Temperature control method for power battery pack of new energy automobile

Abstract

The invention relates to the technical field of new energy vehicles, and particularly discloses a new energy vehicle power battery pack temperature control method, which comprises the following steps: determining the current temperature abnormal degree of a battery pack based on the current working condition record of a new energy vehicle, a dynamic battery pack temperature model under each working condition and the actual temperature record data of the battery pack; when the current temperature abnormal degree of the battery pack exceeds an abnormal degree threshold value, evaluating a current aging factor of the battery pack, determining a first battery pack temperature influence coefficient based on the current aging factor of the battery pack, and determining a second battery pack temperature influence coefficient based on the current environment temperature; the current temperature abnormal degree of the battery pack is corrected based on the first battery pack temperature influence coefficient and the second battery pack temperature influence coefficient, a liquid cooling strategy of the battery pack is determined, and liquid cooling equipment is started to cool the battery pack; the cooling operation can be accurately regulated and controlled according to the actual abnormal temperature condition, and the temperature of the battery pack is effectively controlled.

Description

Technical Field

[0001] The present invention relates to the technical field of new energy vehicles, and particularly relates to a temperature control method for a power battery pack of a new energy vehicle. Background Art

[0002] With the enhancement of environmental awareness and the transformation of the energy structure, new energy vehicles have developed rapidly. The power battery pack, as the core component of new energy vehicles, directly affects the overall performance of the vehicle in terms of its performance and lifespan. Temperature is a key factor affecting the performance and lifespan of the power battery pack. Excessive or too low temperature will lead to a decrease in the charge-discharge efficiency of the battery pack, an accelerated decline in capacity, and even pose safety problems. Therefore, an efficient temperature control method for the power battery pack of new energy vehicles is of crucial importance. It can ensure that the battery pack operates within an appropriate temperature range, improve the charge-discharge performance of the battery, extend the battery lifespan, and thus enhance the driving range and safety of new energy vehicles. This not only helps to enhance the competitiveness of new energy vehicles in the market but also conforms to the concept of sustainable development and is of great significance for promoting the healthy development of the new energy vehicle industry. With the continuous expansion of the new energy vehicle market, advanced temperature control technologies will surely have a broader application prospect and provide strong support for the prosperity of the new energy vehicle industry.

[0003] However, there are some drawbacks in the existing temperature control technologies for the power battery packs of new energy vehicles. It is impossible to accurately judge the degree of temperature abnormality of the battery pack by integrating multi-source data; at the same time, when the temperature abnormality exceeds the threshold, the degree of temperature abnormality cannot be corrected by combining the battery aging factor and the environmental temperature, so an accurate and effective liquid cooling cooling strategy cannot be formulated, which will affect the performance and lifespan of the battery pack and threaten the safe and stable operation of new energy vehicles.

[0004] Therefore, the present invention proposes a temperature control method for a power battery pack of a new energy vehicle. Summary of the Invention

[0005] The present invention provides a method for controlling the temperature of a power battery pack of a new energy vehicle. By determining the current temperature abnormality degree of the battery pack based on the current working condition record of the new energy vehicle, the dynamic battery pack temperature model, and the actual temperature record data, it can comprehensively consider various key factors, accurately identify the temperature abnormality condition of the battery pack, and provide an accurate basis for subsequent taking reasonable measures. When the temperature abnormality degree exceeds the threshold, the aging factor is evaluated based on the usage record of the battery pack, and then the first battery pack temperature influence coefficient is determined. At the same time, the second battery pack temperature influence coefficient is determined in combination with the current ambient temperature, comprehensively considering the influence of battery self-aging and environmental factors on temperature, making the control strategy more scientific and targeted. Using these two influence coefficients to correct the temperature abnormality degree, a corrected temperature abnormality degree that is more in line with the actual situation is obtained, effectively improving the accuracy of temperature abnormality judgment and avoiding deviations caused by single-factor judgment. Based on the corrected temperature abnormality degree, a liquid cooling cooling strategy is determined and the liquid cooling device is started, and the cooling operation of the battery pack can be accurately adjusted according to the actual temperature abnormality condition, not only ensuring that the temperature of the battery pack is effectively controlled, but also avoiding energy waste and equipment loss caused by excessive cooling, protecting the performance and life of the battery pack, and at the same time improving the overall operation efficiency and safety of the new energy vehicle.

[0006] The present invention provides a method for controlling the temperature of a power battery pack of a new energy vehicle, including:

[0007] S1: Based on the current working condition record of the new energy vehicle, the dynamic battery pack temperature model under each working condition, and the actual temperature record data of the battery pack, determine the current temperature abnormality degree of the battery pack;

[0008] S2: When the current temperature abnormality degree of the battery pack exceeds the abnormality degree threshold, evaluate the current aging factor of the battery pack based on the usage record of the battery pack of the new energy vehicle, and determine the first battery pack temperature influence coefficient based on the current aging factor of the battery pack. At the same time, determine the second battery pack temperature influence coefficient based on the current ambient temperature;

[0009] S3: Based on the first battery pack temperature influence coefficient and the second battery pack temperature influence coefficient, correct the current temperature abnormality degree of the battery pack to obtain the corrected temperature abnormality degree of the battery pack;

[0010] S4: Based on the corrected temperature abnormality degree of the battery pack, determine the liquid cooling cooling strategy of the battery pack, and start the liquid cooling device to perform a cooling operation on the battery pack based on the liquid cooling cooling strategy of the battery pack.

[0011] Preferably, S1: Based on the current working condition record of the new energy vehicle, the dynamic battery pack temperature model under each working condition, and the actual temperature record data of the battery pack, determine the current temperature abnormality degree of the battery pack, including:

[0012] Determine the sequence of operating condition types experienced by the new energy vehicle during the current complete continuous working period and the duration of each operating condition type based on the current operating condition record of the new energy vehicle;

[0013] Substitute the sequence of operating condition types experienced by the new energy vehicle during the current complete continuous working period and the duration of each operating condition type into the dynamic battery pack temperature models under all operating conditions to determine the ideal temperature record data of the battery pack during the current complete continuous working period;

[0014] Based on part of the actual temperature record data of the battery pack during the current complete continuous working period in the actual temperature record data of the battery pack and the ideal temperature record data of the battery pack during the current complete continuous working period, determine the current temperature anomaly degree of the battery pack.

[0015] Preferably, based on part of the actual temperature record data of the battery pack during the current complete continuous working period in the actual temperature record data of the battery pack and the ideal temperature record data of the battery pack during the current complete continuous working period, determine the current temperature anomaly degree of the battery pack, including:

[0016] Screen out all abnormal temperature values in part of the actual temperature record data of the battery pack during the current complete continuous working period in the actual temperature record data of the battery pack, and determine the occurrence time periods of all abnormal temperature values;

[0017] Based on all abnormal temperature values and the corresponding occurrence time periods, determine the first temperature anomaly degree of the battery pack;

[0018] Calculate the deviation degree between part of the actual temperature record data of the battery pack during the current complete continuous working period in the actual temperature record data of the battery pack and the ideal temperature record data of the battery pack during the current complete continuous working period as the second temperature anomaly degree of the battery pack;

[0019] Based on the first temperature anomaly degree and the second temperature anomaly degree of the battery pack, determine the current temperature anomaly degree of the battery pack.

