Battery temperature rise detection method, device and electronic equipment

Abstract

The present invention provides a battery temperature rise detection method, device, and electronic device, which relate to the technical field of charging, including: obtaining charging message data and attribute information of a charging order; matching a first target temperature rise model based on current heat generation and a second target temperature rise model based on charging duration in a preset temperature rise model library based on the attribute information; and detecting the charging order using the first target temperature rise model, the second target temperature rise model, and the charging message data to obtain a battery temperature rise detection result for the charging order. When detecting the battery temperature rise of a charging order, this method specifically provides a first target temperature rise model based on current heat generation and a second target temperature rise model based on charging duration. That is, the detection process takes into account both the temperature change of the battery and the influence of the charging current and charging time on the temperature rise. The increase in the dimension of the detection data can effectively improve the accuracy of the battery temperature rise detection result.

Description

Technical Field

[0001] The present invention relates to the technical field of charging, and in particular to a method, device and electronic equipment for detecting battery temperature rise. Background Art

[0002] During the battery's charge and discharge process, changes in battery temperature are a key area of monitoring from a safety perspective. Existing technical solutions typically detect battery temperature rise based on the maximum battery temperature and the rate of change of the battery temperature. Specifically, they monitor the change in the maximum temperature of the battery pack's individual cells within a predetermined time period. If the change is abnormal, the BMS (Battery Management System) or charging station will stop charging, thereby ensuring the safety of the battery charge and discharge process. However, the above method only determines whether the temperature rise is abnormal based on the battery temperature change, which is a single dimension and makes it difficult to ensure the accuracy of the detection results for complex charging conditions. In other words, the existing battery temperature rise detection method has the technical problem of low accuracy. Summary of the Invention

[0003] The present invention aims to provide a method, device and electronic device for detecting battery temperature rise, so as to improve the accuracy of the battery temperature rise detection result.

[0004] In a first aspect, the present invention provides a method for detecting battery temperature rise, comprising: obtaining charging message data of a charging order and attribute information of the charging order; matching a first target temperature rise model based on current heat generation and a second target temperature rise model based on charging time in a preset temperature rise model library based on the attribute information; and detecting the charging order using the first target temperature rise model, the second target temperature rise model, and the charging message data to obtain a battery temperature rise detection result of the charging order.

[0005] In an optional embodiment, the use of the first target temperature rise model, the second target temperature rise model and the charging message data to detect the charging order to obtain a battery temperature rise detection result of the charging order includes: fitting a first predicted temperature rise curve based on the first target temperature rise model and the charging message data; calculating a first root mean square error and a first mean absolute error of the charging order based on the first predicted temperature rise curve and the actual temperature rise curve of the charging order; when the first root mean square error is higher than the root mean square error threshold of the first target temperature rise model, and / or the first mean absolute error is higher than the mean absolute error threshold of the first target temperature rise model, fitting a second predicted temperature rise curve based on the second target temperature rise model and the charging message data; calculating a second root mean square error and a second mean absolute error of the charging order based on the second predicted temperature rise curve and the actual temperature rise curve; when the second root mean square error is higher than the root mean square error threshold of the second target temperature rise model, and / or the second mean absolute error is higher than the mean absolute error threshold of the second target temperature rise model, determining that the charging order is an order with abnormal battery temperature rise.

[0006] In an optional embodiment, when the first root mean square error is less than or equal to the root mean square error threshold of the first target temperature rise model, and the first mean absolute error is less than or equal to the mean absolute error threshold of the first target temperature rise model, the charging order is determined to be a normal battery temperature rise order.

[0007] In an optional embodiment, when the second root mean square error is less than or equal to the root mean square error threshold of the second target temperature rise model, and the second mean absolute error is less than or equal to the mean absolute error threshold of the second target temperature rise model, the charging order is determined to be a heating order; wherein, the heating order indicates that the battery temperature rise is normal during the charging process, and there is an external heat source around the battery during the charging process.

[0008] In an optional embodiment, the first target temperature rise model is expressed as: Wherein, ΔT(k)=T(k)-T(0), T(k) represents the maximum temperature of the battery at the kth sampling time, T(0) represents the initial charging temperature of the battery, K represents the battery temperature rise rate, ΔT represents the sampling interval, I(i) represents the charging current at time i, and B represents the constant value of the battery heat dissipation performance.

[0009] In an optional embodiment, the second target temperature rise model is expressed as: Y=ax+b, where Y represents the temperature rise value of the battery charging process, a represents the temperature rise parameter, x represents the charging time, and b represents the model constant parameter.

[0010] In an optional embodiment, the charging message data includes: charging current, charging time, and battery temperature; the attribute information includes at least one of the following information: charging city, charging order time, and charging vehicle model.

