Method and device for estimating residual energy of power battery and electronic equipment
By establishing an electrothermal coupling model with a first-order equivalent circuit model and a one-dimensional thermal model, the problem of insufficient accuracy in estimating the remaining energy of the power battery under low temperature and high current conditions is solved, achieving high-precision remaining energy estimation and temperature prediction, which is applicable to real vehicle environments with active liquid cooling.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-12-31
- Publication Date
- 2026-04-14
AI Technical Summary
Existing technologies have poor accuracy in estimating the remaining energy of power batteries under complex operating conditions such as low temperature and high current. They fail to effectively decouple the effects of nonlinear changes in temperature and electrochemical characteristics, resulting in large estimation errors.
By establishing an electrothermal coupling model with a first-order equivalent circuit model and a one-dimensional thermal model, and combining iterative integration frameworks of temperature and voltage, the energy loss during battery charging and discharging is predicted, thereby improving the estimation accuracy.
It significantly improves the accuracy of remaining energy estimation under low temperature and high current conditions, and is suitable for real vehicle environments with active liquid cooling, enabling real-time closed-loop correction of temperature and energy.
Smart Images
Figure CN121856808A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of battery management system technology, specifically to methods, devices, and electronic equipment for estimating the remaining energy of power batteries. Background Technology
[0002] Estimating the state of energy (SOE) of a power battery requires decoupling two nonlinear relationships: the nonlinear change in temperature with heat dissipation and the nonlinear change in battery electrochemical characteristics with temperature. Recursive formulas are typically used to simulate the evolution between these two relationships. For example, heat affects temperature over time through thermal capacity and thermal resistance, and temperature affects the battery's terminal voltage characteristics by influencing the capacitance and resistance in the multi-order equivalent circuit model (ECM). Related technologies have poor accuracy in estimating SOE under complex operating conditions such as low temperatures and high currents. Summary of the Invention
[0003] This application provides a method, apparatus, and electronic device for estimating the remaining energy (SOE) of a power battery, aiming to solve the problem of poor SOE estimation accuracy in related technologies under complex operating conditions such as low temperature and high current.
[0004] Firstly, a method for estimating the remaining energy of a power battery is provided, including: A first-order equivalent circuit model is established based on the electrochemical characteristics of the power battery. The first-order equivalent circuit model is used to output the target terminal voltage and target polarization voltage of the next cycle based on the first parameter information of the current cycle. The current cycle and the next cycle belong to the same current calculation cycle. A one-dimensional thermal model is established based on the thermal characteristics of the power battery. The one-dimensional thermal model is used to output the target battery temperature for the next cycle based on the second parameter information of the current cycle. An electrothermal coupling model is constructed based on the spatial state equations of a first-order equivalent circuit model and a one-dimensional thermal model. In the electrothermal coupling model, the target polarization voltage is used as the input of the one-dimensional thermal model, and the target battery temperature is used as the input of the first-order equivalent circuit model. A cyclic iterative integration framework for residual energy was constructed based on an electrothermal coupling model. The target cumulative value of the remaining energy of the power battery in the current calculation cycle is determined by using a cyclic iterative integral framework.
[0005] In some embodiments, the first parameter information includes the current average current, current open-circuit voltage, current state of charge, current polarization voltage, current battery temperature, and battery capacity of the power battery. The calculation process of the first-order equivalent circuit model includes: Based on the current open-circuit voltage, current state of charge, current average current, and battery capacity, determine the target open-circuit voltage for the next iteration cycle. Obtain the ohmic internal resistance, polarization internal resistance, and polarization capacitance corresponding to the current battery temperature, and determine the target polarization voltage based on the current average current, current polarization voltage, ohmic internal resistance, polarization internal resistance, and polarization capacitance. The target terminal voltage is determined based on the difference between the target open-circuit voltage and the target polarization voltage.
[0006] In some embodiments, the second parameter information includes the current average current, current polarization voltage, current battery temperature, thermal capacity, thermal resistance, and the current ambient temperature and current heat dissipation coefficient for the current calculation cycle. The calculation process of the one-dimensional thermal model includes: The product of the current average current and the current polarization voltage is determined as the heat generated by the power battery during the charging and discharging process. Based on the current battery temperature, current ambient temperature, thermal resistance, and current heat dissipation coefficient, determine the amount of heat dissipated by the power battery to the environment; The temperature change value is determined based on the amount of heat generated, the amount of heat dissipated, and the heat capacity. The target battery temperature is determined by summing the current battery temperature and the temperature change value.
[0007] In some embodiments, after the one-dimensional thermal model in the electrothermal coupling model outputs the target battery temperature, the method further includes: In response to the current battery temperature meeting the thermal management activation condition, the target battery temperature is updated to the thermal management deactivation target temperature; wherein, the updated target battery temperature is used as the input of the first-order equivalent circuit model.