[0020] Preferably, evaluate the current aging factor of the battery pack based on the usage record of the battery pack of the new energy vehicle, including:

[0021] Based on the usage record of the battery pack of the new energy vehicle, determine the charge and discharge times of the battery pack, the total usage duration, the environmental record data during each use, the charge and discharge depth and the charge and discharge rate during each charge and discharge process;

[0022] Based on a preset aging evaluation model, perform integrated calculation and analysis on the charge and discharge times of the battery pack, the total usage duration, the environmental record data during each use, the charge and discharge depth and the charge and discharge rate during each charge and discharge process to determine the current aging factor of the battery pack.

[0023] Preferably, determining a first battery pack temperature influence coefficient based on the current aging factor of the battery pack includes:

[0024] Evaluating the aging factors of the battery packs of a large number of currently available new energy vehicles of the current model at different times based on the usage records of the battery packs of a large number of currently available new energy vehicles of the current model collected;

[0025] Based on the aging factors of the battery packs of a large number of currently available new energy vehicles of the current model at different times and the battery pack temperature change data of all currently available new energy vehicles of the current model during the same period under the same operating conditions and the same environmental data, establishing a function model between the aging factor of the battery pack and the battery pack temperature influence coefficient of the currently available new energy vehicle of the current model under each operating condition and each environmental data;

[0026] Substituting the current aging factor of the battery pack into the function model between the aging factor of the battery pack and the battery pack temperature influence coefficient of the currently available new energy vehicle of the current model under the current operating conditions and the current environmental data of the new energy vehicle to determine the first battery pack temperature influence coefficient.

[0027] Preferably, determining a second battery pack temperature influence coefficient based on the current environmental temperature includes:

[0028] Based on the battery pack temperature change data of a large number of currently available new energy vehicles of the current model under the same operating conditions, the same aging factor, and all other environmental data except the environmental temperature being the same as the current environmental data of the new energy vehicle but with different environmental temperatures, establishing a function model between the environmental temperature and the battery pack temperature influence coefficient of the currently available new energy vehicle of the current model under each operating condition, each aging factor, and all other environmental data except the environmental temperature being the same as the current environmental data of the new energy vehicle;

[0029] Substituting the current environmental temperature into the function model between the environmental temperature and the battery pack temperature influence coefficient of the currently available new energy vehicle of the current model under the current operating conditions, the current aging factor, and all other environmental data except the environmental temperature being the same as the current environmental data of the new energy vehicle to determine the second battery pack temperature influence coefficient.

[0030] Preferably, S3: Correcting the current temperature abnormality degree of the battery pack based on the first battery pack temperature influence coefficient and the second battery pack temperature influence coefficient to obtain the corrected temperature abnormality degree of the battery pack, including:

[0031] Generating a continuous aging factor sequence based on the current aging factor, and substituting each aging factor included in the continuous aging factor sequence into the function model between the aging factor of the battery pack and the battery pack temperature influence coefficient of the currently available new energy vehicle of the current model under the current operating conditions and the current environmental data of the new energy vehicle to obtain a first influence coefficient sequence;

[0032] Generate a continuous ambient temperature sequence based on the current ambient temperature, and substitute each ambient temperature in the continuous ambient temperature sequence into the function model of the ambient temperature and the temperature influence coefficient of the battery pack of the current model of new energy vehicle under the condition that the current working condition, the current aging factor, and all other ambient data except the ambient temperature correspond to the current ambient data of the new energy vehicle, to obtain a second influence coefficient sequence;

[0033] Based on the first battery pack temperature influence coefficient, correct the current temperature abnormality degree of the battery pack to obtain the first corrected abnormality degree;

[0034] Based on the second battery pack temperature influence coefficient, correct the current temperature abnormality degree of the battery pack to obtain the second corrected abnormality degree;

[0035] Based on the first influence coefficient sequence, the second influence coefficient sequence, the first corrected abnormality degree, and the second corrected abnormality degree, obtain the corrected temperature abnormality degree of the battery pack.

[0036] Preferably, based on the first influence coefficient sequence, the second influence coefficient sequence, the first corrected abnormality degree, and the second corrected abnormality degree, obtaining the corrected temperature abnormality degree of the battery pack includes:

[0037] Align the continuous aging factor sequence and the continuous ambient temperature sequence, and summarize the aligned each group of aging factors, ambient temperature, the current working condition, and all other ambient data except the ambient temperature corresponding to the current ambient data of the new energy vehicle as the retrieval basis for each group of instances;

[0038] Based on the battery pack temperature change data of the new energy vehicle of the current model when meeting the retrieval basis for each group of instances, determine the actual corrected temperature abnormality degree;

[0039] Take the first influence coefficient and the second influence coefficient with the same sorting value in the first influence coefficient sequence and the second influence coefficient sequence as the abscissa value and the ordinate value respectively, and mark the coordinates of the point to be corrected corresponding to each sorting value in the preset two-dimensional coordinate system;

[0040] At the same time, take each actual corrected temperature abnormality degree determined by the corresponding same sorting value in the continuous aging factor sequence and the continuous ambient temperature sequence as the abscissa value and the ordinate value respectively, and mark the coordinates of the anchor point corresponding to each sorting value in the preset two-dimensional coordinate system;

[0041] Take the vector pointing from the coordinates of the point to be corrected of each sorting value to the coordinates of the anchor point as the two-dimensional correction vector;

[0042] Based on the starting coordinates of all two-dimensional correction vectors and the point coordinates calibrated in a preset two-dimensional coordinate system with the first correction anomaly degree and the second correction anomaly degree as the abscissa value and the ordinate value respectively, determine the interpolation distance of each two-dimensional correction vector;

[0043] Calculate the two-dimensional correction vectors at the first correction anomaly degree and the second correction anomaly degree based on all two-dimensional correction vectors and their corresponding interpolation distances;

[0044] Obtain the corrected temperature anomaly degree of the battery pack based on the two-dimensional correction vectors at the first correction anomaly degree and the second correction anomaly degree.

[0045] Preferably, obtaining the corrected temperature anomaly degree of the battery pack based on the two-dimensional correction vectors at the first correction anomaly degree and the second correction anomaly degree includes:

[0046] Based on the two-dimensional correction vectors at the first correction anomaly degree and the second correction anomaly degree and the point coordinates calibrated in a preset two-dimensional coordinate system with the first correction anomaly degree and the second correction anomaly degree as the abscissa value and the ordinate value respectively, fit the corresponding anchor point coordinates;

[0047] Judge whether the horizontal and vertical coordinate deviation degrees of the corresponding anchor point coordinates are less than the preset deviation degree threshold. If so, take the average value of the horizontal and vertical coordinates of the corresponding anchor point coordinates as the corrected temperature anomaly degree of the battery pack. Otherwise, obtain a new sequence of continuous aging factors and a sequence of continuous ambient temperatures, and determine new two-dimensional correction vectors at the first correction anomaly degree and the second correction anomaly degree based on the new sequence of continuous aging factors and the sequence of continuous ambient temperatures until the horizontal and vertical coordinate deviation degrees of the new anchor point coordinates determined based on the new two-dimensional correction vectors do not exceed the preset deviation degree threshold, then take the average value of the horizontal and vertical coordinates of the corresponding new anchor point coordinates as the corrected temperature anomaly degree of the battery pack.

