Automobile low-voltage storage battery intelligent charging model based on multi-physics field coupling analysis

Abstract

The invention discloses an automobile low-voltage storage battery intelligent electricity supplement model based on multi-physics field coupling analysis, which mainly realizes accurate state evaluation and dynamic electricity supplement strategy optimization of a low-voltage storage battery by establishing a nonlinear state space equation and a fuzzy decision rule. The system specifically comprises a multi-parameter coupling modeling module used for evaluating the terminal voltage of the storage battery, a battery state evaluation module used for combining the terminal voltage to evaluate the charge state of the storage battery, a fuzzy decision module used for determining the charging voltage and current according to the charge state, and a control module used for determining the charging current and the charging voltage in the charging process through the charging voltage and the charging current. And the self-adaptive correction module is used for identifying the internal resistance of the battery on line and carrying out dynamic voltage compensation. According to the method, all key factors can be comprehensively integrated, accurate estimation of the capacity of the low-voltage storage battery is realized, so that the electricity supplementing accuracy is improved, the service life of the low-voltage storage battery is prolonged while the power consumption is reduced, and the method is suitable for accurate evaluation of the battery state and optimization of the electricity supplementing strategy when the vehicle is in a dynamic working condition.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of new energy vehicles, and particularly relates to an intelligent low-voltage battery charging model for a vehicle based on multi-physical field coupling analysis. BACKGROUND

[0002] In a new energy vehicle, a low-voltage battery does not directly drive the vehicle like a high-voltage power battery, but it plays a crucial role in the stable operation of many auxiliary systems, control systems and electronic devices of the vehicle. When the vehicle is parked for a long time or the dark current of the vehicle is large, the vehicle battery will be insufficient, and thus the vehicle cannot be started.

[0003] The common low-voltage battery charging strategy for a new energy vehicle has many deficiencies. The traditional method often simply estimates the battery capacity for charging according to the voltage and temperature of the low-voltage battery, without fully considering the influence of the complex operating conditions of the vehicle, the real-time health status of the low-voltage battery and the changes in the surrounding environment. This easily leads to unreasonable charging, such as overcharging in the case of long-term parking but with some devices consuming power, which accelerates the aging of the low-voltage battery, reduces its service life and reliability, and thus affects the overall use experience and safety of the new energy vehicle.

[0004] Specifically, the existing battery charging method is basically to continuously charge after high voltage or to control charging according to the remaining battery capacity estimated by the battery voltage and temperature. The current low-voltage battery charging technology has at least the following technical defects:

[0005] 1. Insufficient parameter relevance: the traditional model only relies on a single parameter of voltage or SOC, ignoring the coupling influence of key variables such as internal resistance and temperature;

[0006] 2. Dynamic response lag: unable to track in real time the influence of internal resistance changes on the charging efficiency during battery aging;

[0007] 3. Lack of temperature compensation: no effective temperature-voltage-internal resistance mapping relationship is established in low-temperature environments, leading to insufficient or overcharging;

[0008] 4. Health state misjudgment: the internal resistance increment is not used as a health assessment indicator, making it difficult to accurately predict the remaining service life of the battery. SUMMARY

[0009] In view of the above, the present application aims to provide an intelligent low-voltage battery charging model for a vehicle based on multi-physical field coupling analysis to solve the aforementioned technical problems.

[0010] The technical solution adopted by the present application is as follows:

[0011] This invention provides a smart charging model for automotive low-voltage batteries based on multiphysics coupling analysis, including:

[0012] A multi-parameter coupling modeling module, which includes a preset equivalent circuit model and a temperature compensation algorithm, is used to evaluate the terminal voltage of the battery.

[0013] A battery state assessment module, comprising a SOC estimation sub-model and a SOH assessment sub-model, is used to assess the state of charge of the battery by combining the terminal voltage.

[0014] A fuzzy decision-making module is used to generate a charging strategy based on normalized input parameters and to determine the charging voltage and charging current according to the state of charge.

[0015] And an adaptive correction module, which is used to identify the battery internal resistance online and perform dynamic voltage compensation during the charging process by the charging voltage and the charging current.

[0016] In at least one possible implementation, the equivalent circuit model employs a modified Thevenin model, with an internal resistance of ohms. The polarization resistance and polarization capacitance form the expression of a differential equation.

[0017] In at least one possible implementation, the temperature compensation algorithm includes: constructing a temperature-internal resistance correction coefficient using the Arrhenius formula.

[0018] In at least one of the possible implementations, the SOC estimation sub-model includes: based on extended Kalman filtering and fused with current integration and open-circuit voltage methods.

[0019] In at least one of the possible implementations, the SOH evaluation sub-model is expressed as a correlation model between the internal resistance increment and the capacity decay rate.

[0020] In at least one possible implementation, the power generation strategy includes:

[0021] Using the normalized voltage deviation, internal resistance increment, and temperature as input variables, and the charging current and termination voltage as outputs, a Mamdani-type fuzzy system is constructed to form a fuzzy rule base.