[0008] In some embodiments, a target cumulative value of the remaining energy of the power battery in the current calculation cycle is determined using a cyclic iterative integration framework, including: Using a cyclic iterative integration framework, the initial cumulative value of the remaining energy of the power battery in the current calculation cycle is determined based on the terminal voltage and average current from the initial iteration cycle to the current iteration cycle. The target cumulative value is determined based on the initial cumulative value.
[0009] In some embodiments, determining a target cumulative value based on an initial cumulative value includes: If the current battery temperature of the power battery meets the conditions for thermal management to be activated, the target cumulative value is determined based on the difference between the initial cumulative value and the calibrated value of the single thermal management loss. If the current battery temperature does not meet the conditions for enabling thermal management, the initial cumulative value will be used as the target cumulative value.
[0010] In some embodiments, the second parameter information includes the current heat dissipation coefficient for the current calculation cycle, and the above-described residual energy estimation method further includes: Based on the thermal resistance and thermal capacity of the power battery, the initial battery temperature and initial ambient temperature at startup, as well as the actual battery temperature and current ambient temperature in the current calculation cycle, the estimated cumulative heat for the current calculation cycle is determined. Based on the maximum energy that the power battery can release and the actual amount of work done in the current calculation cycle, the actual cumulative heat in the current calculation cycle is determined by the law of conservation of energy. Based on the current heat dissipation coefficient, estimated cumulative heat, and actual cumulative heat, determine the target heat dissipation coefficient for the next calculation period.
[0011] In some embodiments, the above-described residual energy estimation method further includes: When the target terminal voltage is less than or equal to the lower cutoff voltage, output the target cumulative value.
[0012] Secondly, a device for estimating the remaining energy of a power battery is provided, comprising: The first model building unit is configured to build a first-order equivalent circuit model based on the electrochemical characteristics of the power battery. The first-order equivalent circuit model is used to output the target terminal voltage and target polarization voltage of the next cycle based on the first parameter information of the current cycle. The current cycle and the next cycle belong to the same current calculation cycle. The second model building unit is configured to build a one-dimensional thermal model based on the thermal characteristics of the power battery. The one-dimensional thermal model is used to output the target battery temperature for the next cycle based on the second parameter information of the current cycle. The coupling unit is configured to build an electrothermal coupling model based on the spatial state equations of the first-order equivalent circuit model and the one-dimensional thermal model; in the electrothermal coupling model, the target polarization voltage is used as the input of the one-dimensional thermal model, and the target battery temperature is used as the input of the first-order equivalent circuit model. The framework building unit is configured to build a cyclic iterative integral framework for the remaining energy based on the electrothermal coupling model; The determination unit is configured to determine the target cumulative value of the remaining energy of the power battery in the current calculation cycle through a cyclic iterative integration framework.
[0013] Thirdly, an electronic device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and when executed by the processor, the computer program implements the remaining energy estimation method as described in any implementation of the first aspect.
[0014] Beneficial effects: The solution provided in this application establishes a first-order equivalent circuit model based on the electrochemical characteristics of the power battery. This model outputs the target terminal voltage and target polarization voltage for the next iteration cycle based on the first parameter information of the current iteration cycle. The two cycles (the current iteration cycle and the next iteration cycle) belong to the same current calculation cycle. A one-dimensional thermal model is established based on the thermal characteristics of the power battery. This model outputs the target battery temperature for the next iteration cycle based on the second parameter information of the current iteration cycle. Then, an electrothermal coupling model is constructed based on the spatial state equations of the first-order equivalent circuit model and the one-dimensional thermal model. In this model, the target polarization voltage is used as the input to the one-dimensional thermal model, and the target battery temperature is used as the input to the first-order equivalent circuit model. Finally, a cyclic iterative integral framework for the remaining energy is constructed based on the electrothermal coupling model, and the target cumulative value of the remaining energy of the power battery in the current calculation cycle is determined through this framework. This scheme couples a first-order equivalent circuit model with a one-dimensional thermal model, and simultaneously predicts two observables, temperature and voltage, to calculate the energy loss process caused by dynamic changes in temperature and internal resistance during battery charging and discharging, thereby improving the overall accuracy of remaining energy estimation. Attached Figure Description
[0015] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0016] Figure 1 This is a flowchart of a method for estimating the remaining energy of a power battery provided in an embodiment of this application; Figure 2 This is a schematic diagram of the electrothermal coupling model provided in the embodiments of this application; Figure 3 This is a flowchart of a single computation cycle provided in an embodiment of this application; Figure 4 This is a detailed flowchart of the double-cycle nesting provided in the embodiments of this application; Figure 5 This is a schematic diagram of the estimation results provided in the embodiments of this application; Figure 6 This is a schematic diagram of the structure of the power battery remaining energy estimation device provided in the embodiments of this application. Detailed Implementation
[0017] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.