[0048] Preferably, S4: Determine the liquid cooling temperature reduction strategy of the battery pack based on the corrected temperature anomaly degree of the battery pack, and start the liquid cooling device to perform temperature reduction operation on the battery pack based on the liquid cooling temperature reduction strategy of the battery pack, including:

[0049] Build a liquid cooling temperature reduction strategy generation model for the battery pack;

[0050] Input the corrected temperature anomaly degree of the battery pack into the liquid cooling temperature reduction strategy generation model of the battery pack to obtain the liquid cooling temperature reduction strategy of the battery pack;

[0051] Start the liquid cooling device to perform temperature reduction operation on the battery pack based on the liquid cooling temperature reduction strategy of the battery pack.

[0052] The beneficial effects of the present invention compared with the prior art are as follows: By determining the current abnormal degree of the battery pack temperature based on the current working conditions record of new energy vehicles, the dynamic battery pack temperature model, and the actual temperature record data, various key factors can be comprehensively considered to accurately identify the abnormal situation of the battery pack temperature, providing an accurate basis for subsequent taking reasonable measures. When the abnormal degree of temperature exceeds the threshold, the aging factor is evaluated based on the battery pack usage record, and then the first battery pack temperature influence coefficient is determined. At the same time, the second battery pack temperature influence coefficient is determined in combination with the current ambient temperature, comprehensively considering the influence of battery self-aging and environmental factors on temperature, making the control strategy more scientific and targeted. Using these two influence coefficients to correct the abnormal degree of temperature, a corrected abnormal degree of temperature that is more in line with the actual situation is obtained, effectively improving the accuracy of temperature abnormality judgment and avoiding deviations caused by single-factor judgment. Based on the corrected abnormal degree of temperature, a liquid cooling strategy is determined and the liquid cooling equipment is started, and the cooling operation can be accurately regulated according to the actual temperature abnormal situation, not only ensuring that the battery pack temperature is effectively controlled, but also avoiding energy waste and equipment loss caused by overcooling, protecting the performance and life of the battery pack while improving the overall operation efficiency and safety of new energy vehicles.

[0053] Other features and advantages of the present invention will be described in the following specification, and, in part, will be obvious from the specification, or will be understood by implementing the present invention. The objectives and other advantages of the present invention can be achieved and obtained by the structure specifically pointed out in this application document.

[0054] The technical solutions of the present invention will be further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] The accompanying drawings are used to provide a further understanding of the present invention, and constitute a part of the specification, and are used to explain the present invention together with the embodiments of the present invention, and do not constitute a limitation to the present invention. In the accompanying drawings:

[0056] Figure 1 is a schematic flow chart of a temperature control method for a power battery pack of a new energy vehicle in an embodiment of the present invention;

[0057] Figure 2 is a schematic implementation flow chart of step S1 in an embodiment of the present invention;

[0058] Figure 3 is a schematic sight flow chart of step S3 in an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0059] The following describes the preferred embodiments of the present invention with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0060] Embodiment 1:

[0061] The present invention provides a temperature control method for a power battery pack of a new energy vehicle, with reference to Figure 1 , including:

[0062] S1: Based on the current working condition record of the new energy vehicle, the dynamic battery pack temperature model under each working condition, and the actual temperature record data of the battery pack, determine the current temperature abnormality degree of the battery pack;

[0063] S2: When the current temperature abnormality degree of the battery pack exceeds the abnormality degree threshold, evaluate the current aging factor of the battery pack based on the usage record of the battery pack of the new energy vehicle, and determine the first battery pack temperature influence coefficient based on the current aging factor of the battery pack. At the same time, determine the second battery pack temperature influence coefficient based on the current ambient temperature;

[0064] S3: Correct the current temperature abnormality degree of the battery pack based on the first battery pack temperature influence coefficient and the second battery pack temperature influence coefficient to obtain the corrected temperature abnormality degree of the battery pack;

[0065] S4: Determine the liquid cooling temperature reduction strategy of the battery pack based on the corrected temperature abnormality degree of the battery pack, and start the liquid cooling device to cool the battery pack based on the liquid cooling temperature reduction strategy of the battery pack.

[0066] In this embodiment, the current working condition record refers to the working states such as acceleration, deceleration, and constant speed experienced during the current complete continuous working period of the new energy vehicle and the duration of each state.

[0067] In this embodiment, the dynamic battery pack temperature model under each working condition is used to describe the change of the temperature of the battery pack with time and other factors under different working conditions such as acceleration, deceleration, and constant speed in an ideal state (i.e., not affected by the ambient temperature and not affected by the aging of the battery pack).

[0068] In this embodiment, the actual temperature record data of the battery pack is the temperature data actually recorded during the working process of the battery pack.

[0069] In this embodiment, the current temperature abnormality degree of the battery pack is determined by comprehensively considering the actual temperature of the battery pack and the ideal temperature record data, reflecting the degree of deviation of the current temperature from the normal state.

[0070] In this embodiment, the abnormality degree threshold is preset and is a standard value used to judge whether the current temperature abnormality degree of the battery pack exceeds the normal range.

[0071] In this embodiment, the usage record of the battery pack includes the number of charge and discharge cycles of the battery pack, the total usage duration, the ambient and working condition record data for each use, the charge and discharge depth, and the rate, etc.

[0072] In this embodiment, the current aging factor of the battery pack is obtained through integrated calculation and analysis by a preset aging evaluation model based on the usage record of the battery pack, reflecting the degree of performance degradation of the battery pack.

[0073] In this embodiment, the first battery pack temperature influence coefficient is calculated through a function model established based on the current aging factor of the battery pack, which is a coefficient measuring the influence of battery aging on the degree of temperature abnormality.

[0074] In this embodiment, the current ambient temperature refers to the temperature of the external environment where the battery pack is currently located.

[0075] In this embodiment, the second battery pack temperature influence coefficient is determined through a function model established based on the current ambient temperature, which is a coefficient reflecting the influence of ambient temperature on the degree of temperature abnormality of the battery pack.

