Charging parameter determination method and device, computer equipment and storage medium

Abstract

The invention relates to a charging parameter determination method and device, computer equipment and a storage medium. The method comprises the steps of obtaining an actual measurement value of a first type of parameters of a current sampling point, obtaining a prediction value of the first type of parameters according to the actual measurement value of the first type of parameters of the current sampling point, a preset second type of parameters and a pre-constructed prediction model, the second type of parameters comprise thermal management control parameters, the thermal management control parameters are parameters related to the thermal management device, and an optimized value of the second type of parameters is determined according to the predicted value of the first type of parameters and a pre-constructed target function and sent to the thermal management device so that the temperature of the battery can be controlled through the thermal management device. The objective function is constructed according to the charging duration determined by the temperature related parameters of the battery, and the thermal management device is controlled to operate according to the optimized value of the thermal management control parameter, so that the temperature of the battery is controlled through the thermal management device. By adopting the method, the temperature of the battery can be controlled within a proper working range while the charging duration is optimized.

Description

Technical Field

[0001] The present application relates to the field of battery technology, and in particular to a method, apparatus, computer device, and storage medium for determining charging parameters. Background Art

[0002] As the number of electric vehicles grows, users are increasingly demanding shorter charging times. Advances in battery materials are also paving the way for the development of fast-charging technology. However, while fast charging can replenish a vehicle's battery with significant energy in a short period of time, meeting the needs of long-distance travel, it also comes with a significant drawback: the heat generated by charging can cause the battery to overheat, impacting performance, shortening its lifespan, and posing a safety hazard. Therefore, achieving fast charging while simultaneously controlling battery temperature within a reasonable range is a pressing issue.

[0003] The current battery charging strategy is mostly constant current and constant voltage charging. This method mechanically uses a certain gradient current for charging and solely pursues fast charging, which can easily accelerate battery aging, shorten battery life, and even cause the opposite effect of longer charging time. Summary of the Invention

[0004] Based on this, it is necessary to provide a charging parameter determination method, device, computer equipment and storage medium that can optimize the charging time while controlling the battery temperature within an appropriate working range to address the above technical problems.

[0005] In a first aspect, a method for determining a charging parameter is provided, comprising:

[0006] Obtaining the measured values of the first type of parameters at the current sampling point, where the first type of parameters include temperature-related parameters of the battery;

[0007] Obtaining predicted values of the first-category parameters based on the measured values of the first-category parameters at the current sampling point, the pre-set second-category parameters, and the pre-built prediction model, wherein the second-category parameters include thermal management control parameters, which are parameters related to the thermal management device;

[0008] Optimized values of the second type of parameters are determined based on the predicted values of the first type of parameters and a pre-constructed objective function, and sent to a thermal management device to control the temperature of the battery via the thermal management device. The objective function is constructed based on a charging time determined based on the temperature-related parameters of the battery.

[0009] In one embodiment, the temperature-related parameters of the battery include the highest temperature value and the lowest temperature value among the temperature values of each battery cell in the battery, and the first-category parameters also include the battery state of charge. Based on the predicted values of the first-category parameters and a pre-established objective function, the optimized values of the second-category parameters are determined, including:

[0010] Determine the charging time based on the predicted value of the maximum temperature of the battery cell and the predicted value of the battery state of charge;

[0011] Determining a target temperature based on a predicted maximum temperature and a predicted minimum temperature of the battery cell;

[0012] Determine the value of the objective function based on the target temperature and charging time;

[0013] Adjust the value of the second type parameter, return to the step of inputting the value of the first type parameter of the current sampling point and the preset second type parameter into the pre-built prediction model to obtain the predicted value of the first type parameter, and perform multiple iterations until the number of iterations reaches the preset number, and obtain the optimized value of the second type parameter. The optimized value of the second type parameter is the value of the second type parameter when the value of the objective function is minimum.

[0014] In one embodiment, the second type of parameters include parameter values of each sampling point within a preset time, and the predicted value of the first type of parameters includes the predicted value corresponding to each sampling point. The predicted value of the first type of parameters is obtained based on the value of the first type of parameters at the current sampling point, the preset second type of parameters, and the pre-built prediction model, including:

[0015] According to the measured value of the first type parameter of the current sampling point and the value of the second type parameter of the current sampling point, the value of the first type parameter of the next sampling point is determined until the last sampling point within the preset time, and the predicted value corresponding to each sampling point is obtained.

[0016] In one embodiment, the first-category parameter further includes the battery state of charge, the second-category parameter further includes the charging current, and the thermal management control parameter includes the flow rate and temperature of the cooling medium. Determining the value of the first-category parameter at the next sampling point based on the measured value of the first-category parameter at the current sampling point and the value of the second-category parameter at the current sampling point includes:

[0017] Determine the battery heat generation based on the charging current corresponding to the current sampling point;

[0018] Determine the battery's thermal management heat exchange based on the charging current, cooling medium flow rate, and temperature corresponding to the current sampling point;

[0019] Determine the value of the temperature-related parameter of the battery corresponding to the next sampling point based on the heat generation and thermal management heat exchange;

[0020] The battery state of charge corresponding to the next sampling point is determined based on the battery state of charge, charging current and battery rated power at the current sampling point.

[0021] In one embodiment, the second type of parameter further includes charging current, and the method further includes:

[0022] The operation of the charging device is controlled according to the optimized value of the charging current to control the charging process of the battery by the charging device.