[0018] In the description of this application, it should be understood that the terms "center," "longitudinal," "lateral," "length," "width," "thickness," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," and "outer," etc., indicating orientation or positional relationships based on the orientation or positional relationships shown in the accompanying drawings, are used only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of this application. Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, features defined with "first" and "second" may explicitly or implicitly include one or more of the stated features. In the description of this application, "a plurality of" means two or more, unless otherwise explicitly specified.
[0019] "A and / or B" includes the following three combinations: A only, B only, and a combination of A and B.
[0020] The use of "applies to" or "configured to" in this application implies open and inclusive language, which does not exclude the applicability to or configuration to devices performing additional tasks or steps. Additionally, the use of "based on" implies openness and inclusivity, because processes, steps, calculations, or other actions "based on" one or more conditions or values may in practice be based on additional conditions or values beyond those stated.
[0021] In this application, the term "exemplary" is used to mean "used as an example, illustration, or description." Any embodiment described as "exemplary" in this application is not necessarily to be construed as being more preferred or advantageous than other embodiments. The following description is provided to enable any person skilled in the art to make and use this application. Details are set forth in the following description for purposes of explanation. It should be understood that those skilled in the art will recognize that this application can be made without using these specific details. In other instances, well-known structures and processes are not described in detail to avoid obscuring the description of this application with unnecessary detail. Therefore, this application is not intended to be limited to the embodiments shown, but is consistent with the broadest scope of the principles and features disclosed in this application.
[0022] Estimating the SOE (Self-Equivalent Energy) of a power battery requires decoupling two nonlinear relationships: the nonlinear change in temperature with heat dissipation and the nonlinear change in battery electrochemical characteristics with temperature. Recursive formulas are typically used to simulate the evolution between these two relationships. For example, heat affects temperature over time through thermal capacity and thermal resistance, while temperature alters the battery's terminal voltage characteristics by influencing the capacitance and resistance in a multi-order equivalent circuit model.
[0023] Related technologies can be categorized into three types based on their complexity: open circuit voltage (OCV) integration, equivalent circuit model state observation, and data-driven methods. The open circuit voltage integration method obtains the corresponding OCV from a table based on the current state of charge (SOC), and the SOE value is the product of the remaining charge (in ampere-hours) and the OCV (in volts). This method is only suitable for energy estimation under low-current, constant-condition operation and cannot describe the energy loss differences caused by different currents under dynamic conditions. The equivalent circuit model observation method uses the current temperature and current time series as input to calculate the future terminal voltage time series, and obtains the SOE value by integrating the time series of the terminal voltage and current product. Compared to the OCV integration method, this method incorporates the dynamic process of the battery and improves the estimation accuracy under high current conditions, but it does not include SOE fluctuations caused by battery temperature rise changes at low temperatures, and the estimation accuracy is directly limited by the accuracy of the future current series. The data-driven method relies on massive amounts of labeled data on operating conditions and SOE for neural network training. The diversity of the data directly determines the model's generalization estimation ability, and the scarcity of sample data for new products directly limits model development.
[0024] To overcome the problems of poor energy prediction under future operating conditions using the equivalent circuit model observation method and poor robustness of the data-driven method, a method combining the two has been proposed. The current sequence output by the data-driven model is fed to the equivalent circuit model, and the equivalent circuit model iteratively calculates and outputs the future energy value. This method improves the average estimation accuracy and includes physical constraints. However, it lacks a closed-loop feedback mechanism and cannot simulate energy fluctuations caused by temperature changes due to thermal management intervention.
[0025] The related technologies have the following drawbacks: 1) When battery mechanism-based technologies are used in low-temperature conditions, the dynamic energy loss process of battery self-heating and heat dissipation cannot be directly linked to SOE, resulting in a large estimation error. The reason is that the relevant technologies use the terminal voltage output by the equivalent circuit as the state observation, and obtain the filtering gain by the difference between it and the current actual collected voltage. At the same time, the SOC and SOE are corrected in real time, which cannot cover the changes in battery performance caused by future temperature changes. For example, after a cold start at low temperatures, the temperature of the vehicle continues to rise as driving, and the actual energy released is higher than the estimated value. 2) When data-driven technologies are used in extremely complex working conditions, if the actual vehicle working conditions are not covered in the training dataset, it will lead to a large model estimation error. The reason is that the data-driven methods of related technologies have poor extrapolation ability for extreme scenarios and lack physical constraints, which may cause the estimation error to increase sharply. In addition, there is a lack of sample data for neural network training during the development phase, resulting in poor robustness of the model and low average estimation accuracy. 3) When the technology based on battery mechanism and data-driven approach is used under temperature fluctuation conditions, it lacks the influence of temperature on heat generation, resulting in a large estimation error. The reason is that the technology of merging the two related methods in series lacks the dynamic influence of temperature on battery heat loss and lacks a closed-loop feedback process. The accuracy depends on the future operating condition time series output by the data-driven model, and the accuracy is poor under the condition of large battery temperature changes. 4) The relevant technologies for estimating remaining energy and predicting temperature do not take into account active thermal management conditions, and the estimation accuracy error is large under non-constant heat dissipation conditions. The reason is that the relevant methods cannot simulate the process of battery heat dissipation changing dynamically with temperature, which leads to the energy consumption of energy management not being included in the calculation of remaining energy. In addition, the temperature change after the intervention of thermal management will also directly affect the timing of the voltage reaching the lower cutoff voltage. The dual impact makes the accuracy of thermal management start-up and shutdown under extreme temperature conditions poor.