[0022] The centroid method is used to perform defuzzification calculations on the fuzzy system to obtain real-time power compensation parameters.

[0023] In at least one possible implementation, the dynamic voltage compensation includes:

[0024] During the charging process, the current internal resistance of the battery, the real-time charge, and the real-time charging voltage are obtained dynamically. Combined with a preset temperature-voltage mapping table, the charging voltage correction result is obtained.

[0025] Compared with existing technologies, the main design concept of this invention lies in providing an intelligent charging algorithm model that integrates multi-dimensional parameters such as voltage, internal resistance, and temperature. By establishing nonlinear state-space equations and fuzzy decision rules, it achieves accurate state assessment and dynamic charging strategy optimization for low-voltage batteries. Specifically, it includes: a multi-parameter coupled modeling module for evaluating the battery's terminal voltage; a battery state assessment module for evaluating the battery's state of charge (SOC) based on the terminal voltage; a fuzzy decision module for determining the charging voltage and charging current based on the SOC; and an adaptive correction module for identifying the battery's internal resistance online and performing dynamic voltage compensation during the charging process using the charging voltage and charging current. This invention comprehensively integrates various key factors to achieve accurate estimation of low-voltage battery capacity, thereby improving charging accuracy and extending the battery's lifespan while reducing power consumption. It is particularly suitable for accurate battery state assessment and charging strategy optimization under dynamic vehicle operating conditions.

[0026] The results of the above-mentioned scheme, verified by actual testing, are as follows:

[0027] 1) Accurate State Assessment: By fusing multi-sensor data using the EKF algorithm, the theoretical error in SOC estimation is less than 2%;

[0028] 2) Dynamic strategy optimization: Real-time identification of internal resistance changes reduces the response time of the compensation current adjustment to within 50ms;

[0029] 3) Wide temperature range adaptability: Charging efficiency can be improved by 18% to 25% within the range of -20℃ to 60℃;

[0030] 4) Life extension mechanism: Based on the dynamic adjustment of the termination voltage of SOH, the cycle life can be extended by more than 20%. Attached Figure Description

[0031] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described below with reference to the accompanying drawings, wherein:

[0032] Figure 1 This is a schematic diagram of an intelligent charging model for automotive low-voltage batteries based on multiphysics coupling analysis, provided in an embodiment of the present invention. Detailed Implementation

[0033] Embodiments of the present invention are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention.

[0034] This invention proposes an embodiment of an intelligent charging model for automotive low-voltage batteries based on multiphysics coupling analysis. Specifically, as follows: Figure 1 As shown, it includes:

[0035] A multi-parameter coupling modeling module, which includes a preset equivalent circuit model and a temperature compensation algorithm, is used to evaluate the terminal voltage of the battery.

[0036] A battery state assessment module, comprising a SOC estimation sub-model and a SOH assessment sub-model, is used to assess the state of charge of the battery in conjunction with the terminal voltage.

[0037] A fuzzy decision module is used to generate a charging strategy based on normalized input parameters and to determine the charging voltage and charging current according to the state of charge.

[0038] And an adaptive correction module, which is used to identify the battery internal resistance online and perform voltage compensation during the charging process by the charging voltage and the charging current.

[0039] To elaborate, the equivalent circuit model can preferably be a modified Thevenin model, specifically including: ohmic internal resistance Polarization internal resistance and polarization capacitor And it can be formed in the form of the following differential equation:

[0040]

[0041] in, Open circuit voltage, I is the terminal voltage, and I is the charging / discharging current. This is the polarization voltage.

[0042] The temperature compensation algorithm can be implemented by constructing a temperature-internal resistance correction coefficient using the Arrhenius formula:

[0043]

[0044] in, For activation energy, The gas constant is... Absolute temperature The reference temperature is 25°C.

[0045] For the SOC estimation sub-model, it can be based on the Extended Kalman Filter (EKF) and combined with the current integration method and the open-circuit voltage method, as shown in the following equation:

[0046]

[0047] in, This is the estimated value of SOC. For Kalman gain, To measure the noise covariance.

[0048] The SOH evaluation sub-model can be expressed as the internal resistance increment in actual operation. With capacity decay rate The association model is as follows:

[0049]

[0050] Where a and b are fitting coefficients, determined by electrochemical impedance spectroscopy (EIS).

[0051] Continuing from the previous text, the power generation strategy includes:

[0052] The normalized voltage deviation Internal resistance increment ,temperature As input variables;

[0053] Establish the above three inputs and two outputs (charging current) Termination voltage The Mamdani-type fuzzy system is used to form a fuzzy rule base, and the typical rules involved can be referred to as follows:

[0054] If ( is High) and ( is Medium) and ( is Low) then ( is Low) and ( (is Medium)

[0055] The centroid method is used to defuzzify the fuzzy system, and the output is used to generate real-time electrical compensation parameters, for example:

[0056]

[0057]

[0058] Finally, regarding the explanation of the adaptive correction mechanism, it can be combined with a mature online internal resistance identification method. During the charging task, determined by the charging voltage and current in the above steps, the dynamic current battery internal resistance, real-time charge, and real-time charging voltage are obtained. Combined with a preset temperature-voltage mapping table, the charging voltage correction result is obtained. More preferably, a charging voltage compensation algorithm for a preset low-temperature environment can be established using a BP neural network, as shown in the following equation:

[0059]

[0060] In this algorithm structure, the input layer has three nodes (nominal voltage, temperature, and SOC), and the final output layer has one node, namely the voltage compensation value.