[0023] In one embodiment, the optimized value of the second type of parameter includes the optimized value corresponding to each sampling point within a preset time, and controlling the operation of the charging device according to the optimized value of the charging current to control the charging process of the battery by the charging device includes:

[0024] According to the value of the first type parameter at the next sampling point, a preset data table is queried to obtain a calibration current, wherein the preset data table records a mapping relationship between the value of the first type parameter and the calibration current;

[0025] The calibration current and the optimized value of the charging current corresponding to the next sampling point are minimized to obtain the target control current;

[0026] The operation of the charging device is controlled according to the target control current to control the charging process of the battery by the charging device.

[0027] In one embodiment, the charging parameter determination method further includes:

[0028] Determine the temperature range of the cooling medium based on the average temperature of each battery cell in the battery and the optimized value of the temperature of the cooling medium at the last sampling point;

[0029] The step of determining the optimized value of the temperature of the cooling medium includes:

[0030] An optimized value of the temperature of the cooling medium at the current sampling point is determined according to the temperature range of the cooling medium, the predicted value of the first type parameter, and the objective function.

[0031] In a second aspect, a charging control device is provided, comprising:

[0032] an acquisition module, configured to obtain the measured values of the first type of parameters at the current sampling point, the first type of parameters including temperature-related parameters of the battery;

[0033] a prediction module, configured to obtain predicted values of the first-category parameters based on the measured values of the first-category parameters at the current sampling point, pre-set second-category parameters, and a pre-built prediction model, wherein the second-category parameters include thermal management control parameters, which are parameters related to the thermal management device;

[0034] An optimization module is used to determine optimized values of the second type of parameters based on the predicted values of the first type of parameters and a pre-constructed objective function, and send the optimized values to the thermal management device to control the temperature of the battery through the thermal management device. The objective function is constructed based on the charging time determined according to the temperature-related parameters of the battery.

[0035] In a third aspect, the present application provides a controller comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the charging parameter determination method provided in any embodiment of the present application in the first aspect are implemented.

[0036] In a fourth aspect, the present application provides a vehicle comprising a controller according to the third aspect.

[0037] In a fifth aspect, the present application provides a vehicle comprising a controller according to the third aspect.

[0038] In a sixth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the steps of the charging parameter determination method provided in any embodiment of the first aspect of the present application are implemented.

[0039] The aforementioned charging parameter determination method, apparatus, computer device, and storage medium predict the predicted values of the first-category parameters at future sampling points using the measured values of the first-category parameters at the current sampling point and pre-set second-category parameters. The second-category parameters are further optimized based on the predicted values of the first-category parameters and a pre-established objective function. The second-category parameters are then used to control the operation of a thermal management device, thereby controlling the battery temperature within an appropriate range through the thermal management device. When constructing the objective function, the dual-objective optimization of charging time and temperature is simultaneously considered. With shortening charging time and controlling battery temperature within an ideal range as the two objectives, a multi-objective function is constructed to ensure an effective balance between the two requirements at different charging stages. This achieves the goal of simultaneously optimizing charging time while controlling battery temperature within an appropriate operating range, extending battery life, and improving driving safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 A diagram illustrating an application environment of a method for determining charging parameters in some embodiments;

[0041] Figure 2 is a flow chart of a method for determining charging parameters in some embodiments;

[0042] Figure 3 A schematic diagram of the internal processing flow of a prediction model in some embodiments;

[0043] Figure 4 is a structural block diagram of a charging control device in some embodiments;

[0044] Figure 5 2 is a diagram of the internal structure of the controller in some embodiments. DETAILED DESCRIPTION

[0045] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0046] Please refer to Figure 1 , Figure 1 This is a schematic diagram of an application environment of a charging parameter determination method provided by an exemplary embodiment of the present application. Figure 1 As shown, the application environment includes a controller 100, a battery 101, a thermal management device 102 and a charging device 103, wherein the controller 100 is connected to the battery 101, the thermal management device 102 and the charging device 103 respectively, the battery 101 is connected to the thermal management device 102, and the battery 101 is also connected to the charging device 103.

[0047] The controller 100 is used to obtain the actual measured values of the first-category parameters at the current sampling point. The first-category parameters include temperature-related parameters of the battery. The predicted values of the first-category parameters are obtained based on the actual measured values of the first-category parameters at the current sampling point, pre-set second-category parameters, and a pre-constructed prediction model. The second-category parameters include thermal management control parameters. The thermal management control parameters are parameters related to the thermal management device. The optimized values of the second-category parameters are determined based on the predicted values of the first-category parameters and a pre-constructed objective function, and sent to the thermal management device to control the temperature of the battery through the thermal management device. The objective function is constructed based on the charging time determined according to the temperature-related parameters of the battery. The operation of the thermal management device 102 is controlled based on the optimized values of the thermal management control parameters to control the temperature of the battery 101 through the thermal management device 102.

[0048] The controller 100 is further configured to send the optimized values of the second type of parameters to the charging device 103 to control the charging process of the battery 101 by the charging device 103 .

[0049] The thermal management device 102 is used to control the temperature of the battery 101 according to the optimized value of the thermal management control parameter.

[0050] The charging device 103 is used to control the charging process of the battery 101 according to the optimized value of the second type of parameter. The charging device 103 can be a charging pile.

[0051] The controller 100 can be located in the vehicle, specifically as a Powertrain and Chassis Master Unit (PCMU). The PCMU is primarily responsible for the integrated control and management of the vehicle's powertrain and chassis systems. It can communicate and collaborate with other units, such as thermal management devices and charging devices, to ensure safe and efficient operation of the vehicle's batteries. The controller 100 can also be located in the charging device 103.