[0026] In view of this, embodiments of this application provide a method, apparatus, and electronic device for estimating the remaining energy of a power battery. By coupling a first-order equivalent circuit model with a one-dimensional thermal model, and simultaneously predicting two observables, temperature and voltage, the method calculates the energy loss process caused by dynamic changes in temperature and internal resistance during battery charging and discharging, thereby improving the overall accuracy of remaining energy estimation and solving at least one of the aforementioned technical problems.
[0027] Figure 1 This is a flowchart of a method for estimating the remaining energy of a power battery provided in an embodiment of this application. The method includes the following steps: S101: A first-order equivalent circuit model is established based on the electrochemical characteristics of the power battery. The first-order equivalent circuit model is used to output the target terminal voltage and target polarization voltage of the next cycle based on the first parameter information of the current cycle iteration. The current cycle iteration and the next cycle iteration belong to the same current calculation cycle. S103: Establish a one-dimensional thermal model based on the thermal characteristics of the power battery. The one-dimensional thermal model is used to output the target battery temperature for the next cycle based on the second parameter information of the current cycle. S105: An electrothermal coupling model is built based on the spatial state equations of the first-order equivalent circuit model and the one-dimensional thermal model. In the electrothermal coupling model, the target polarization voltage is used as the input of the one-dimensional thermal model, and the target battery temperature is used as the input of the first-order equivalent circuit model. S107: A cyclic iterative integration framework for residual energy is built based on an electrothermal coupling model; S109: Determine the target cumulative value of the remaining energy of the power battery in the current calculation cycle through a cyclic iterative integral framework.
[0028] Figure 1 The corresponding embodiment provides a solution that establishes a first-order equivalent circuit model based on the electrochemical characteristics of the power battery. This model outputs the target terminal voltage and target polarization voltage for the next iteration cycle based on the first parameter information of the current iteration cycle. The current and next iteration cycles belong to the same current calculation cycle. A one-dimensional thermal model is then established based on the thermal characteristics of the power battery. This model outputs the target battery temperature for the next iteration cycle based on the second parameter information of the current iteration cycle. Next, an electrothermal coupling model is constructed based on the spatial state equations of the first-order equivalent circuit model and the one-dimensional thermal model. In this model, the target polarization voltage and the target battery temperature are used as inputs to the first-order equivalent circuit model. Then, a cyclic iterative integration framework for remaining energy is constructed based on the electrothermal coupling model. This framework is used to determine the target cumulative value of the remaining energy of the power battery in the current calculation cycle. This solution couples the first-order equivalent circuit model with the one-dimensional thermal model, simultaneously predicting both temperature and voltage, and calculating the energy loss process caused by dynamic changes in temperature and internal resistance during battery charging and discharging, thereby improving the overall accuracy of remaining energy estimation.
[0029] Steps S101 to S109 will be explained below.
[0030] In step S101, a first-order equivalent circuit model is established based on the electrochemical characteristics of the power battery. The first-order equivalent circuit model is used to output the target terminal voltage and target polarization voltage of the next cycle based on the first parameter information of the current cycle iteration. The current cycle iteration and the next cycle iteration belong to the same current calculation cycle.
[0031] by Taking the current cycle iteration period as an example, the first parameter information may include the current average current of the power battery. Current open circuit voltage Current state of charge Current polarization voltage Current battery temperature and battery capacity The computational process of a first-order equivalent circuit model may include: Based on the current open circuit voltage Current state of charge Current average current and battery capacity Determine the next iteration period. Target open circuit voltage ; Get the current battery temperature Corresponding ohmic internal resistance Polarization internal resistance and polarization capacitor And based on the current average current Current polarization voltage Ohmic internal resistance Polarization internal resistance and polarization capacitor Determine the target polarization voltage ; Based on target open-circuit voltage and target polarization voltage The difference is used to determine the target terminal voltage. .