[0076] In this embodiment, the corrected temperature abnormality degree of the battery pack is the value obtained by correcting the current temperature abnormality degree in combination with the first and second battery pack temperature influence coefficients, which can more accurately reflect the temperature abnormality situation. When the battery pack ages or the ambient temperature is too high, the actual temperature abnormality of the battery pack is usually more serious than the calculated surface value (i.e., the current temperature abnormality degree). At this time, stronger cooling measures are required. If liquid cooling control is carried out according to the current temperature abnormality degree, the cooling effect of the battery pack will be slowed down. Therefore, when determining the liquid cooling strategy, it is necessary to consider the influence of battery aging and ambient temperature on the calculated surface value (i.e., the current temperature abnormality degree), and then determine the corrected temperature abnormality degree of the battery pack based on this logic. The liquid cooling strategy determined based on the corrected temperature abnormality degree of the battery pack will also result in better temperature control effect for the battery pack; conversely, when the ambient temperature is too low, the actual temperature abnormality (in this case, the abnormal situation of the characteristic temperature being too high) of the battery pack is less serious than the calculated surface value (i.e., the current temperature abnormality degree) because the ambient temperature will play a part in the cooling effect. Therefore, the liquid cooling strategy determined based on the corrected temperature abnormality degree of the battery pack considering this part of the influence can avoid excessive cooling causing energy waste and equipment loss.

[0077] In this embodiment, the liquid cooling strategy for the battery pack is generated based on the corrected temperature abnormality degree of the battery pack, which is a strategy used to control the liquid cooling device to cool the battery pack.

[0078] In this embodiment, the liquid cooling device is a device for cooling the battery pack.

[0079] In this embodiment, the liquid cooling device is started based on the liquid cooling strategy of the battery pack to perform a cooling operation on the battery pack, that is, the liquid cooling device is turned on according to the generated liquid cooling strategy to implement cooling.

[0080] Embodiment 2:

[0081] Based on the current working condition record of the new energy vehicle, the dynamic battery pack temperature model under each working condition, and the actual temperature record data of the battery pack, determine the current temperature abnormality degree of the battery pack, referring to Figure 2 , including:

[0082] Determine the sequence of working condition types experienced by the new energy vehicle during the current complete continuous working period and the duration of each working condition type based on the current working condition record of the new energy vehicle;

[0083] Substitute the sequence of working condition types experienced by the new energy vehicle during the current complete continuous working period and the duration of each working condition type into the dynamic battery pack temperature models under all working conditions to determine the ideal temperature record data of the battery pack during the current complete continuous working period;

[0084] Based on a part of the actual temperature record data during the current complete continuous working period in the actual temperature record data of the battery pack and the ideal temperature record data of the battery pack during the current complete continuous working period, determine the current temperature abnormality degree of the battery pack.

[0085] In this embodiment, the current complete continuous working period refers to the time period from the start of the current continuous working state of the new energy vehicle to the current moment, during which the vehicle working state is continuous without interruption.

[0086] In this embodiment, the sequence of working condition types refers to the sequence formed by the working states such as acceleration, deceleration, and constant speed that the new energy vehicle experiences in sequence during the current complete continuous working period.

[0087] In this embodiment, the duration of each working condition type is the time length that each working condition state such as acceleration, deceleration, and constant speed lasts during the current complete continuous working period.

[0088] In this embodiment, substituting the sequence of working condition types experienced by the new energy vehicle during the current complete continuous working period and the duration of each working condition type into the dynamic battery pack temperature models under all working conditions to determine the ideal temperature record data of the battery pack during the current complete continuous working period, the specific process is as follows: Assume that the sequence of working condition types is acceleration - constant speed - deceleration, the acceleration lasts for 5 minutes, the constant speed lasts for 10 minutes, and the deceleration lasts for 3 minutes. Input this information into the dynamic model that describes the temperature change of the battery pack over time under all working conditions such as acceleration, constant speed, and deceleration. The model calculates and outputs the theoretical temperature value that the battery pack should have at each time point during this 18 - minute current complete continuous working period, so as to obtain the ideal temperature record data.

[0089] In this embodiment, the ideal temperature record data refers to the data record of the temperature change of the battery pack over time that should theoretically be, calculated based on the dynamic battery pack temperature model, combined with the sequence of working condition types and the duration of each working condition within the current complete continuous working period of the vehicle.

[0090] The beneficial effects of the above technology are as follows: By recording the current working conditions of new energy vehicles, clarifying the sequence of working condition types and the duration of each working condition within the current complete continuous working period, accurately reflecting the dynamic changes in the vehicle's operating state, and providing a core basis for subsequent simulation of the battery pack temperature. Substituting this working condition information into the dynamic battery pack temperature model, obtaining the ideal temperature record data, simulating the theoretical temperature change, and providing a reasonable reference standard for judging the actual temperature abnormality. Combining some of the actual temperature record data within the current complete continuous working period with the ideal temperature record data, comprehensively considering the actual and theoretical temperatures, making the determination of the temperature abnormality degree more scientific and accurate, and sensitively detecting the degree to which the battery pack temperature deviates from the normal state. This greatly improves the accuracy of judging the battery pack temperature abnormality, lays a solid foundation for taking appropriate temperature control measures, effectively ensures the stable operation of the power battery pack of new energy vehicles under different working conditions, extends the service life of the battery pack, and comprehensively improves the safety and reliability of new energy vehicles.

[0091] Embodiment 3:

[0092] Based on some of the actual temperature record data within the current complete continuous working period in the actual temperature record data of the battery pack and the ideal temperature record data of the battery pack within the current complete continuous working period on the basis of Embodiment 2, determine the current temperature abnormality degree of the battery pack, refer to Figure 2 , including:

[0093] Screen out all abnormal temperature values in some of the actual temperature record data within the current complete continuous working period in the actual temperature record data of the battery pack, and determine the occurrence time periods of all abnormal temperature values;

[0094] Based on all abnormal temperature values and the corresponding occurrence time periods, determine the first temperature abnormality degree of the battery pack;

[0095] Calculate the deviation degree between some of the actual temperature record data within the current complete continuous working period in the actual temperature record data of the battery pack and the ideal temperature record data of the battery pack within the current complete continuous working period, as the second temperature abnormality degree of the battery pack;

[0096] Based on the first temperature abnormality degree and the second temperature abnormality degree of the battery pack, determine the current temperature abnormality degree of the battery pack.

[0097] In this embodiment, screening out all abnormal temperature values from the partial actual temperature record data of the battery pack within the current complete continuous working period means finding out the temperature values that significantly deviate from the normal range from the partial battery pack temperature data actually recorded within the current complete continuous working period. For example, if the normal temperature range is set to 20°C - 30°C, 35°C, 15°C, etc. in the actual recorded data belong to abnormal temperature values.