[0011] In a second aspect, the present invention provides a battery temperature rise detection device, comprising: an acquisition module for acquiring charging message data of a charging order and attribute information of the charging order; a matching module for matching a first target temperature rise model based on current heat generation and a second target temperature rise model based on charging time in a preset temperature rise model library based on the attribute information; a detection module for detecting the charging order using the first target temperature rise model, the second target temperature rise model and the charging message data to obtain a battery temperature rise detection result of the charging order.

[0012] In a third aspect, the present invention provides an electronic device comprising a memory and a processor, wherein the memory stores a computer program that can be run on the processor, and when the processor executes the computer program, the steps of the method described in any one of the aforementioned embodiments are implemented.

[0013] In a fourth aspect, the present invention provides a computer-readable medium having a non-volatile program code executable by a processor, wherein the program code enables the processor to execute the method described in any one of the aforementioned embodiments.

[0014] The battery temperature rise detection method provided by the present invention includes: obtaining charging message data and attribute information of a charging order; matching a first target temperature rise model based on current heat generation and a second target temperature rise model based on charging duration in a preset temperature rise model library based on the attribute information; and detecting the charging order using the first target temperature rise model, the second target temperature rise model, and the charging message data to obtain a battery temperature rise detection result for the charging order.

[0015] The battery temperature rise detection method provided by the present invention specifically provides a first target temperature rise model based on current heat generation and a second target temperature rise model based on charging time when detecting the battery temperature rise of a charging order. That is, the detection process takes into account both the temperature change of the battery and the influence of the charging current and charging time on the temperature rise. The increase in the dimension of the detection data can effectively improve the accuracy of the battery temperature rise detection results. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the specific embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0017] Figure 1 A flow chart of a method for detecting battery temperature rise provided by an embodiment of the present invention;

[0018] Figure 2 A flowchart of an embodiment of the present invention provides a method for detecting a charging order using a first target temperature rise model, a second target temperature rise model, and charging message data to obtain a battery temperature rise detection result for the charging order;

[0019] Figure 3 A schematic diagram of a first predicted temperature rise curve and an actual temperature rise curve provided in an embodiment of the present invention;

[0020] Figure 4 A schematic diagram of another first predicted temperature rise curve and an actual temperature rise curve provided by an embodiment of the present invention;

[0021] Figure 5 A schematic diagram of another first predicted temperature rise curve and an actual temperature rise curve provided by an embodiment of the present invention;

[0022] Figure 6 A schematic diagram of the distribution of RMSE and MAE provided in an embodiment of the present invention;

[0023] Figure 7 A functional module diagram of a battery temperature rise detection device provided by an embodiment of the present invention;

[0024] Figure 8 A schematic diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0025] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions of the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings herein can be arranged and designed in various different configurations.

[0026] Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the invention as claimed, but rather merely represents selected embodiments of the present invention. All other embodiments derived by persons of ordinary skill in the art based on the embodiments of the present invention without creative effort shall fall within the scope of protection of the present invention.

[0027] The following embodiments of the present invention are described in detail with reference to the accompanying drawings. In the absence of conflict, the following embodiments and features in the embodiments may be combined with each other.

[0028] During the battery charging and discharging process, the change in battery temperature is the key monitoring object from a safety perspective. The existing technical solutions usually detect the battery temperature rise based on the maximum battery temperature and the rate of change of the battery temperature. Specifically, the change value of the maximum temperature of the battery pack single cell within a predetermined time period is monitored. If the change value is abnormal, the BMS (Battery Management System) or the charging pile will stop charging, thereby ensuring the safety of the battery charging and discharging process. However, the above method only determines whether the temperature rise is abnormal based on the change in battery temperature. The dimension is single and it is difficult to ensure the accuracy of the detection results of complex charging conditions. In addition, the change in battery temperature is simply looked at, and the influence of the external environment on the current battery is not considered. Moreover, the above method cannot identify the phenomenon of abnormal slow increase in battery temperature. Therefore, the existing battery temperature rise abnormality detection method has the technical problem of low accuracy. In view of this, the embodiment of the present invention provides a battery temperature rise detection method to alleviate the technical problems raised above.

[0029] Example 1

[0030] Figure 1 A flow chart of a method for detecting battery temperature rise provided by an embodiment of the present invention is shown in FIG. Figure 1 As shown, the method specifically includes the following steps:

[0031] Step S102: Acquire charging message data and attribute information of the charging order.