[0052] In a first aspect, the present application provides a method for determining charging parameters, such as Figure 2 As shown, this method is applied to Figure 1 The controller in the example is used to illustrate the following steps:

[0053] Step S21 : obtaining the measured values of the first type of parameters at the current sampling point, where the first type of parameters include temperature-related parameters of the battery.

[0054] The sampling point can refer to a sampling instant, a specific moment selected when discretely sampling a continuous-time signal. By measuring and recording the signal amplitude at these instants, the continuous signal is converted into a discrete digital signal for processing and analysis by a computer or other digital system. The current sampling point can be the current sampling instant.

[0055] The first type of parameters refers to parameters related to the battery. Specifically, the first type of parameters may include parameters related to the temperature of the battery.

[0056] Battery temperature parameters can include the battery body temperature, battery surface temperature, and internal temperature gradient. The controller obtains these temperature parameters through temperature sensors. For example, multiple temperature sensors can be installed on the battery surface to collect temperature values at different locations on the surface, or a temperature sensor can be embedded inside the battery to collect internal temperature. The controller receives the temperature data collected by these temperature sensors through a communication interface as the measured value of the first-category parameter at the current sampling point.

[0057] Specifically, the controller can monitor various parameters of the battery in real time, such as measuring the temperature of the battery through a temperature sensor, measuring the voltage of the battery through a voltage sensor, etc.

[0058] In step S22, a predicted value of the first type of parameter is obtained based on the measured value of the first type of parameter at the current sampling point, the preset second type of parameter, and the pre-built prediction model. The second type of parameter includes a thermal management control parameter, which is a parameter related to the thermal management device.

[0059] The second category of parameters refers to parameters used to control other battery-related units, which may include thermal management devices. For thermal management devices, the corresponding second category of parameters are thermal management control parameters, which are parameters related to the thermal management device and are used to control the thermal management device, such as controlling the state of the thermal management device's cooling medium.

[0060] A prediction model is a model that predicts the future value of a first-category parameter. The predicted value of a first-category parameter is the predicted value of the first-category parameter at a future time. For example, assuming the current sampling point is time t, the prediction model can predict the value of the first-category parameter at time t+1, t+2, ..., t+n based on the value of the first-category parameter at time t.

[0061] Specifically, the prediction model can perform a comprehensive analysis based on the battery's heat generation, thermal management heat exchange, environmental heat exchange, and inter-battery heat exchange to predict the value of the first-category parameter of the battery.

[0062] In step S23, the optimized values of the second type of parameters are determined based on the predicted values of the first type of parameters and a pre-constructed objective function, and are sent to the thermal management device to control the temperature of the battery through the thermal management device. The objective function is constructed based on the charging time determined according to the temperature-related parameters of the battery.

[0063] The objective function in this application refers to a multi-objective function. A multi-objective function refers to an optimization problem in which there are multiple interrelated objective functions that need to be optimized simultaneously. These objective functions usually represent different performance indicators or decision criteria, and there may be conflicting relationships between them.

[0064] Specifically, the objective function of the present application can optimize the battery temperature and charging time at the same time. When constructing the objective function, it is constructed based on the battery temperature and charging time.

[0065] A thermal management device is a device or system used to monitor and regulate battery temperature. It typically consists of sensors, controllers, heat sinks, heaters, and related circuits and software. It monitors the battery's temperature in real time and uses various methods to keep it within an appropriate range.

[0066] The optimized value of the second type of parameter refers to the value of the second type of parameter when the battery can be controlled to reach the target state. In other words, the value of the second type of parameter when the objective function can be controlled to meet the preset target.

[0067] Specifically, the controller can continuously adjust the value of the second-category parameter until the value of the objective function reaches the preset target. The value of the second-category parameter obtained at this time is the optimized value of the second-category parameter, and is sent to the thermal management device to control the operation of the thermal management device according to the optimized value of the thermal management control parameter, thereby controlling the temperature of the battery through the thermal management device.

[0068] In one embodiment, the temperature-related parameters of the battery include the highest temperature value and the lowest temperature value among the temperature values of each battery cell in the battery, and the first-category parameters also include the battery state of charge. According to the predicted value of the first-category parameters and the pre-constructed objective function, the optimized value of the second-category parameters is determined and sent to the thermal management device to control the temperature of the battery through the thermal management device, including: determining the charging time according to the predicted value of the highest temperature of the battery cell and the predicted value of the battery state of charge, determining the target temperature according to the predicted value of the highest temperature of the battery cell and the predicted value of the lowest temperature, determining the value of the objective function according to the target temperature and the charging time, adjusting the value of the second-category parameters, returning to the step of inputting the value of the first-category parameters at the current sampling point and the pre-set second-category parameters into the pre-constructed prediction model to obtain the predicted value of the first-category parameters, and performing multiple iterations until the number of iterations reaches a preset number, and the optimized value of the second-category parameters is obtained, and the optimized value of the second-category parameters is the value of the second-category parameters when the value of the objective function is minimum.

[0069] A battery is equipped with multiple cells, which are the basic units that make up a battery pack. They have independent electrochemical systems and are capable of storing and releasing electrical energy. Battery temperature-related parameters may include the temperature values of each cell within the battery, as well as the highest and lowest temperature values among the cell temperatures.

[0070] In this application, when predicting the value of the first category parameter, the first category parameter within a time period is predicted. Specifically, this application pre-sets the value of the second category parameter corresponding to each sampling point within a preset time, inputs the prediction model, and processes it through the prediction model to obtain the predicted value of the first category parameter corresponding to the preset time. Subsequently, the second category parameter is optimized according to the predicted value of the first category parameter corresponding to the preset time.