[0032] Furthermore, the first-order equivalent circuit model, after discretization, can be expressed as the following formulas (1), (2), and (3): (1) (2) (3) in, This can represent the step time of the iterative calculation, in seconds (s). Current average current. The unit is A (ampere). Current open-circuit voltage Current polarization voltage Target open-circuit voltage Target polarization voltage and target terminal voltage The unit for all values is V (volt). Current state of charge. The unit is %. Current battery temperature The unit is °C. Battery capacity. The unit is Ah (ampere-hour). Ohmic internal resistance and polarization resistance The unit is Ω (ohm). Polarized capacitance. The unit is F (farad).
[0033] It should be noted that this can be based on the current battery temperature. Find the preset parameter table and obtain the current battery temperature from it. Corresponding ohmic internal resistance Polarization internal resistance and polarization capacitor This parameter table records different battery temperatures and the corresponding ohmic internal resistance, polarization internal resistance, and polarization capacitance. Additionally, it includes the polarization voltage (such as the current polarization voltage). Target polarization voltage ) is the internal resistance of the ohm and polarization resistance The resulting pressure difference is the main source of battery heat loss.
[0034] In step S103, a one-dimensional thermal model is established based on the thermal characteristics of the power battery. The one-dimensional thermal model is used to output the target battery temperature for the next cycle based on the second parameter information of the current cycle iteration.
[0035] by Indicates the current iteration period. Taking the current calculation period as an example, the second parameter information includes the current average current. Current polarization voltage Current battery temperature Heat capacity Thermal resistance and the current ambient temperature during the current calculation period. and current heat dissipation coefficient The calculation process of a one-dimensional thermal model may include: Current average current and current polarization voltage The product of these two factors is determined as the heat generated by the power battery during the charging and discharging process. ; Based on the current battery temperature Current ambient temperature Thermal resistance and current heat dissipation coefficient Determine the amount of heat dissipated by the power battery to the environment. ; Based on heat generation Heat dissipation and heat capacity Determine the temperature change value ; Current battery temperature and temperature change value The sum is determined as the target battery temperature. .
[0036] Furthermore, the discretized one-dimensional thermal model can be expressed as the following formulas (4), (5), (6) and (7): (4) (5) (6) (7) in, This can represent the step time of the iterative calculation, in seconds. Current average current. The unit is A (ampere). Current polarization voltage The units for all values are V. (Heat capacity) The unit is J / ℃ (joules per degree Celsius). Thermal resistance The unit is ℃ s / J (degrees Celsius) (seconds per joule). Current battery temperature. Current ambient temperature Temperature change value and target battery temperature The unit is °C. Calorific value. and heat dissipation The unit is W (watt).
[0037] It should be noted that in this application, The initial value is the actual sampled battery temperature of the current calculation cycle. .
[0038] In step S105, an electrothermal coupling model is constructed based on the spatial state equations of the first-order equivalent circuit model and the one-dimensional thermal model. For example... Figure 2 As shown, in the electrothermal coupling model, the target polarization voltage The target battery temperature is used as input to the one-dimensional thermal model. As input to the first-order equivalent circuit model. Wherein, Figure 2 This is a schematic diagram of the electrothermal coupling model provided in the embodiments of this application.
[0039] It should be noted that during each iteration, the first-order equivalent circuit model can use the target battery temperature output by the first-dimensional thermal model in the previous iteration cycle to complete the RC parameter lookup table and realize the polarization voltage prediction for the next iteration cycle; the first-dimensional thermal model can use the target polarization voltage output by the first-order equivalent circuit model in the previous iteration cycle to complete the heat generation calculation and realize the battery temperature prediction for the next iteration cycle.
[0040] In step S107, a cyclic iterative integration framework for the remaining energy is established based on the electrothermal coupling model. Then, by executing step S109, the target cumulative value of the remaining energy of the power battery in the current calculation cycle is determined using the cyclic iterative integration framework.
[0041] Furthermore, the initial cumulative value of the remaining energy of the power battery in the current calculation cycle can be determined by using a cyclic iterative integration framework, based on the terminal voltage and average current from the initial iteration cycle to the current iteration cycle, and the target cumulative value can be determined based on the initial cumulative value.
[0042] Furthermore, such as Figure 3 As shown, at the current battery temperature of the power battery Under the condition that thermal management is enabled, based on the initial cumulative value and the amount of thermal management loss per cycle. calibration value The difference between them determines the target cumulative value. .in, Figure 3 This is a flowchart of a single calculation cycle provided in an embodiment of this application. At the current battery temperature... If the conditions for activating thermal management are not met, the initial accumulated value will be used as the target accumulated value. One example of a thermal management activation condition is the current battery temperature. Greater than or equal to the thermal management start temperature. The unit of thermal management start temperature is °C.