[0032] In the prior art, when detecting battery temperature rise, the change in battery temperature within a preset time period is generally considered. However, the above detection method cannot identify abnormalities caused by slow abnormal temperature increases, which leads to a decrease in the accuracy of the detection results. Compared with the prior art, the battery temperature rise detection method provided in the embodiment of the present invention uses the charging message data generated during the entire charging process, that is, the charging message data generated from the start of charging to the moment of detection. Therefore, the method of the present invention can detect battery temperature rise anomalies in charging orders in real time, promptly identifying charging anomalies so that users can quickly respond to abnormal situations.

[0033] In the embodiments of the present invention, battery temperature specifically refers to the maximum temperature of a battery cell. Therefore, battery temperature rise refers to the change in the maximum battery temperature during charging. For example, if the maximum temperature of a battery cell is 47°C at the 20th minute of charging and rises to 48°C at the 21st minute of charging, the battery temperature rise over this one-minute period is expressed as 48-47 = 1°C. In the above example, the battery temperature rise monitoring period is 1 minute. Users can also set the monitoring period duration based on actual needs, and this is not specifically limited in the embodiments of the present invention.

[0034] In an embodiment of the present invention, the charging message data includes: charging current, charging time and battery temperature. That is, the charging current and battery temperature at each charging moment are obtained. In addition, while obtaining the charging message data, it is also necessary to obtain the attribute information of the charging order, wherein the attribute information includes at least one of the following information: charging city, charging order time and charging model. The charging city can be understood as the city where the charging order is located, and the charging order time can be understood as the time when the charging order occurs. For example, from 10:00 to 14:00 on October 1, 2021 (charging order time), a Volvo XC60 (charging model) was charged in Beijing (charging city). The acquisition of attribute information is to more accurately match the temperature rise model for detecting the battery temperature rise in the following steps.

[0035] Step S104 : matching a first target temperature rise model based on current heat generation and a second target temperature rise model based on charging duration in a preset temperature rise model library based on the attribute information.

[0036] In this embodiment of the present invention, the preset model library contains two types of temperature-rise models: a temperature-rise model based on current heat generation and a temperature-rise model based on charging duration. Each type of temperature-rise model is differentiated based on attribute information. Specifically, the temperature-rise models are subdivided based on one or more of the following information: the charging city, the time of the charging order, and the vehicle type. After obtaining the attribute information of a charging order, the preset temperature-rise model library is searched for a matching first target temperature-rise model based on current heat generation and a second target temperature-rise model based on charging duration.

[0037] Assuming that each type of temperature rise model in the preset model library is differentiated according to the three dimensions of charging city, charging order time and charging vehicle model, the training process of any temperature rise model in the preset model library (either a temperature rise model based on current heat generation or a temperature rise model based on charging time) should be: first, based on massive charging data, the training set and test set are split according to the specified charging city, charging order time and charging vehicle model; then the initial temperature rise model is trained using the training set, and the trained model is verified using the test set. After determining that the error meets the preset requirements (for example, the error of the test set is less than 3%), the model training is completed and the target temperature rise model is obtained.

[0038] Step S106 , detecting the charging order using the first target temperature rise model, the second target temperature rise model, and the charging message data to obtain a battery temperature rise detection result of the charging order.

[0039] Given that the first target temperature rise model is a temperature rise model based on current heat generation, and the second target temperature rise model is a temperature rise model based on charging time, if the above two models are used to detect the charging message data, it is possible to comprehensively consider the impact of charging current and charging time on battery temperature changes, and comprehensively judge whether there is an abnormal battery temperature rise in the charging order from multiple dimensions, thereby ensuring the accuracy of the battery temperature rise detection results.

[0040] The battery temperature rise detection method provided by the present invention includes: obtaining charging message data and attribute information of a charging order; matching a first target temperature rise model based on current heat generation and a second target temperature rise model based on charging duration in a preset temperature rise model library based on the attribute information; and detecting the charging order using the first target temperature rise model, the second target temperature rise model, and the charging message data to obtain a battery temperature rise detection result for the charging order.

[0041] The battery temperature rise detection method provided by the present invention specifically provides a first target temperature rise model based on current heat generation and a second target temperature rise model based on charging time when detecting the battery temperature rise of a charging order. That is, the detection process takes into account both the temperature change of the battery and the influence of the charging current and charging time on the temperature rise. The increase in the dimension of the detection data can effectively improve the accuracy of the battery temperature rise detection results.

[0042] In an optional embodiment, if Figure 2 As shown, the above step S106, using the first target temperature rise model, the second target temperature rise model and the charging message data to detect the charging order, to obtain the battery temperature rise detection result of the charging order, specifically includes the following steps:

[0043] Step S1061: Fitting a first predicted temperature rise curve based on a first target temperature rise model and charging message data.