[0061] In summary, the main design concept of this invention lies in providing an intelligent charging algorithm model that integrates multi-dimensional parameters such as voltage, internal resistance, and temperature. By establishing nonlinear state-space equations and fuzzy decision rules, it achieves accurate state assessment and dynamic charging strategy optimization for low-voltage batteries. Specifically, it includes: a multi-parameter coupled modeling module for evaluating the battery's terminal voltage; a battery state assessment module for evaluating the battery's state of charge (SOC) based on the terminal voltage; a fuzzy decision module for determining the charging voltage and charging current based on the SOC; and an adaptive correction module for identifying the battery's internal resistance online and performing dynamic voltage compensation during the charging process using the charging voltage and charging current. This invention comprehensively integrates various key factors to achieve accurate estimation of low-voltage battery capacity, thereby improving charging accuracy and extending the battery's lifespan while reducing power consumption. It is particularly suitable for accurate battery state assessment and charging strategy optimization under dynamic vehicle operating conditions.

[0062] In this invention, when directional terms are mentioned, they are relative concepts based on the embodiments. Furthermore, "at least one" refers to one or more, and "more than one" refers to two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent the existence of A alone, A and B simultaneously, or B alone. A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects have an "or" relationship. "At least one of the following" and similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, and c can represent: a, b, c, a and b, a and c, b and c, or a and b and c, where a, b, and c can be single or multiple.

[0063] The above description of the structure, features, and effects of the present invention is based on the embodiments shown in the figures. However, the above are only preferred embodiments of the present invention. It should be noted that the technical features involved in the above embodiments and their preferred methods can be reasonably combined and matched by those skilled in the art to form a variety of equivalent solutions without departing from or changing the design concept and technical effects of the present invention. Therefore, the present invention is not limited to the scope of implementation shown in the figures. Any changes made in accordance with the concept of the present invention, or modifications to equivalent embodiments, that do not exceed the spirit covered by the specification and figures, should be within the protection scope of the present invention.

Claims

1. A smart charging model for automotive low-voltage batteries based on multiphysics coupling analysis, characterized in that, include: A multi-parameter coupling modeling module, which includes a preset equivalent circuit model and a temperature compensation algorithm, is used to evaluate the terminal voltage of the battery. A battery state assessment module, comprising a SOC estimation sub-model and a SOH assessment sub-model, is used to assess the state of charge of the battery by combining the terminal voltage. A fuzzy decision-making module is used to generate a charging strategy based on normalized input parameters and to determine the charging voltage and charging current according to the state of charge. And an adaptive correction module, which is used to identify the battery internal resistance online and perform dynamic voltage compensation during the charging process by the charging voltage and the charging current.

2. The intelligent charging model for automotive low-voltage batteries based on multiphysics coupling analysis according to claim 1, characterized in that, The equivalent circuit model adopts the improved Thevenin model, and uses an internal resistance of ohms. The polarization resistance and polarization capacitance form the expression of a differential equation.

3. The intelligent charging model for automotive low-voltage batteries based on multiphysics coupling analysis according to claim 1, characterized in that, The temperature compensation algorithm includes: constructing a temperature-internal resistance correction coefficient using the Arrhenius formula.

4. The intelligent charging model for automotive low-voltage batteries based on multiphysics coupling analysis according to claim 1, characterized in that, The SOC estimation sub-model includes: based on extended Kalman filtering, and fused with current integration method and open-circuit voltage method.

5. The intelligent charging model for automotive low-voltage batteries based on multiphysics coupling analysis according to claim 1, characterized in that, The SOH evaluation sub-model is expressed as a correlation model between the internal resistance increment and the capacity decay rate.

6. The intelligent charging model for automotive low-voltage batteries based on multiphysics coupling analysis according to claim 1, characterized in that, The power generation strategy includes: Using the normalized voltage deviation, internal resistance increment, and temperature as input variables, and the charging current and termination voltage as outputs, a Mamdani-type fuzzy system is constructed to form a fuzzy rule base. The centroid method is used to perform defuzzification calculations on the fuzzy system to obtain real-time power compensation parameters.

7. The intelligent charging model for automotive low-voltage batteries based on multiphysics coupling analysis according to any one of claims 1 to 6, characterized in that, The dynamic voltage compensation includes: During the charging process, the current internal resistance of the battery, the real-time charge, and the real-time charging voltage are obtained dynamically. Combined with a preset temperature-voltage mapping table, the charging voltage correction result is obtained.

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