[0098] In this embodiment, determining the first temperature abnormality degree of the battery pack based on all abnormal temperature values and the corresponding occurrence time periods means quantifying a dimension of the battery pack temperature abnormality by analyzing the screened abnormal temperature values and their specific occurrence time points. For example, first set a basic parameter value related to temperature abnormality. For instance, each 1°C deviation of the abnormal temperature from the normal range is counted as 1 point, and each 1-minute duration of the occurrence of the abnormal temperature is counted as 0.5 points. Suppose the screened abnormal temperature value is 35°C (the normal range is assumed to be 20 - 30°C), deviating from the normal range by 5°C, and the occurrence time period starts at 10:00 and lasts for 10 minutes. Then the calculation is as follows: Temperature deviation score = 5×1 = 5 points, duration score = 10×0.5 = 5 points, and the sum of the two gives the first temperature abnormality degree of 10 points. Here, by comprehensively considering the deviation amplitude and duration of the abnormal temperature, the first temperature abnormality degree is determined to reflect one aspect of the battery pack temperature abnormality situation. In actual calculation, the scoring rules will be formulated more scientifically and reasonably according to the battery characteristics and actual requirements.

[0099] In this embodiment, calculating the deviation degree between the partial actual temperature record data of the battery pack within the current complete continuous working period and the ideal temperature record data of the battery pack within the current complete continuous working period as the second temperature abnormality degree of the battery pack means taking the average of the ratios of the absolute values of the differences between the actual temperature and the ideal temperature at all corresponding time points to the ideal temperature as the second temperature abnormality degree. This deviation degree reflects the overall degree of deviation of the actual temperature from the ideal temperature.

[0100] In this embodiment, determining the current temperature abnormality degree of the battery pack based on the first temperature abnormality degree and the second temperature abnormality degree of the battery pack. For example, if the first temperature abnormality degree is determined to be 3 (a hypothetical quantified value) according to the abnormal temperature value and the occurrence time period, and the second temperature abnormality degree is 2.25 calculated as above, through a certain preset algorithm, such as simple weighted average (assuming the weights are both 0.5), then the current temperature abnormality degree = 3×0.5 + 2.25×0.5 = 2.625. By comprehensively considering the abnormality degrees of these two dimensions, the situation of the current temperature of the battery pack deviating from the normal state can be measured more comprehensively and accurately.

[0101] The beneficial effects of the above technology are as follows: Screen abnormal temperature values and their occurrence periods in the actual temperature record data, focus on the abnormal fluctuation points of the battery pack temperature, accurately locate the specific conditions of temperature deviation from the normal state, and provide key details for judging the degree of abnormality. Based on this, determine the first degree of temperature abnormality, which intuitively reflects the impact of the actual situation of abnormal temperature on the battery pack temperature abnormality. Calculate the deviation degree between the actual and ideal temperature record data as the second degree of temperature abnormality, which measures the deviation between the actual temperature and the theoretical expectation as a whole, and comprehensively considers the difference between the temperature change trend and the ideal state. Combine the first and second degrees of temperature abnormality to determine the current degree of temperature abnormality, taking into account both specific abnormal points and overall temperature deviation, making the judgment more comprehensive and accurate. This accurate judgment helps to take temperature control measures in a timely and precise manner, ensure the stable operation of the battery pack, guarantee the safe and reliable driving of new energy vehicles, extend the service life of the battery pack at the same time, and reduce the maintenance cost.

[0102] Embodiment 4:

[0103] Based on the usage records of the battery pack of a new energy vehicle in Embodiment 1, evaluate the current aging factor of the battery pack, including:

[0104] Determine the charge-discharge cycles, total usage duration, environmental record data during each usage process, charge-discharge depth and charge-discharge rate during each charge-discharge process of the battery pack based on the usage records of the battery pack of a new energy vehicle;

[0105] Integrate, calculate and analyze the charge-discharge cycles, total usage duration, environmental record data during each usage process, charge-discharge depth and charge-discharge rate during each charge-discharge process of the battery pack based on a preset aging evaluation model to determine the current aging factor of the battery pack.

[0106] In this embodiment, the charge-discharge cycles of the battery pack refer to the number of cycles when the battery is fully charged to fully discharged, or from fully discharged to fully charged; the total usage duration is the cumulative working time of the battery pack from the start of use to the current moment; the environmental record data during each usage process includes information such as the environmental temperature and humidity during use; the charge-discharge depth during each charge-discharge process refers to the proportion of the amount of electricity discharged or charged during the charge-discharge process of the battery to the total capacity of the battery, and the charge-discharge rate is the ratio of the current value output or input when the battery discharges its rated capacity within a specified time to the rated capacity. For example, the charge-discharge cycles are 500 times, the total usage duration is 500 hours, the environmental temperature during a certain use is 25°C, the humidity is 60%, the working condition is uniform driving, the charge-discharge depth is 80%, and the charge-discharge rate is 1C.

[0107] In this embodiment, the above data is integrated, calculated and analyzed based on a preset aging evaluation model to determine the current aging factor of the battery pack. Assume the preset aging evaluation model is: aging factor = 0.3 × number of charge and discharge cycles / 1000 + 0.2 × total usage duration / 1000 + 0.2 × (average ambient temperature - 25)² / 25 + 0.1 × average charge and discharge depth + 0.2 × average charge and discharge rate (this is an example model). If the number of charge and discharge cycles is 500 times, the total usage duration is 500 hours, the average ambient temperature during all uses is 28 °C, the charge and discharge depth during all charge and discharge processes is 80%, and the charge and discharge rate during all charge and discharge processes is 1C, substituting into the model gives: aging factor = 0.3 × 500 / 1000 + 0.2 × 500 / 1000 + 0.2 × (28 - 25)² / 25 + 0.1 × 0.8 + 0.2 × 1 = 0.15 + 0.1 + 0.072 + 0.08 + 0.2 = 0.602, that is, the current aging factor is 0.602, thus quantifying the aging degree of the battery pack.

[0108] The beneficial effects of the above technology are as follows: In Embodiment 4, by evaluating the current aging factor based on the usage records of the battery pack, it brings many positive effects. The charge and discharge times of the battery pack, the total usage duration, the environmental and working condition record data of each use, as well as the charge and discharge depth and rate of each charge and discharge process are comprehensively collected. These data comprehensively reflect the usage history and conditions of the battery pack. Using the preset aging evaluation model to integrate, calculate and analyze a large number of data can comprehensively consider various factors affecting battery aging, avoid the one-sidedness of single-factor evaluation, and make the determination of the aging factor more scientific and accurate. The accurate evaluation of the current aging factor can help the system more accurately judge the degree of performance decline of the battery pack, providing a reliable basis for adjusting the temperature control strategy based on the aging status. This helps to more reasonably control the temperature of the battery pack, delay the battery aging speed, ensure the stable operation of the battery pack at different aging stages, thereby improving the overall performance and safety of new energy vehicles, extending the service life of the battery pack, and reducing the vehicle operation cost.

[0109] Embodiment 5:

[0110] Based on the current aging factor of the battery pack in Embodiment 1, a first battery pack temperature influence coefficient is determined, including:

[0111] Based on the usage records of the battery packs of a large number of current models of new energy vehicles collected, the aging factors of the battery packs of a large number of current models of new energy vehicles at different times are evaluated;

[0112] Based on the aging factors of the battery packs of a large number of current models of new energy vehicles at different times and the corresponding battery pack temperature change data of all current models of new energy vehicles during the same time period under the same working conditions and the same environmental data, a function model of the aging factor of the battery pack and the battery pack temperature influence coefficient of the current models of new energy vehicles is established under each working condition and each environmental data;

[0113] Substitute the current aging factor of the battery pack into the function model of the aging factor of the battery pack and the battery pack temperature influence coefficient of the current models of new energy vehicles under the current working conditions and the current environmental data of the new energy vehicle to determine the first battery pack temperature influence coefficient.