[0044] In an embodiment of the present invention, to determine whether the temperature rise of the battery in the current charging order is abnormal, a first predicted temperature rise curve is first fitted using a first target temperature rise model and the charging message data. Optionally, the curve fitting result can be determined based on a robust least squares method. Given that the first target temperature rise model is a temperature rise model based on current heat generation, the first predicted temperature rise curve is determined based on the charging current in the charging message data combined with the model parameters of the first target temperature rise model. In other words, the current-temperature rise curve of the current charging order can be predicted using the first target temperature rise model.

[0045] Step S1062 : Calculate a first root mean square error and a first mean absolute error of the charging order based on the first predicted temperature rise curve and the actual temperature rise curve of the charging order.

[0046] When the first root mean square error is higher than the root mean square error threshold of the first target temperature rise model, and / or the first mean absolute error is higher than the mean absolute error threshold of the first target temperature rise model, execute the following step S1063; when the first root mean square error is less than or equal to the root mean square error threshold of the first target temperature rise model, and the first mean absolute error is less than or equal to the mean absolute error threshold of the first target temperature rise model, execute the following step S1066.

[0047] Specifically, since the root mean square error can detect irregular charging methods, the mean absolute error combined with robust least squares can detect curves with temperature jumps. These two indicators can detect the smoothness of the curve. Therefore, after obtaining the first predicted temperature rise curve, combined with the actual temperature rise curve determined by the battery temperature data in the charging order, the first root mean square error RMSE1 and the first mean absolute error MAE1 of the charging order are calculated, and then RMSE1 is compared with the root mean square error threshold RMSE of the first target temperature rise model. r1 Compare MAE1 with the mean absolute error threshold MAE of the first target temperature rise model r1 Compare, if RMSE1>RMSE r1 , and / or, MAE1>MAE r1 , it means that the current-temperature rise trend of the charging order does not conform to the conventional battery temperature rise law. Therefore, it is necessary to further use the second target temperature rise model based on the charging time to verify whether there is any abnormal battery temperature rise in the charging order; but if RMSE1≤RMSE r1 And MAE1≤MAE r1 , it means that the current-temperature rise trend of the charging order conforms to the conventional battery temperature rise law, and it is a normal battery temperature rise order. Because vehicles of the same type, in the same period, in the same area, should have similar temperature rise coefficients, if there is a deviation, it means that the battery temperature rises too quickly.

[0048] Figure 3 A schematic diagram of a first predicted temperature rise curve and an actual temperature rise curve provided in an embodiment of the present invention, Figure 3 In the figure, the continuous curve is the first predicted temperature rise curve, and the discrete points are the actual charging data for constructing the actual temperature rise curve. Figure 3 The difference between the first predicted temperature rise curve and the actual temperature rise curve in the example shows that the current-temperature rise trend of this charging order conforms to the conventional battery temperature rise law and is a normal battery temperature rise order.

[0049] Figure 4 and Figure 5 Both are schematic diagrams of another first predicted temperature rise curve and actual temperature rise curve provided by embodiments of the present invention. Figure 4 and Figure 5 In the figure, the continuous curve is the first predicted temperature rise curve, and the discrete points are the actual charging data for constructing the actual temperature rise curve. Figure 4 and Figure 5 The difference between the first predicted temperature rise curve and the actual temperature rise curve in the example shows that the current-temperature rise trends of the two charging orders do not conform to the conventional battery temperature rise law and need to be further verified using the second target temperature rise model.

[0050] In this embodiment of the present invention, the root mean square error (RMSE) and mean absolute error (MAE) thresholds for the first target temperature rise model are determined using a large amount of charging order data in conjunction with statistical methods. Specifically, after training the first target temperature rise model, the corresponding RMSE and MAE are calculated for each charging order. By combining the RMSE and MAE of a large number of charging orders, the root mean square error (RMSE) and mean absolute error (MAE) thresholds for the first target temperature rise model can be determined based on actual needs.

[0051] Figure 6 A schematic diagram of the distribution of RMSE and MAE provided in an embodiment of the present invention is shown in FIG. Figure 6 From the data distribution, it can be determined that most of the orders of this model have RMSE < 2.5 and MAE < 7. Therefore, the root mean square error threshold RMSE of the first target temperature rise model can be r1 Set to 2.5, the mean absolute error threshold MAE r1 Set to 7. The embodiment of the present invention does not specifically limit the method for determining the root mean square error threshold and the mean absolute error threshold, and the user can set them according to actual needs.

[0052] Step S1063: Fitting a second predicted temperature rise curve based on the second target temperature rise model and the charging message data.