[0043] In one example, the iterative integral framework for the remaining energy can be expressed by the following formula (8): (8) It should be noted that at the current battery temperature of the power battery... When the thermal management activation conditions are met, in formula (8) equal to the calibration value At the current battery temperature of the power battery If the thermal management activation conditions are not met, in formula (8) .
[0044] In some embodiments, the one-dimensional thermal model in the electrothermal coupling model outputs the target battery temperature. After that, it can be like Figure 3 As shown, in response to the current battery temperature Meet the thermal management activation conditions and reduce the target battery temperature. Updated to thermal management off target temperature For example, let Among them, the updated target battery temperature This serves as the input to the first-order equivalent circuit model. Therefore, it is possible to predict the temperature while estimating the remaining energy, with an active thermal management strategy accompanying the temperature iteration process. This makes this application applicable to the estimation of remaining energy in real vehicles with active liquid cooling.
[0045] In some embodiments, the thermal resistance of the power battery can be used as a basis. Heat capacity Initial battery temperature at startup and initial ambient temperature And the actual battery temperature value in the current calculation cycle. and current ambient temperature Determine the estimated cumulative heat for the current calculation period. Based on the maximum energy that the power battery can release in the current calculation cycle. and actual external work done The actual cumulative heat for the current calculation period is determined using the law of conservation of energy. Then, based on the current heat dissipation coefficient of the current calculation cycle. Estimate cumulative heat and actual cumulative heat Determine the next calculation cycle. Target heat dissipation coefficient Target heat dissipation coefficient As parameters for the one-dimensional thermal model in the next calculation cycle.
[0046] Furthermore, the estimated cumulative heat for the current calculation period It can be calculated using the following formula (9): (9) in, This can represent the step time of the calculation period, in seconds. It estimates the cumulative heat. Actual cumulative heat and actual external work done The unit can be J (joule).
[0047] According to the law of conservation of energy, as shown in the following formula (10), from Time's up Maximum energy that can be released at any moment Actual cumulative heat Actual external work done sum: (10) Among them, the maximum energy that can be released The fixed value for each battery cell, the actual amount of work done externally. The accurate value can be obtained by multiplying the voltage and current by the accumulator, and the actual accumulated heat can be obtained by subtracting the two. .
[0048] The following formula (11) can be used to estimate the cumulative heat based on the current calculation period. and actual cumulative heat Determine the next calculation cycle. Target heat dissipation coefficient : (11) In some embodiments, at the target terminal voltage Less than or equal to the lower cutoff voltage In this case, output the target cumulative value. .
[0049] It should be noted that the dispersion coefficient is continuously updated using historical temperature data to achieve closed-loop temperature prediction while simultaneously calculating remaining energy online. The flowchart of the iterative process within one calculation cycle is as follows: Figure 3 As shown. Among them, Figure 3 The cumulative energy consumption integral value in the figure represents the target cumulative value. .
[0050] in addition, Figure 4 The detailed process of the nested double cycle is shown. Among them, Figure 4 This is a detailed flowchart of the double-cycle nesting provided in the embodiments of this application. Figure 4 middle, It can represent the terminal voltage of the next cycle being calculated iteratively during the energy integration process; Indicates the state of charge in the current calculation cycle; This represents the average current for the current calculation period; Indicates ambient temperature. This indicates the battery temperature during the current calculation cycle.
[0051] As verified, such as Figure 5 As shown, the SOE estimation error is within 0.22 kWh (error = 0.76%) at both room temperature and high temperature, and the temperature error is within 2℃; the maximum SOE estimation error at low temperature can reach 1.7 kWh (error = 5.90%), with a maximum temperature error of 6℃, all occurring at 0℃. Figure 5 This is a schematic diagram of the estimation results provided in the embodiments of this application. It should be noted that "normal temperature" usually refers to the general temperature range in which the battery operates normally, such as 10℃~35℃; "high temperature" refers to the temperature that is close to or exceeds the upper limit of battery operation, such as >45℃; "low temperature" usually refers to the temperature of 0℃ or below 0℃.