[0071] Specifically, the process of creating the objective function in this application includes:

[0072] The first function is constructed based on the battery charging time J1. The expression of J1 is as follows:

[0073] J1=MAE(SOC_list-a) ifmax(Tmax_list)

[0074] The second function is constructed based on the battery temperature J2. The expression of J2 is as follows:

[0075] J2=MAE(Tmax_list+Tmin_list-2*d) (2)

[0076] The objective function J is constructed based on J1 and J2. The expression of J is as follows:

[0077] ​J=J1 / e+J2 / f (3)

[0078] In expression (1), the suffix _list represents the set of results of the parameter within the preset time, SOC (State of Charge) represents the battery state of charge, Tmax represents the highest temperature value among the temperature values of each battery cell, MAE represents the mean absolute error, and a, b, and c are constants set according to actual conditions, where a represents the charging target SOC, b represents the maximum safe temperature of the battery, and c represents the penalty term, which has no actual physical meaning.

[0079] Expression (1) means: when the maximum value of the highest temperature value of the battery cell within the preset time is less than b, J1 = MAE (SOC_list-a), at this time J1 is the average absolute error between the estimated SOC and the charging target SOC within this time domain, and when the maximum value of the highest temperature value of the battery cell is greater than or equal to b, J1 = c. The maximum value here refers to the comparison between different moments within the preset time. For example, if there are N moments (sampling points) within the preset time, then the maximum value obtained by comparing N moments. The average absolute error here also refers to the calculation using the SOC at different moments. Among them, SOC is used to describe the amount of remaining power in the battery, usually expressed as a percentage, 0% means the battery is fully discharged, and 100% means the battery is fully charged.

[0080] In expression (2), Tmin represents the lowest temperature value among the battery cell temperatures, and d is a constant set according to actual conditions, representing the battery's optimal charging temperature. Expression (2) means: the mean absolute error of the optimal battery charging temperature is calculated by subtracting twice the mean absolute error of the optimal battery charging temperature from the sum of the highest and lowest battery cell temperatures within the preset time period.

[0081] In expression (3), e and f are constants set according to actual conditions. The total objective function J is the weighted sum of the first function J1 and the second function J2, and e and f are the weight coefficients of J1 and J2 respectively.

[0082] Furthermore, the controller can calculate the charging time according to the above expression (1), calculate the target temperature according to the above expression (2), and further calculate the value of the objective function according to the above expression (3) to perform one iteration. Further, the value of the second type parameter is adjusted, and the step of returning to the step of inputting the value of the first type parameter of the current sampling point and the pre-set second type parameter into the pre-built prediction model to obtain the predicted value of the first type parameter is performed to perform the next iteration. And so on, multiple iterations are performed until the number of iterations reaches the preset number, and the value of the objective function after each iteration is obtained. The value of the second type parameter corresponding to the minimum value of the objective function is selected as the optimized value of the second type parameter.

[0083] The beneficial effect of this embodiment is that by constructing a multi-objective function and optimizing the battery temperature and charging time at the same time, it is possible to optimize the battery charging time while ensuring that the battery temperature remains within a safe and efficient operating range, thereby extending the battery life and improving the overall safety of the vehicle.

[0084] Furthermore, during the battery charging process, the state of the battery changes differently at different stages, especially the changing trend of the battery temperature is very complex. To address this issue, this application uses the predicted values of the first-category parameters at each future sampling moment when optimizing the objective function, fully considering the dynamic changes of the battery at different future charging stages, thereby effectively improving the accuracy of the objective function optimization and making the optimization results more in line with the actual situation.

[0085] In one embodiment, the second-category parameter includes the parameter value of each sampling point within a preset time, and the predicted value of the first-category parameter includes the predicted value corresponding to each sampling point. The predicted value of the first-category parameter is obtained based on the value of the first-category parameter at the current sampling point, the preset second-category parameter, and the pre-built prediction model, including: determining the value of the first-category parameter at the next sampling point based on the measured value of the first-category parameter at the current sampling point and the value of the second-category parameter at the current sampling point, until the last sampling point within the preset time, to obtain the predicted value corresponding to each sampling point.

[0086] Specifically, the present application can use the measured value of the first-category parameter at the current sampling point and the value of the second-category parameter at the current sampling point to infer the value of the first-category parameter at the next sampling point. This method is then repeated until the last sampling point within a preset time, ultimately obtaining the predicted value of the first-category parameter corresponding to each sampling point.

[0087] Please refer to Figure 3 , Figure 3 FIG. 1 is a schematic diagram of the internal processing logic of the prediction model in one embodiment. Figure 3 In the prediction model, the second-category parameters are X(t), X(t+1), X(t+2)...X(t+p-1), where X(t) is the value of the second-category parameter at time t, and so on. The value of the first-category parameter at the current sampling point of the prediction model is Y(t). The prediction model processing steps include:

[0088] Use X(t) and Y(t) to make predictions and get Y(t+1);

[0089] Use X(t+1) and Y(t+1) to make a prediction and get Y(t+2);

[0090] This process is deduced in this way until Y(t+p) is obtained. The time from time t to time t+p-1 is a preset time, and each sampling point within the preset time is predicted.

[0091] The final set of predicted values, namely Y(t+1), Y(t+2), ...Y(t+p), are used as the predicted values corresponding to each sampling point.

[0092] The beneficial effects of this embodiment are: calculation is performed in sequence according to the order of sampling points, the characteristics of the time series are fully considered, the evolution trend of the first type of parameters over time can be captured, and it is helpful to more accurately capture the state changes of the battery at different times.