[0053] If RMSE1>RMSE r1 , and / or, MAE1>MAE r1 , the second target temperature rise model and the charging message data are further used to fit the second predicted temperature rise curve. Given that the second target temperature rise model is a temperature rise model based on the charging duration, the second predicted temperature rise curve is determined based on the charging time in the charging message data combined with the model parameters of the second target temperature rise model. That is, the second target temperature rise model can be used to predict the charging duration-temperature rise curve of the current charging order.

[0054] Step S1064 , calculating a second root mean square error and a second mean absolute error of the charging order based on the second predicted temperature rise curve and the actual temperature rise curve.

[0055] When the second root mean square error is higher than the root mean square error threshold of the second target temperature rise model, and / or the second mean absolute error is higher than the mean absolute error threshold of the second target temperature rise model, execute the following step S1065; when the second root mean square error is less than or equal to the root mean square error threshold of the second target temperature rise model, and the second mean absolute error is less than or equal to the mean absolute error threshold of the second target temperature rise model, execute the following step S1067.

[0056] Step S1065 , determining that the charging order is an order with abnormal battery temperature rise.

[0057] Step S1066: Determine that the charging order is a normal battery temperature rise order.

[0058] Step S1067: Determine whether the charging order is a heating order.

[0059] Similarly, after obtaining the second predicted temperature rise curve, the second root mean square error RMSE2 and the second mean absolute error MAE2 of the charging order can be calculated in combination with the actual temperature rise curve, and then RMSE2 is compared with the root mean square error threshold RMSE of the second target temperature rise model. r2 Compare MAE2 with the mean absolute error threshold MAE of the second target temperature rise model r2 Compare, if RMSE2>RMSE r2 , and / or, MAE2>MAE r2 , it means that the charging time-temperature rise trend of the charging order does not conform to the normal battery temperature rise law, and the charging order is determined to be an abnormal battery temperature rise order; but if RMSE2≤RMSE r2 And MAE2≤MAE r2 , it means that the charging time-temperature rise trend of the charging order conforms to the conventional battery temperature rise law, and the charging order is determined to be a heating order. Among them, a heating order means that the battery temperature rise is normal during the charging process, and there is an external heat source around the battery during the charging process.

[0060] The heating order does not belong to the abnormal temperature rise order, but is caused by the interference of external heat sources, which causes the battery temperature to rise too quickly. However, the second target temperature rise model can be used to identify the heating order, reducing the misjudgment rate of the detection results.

[0061] According to the above introduction, Figure 4 and Figure 5 The orders that are judged by the first target temperature rise model as not conforming to the normal battery temperature rise law, but after verification by the second target temperature rise model, Figure 4 The heating order provided is identified, i.e. Figure 4 The temperature rise curve belongs to the heating order, Figure 5 The temperature rise curve belongs to the abnormal battery temperature rise order (there is a temperature jump point).

[0062] The method for determining the root mean square error threshold and the mean absolute error threshold of the first target temperature rise model has been described above. The above two indicators of the second target temperature rise model are similar and will not be described again here.

[0063] In an optional embodiment, the first target temperature rise model is expressed as: Wherein, ΔT(k)=T(k)-T(0), T(k) represents the maximum temperature of the battery at the kth sampling time, T(0) represents the initial charging temperature of the battery, K represents the battery temperature rise rate, ΔT represents the sampling interval, I(i) represents the charging current at time i, and B represents the constant value of the battery heat dissipation performance.

[0064] Battery pack temperature fluctuations are primarily influenced by internal factors such as the battery pack's heat dissipation characteristics, the internal resistance of the battery cells and connectors, the performance of the battery pack's heat dissipation system, and ambient temperature, as well as external factors such as battery charging current, user charging habits, and ambient temperature. Therefore, abnormal battery pack temperature fluctuations require research into the battery's temperature rise rate and heat dissipation rate under varying ambient temperatures, heat dissipation conditions, and user habits.

[0065] According to Joule's law: the battery temperature rise is proportional to the battery's net heat generation power, and the battery's heat generation power is proportional to the square of the battery's input current. Therefore, the battery temperature change law can be expressed by the following formula (1): In formula (1), T(k) represents the maximum temperature of the battery at the kth sampling time, T(0) represents the initial charging temperature of the battery, and Q gen (k) represents the total heat generated by the battery from time 0 to time k, Q diff (k) represents the total heat dissipation of the battery from time 0 to time k, then Q gen (k)-Q diff (k) represents the net heat generated by the battery pack from time 0 to k, which is proportional to the change in battery temperature. The most important part of the total heat generated by the battery is the heat loss on the battery pack resistance, which can be expressed as R is the internal resistance of the battery pack; and Q diff (k) It is difficult to model. The main reason is that the battery heat dissipation power is mainly affected by the heat dissipation medium used by the battery pack, the ambient temperature, and whether the battery pack has a cooling system turned on. Therefore, based on experience, charging orders can be diverted according to the ambient temperature and whether the battery pack has a cooling system turned on.