[0052] Figure 6 This is a schematic diagram of the structure of the remaining energy estimation device for a power battery provided in an embodiment of this application. The remaining energy estimation device includes: The first model building unit 601 is configured to build a first-order equivalent circuit model based on the electrochemical characteristics of the power battery. The first-order equivalent circuit model is used to output the target terminal voltage and target polarization voltage of the next cycle based on the first parameter information of the current cycle iteration. The current cycle iteration and the next cycle iteration belong to the same current calculation cycle. The second model building unit 602 is configured to build a one-dimensional thermal model based on the thermal characteristics of the power battery. The one-dimensional thermal model is used to output the target battery temperature for the next cycle based on the second parameter information of the current cycle. The coupling unit 603 is configured to build an electrothermal coupling model based on the spatial state equations of a first-order equivalent circuit model and a one-dimensional thermal model; in the electrothermal coupling model, the target polarization voltage is used as the input of the one-dimensional thermal model, and the target battery temperature is used as the input of the first-order equivalent circuit model. The framework building unit 604 is configured to build a cyclic iterative integral framework for the remaining energy based on the electrothermal coupling model; The determination unit 605 is configured to determine the target cumulative value of the remaining energy of the power battery in the current calculation cycle through a cyclic iterative integration framework.
[0053] For an explanation of the aforementioned residual energy estimation device and its various units, please refer to the relevant descriptions above, which will not be repeated here.
[0054] To address the problems existing in related technologies, this application proposes a method for estimating the remaining energy (SOE) of a power battery based on temperature prediction. This method adds temperature and heat as input and output of the electrical model, respectively, coupling them into an electro-thermal model to iteratively integrate current and voltage. The cutoff voltage is used as the cycle endpoint to obtain the remaining energy estimate. Simultaneously, the method uses the ambient temperature difference and battery temperature difference from startup to the current moment to calculate heat change online, and corrects the SOE estimate in real time using a closed-loop method. This solves two problems that lead to error amplification in related technologies: firstly, the lack of incorporation of dynamic temperature changes leading to SOE system errors; and secondly, real-time changes in operating conditions leading to cumulative SOE errors. Furthermore, this application does not rely on data-driven methods, ensuring estimation accuracy while reducing development costs.
[0055] The solution provided in this application has the following beneficial effects: A battery charging and discharging iterative model was built to simulate the process of voltage decreasing to the lower cutoff voltage under different currents and temperatures within one calculation cycle, thereby improving the accuracy of remaining energy estimation under low temperature and low SOC conditions. By coupling the first-order equivalent circuit model with the one-dimensional thermal model, and simultaneously predicting the two observables of temperature and voltage, the energy loss process caused by the dynamic changes in temperature and internal resistance during battery charging and discharging is calculated, thereby improving the overall accuracy of remaining energy estimation. While estimating the remaining energy, the temperature is predicted. The temperature iteration process is accompanied by an active thermal management strategy, making the present invention applicable to the estimation of the remaining energy of a real vehicle with active liquid cooling. By using historical temperature data to calculate battery heat online, and continuously adjusting the temperature prediction results by updating the heat dissipation coefficient, the SOE is corrected. This achieves model closed-loop error convergence without the need to predict future operating conditions.
[0056] This application also provides an electronic device, including a memory and a processor. The memory stores a computer program, and when the computer program is executed by the processor, it implements the method of any of the above embodiments.
[0057] This application also provides a computer-readable storage medium storing a computer program thereon, which is loaded by a processor to execute the steps of any of the methods described in the above embodiments. In this application, the storage medium may be a magnetic disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM), etc.
[0058] This application also provides a computer program product or computer program that includes computer instructions stored in a computer-readable storage medium. A processor of an electronic device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the electronic device to perform the methods provided in the various optional implementations of the above embodiments.
[0059] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.
[0060] The remaining energy estimation method, apparatus, and electronic equipment of the power battery provided in the embodiments of this application have been described in detail above. Specific examples have been used to illustrate the principles and implementation methods of this application. The description of the above embodiments is only for the purpose of helping to understand the method and core ideas of this application. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this application. Therefore, the content of this specification should not be construed as a limitation of this application.
Claims
1. A method for estimating the remaining energy of a power battery, characterized in that, include: A first-order equivalent circuit model is established based on the electrochemical characteristics of the power battery. The first-order equivalent circuit model is used to output the target terminal voltage and target polarization voltage of the next cycle iteration based on the first parameter information of the current cycle iteration. The current cycle iteration and the next cycle iteration belong to the same current calculation cycle. A one-dimensional thermal model is established based on the thermal characteristics of the power battery. The one-dimensional thermal model is used to output the target battery temperature for the next cycle based on the second parameter information of the current cycle. An electrothermal coupling model is constructed based on the spatial state equations of the first-order equivalent circuit model and the one-dimensional thermal model. In the electrothermal coupling model, the target polarization voltage is used as the input of the one-dimensional thermal model, and the target battery temperature is used as the input of the first-order equivalent circuit model. A cyclic iterative integration framework for the remaining energy is constructed based on the electrothermal coupling model. The target cumulative value of the remaining energy of the power battery in the current calculation cycle is determined by the cyclic iterative integration framework.