[0114] In this embodiment, the aging factors of the battery packs of a large number of current models of new energy vehicles are evaluated based on the usage records of the battery packs of a large number of current models of new energy vehicles collected, which means that by obtaining the usage record information such as the charge and discharge times, total usage duration, environmental data for each use, charge and discharge depth, and rate of a large number of battery packs of new energy vehicles of the same model, and using a preset aging evaluation model, the aging factor values of these battery packs at different usage times are calculated. For example, the usage records of the battery packs of 100 vehicles of the same model are collected, and the records of each vehicle at different usage stages are analyzed to obtain the aging factors at each stage.

[0115] In this embodiment, based on the aging factors of the battery packs of a large number of current models of new energy vehicles at different times and the corresponding battery pack temperature change data of all current models of new energy vehicles during the same time period under the same working conditions and the same environmental data, a function model of the aging factor of the battery pack and the battery pack temperature influence coefficient of the current models of new energy vehicles is established under each working condition and each environmental data. Specifically, assume that when the working condition is uniform speed and the environmental temperature is 25°C, the aging factors at different times are 0.2, 0.3, and 0.4 respectively, and the corresponding battery pack temperature changes are an increase of 2°C, an increase of 3°C, and an increase of 4°C respectively. Taking the aging factor as the independent variable x and the temperature influence coefficient (which can be obtained from the relationship between the temperature change and a reference value, such as temperature influence coefficient = temperature change value / 10) as the dependent variable y, a simple function model y = 10x can be established. In practice, data under various working conditions (acceleration, deceleration, etc.) and environmental data (different temperatures, humidities, etc.) will be collected to establish a more complex and general function model to describe the relationship between the aging factor and the temperature influence coefficient.

[0116] The beneficial effects of the above technology are as follows: By collecting a large number of usage records of the battery packs of current models of new energy vehicles and evaluating the aging factors at different times, a rich and comprehensive data foundation has been accumulated, making the research on the aging of battery packs more universal and representative. Using these aging factor data and combining with the battery pack temperature change data under the same working conditions and environment, a function model is established, which can accurately reflect the internal relationship between the aging factor and the temperature influence coefficient under different working conditions and environmental conditions, providing a scientific basis for accurately calculating the temperature influence coefficient in the future. Substituting the current aging factor of the battery pack into the function model under specific working conditions and environment, the first battery pack temperature influence coefficient is obtained, and the degree of influence of aging on temperature can be accurately measured according to the real-time aging state of the battery pack. This helps the system to more reasonably adjust the temperature control strategy according to the aging condition of the battery pack, achieve refined management of the battery pack temperature, ensure the stable operation of the battery pack, extend the battery life, and improve the overall performance and reliability of new energy vehicles.

[0117] Embodiment 6:

[0118] Based on Embodiment 1, determining the second battery pack temperature influence coefficient based on the current ambient temperature, including:

[0119] Based on the collected temperature change data of the battery packs of a large number of current models of new energy vehicles under the same working conditions, the same aging factors, and when all other environmental data except the ambient temperature are consistent with the current environmental data of the new energy vehicle and the ambient temperature is different, establish a function model of the ambient temperature and the battery pack temperature influence coefficient of the current model of new energy vehicle under the condition that each working condition, each aging factor, and all other environmental data except the ambient temperature are consistent with the current environmental data of the new energy vehicle;

[0120] Substitute the current ambient temperature into the function model of the ambient temperature and the battery pack temperature influence coefficient of the current model of new energy vehicle under the current working condition, the current aging factor, and the condition that all other environmental data except the ambient temperature are consistent with the current environmental data of the new energy vehicle to determine the second battery pack temperature influence coefficient.

[0121] In this embodiment, the environmental data other than the ambient temperature refers to other environmental data that may affect the battery pack temperature, such as environmental humidity, air pressure, etc., in addition to the ambient temperature. For example, environmental humidity may affect battery heat dissipation, and air pressure may also affect the internal chemical reaction of the battery to a certain extent and then affect the temperature. These data together with the ambient temperature constitute the environmental conditions for the battery to work, but here specifically point out the data of other environmental factors excluding the ambient temperature.

[0122] In this embodiment, based on the battery pack temperature change data of a large number of current models of new energy vehicles collected under the same working conditions, the same aging factor, and when all other environmental data except the ambient temperature correspond to the current environmental data of the new energy vehicle and the ambient temperature is different, a function model of the ambient temperature and the battery pack temperature influence coefficient of the current model of new energy vehicle is established under the condition that all other environmental data except the ambient temperature correspond to the current environmental data of the new energy vehicle for each working condition, each aging factor. For example, assume that the current working condition is uniform driving, the aging factor is 0.5, and except for the ambient temperature, the ambient humidity is 60% and the air pressure is standard atmospheric pressure. Collect the battery pack temperature change data at different ambient temperatures (such as 20 °C, 25 °C, 30 °C, etc.) under such environmental conditions. Taking the ambient temperature as the independent variable and the temperature influence coefficient calculated based on the battery pack temperature change as the dependent variable (for example, the temperature influence coefficient is determined by comparing the ratio of the battery pack temperature change at different ambient temperatures to the temperature change under a certain fixed standard state), by analyzing a large amount of such data, a function model reflecting the relationship between the ambient temperature and the temperature influence coefficient under this specific working condition, aging factor, and fixed other environmental data conditions is established. It is possible to obtain a function relationship such as y = 0.2x + 1 (only for example, it will be more complex in reality), which is used to determine the temperature influence coefficient according to the current ambient temperature in the future.

[0123] The beneficial effects of the above technology are as follows: Collect a large amount of battery pack temperature change data of current models of new energy vehicles under specific conditions, where only the ambient temperature is different. This targeted data collection method highlights the influence of the ambient temperature variable on the battery pack temperature, providing detailed basis for accurate modeling. Based on this, a function model of the ambient temperature and the battery pack temperature influence coefficient under various working conditions, aging factors, and other environmental data is established, comprehensively covering different situations and accurately depicting the complex relationship between the ambient temperature and the temperature influence coefficient. Substituting the current ambient temperature into the corresponding function model to determine the second battery pack temperature influence coefficient can reflect the influence degree of the current ambient temperature on the battery pack temperature in real time and accurately. This enables full consideration of the ambient temperature factor when regulating the battery pack temperature, improving the accuracy and effectiveness of temperature regulation, optimizing the working environment of the battery pack, ensuring the stable operation of the battery pack, further extending the service life of the battery pack, and guaranteeing the performance and safety of new energy vehicles in different environments.