[0066] In this case, the battery heat dissipation power can be considered to be a constant value. Therefore, based on Joule's law, the battery thermal dynamic behavior can be fitted by formula (2) to establish a temperature rise model based on current heat generation: Formula (2) can be used to analyze the temperature rise rate and heat dissipation performance index corresponding to each charging order. At the same time, according to the maximum deviation error of the fitting result, the abnormal points in the order can be analyzed.

[0067] In one optional embodiment, the second target temperature rise model is expressed as: Y = ax + b, where Y represents the temperature rise value during the battery charging process, a represents the temperature rise parameter, x represents the charging duration, and b represents the model constant parameter. That is, the temperature rise value during the battery charging process is linearly related to the charging duration, with the charging duration x starting at 0. The temperature rise value Y during the battery charging process represents the difference between the battery temperature after charging for x time period and the initial charging temperature. Both a and b are parameters that can be determined after model training.

[0068] In summary, the battery temperature rise detection method provided in the embodiment of the present invention takes into account the temperature changes of the battery during the detection process, and also comprehensively considers the effects of the charging current and charging time on the temperature rise, and comprehensively judges from multiple dimensions whether there is an abnormal battery temperature rise in the charging order, thereby ensuring the accuracy of the battery temperature rise detection results; further, compared with the prior art, the battery temperature rise detection method provided in the embodiment of the present invention uses the charging message data generated by the entire charging process when detecting the battery temperature rise, and can also identify the phenomenon of a slow and abnormal increase in battery temperature, thereby effectively improving the accuracy of the battery temperature rise detection results.

[0069] Example 2

[0070] An embodiment of the present invention further provides a battery temperature rise detection device, which is mainly used to execute the battery temperature rise detection method provided in the above-mentioned embodiment 1. The battery temperature rise detection device provided in the embodiment of the present invention is specifically introduced below.

[0071] Figure 7 This is a functional module diagram of a battery temperature rise detection device provided by an embodiment of the present invention. Figure 7 As shown, the device mainly includes: an acquisition module 10, a matching module 20, and a detection module 30, wherein:

[0072] The acquisition module 10 is used to acquire the charging message data of the charging order and the attribute information of the charging order.

[0073] The matching module 20 is configured to match a first target temperature rise model based on current heat generation and a second target temperature rise model based on charging duration in a preset temperature rise model library based on the attribute information.

[0074] The detection module 30 is used to detect the charging order using the first target temperature rise model, the second target temperature rise model and the charging message data to obtain a battery temperature rise detection result of the charging order.

[0075] The battery temperature rise detection device provided by the present invention includes: an acquisition module 10, which is used to obtain charging message data and attribute information of a charging order; a matching module 20, which is used to match a first target temperature rise model based on current heat generation and a second target temperature rise model based on charging duration in a preset temperature rise model library based on the attribute information; and a detection module 30, which is used to detect the charging order using the first target temperature rise model, the second target temperature rise model and the charging message data to obtain a battery temperature rise detection result of the charging order.

[0076] The battery temperature rise detection device provided by the present invention, when detecting the battery temperature rise of a charging order, specifically provides a first target temperature rise model based on current heat generation and a second target temperature rise model based on charging time. That is, the detection process takes into account both the temperature change of the battery and the influence of the charging current and charging time on the temperature rise. The increase in the dimension of the detection data can effectively improve the accuracy of the battery temperature rise detection results.

[0077] Optionally, the detection module includes:

[0078] The first fitting unit is configured to fit a first predicted temperature rise curve based on the first target temperature rise model and the charging message data.

[0079] The first calculation unit is configured to calculate a first root mean square error and a first mean absolute error of the charging order based on the first predicted temperature rise curve and the actual temperature rise curve of the charging order.

[0080] The second fitting unit is used to fit a second predicted temperature rise curve based on the second target temperature rise model and the charging message data when the first root mean square error is higher than the root mean square error threshold of the first target temperature rise model and / or the first mean absolute error is higher than the mean absolute error threshold of the first target temperature rise model.

[0081] The second calculation unit is used to calculate a second root mean square error and a second mean absolute error of the charging order based on the second predicted temperature rise curve and the actual temperature rise curve.

[0082] The first determination unit is used to determine that the charging order is an abnormal battery temperature rise order when the second root mean square error is higher than the root mean square error threshold of the second target temperature rise model and / or the second mean absolute error is higher than the mean absolute error threshold of the second target temperature rise model.