2. The residual energy estimation method according to claim 1, characterized in that, The first parameter information includes the current average current, current open-circuit voltage, current state of charge, current polarization voltage, current battery temperature, and battery capacity of the power battery; The calculation process of the first-order equivalent circuit model includes: Based on the current open-circuit voltage, the current state of charge, the current average current, and the battery capacity, determine the target open-circuit voltage for the next cycle iteration. Obtain the ohmic internal resistance, polarization internal resistance, and polarization capacitance corresponding to the current battery temperature, and determine the target polarization voltage based on the current average current, the current polarization voltage, the ohmic internal resistance, the polarization internal resistance, and the polarization capacitance; The target terminal voltage is determined based on the difference between the target open-circuit voltage and the target polarization voltage.
3. The residual energy estimation method according to claim 1, characterized in that, The second parameter information includes the current average current, current polarization voltage, current battery temperature, thermal capacity, thermal resistance, and the current ambient temperature and current heat dissipation coefficient for the current calculation cycle; The calculation process of the one-dimensional thermal model includes: The product of the current average current and the current polarization voltage is determined as the heat generated by the power battery during the charging and discharging process. Based on the current battery temperature, current ambient temperature, thermal resistance, and current heat dissipation coefficient, determine the amount of heat dissipated by the power battery to the environment; The temperature change value is determined based on the heat generated, the heat dissipated, and the heat capacity. The sum of the current battery temperature and the temperature change value is determined as the target battery temperature.
4. The residual energy estimation method according to claim 3, characterized in that, After the one-dimensional thermal model in the electrothermal coupling model outputs the target battery temperature, the model further includes: In response to the current battery temperature meeting the thermal management activation condition, the target battery temperature is updated to the thermal management deactivation target temperature; wherein, the updated target battery temperature is used as the input of the first-order equivalent circuit model.
5. The residual energy estimation method according to claim 1, characterized in that, The target cumulative value of the remaining energy of the power battery in the current calculation cycle is determined through the cyclic iterative integration framework, including: Using the cyclic iterative integration framework, based on the terminal voltages and average currents from the initial iterative cycle to the current iterative cycle within the current calculation cycle, the initial cumulative value of the remaining energy of the power battery in the current calculation cycle is determined. The target cumulative value is determined based on the initial cumulative value.
6. The residual energy estimation method according to claim 5, characterized in that, Determining the target cumulative value based on the initial cumulative value includes: When the current battery temperature of the power battery meets the thermal management activation conditions, the target cumulative value is determined based on the difference between the initial cumulative value and the calibrated value of the single thermal management loss. If the current battery temperature does not meet the thermal management activation conditions, the initial cumulative value will be used as the target cumulative value.
7. The method for estimating residual energy according to claim 1, characterized in that, The second parameter information includes the current heat dissipation coefficient for the current calculation cycle, and the remaining energy estimation method further includes: Based on the thermal resistance and thermal capacity of the power battery, the initial battery temperature and the initial ambient temperature at startup, and the actual battery temperature and the current ambient temperature in the current calculation cycle, the estimated cumulative heat for the current calculation cycle is determined. Based on the maximum energy that the power battery can release and the actual amount of work done in the current calculation cycle, the actual cumulative heat in the current calculation cycle is determined by the law of conservation of energy. Based on the current heat dissipation coefficient, the estimated cumulative heat, and the actual cumulative heat, the target heat dissipation coefficient for the next calculation cycle is determined.
8. The residual energy estimation method according to claim 1, characterized in that, Also includes: When the target terminal voltage is less than or equal to the lower cutoff voltage, the target cumulative value is output.
9. A device for estimating the remaining energy of a power battery, characterized in that, include: The first model building unit is configured to build a first-order equivalent circuit model based on the electrochemical characteristics of the power battery. The first-order equivalent circuit model is used to output the target terminal voltage and target polarization voltage of the next cycle iteration based on the first parameter information of the current cycle iteration. The current cycle iteration and the next cycle iteration belong to the same current calculation cycle. The second model building unit is configured to build a one-dimensional thermal model based on the thermal characteristics of the power battery. The one-dimensional thermal model is used to output the target battery temperature for the next cycle based on the second parameter information of the current cycle. The coupling unit is configured to build an electrothermal coupling model based on the spatial state equations of the first-order equivalent circuit model and the one-dimensional thermal model; in the electrothermal coupling model, the target polarization voltage is used as the input of the one-dimensional thermal model, and the target battery temperature is used as the input of the first-order equivalent circuit model. The framework building unit is configured to build a cyclic iterative integral framework for the remaining energy based on the electrothermal coupling model. The determining unit is configured to determine, through the cyclic iterative integration framework, a target cumulative value of the remaining energy of the power battery in the current calculation cycle.
10. An electronic device, characterized in that, It includes a memory and a processor, wherein the memory stores a computer program that, when executed by the processor, implements the remaining energy estimation method as described in any one of claims 1-8.