[0093] In one embodiment, the first category of parameters also includes the battery state of charge, the second category of parameters also includes the charging current, and the thermal management control parameters include the flow rate and temperature of the cooling medium. The value of the first category of parameters at the next sampling point is determined based on the measured value of the first category of parameters at the current sampling point and the value of the second category of parameters at the current sampling point, including: determining the heating value of the battery based on the charging current corresponding to the current sampling point, determining the thermal management heat exchange of the battery based on the charging current, flow rate and temperature of the cooling medium corresponding to the current sampling point, determining the value of the temperature-related parameter of the battery corresponding to the next sampling point based on the heating value and thermal management heat exchange, and determining the battery state of charge corresponding to the next sampling point based on the battery state of charge, charging current and battery rated power at the current sampling point.

[0094] Among them, the battery state of charge is used to describe the amount of remaining power in the battery, usually expressed as a percentage.

[0095] The cooling medium in a thermal management device refers to the substance used to transfer and dissipate heat within the thermal management system to cool the associated equipment or components. Common cooling media include water, air, and ethylene glycol-water solutions. If the thermal management device is liquid-cooled, the corresponding thermal management control parameters may include the coolant temperature and flow rate.

[0096] Specifically, the calculation formula for determining the heat amount of the battery according to the charging current corresponding to the current sampling point can be:

[0097] Heat generation Q(t)=Xi*f(I,R)

[0098] Where I is the charging current, R is the internal resistance of the battery, and Xi = [X1, X2, X3] is a set of temperature coefficients obtained by fitting, where X1 is the parameter corresponding to the highest temperature value among the temperature values of the battery cells, X2 is the parameter corresponding to the lowest temperature value among the temperature values of the battery cells, and X3 is the parameter corresponding to the average temperature value of the battery cells.

[0099] The calculation formula for determining the thermal management heat exchange of the battery based on the charging current, flow rate and temperature of the cooling medium corresponding to the current sampling point can be:

[0100] Thermal management heat exchange P(t) = Xi*f(Tcoolt(t), Tbatt(t), Flw(t))

[0101] Where Tcoolt is the temperature change of the cooling medium, Tbatt is the battery temperature, and Flw is the flow rate of the cooling medium.

[0102] Furthermore, the present application can also calculate the ambient heat exchange, the formula is as follows:

[0103] Environmental heat transfer Ptamb(t)=P(t)=Xi*f(Tbatt(t),Ttamb(t))

[0104] Among them, Ttamb represents the ambient temperature.

[0105] Furthermore, the present application can also calculate the ambient heat exchange, the formula is as follows:

[0106] Heat exchange between cells Pcell(t)=Xi*f(Tmax(t),Tmin(t),Tavg(t))

[0107] Furthermore, the formula for calculating the predicted value of each first-category parameter in this application is as follows:

[0108] Tmax(t+1)=(Q(t)-P(t)-Ptamb(t)-Pcell(t)) / (Mbatt*Cbatt)+Tmax(t)

[0109] Wherein, Tmax(t+1) is the predicted value of the highest temperature value among the temperature values of the battery cells at time t+1, Mbatt is the battery mass, and Cbatt is the battery mass specific heat capacity.

[0110] Tmin(t+1)=(Q(t)-P(t)-Ptamb(t)-Pcell(t)) / (Mbatt*Cbatt)+Tmin(t)

[0111] Wherein, Tmin(t+1) is the predicted value of the lowest temperature value among the temperature values of the battery cells at time t+1, Mbatt is the battery mass, and Cbatt is the battery mass specific heat capacity.

[0112] Tavg(t+1)=(Q(t)-P(t)-Ptamb(t)-Pcell(t)) / (Mbatt*Cbatt)+Tavg(t)

[0113] Wherein, Tavg(t+1) is the predicted value of the average temperature of the battery cell at time t+1, Mbatt is the battery mass, and Cbatt is the battery mass specific heat capacity.

[0114] Furthermore, the prediction of battery state of charge includes:

[0115] SOCmax(t+1)=SOCmax(t)+I*Δt / 3600 / Qbatt

[0116] Wherein, SOCmax(t+1) refers to the predicted value of the maximum state of charge among the battery cell states of charge, and Qbatt represents the rated capacity of the battery.

[0117] The predicted value of the minimum state of charge among the battery cell states of charge is SOC min(t+1)=SOC min(t)+I*Δt / 3600 / Qbatt.

[0118] Wherein, SOC min(t+1) represents the predicted value of the minimum state of charge among the battery cell states of charge, and Qbatt represents the rated capacity of the battery.

[0119] The beneficial effect of this embodiment is that the present application combines battery heat generation, thermal management heat exchange, environmental heat exchange and inter-battery heat exchange to predict the first type of parameters, making the predicted values of the first type of parameters more accurate.

[0120] In one embodiment, the second type of parameters further includes charging current, and the method further includes: controlling the operation of the charging device according to the optimized value of the charging current to control the charging process of the battery by the charging device.

[0121] Among them, the charging device is mainly a device that converts the electrical energy of the external power supply into an electrical energy form suitable for battery charging, and charges the battery according to a certain control strategy.

[0122] Specifically, the controller may transmit the optimized value of the charging current to the charging device through the control circuit or the communication interface. The charging device adjusts its own working state according to the received optimized value, thereby controlling the charging process of the battery.

[0123] The beneficial effect of this embodiment is that by controlling the charging device according to the optimized value of the charging current, the charging process can be made more in line with the characteristics and requirements of the battery, avoiding the problem of battery temperature increase due to excessive charging current, extending the battery life and improving charging efficiency.