[0083] Optionally, the detection module further includes:

[0084] The second determination unit is used to determine that the charging order is a normal battery temperature rise order when the first root mean square error is less than or equal to the root mean square error threshold of the first target temperature rise model, and the first mean absolute error is less than or equal to the mean absolute error threshold of the first target temperature rise model.

[0085] Optionally, the detection module further includes:

[0086] The third determination unit is used to determine that the charging order is a heating order when the second root mean square error is less than or equal to the root mean square error threshold of the second target temperature rise model, and the second mean absolute error is less than or equal to the mean absolute error threshold of the second target temperature rise model; wherein, the heating order indicates that the battery temperature rise is normal during the charging process and there is an external heat source around the battery during the charging process.

[0087] Optionally, the first target temperature rise model is expressed as: Wherein, ΔT(k)=T(k)-T(0), T(k) represents the maximum temperature of the battery at the kth sampling time, T(0) represents the initial charging temperature of the battery, K represents the battery temperature rise rate, ΔT represents the sampling interval, I(i) represents the charging current at time i, and B represents the constant value of the battery heat dissipation performance.

[0088] Optionally, the second target temperature rise model is expressed as: Y=ax+b, where Y represents the temperature rise value of the battery charging process, a represents the temperature rise parameter, x represents the charging time, and b represents the model constant parameter.

[0089] Optionally, the charging message data includes: charging current, charging time and battery temperature; the attribute information includes at least one of the following information: charging city, charging order time and charging vehicle model.

[0090] Example 3

[0091] See also Figure 8 An embodiment of the present invention provides an electronic device, which includes: a processor 60, a memory 61, a bus 62 and a communication interface 63, wherein the processor 60, the communication interface 63 and the memory 61 are connected via the bus 62; the processor 60 is used to execute an executable module stored in the memory 61, such as a computer program.

[0092] The memory 61 may include high-speed random access memory (RAM) and may also include non-volatile memory, such as at least one disk storage. The system network element communicates with at least one other network element via at least one communication interface 63 (which may be wired or wireless), and may utilize the Internet, a wide area network, a local area network, a metropolitan area network, or the like.

[0093] The bus 62 may be an ISA bus, a PCI bus, or an EISA bus. The bus may be divided into an address bus, a data bus, a control bus, and the like. For ease of representation, Figure 8Only one bidirectional arrow is used in the diagram, but this does not mean that there is only one bus or one type of bus.

[0094] Among them, the memory 61 is used to store programs, and the processor 60 executes the program after receiving the execution instruction. The method executed by the device for flow process definition disclosed in any embodiment of the above-mentioned embodiment of the present invention can be applied to the processor 60 or implemented by the processor 60.

[0095] The processor 60 may be an integrated circuit chip with signal processing capabilities. During implementation, the steps of the above method may be performed by hardware integrated logic circuits or software instructions within the processor 60. The processor 60 may be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it may also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It may implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of the present invention. The general-purpose processor may be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of the present invention may be directly executed by a hardware decoding processor or by a combination of hardware and software modules within the decoding processor. The software modules may be located in storage media well-known in the art, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or the like. The storage medium is located in the memory 61 , and the processor 60 reads the information in the memory 61 and completes the steps of the above method in combination with its hardware.

[0096] The embodiments of the present invention provide a battery temperature rise detection method, device, and computer program product for an electronic device, including a computer-readable storage medium storing non-volatile program code executable by a processor. The program code includes instructions that can be used to execute the methods described in the previous method embodiments. For specific implementation, please refer to the method embodiments and will not be repeated here.

[0097] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0098] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a non-volatile computer-readable storage medium that is executable by a processor. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0099] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, it does not need to be further defined or explained in subsequent drawings.

[0100] In the description of the present invention, it should be noted that the terms "center," "upper," "lower," "left," "right," "vertical," "horizontal," "inner," and "outer" and the like indicate positions or locations based on the positions shown in the accompanying drawings, or the positions or locations in which the inventive product is typically placed when in use. These terms are intended solely to facilitate the description of the present invention and to simplify the description, and are not intended to indicate or imply that the devices or components referred to must have a specific orientation, be constructed, or operate in a specific orientation. Therefore, they should not be construed as limitations on the present invention. Furthermore, the terms "first," "second," and "third," etc., are used solely to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0101] Furthermore, terms such as "horizontal," "vertical," and "overhanging" do not necessarily imply that a component must be absolutely horizontal or overhanging, but rather that it can be slightly tilted. For example, "horizontal" simply means that its direction is more horizontal than "vertical," and does not mean that the structure must be completely horizontal, but rather that it can be slightly tilted.