[0124] Embodiment 7:

[0125] On the basis of Embodiment 1, S3: Correct the current temperature abnormality degree of the battery pack based on the first battery pack temperature influence coefficient and the second battery pack temperature influence coefficient to obtain the corrected temperature abnormality degree of the battery pack. Refer to Figure 3 , including:

[0126] Generate a continuous aging factor sequence based on the current aging factor, and substitute each aging factor included in the continuous aging factor sequence into the function model of the aging factor of the battery pack and the temperature influence coefficient of the new energy vehicle battery pack of the current model under the current working condition and current environmental data of the new energy vehicle to obtain a first influence coefficient sequence;

[0127] Generate a continuous ambient temperature sequence based on the current ambient temperature, and substitute each ambient temperature in the continuous ambient temperature sequence into the function model of the ambient temperature and the temperature influence coefficient of the new energy vehicle battery pack of the current model under the current working condition, current aging factor, and all other environmental data except the ambient temperature being consistent with the current environmental data of the new energy vehicle to obtain a second influence coefficient sequence;

[0128] Correct the current temperature anomaly degree of the battery pack based on the first battery pack temperature influence coefficient to obtain a first corrected anomaly degree;

[0129] Correct the current temperature anomaly degree of the battery pack based on the second battery pack temperature influence coefficient to obtain a second corrected anomaly degree;

[0130] Obtain the corrected temperature anomaly degree of the battery pack based on the first influence coefficient sequence, the second influence coefficient sequence, the first corrected anomaly degree, and the second corrected anomaly degree.

[0131] In this embodiment, generating a continuous aging factor sequence based on the current aging factor means generating a series of continuously changing aging factors in a certain pattern based on the currently obtained aging factor of the battery pack to form a sequence. For example, assume the current aging factor is 0.3, and generate a sequence with a step size of 0.05: [0.25, 0.3, 0.35, 0.4].

[0132] In this embodiment, substituting each aging factor included in the continuous aging factor sequence into the function model of the aging factor of the battery pack and the temperature influence coefficient of the new energy vehicle battery pack of the current model under the current working condition and current environmental data to obtain a first influence coefficient sequence. That is, for each value in the generated continuous aging factor sequence, such as 0.25, 0.3, 0.35, 0.4, substitute them into the previously established function model reflecting the relationship between the aging factor and the temperature influence coefficient (assuming the function model is y = 2x + 0.5), calculate the corresponding temperature influence coefficient values, and form a new sequence. Substituting 0.25 gives y = 2×0.25 + 0.5 = 1, substituting 0.3 gives y = 2×0.3 + 0.5 = 1.1, and so on, to obtain the first influence coefficient sequence [1, 1.1, 1.2, 1.3].

[0133] In this embodiment, a continuous ambient temperature sequence is generated based on the current ambient temperature, that is, a set of continuously changing ambient temperature values ​​is generated according to certain rules with the current ambient temperature as the middle value to form a sequence. For example, if the current ambient temperature is 25°C, a sequence is generated at intervals of 2°C: [21°C, 23°C, 25°C, 27°C, 29°C].

[0134] In this embodiment, each ambient temperature in the continuous ambient temperature sequence is substituted into the function model under which the ambient temperature and the temperature influence coefficient of the battery pack of the current model of new energy vehicle correspond to the current working condition, the current aging factor, and all the remaining environmental data except the ambient temperature correspond to the current environmental data of the new energy vehicle to obtain the second influence coefficient sequence. For each temperature value in the generated continuous ambient temperature sequence, such as 21°C, 23°C, etc., they are respectively substituted into a specific function model reflecting the relationship between the ambient temperature and the temperature influence coefficient (assuming that the function model is y=0.1x-0.5), and the corresponding temperature influence coefficient value is calculated and formed into a sequence. Substituting 21°C into y=0.1×21-0.5=1.6, substituting 23°C into y=0.1×23-0.5=1.8, and obtaining the second influence coefficient sequence [1.6, 1.8, 2, 2.2, 2.4].

[0135] In this embodiment, the current temperature abnormality of the battery pack is corrected based on the first battery pack temperature influence coefficient to obtain a first corrected abnormality. Assuming that the current temperature abnormality is 0.8 and the first battery pack temperature influence coefficient is 1.2, through a certain preset correction algorithm (such as multiplication), the first corrected abnormality is obtained as 0.8×1.2=0.96, which preliminarily considers the impact of battery aging on the current temperature abnormality.

[0136] In this embodiment, the current temperature abnormality of the battery pack is corrected based on the second battery pack temperature influence coefficient to obtain a second corrected abnormality. Also assuming that the current temperature abnormality is 0.8 and the second battery pack temperature influence coefficient is 1.5, according to a preset correction algorithm (such as multiplication), the second corrected abnormality is 0.8×1.5=1.2, which preliminarily considers the influence of the ambient temperature on the current temperature abnormality.

[0137] The beneficial effects of the above technology are as follows: Generating a continuous aging factor sequence and substituting it into the function model to obtain the first influence coefficient sequence can dynamically analyze the change of the temperature influence coefficient under different aging degrees, fully consider the dynamic influence on the abnormal temperature judgment during the battery aging process, and improve the accuracy of the judgment. Generating a continuous ambient temperature sequence and substituting it into the corresponding function model to obtain the second influence coefficient sequence can simulate the change of the temperature influence coefficient under different ambient temperatures, making the abnormal temperature judgment more in line with the actual environmental changes. Respectively, based on the temperature influence coefficients of the first and second battery packs, the current temperature abnormality degree is corrected to obtain the first and second corrected abnormality degrees, and the abnormality degree is preliminarily adjusted from two key dimensions of aging and ambient temperature, laying a foundation for the final accurate correction. Combining the first and second influence coefficient sequences and the two corrected abnormality degrees to obtain the corrected temperature abnormality degree takes into account the comprehensive effects of battery aging and ambient temperature changes on the abnormal temperature judgment, greatly optimizing the evaluation of the temperature abnormality degree, providing a reliable basis for formulating a more reasonable and accurate battery pack temperature control strategy, effectively ensuring the stable operation of the battery pack, extending its service life, and improving the safety and reliability of new energy vehicles.