[0124] In one embodiment, the optimized value of the second type of parameter includes the optimized value corresponding to each sampling point within a preset time, and the operation of the charging device is controlled according to the optimized value of the charging current to control the charging process of the battery by the charging device, including: querying a preset data table according to the value of the first type of parameter at the next sampling point to obtain a calibration current, the preset data table records the mapping relationship between the value of the first type of parameter and the calibration current, taking the smaller of the calibration current and the optimized value of the charging current corresponding to the next sampling point to obtain a target control current, and controlling the operation of the charging device according to the target control current to control the charging process of the battery by the charging device.

[0125] The preset data table can be a SOC-temperature-current table, which records the mapping relationship between the battery state of charge, battery temperature, and charging current. The present application can use the battery state of charge and battery temperature values corresponding to the current sampling point as indexes to query the preset data table and obtain the calibration current.

[0126] Furthermore, the smallest value between the calibration current and the optimized value of the charging current corresponding to the next sampling point is selected as the target control current, and the target control current is sent to the charging device for execution, so that the charging device controls the charging process of the battery according to the target control current.

[0127] The beneficial effect of this embodiment is that since the calibration current of the next sampling point is the maximum current that the battery can withstand under the temperature and SOC of the battery at the next sampling point, the application performs a small operation to prevent overcurrent, protect the battery, and ensure charging safety.

[0128] In one embodiment, the charging parameter determination method may further include: determining the temperature range of the cooling medium based on the average temperature of each battery cell in the battery and the optimized value of the temperature of the cooling medium at the previous sampling point, wherein the step of determining the optimized value of the temperature of the cooling medium includes: determining the optimized value of the temperature of the cooling medium at the current sampling point based on the temperature range of the cooling medium, the predicted value of the first type of parameter and the objective function.

[0129] The step of determining the temperature range of the cooling medium in this application may include:

[0130] The first temperature boundary of the cooling medium is determined based on the average temperature Tavg of each battery cell in the battery. The formula is as follows:

[0131] Treq_min_1=Tavg(t)-h (4)

[0132] Treq_max_1=Tavg(t)+h (5)

[0133] Where h is a constant representing the maximum temperature change, and Treq represents the temperature of the cooling medium;

[0134] Formula (4) represents the lower limit of the first temperature boundary of the cooling medium obtained based on the average temperature of the battery. The logic is the average temperature at time t minus h.

[0135] Formula (5) represents the upper limit of the first temperature boundary of the cooling medium obtained based on the average temperature of the battery. The logic is the average temperature at time t plus h.

[0136] Furthermore, the second temperature boundary of the cooling medium is determined according to the water temperature change rate:

[0137] Treq_min_2=Top(t-1)-i (6)

[0138] Treq_max_2=Top(t-1)+i (7)

[0139] Where i is a constant representing the adjacent temperature change, and Top represents the temperature of the cooling medium;

[0140] Formula (6) represents the lower limit of the second temperature boundary obtained based on the temperature of the cooling medium, and its logic is the temperature of the cooling medium at time t-1 minus i.

[0141] Formula (7) indicates that the upper limit of the second temperature boundary is obtained based on the temperature of the cooling medium. The logic is the temperature of the cooling medium at time t-1 plus i.

[0142] Furthermore, the two boundaries are intersected to obtain the final boundary:

[0143] Treq_min=max(Treq_min_1,Treq_min_2) (8)

[0144] Treq_max=min(Treq_max_1,Treq_max_2) (9)

[0145] Among them, if Treq_min>Treq_max, an exception is thrown;

[0146] Formula (8) indicates that the maximum value of the lower limit based on the average temperature of the battery cells and the lower limit based on the temperature of the cooling medium is taken as the lower limit of the final cooling medium temperature.

[0147] Formula (9) indicates that the minimum value of the upper limit based on the average temperature of the battery cells and the upper limit based on the temperature of the cooling medium is taken as the upper limit of the final cooling medium temperature, thereby obtaining the temperature range of the cooling medium.

[0148] Furthermore, when calculating the optimal value of the temperature of the cooling medium, the optimal value of the temperature of the cooling medium may be calculated within a temperature range of the cooling medium.

[0149] The beneficial effect of this embodiment is that by considering the average temperature of each battery cell in the battery and the optimized value of the cooling medium temperature at the previous sampling point to determine the temperature range, the heat exchange relationship between the battery and the cooling medium can be grasped more accurately, so that the control of the cooling medium temperature is more in line with actual needs, thereby improving the control accuracy of the thermal management system.

[0150] In one embodiment, the first type of parameters also includes the battery state of charge, and the method further includes: determining the range of the charging current based on the parameters related to the battery temperature and the battery state of charge, wherein the step of determining the optimized value of the charging current includes: determining the optimized value of the charging current based on the range of the charging current, the predicted value of the first type of parameters and the objective function.

[0151] Specifically, the application can calculate the range of charging current by the following formula:

[0152] Imax_1=Map(floor(SOCmax(t)),round(Tmin(t)) (10)

[0153] Imax_2=Map(floor(SOCmax(t)),round(Tmax(t)) (11)

[0154] Imax_3=Imax_max (12)

[0155] Imax=min(Imax_1,Imax_2,Imax_3) (13)

[0156] Where Map represents a table lookup function, Imax_1, Imax_2, and Imax_3 represent the charging currents output by the table lookup function, floor represents a floor function, round represents rounding, Imax_max represents the maximum current in the table and is a constant, SOCmax represents the maximum SOC of a battery cell, and SOCmin represents the minimum SOC of a battery cell. The current boundary is set to Iop∈(0,Imax], and this boundary is used as the current boundary for a preset time in the future.

