into the cyclic aging model. Then, based on the charge-discharge cycle test data, use a fitting algorithm to obtain other model parameters of the cyclic aging model, such as the cyclic activation energy. Initial capacity attenuation amplitude coefficient and long-term capacity decay amplitude coefficient .

[0056] In this embodiment, optionally, the model parameters of the calendar aging model are obtained by fitting experimental data, including:

[0057] 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.

[0058] 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 the model using a fitting algorithm.

[0059] Specifically, in this embodiment, the calendar aging model used is as follows:

[0060] ;

[0061] in, As a pre-exponential factor for aging rate, For time, For time index, As a calendar activation energy, For reference temperature, In a charged state, The positive power exponent of SOC. Here, SOC is the inverse power exponent. The forward power exponent of SOC describes the amplification effect of high state of charge on the capacity decay of agricultural tractor battery systems, while the inverse power exponent of SOC describes the impact of low state of charge on the capacity decay of agricultural tractor battery systems.

[0062] 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.

[0063] 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.

[0064] 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:

[0065] .

[0066] in, This is the calendar aging index.

[0067] like Figure 2 The flowchart shown illustrates a lifespan prediction method for an agricultural tractor battery system. This prediction method includes:

[0068] Step S1: Using the above-mentioned life degradation model construction method, a life degradation model for the agricultural tractor battery system is constructed.

[0069] 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.

[0070] 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.

[0071] 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:

[0072] 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.

[0073] The decay model construction module is configured to integrate the cyclic aging model and the calendar aging model to construct a lifespan decay model.

[0074] 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.

[0075] 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:

[0076] 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.

[0077] 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.

[0078] 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.

[0079] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be covered within the scope of the claims and specification of the present invention.

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. The model parameters of the cycle aging model and the calendar aging model were obtained by fitting the experimental data using the least squares method. The fitted model parameters were then substituted into the cycle aging model and the calendar aging model. A lifespan degradation model is constructed by integrating the cyclic aging model and the calendar aging model. The lifespan degradation model is as follows: ； in, Calendar aging index; Constructing a cyclic aging model for agricultural tractor battery systems includes: The cyclic aging model was constructed by combining the Arrhenius equation and the double exponential model. The Arrhenius equation was used to capture the effect of temperature on the aging rate, and the double exponential model was used to capture the role of different aging mechanisms in the cyclic charge-discharge process of agricultural tractor battery systems. The specific cyclic aging model constructed is as follows: ； ； in, This is the initial capacity decay amplitude coefficient. This is the long-term capacity decay amplitude coefficient. This is the initial decay rate constant. This is the long-term decay rate constant. As the activation energy for the cycle, The gas constant is For temperature, For hourly throughput, This refers to the number of charge-discharge cycles. Depth of discharge, This refers to the cell capacity; The double exponential model is as follows: 。 2. 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.

3. The method for constructing a lifespan decay model according to claim 2, 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.

4. 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.

5. 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 1-4. 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.

6. 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. The model parameters of the cycle aging model and the calendar aging model are obtained by fitting the experimental data using the least squares method, and the fitted model parameters are substituted into the cycle aging model and the calendar aging model. The degradation model construction module is configured to integrate the cyclic aging model and the calendar aging model to construct a lifetime degradation model, which is specifically as follows: ； in, Calendar aging index; The single-item model building module combines the Arrhenius equation and the double-exponential model to construct the cyclic aging model. The Arrhenius equation captures the effect of temperature on the aging rate, while the double-exponential model captures the role of different aging mechanisms in the cyclic charge-discharge process of agricultural tractor battery systems. The constructed cyclic aging model is as follows: ； ； in, This is the initial capacity decay amplitude coefficient. This is the long-term capacity decay amplitude coefficient. This is the initial decay rate constant. This is the long-term decay rate constant. As the activation energy for the cycle, The gas constant is For temperature, For hourly throughput, This refers to the number of charge-discharge cycles. Depth of discharge, This refers to the cell capacity; The double exponential model is as follows: 。 7. 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 1-4 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.

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