[0102] In the description of the present invention, it should also be noted that, unless otherwise expressly specified or limited, the terms "disposed," "installed," "connected," and "connected" should be understood in a broad sense. For example, they may refer to fixed connections, detachable connections, or integral connections; they may refer to mechanical connections or electrical connections; they may refer to direct connections or indirect connections through an intermediate medium; and they may refer to internal communication between two components. Those skilled in the art will understand the specific meanings of the above terms in the present invention based on the specific circumstances.

[0103] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for detecting battery temperature rise, characterized in that: include: Obtaining charging message data of a charging order and attribute information of the charging order; The attribute information includes at least one of the following information: charging city, charging order time, and charging vehicle type; Matching a first target temperature rise model based on current heat generation and a second target temperature rise model based on charging duration in a preset temperature rise model library based on the attribute information; The charging order is detected using the first target temperature rise model, the second target temperature rise model and the charging message data to obtain a battery temperature rise detection result of the charging order; wherein, the battery temperature rise detection result of the charging order includes one of the following: the charging order is an order with abnormal battery temperature rise, the charging order is an order with normal battery temperature rise, and the charging order is a heating order; the heating order indicates that the battery temperature rise is normal during the charging process and there is an external heat source around the battery during the charging process.

2. The method according to claim 1, characterized in that The detecting the charging order by using the first target temperature rise model, the second target temperature rise model, and the charging message data to obtain a battery temperature rise detection result of the charging order includes: Fitting a first predicted temperature rise curve based on the first target temperature rise model and the charging message data; Calculating a first root mean square error and a first mean absolute error of the charging order based on the first predicted temperature rise curve and the actual temperature rise curve of the charging order; Fitting a second predicted temperature rise curve based on the second target temperature rise model and the charging message data when the first root mean square error is higher than a root mean square error threshold of the first target temperature rise model and / or the first mean absolute error is higher than a mean absolute error threshold of the first target temperature rise model; Calculating a second root mean square error and a second mean absolute error of the charging order based on the second predicted temperature rise curve and the actual temperature rise curve; When the second root mean square error is higher than a root mean square error threshold of the second target temperature rise model, and / or the second mean absolute error is higher than a mean absolute error threshold of the second target temperature rise model, the charging order is determined to be an order with abnormal battery temperature rise.

3. The method according to claim 2, characterized in that When the first root mean square error is less than or equal to a root mean square error threshold of the first target temperature rise model, and the first mean absolute error is less than or equal to a mean absolute error threshold of the first target temperature rise model, the charging order is determined to be a normal battery temperature rise order.

4. The method according to claim 2, characterized in that When the second root mean square error is less than or equal to a root mean square error threshold of the second target temperature rise model, and the second mean absolute error is less than or equal to a mean absolute error threshold of the second target temperature rise model, the charging order is determined to be a heating order.

5. The method according to claim 1, characterized in that The first target temperature rise model is expressed as: Wherein, ΔT(k)=T(k)-T(0), T(k) represents the maximum temperature of the battery at the kth sampling time, T(0) represents the initial charging temperature of the battery, K represents the battery temperature rise rate, ΔT represents the sampling interval, I(i) represents the charging current at time i, and B represents the constant value of the battery heat dissipation performance.

6. The method according to claim 1, wherein The second target temperature rise model is expressed as: Y=ax+b, where Y represents the temperature rise value during the battery charging process, a represents the temperature rise parameter, x represents the charging time, and b represents the model constant parameter.

7. The method according to claim 1, characterized in that The charging message data includes: charging current, charging time and battery temperature.

8. A battery temperature rise detection device, characterized in that: include: An acquisition module, configured to acquire charging message data of a charging order and attribute information of the charging order; The attribute information includes at least one of the following information: charging city, charging order time, and charging vehicle type; a matching module, configured to match a first target temperature rise model based on current heat generation and a second target temperature rise model based on charging duration in a preset temperature rise model library based on the attribute information; A detection module is used to detect the charging order using the first target temperature rise model, the second target temperature rise model and the charging message data to obtain a battery temperature rise detection result of the charging order; wherein the battery temperature rise detection result of the charging order includes one of the following: the charging order is an order with abnormal battery temperature rise, the charging order is an order with normal battery temperature rise, and the charging order is a heating order; the heating order indicates that the battery temperature rise is normal during the charging process and there is an external heat source around the battery during the charging process.

9. An electronic device comprising a memory and a processor, wherein the memory stores a computer program that can be run 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 computer-readable medium having a non-volatile program code executable by a processor, characterized in that The program code causes the processor to execute the method of any one of claims 1 to 7.

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