[0157] Specifically, formula (10) indicates that the SOCmax at time t is rounded down, the Tmin value at time t is rounded up, and then the SOC-temperature-current table is looked up to obtain the queried current Imax_1.

[0158] Formula (11) indicates that the SOCmax value at time t is rounded down, the Tmax value at time t is rounded up, and then the SOC-temperature-current table is consulted to obtain the queried current Imax_2.

[0159] Formula (12) indicates that Imax_3 is equal to the maximum current value in the SOC-temperature-current table.

[0160] This embodiment has the beneficial effect of determining the charging current range based on battery temperature-related parameters and the battery's state of charge (SOC), effectively avoiding safety issues caused by improper charging current. For example, when the battery temperature is high or the SOC is close to full, appropriately limiting the charging current can prevent battery overheating and overcharging, reducing safety risks such as battery fire and explosion, and ensuring battery safety during use.

[0161] In a second aspect, the present application provides a charging control device, such as Figure 4 As shown, the charging control device includes: an acquisition module 41, a prediction module 42 and an optimization module 43, wherein:

[0162] An acquisition module 41 is configured to acquire measured values of first-category parameters at a current sampling point, where the first-category parameters include temperature-related parameters of the battery;

[0163] a prediction module 42 for obtaining predicted values of the first-category parameters based on the measured values of the first-category parameters at the current sampling point, preset second-category parameters, and a pre-established prediction model, wherein the second-category parameters include thermal management control parameters, which are parameters related to the thermal management device;

[0164] The optimization module 43 is used to determine the optimized values of the second type of parameters based on the predicted values of the first type of parameters and a pre-constructed objective function, and send the optimized values to the thermal management device to control the temperature of the battery through the thermal management device. The objective function is constructed based on the charging time determined according to the temperature-related parameters of the battery.

[0165] In some embodiments, the temperature-related parameters of the battery include the maximum temperature value and the minimum temperature value among the temperature values of each battery cell in the battery. The optimization module 43 can determine the charging time based on the predicted value of the maximum temperature of the battery cell and the predicted value of the battery state of charge, determine the target temperature based on the predicted value of the maximum temperature of the battery cell and the predicted value of the minimum temperature, determine the value of the objective function based on the target temperature and the charging time, adjust the value of the second-category parameter, return to the step of inputting the value of the first-category parameter at the current sampling point and the pre-set second-category parameter into the pre-built prediction model to obtain the predicted value of the first-category parameter, and perform multiple iterations until the number of iterations reaches the preset number, and obtain the optimized value of the second-category parameter. The optimized value of the second-category parameter is the value of the second-category parameter when the value of the objective function is minimum.

[0166] In some embodiments, the second-category parameters include parameter values at each sampling point within a preset time, and the predicted values of the first-category parameters include predicted values corresponding to each sampling point. The prediction module 42 can determine the value of the first-category parameter at the next sampling point based on the measured value of the first-category parameter at the current sampling point and the value of the second-category parameter at the current sampling point, until the last sampling point within the preset time, to obtain the predicted values corresponding to each sampling point.

[0167] In some embodiments, the first category of parameters mentioned above also includes the battery state of charge, and the second category of parameters also includes the charging current. The prediction module 42 can determine the battery's heating value based on the charging current corresponding to the current sampling point, determine the battery's thermal management heat exchange based on the charging current, the flow rate and the temperature of the cooling medium corresponding to the current sampling point, determine the value of the battery's temperature-related parameters corresponding to the next sampling point based on the heating value and the thermal management heat exchange, and determine the battery state of charge corresponding to the next sampling point based on the battery state of charge, the charging current and the battery rated power at the current sampling point.

[0168] In some embodiments, the second type of parameters further includes charging current. The optimization module 43 may further control the operation of the charging device according to the optimized value of the charging current to control the charging process of the battery by the charging device.

[0169] In some embodiments, the optimized values of the above-mentioned second-category parameters include optimized values corresponding to each sampling point within a preset time. The optimization module 43 can also query a preset data table based on the value of the first-category parameter at the next sampling point to obtain a calibration current. The preset data table records the mapping relationship between the value of the first-category parameter and the calibration current. The calibration current and the optimized value of the charging current corresponding to the next sampling point are taken as the smaller one to obtain the target control current. The operation of the charging device is controlled according to the target control current to control the charging process of the battery by the charging device.

[0170] In some embodiments, the above-mentioned optimization module 43 can also determine the temperature range of the cooling medium based on the average temperature of each battery cell in the battery and the optimized value of the temperature of the cooling medium at the previous sampling point, wherein the step of determining the optimized value of the temperature of the cooling medium includes: determining the optimized value of the temperature of the cooling medium at the current sampling point based on the temperature range of the cooling medium, the predicted value of the first type of parameter and the objective function.

[0171] In a third aspect, the present application provides a controller comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the charging parameter determination method provided in any embodiment of the present application in the first aspect are implemented.

[0172] In one embodiment, the internal structure of the controller can be as follows: Figure 5As shown. The controller includes a processor, memory, a network interface, and a database connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external terminal via a network connection. When executed by the processor, the computer program implements the charging parameter determination method.

[0173] In a fourth aspect, the present application provides a vehicle comprising the controller of the third aspect.

[0174] In a fifth aspect, the present application provides a charging device comprising the controller of the third aspect.

[0175] In a sixth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the steps of the charging parameter determination method provided in any embodiment of the first aspect of the present application are implemented.

[0176] The computer readable storage medium may be Figure 5 A computer-readable storage medium in the computer device shown.

[0177] Those skilled in the art will understand that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing the relevant hardware through a computer program, and the above-mentioned computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0178] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0179] The above embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that a person skilled in the art could make various modifications and improvements without departing from the spirit of the present application, all of which fall within the scope of protection of the present application. Therefore, the scope of protection of the present patent application shall be determined by the appended claims.

Claims

1. A method for determining charging parameters, characterized in that: The method comprises: Obtaining measured values of first-category parameters at a current sampling point, where the first-category parameters include temperature-related parameters of the battery; Obtaining predicted values of the first-category parameters based on the measured values of the first-category parameters at the current sampling point, pre-set second-category parameters, and a pre-built prediction model, wherein the second-category parameters include thermal management control parameters, which are parameters related to a thermal management device; The optimized values of the second type of parameters are determined based on the predicted values of the first type of parameters and a pre-constructed objective function, and are sent to the thermal management device to control the temperature of the battery through the thermal management device. The objective function is constructed based on the charging time determined according to the temperature-related parameters of the battery.

2. The method according to claim 1, characterized in that The temperature-related parameters of the battery include the highest temperature value and the lowest temperature value among the temperature values of each battery cell in the battery. The first type of parameters also includes the battery state of charge. Determining the optimized values of the second type of parameters based on the predicted values of the first type of parameters and a pre-established objective function includes: Determining the charging time according to the predicted value of the maximum temperature of the battery cell and the predicted value of the battery state of charge; determining a target temperature according to a predicted value of a maximum temperature and a predicted value of a minimum temperature of the battery cell; determining a value of the objective function according to the target temperature and the charging duration; Adjust the value of the second-category parameter, and return to the step of inputting the value of the first-category parameter of the current sampling point and the preset second-category parameter into the pre-built prediction model to obtain the predicted value of the first-category parameter, so as to perform multiple iterations until the number of iterations reaches a preset number, and obtain the optimized value of the second-category parameter, which is the value of the second-category parameter when the value of the objective function is minimum.

3. The method according to claim 1, characterized in that The second-category parameters include parameter values of each sampling point within a preset time, and the predicted values of the first-category parameters include predicted values corresponding to each of the sampling points. The predicted values of the first-category parameters are obtained based on the values of the first-category parameters at the current sampling point, the preset second-category parameters, and the pre-built prediction model, including: According to the measured value of the first type parameter of the current sampling point and the value of the second type parameter of the current sampling point, the value of the first type parameter of the next sampling point is determined until the last sampling point within the preset time, and the predicted value corresponding to each sampling point is obtained.

4. The method according to claim 3, characterized in that The first-category parameters further include a battery state of charge, the second-category parameters further include a charging current, and the thermal management control parameters include a flow rate and a temperature of a cooling medium. Determining the value of the first-category parameter at a next sampling point based on the measured value of the first-category parameter at the current sampling point and the value of the second-category parameter at the current sampling point includes: Determine the heat generated by the battery according to the charging current corresponding to the current sampling point; Determining the thermal management heat exchange of the battery according to the charging current, flow rate and temperature of the cooling medium corresponding to the current sampling point; Determining a value of a temperature-related parameter of the battery corresponding to the next sampling point according to the heat generation and the thermal management heat exchange; The battery state of charge corresponding to the next sampling point is determined according to the battery state of charge, charging current, and battery rated power at the current sampling point.

5. The method according to claim 1, wherein The second type of parameters also includes charging current, and the method further includes: The operation of the charging device is controlled according to the optimized value of the charging current to control the charging process of the battery by the charging device.

6. The method according to claim 5, characterized in that The optimized values of the second type of parameters include optimized values corresponding to each sampling point within a preset time, and controlling the operation of the charging device according to the optimized value of the charging current to control the charging process of the battery by the charging device includes: querying a preset data table according to the value of the first type parameter at the next sampling point to obtain a calibration current, wherein the preset data table records a mapping relationship between the value of the first type parameter and the calibration current; The calibration current and the optimized value of the charging current corresponding to the next sampling point are minimized to obtain the target control current; The charging device is controlled to operate according to the target control current to control the charging process of the battery by the charging device.

7. The method according to claim 4, characterized in that The method further comprises: Determining a temperature range of the cooling medium according to an average temperature of each battery cell in the battery and an optimized value of the temperature of the cooling medium at a previous sampling point; The step of determining the optimized value of the temperature of the cooling medium includes: An optimized value of the temperature of the cooling medium at the current sampling point is determined according to the temperature range of the cooling medium, the predicted value of the first type of parameter, and the objective function.

8. A device for determining charging parameters, characterized in that: The device comprises: an acquisition module, configured to acquire a measured value of a first type of parameter at a current sampling point, wherein the first type of parameter includes a temperature-related parameter of the battery; a prediction module, configured to obtain predicted values of the first-category parameters based on the measured values of the first-category parameters at the current sampling point, preset second-category parameters, and a pre-established prediction model, wherein the second-category parameters include thermal management control parameters, which are parameters related to a thermal management device; an optimization module for determining optimized values of the second type of parameters based on the predicted values of the first type of parameters and a pre-constructed objective function, and sending the optimized values to the thermal management device so as to control the temperature of the battery through the thermal management device, wherein the objective function is constructed based on a charging time determined according to the temperature-related parameters of the battery.

9. A controller comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A vehicle, characterized in that: Comprising the controller as claimed in claim 9.

11. A charging device, characterized in that: Comprising the controller as claimed in claim 9.

12. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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