[0138] Example 8:

[0139] On the basis of Example 7, based on the first influence coefficient sequence, the second influence coefficient sequence, the first corrected abnormality degree, and the second corrected abnormality degree, the corrected temperature abnormality degree of the battery pack is obtained, referring to Figure 3 , including:

[0140] Align the continuous aging factor sequence and the continuous ambient temperature sequence, and summarize all the remaining environmental data except the ambient temperature corresponding to the aligned each group of aging factors, ambient temperature, current working conditions, and the current environmental data of the new energy vehicle as the retrieval basis for a single group of examples;

[0141] Based on the battery pack temperature change data of the current model of new energy vehicle when meeting the retrieval basis of each group of examples, determine the actual corrected temperature abnormality degree;

[0142] Take the first influence coefficient and the second influence coefficient with the same sorting value in the first influence coefficient sequence and the second influence coefficient sequence as the abscissa value and the ordinate value respectively, and mark the coordinate of the point to be corrected corresponding to each sorting value in the preset two-dimensional coordinate system;

[0143] At the same time, take each actual corrected temperature abnormality degree determined by the corresponding same sorting value in the continuous aging factor sequence and the continuous ambient temperature sequence as the abscissa value and the ordinate value respectively, and mark the coordinate of the anchor point corresponding to each sorting value in the preset two-dimensional coordinate system;

[0144] Take the vector pointing from the coordinate of the point to be corrected of each sorting value to the coordinate of the corresponding anchor point as the two-dimensional correction vector;

[0145] Based on the starting point coordinates of all two-dimensional correction vectors and the point coordinates calibrated in a preset two-dimensional coordinate system using the first correction abnormality degree and the second correction abnormality degree as the abscissa value and the ordinate value, an interpolation distance of each two-dimensional correction vector is determined;

[0146] Calculate the two-dimensional correction vectors at the first correction abnormality degree and the second correction abnormality degree based on all the two-dimensional correction vectors and the corresponding interpolation distances;

[0147] A corrected temperature abnormality degree of the battery pack is obtained based on the two-dimensional correction vectors at the first corrected abnormality degree and the second corrected abnormality degree.

[0148] In this embodiment, the single group instance retrieval basis is to align the continuous aging factor sequence and the continuous ambient temperature sequence, and then aggregate the corresponding aging factor and ambient temperature of each group together with the current working condition and all the remaining environmental data except the ambient temperature in the current environmental data of the new energy vehicle to form a basis for retrieving the temperature change data of the relevant battery pack. For example, a group of aligned aging factors is 0.3, the ambient temperature is 25°C, the current working condition is uniform speed, and other environmental data such as humidity 60%, air pressure 101kPa, etc. are combined to form a single group instance retrieval basis.

[0149] In this embodiment, the temperature change data of the battery pack of the current model of new energy vehicles that meets each group of instance retrieval criteria refers to the temperature change data of the corresponding battery pack of the new energy vehicle model when the conditions set by the above single group of instance retrieval criteria are met. For example, under the conditions of aging factor 0.3, ambient temperature 25°C, uniform speed, humidity 60%, and air pressure 101kPa, the temperature change data of the battery pack recorded within a period of time rises from 28°C to 32°C.

[0150] In this embodiment, based on the battery pack temperature change data of the current model of new energy vehicles that meet the retrieval criteria of each group of instances, the actual corrected temperature abnormality is determined, that is, by analyzing and processing these qualified battery pack temperature change data, according to certain rules (which may be preset algorithms, such as combining the ideal temperature range, temperature change rate and other factors to calculate), a value that can reflect the temperature abnormality in the actual situation is obtained. For example:

[0151] First, calculate the maximum temperature deviation during the temperature change process. The ideal temperature range is 20-30°C, and the actual temperature rises from 28°C to 32°C. The maximum deviation is 32-30=2°C (only the part exceeding the ideal temperature upper limit is considered, of course, the lower limit deviation can also be considered according to the actual situation).

[0152] If 0.2 points are given for every 1°C exceeding the ideal temperature range, then the temperature deviation score = 2 × 0.2 = 0.4 points.

[0153] Suppose it is recorded that the temperature rises from 28°C to 32°C over 10 minutes. Calculate the temperature change rate as 0.4°C per minute.

[0154] Set a reference rate, such as 0.2°C per minute. Add 0.1 point for every 0.1°C exceeding the reference rate (here, deductions are made because a too-fast temperature rise may imply a more serious anomaly).

[0155] The part exceeding the reference rate is 0.2°C per minute, corresponding to a deduction of 0.2 ÷ 0.1 × 0.1 = 0.2 points. So the temperature change rate score = 0.2 points.

[0156] Set a base value of 1.0. The actual degree of temperature anomaly correction = base value + temperature deviation score + temperature change rate score.

[0157] Substitute the above calculation results. The actual degree of temperature anomaly correction = 1.0 + 0.4 + 0.2 = 1.6.

[0158] In this embodiment, the preset two-dimensional coordinate system is a two-dimensional coordinate system set in advance, used for subsequent calibration of coordinate points to analyze and process data from a geometric perspective. It has a horizontal axis and a vertical axis, and the specific physical meanings represented are set according to actual needs. Here, the horizontal axis may represent factors related to aging, and the vertical axis may represent factors related to ambient temperature (or vice versa).

[0159] In this embodiment, each actual degree of temperature anomaly correction determined by the corresponding same sorting values in the continuous aging factor sequence and the continuous ambient temperature sequence is used as both the abscissa value and the ordinate value at the same time, and the anchor point coordinates corresponding to each sorting value are calibrated in the preset two-dimensional coordinate system. For example, for the continuous aging factor sequence [0.25, 0.3, 0.35] and the continuous ambient temperature sequence [21°C, 23°C, 25°C], for the case where the sorting value is 1, the actual degree of temperature anomaly correction determined by the corresponding aging factor 0.25 and ambient temperature 21°C is 1.2, and the coordinate point (1.2, 1.2) is calibrated as the anchor point coordinate in the preset two-dimensional coordinate system; similarly, other sorting values are calibrated.

[0160] In this embodiment, based on the starting coordinates of all two-dimensional correction vectors and the point coordinates calibrated in the preset two-dimensional coordinate system with the first correction anomaly degree and the second correction anomaly degree as the abscissa value and the ordinate value respectively, the interpolation distance of each two-dimensional correction vector is determined. Suppose the first correction anomaly degree is 0.9 and the second correction anomaly degree is 1.0, and the point (0.9, 1.0) is calibrated in the coordinate system. The starting coordinate of the two-dimensional correction vector is (0.5, 0.6). According to the distance formula between two points (such as the Euclidean distance formula), the distance between the two points is calculated to obtain the interpolation distance of this two-dimensional correction vector.

[0161] In this embodiment, based on all two-dimensional correction vectors and the corresponding interpolation distances, the two-dimensional correction vectors under the first correction anomaly degree and the second correction anomaly degree are calculated. Suppose there are multiple two-dimensional correction vectors and their corresponding interpolation distances. For example, vector A = (1, 1) and the interpolation distance is 0.5; vector B = (-1, 2) and the interpolation distance is 0.3. Through a certain weighted calculation (such as weighting the vector according to the interpolation distance, and the weight is the ratio of the interpolation distance to the sum of all interpolation distances). Suppose the sum of all interpolation distances is 0.8 = (0.5 + 0.3), then after weighting, vector A is (1×0.5÷0.8, 1×0.5÷0.8) = (0.625, 0.625), and vector B is (-1×0.3÷0.8, 2×0.3÷0.8) = (-0.375, 0.75). Then the weighted vectors are synthesized (for example, adding the corresponding coordinates) to obtain the two-dimensional correction vector (0.625 - 0.375, 0.625 + 0.75) = (0.25, 1.375) under the first correction anomaly degree and the second correction anomaly degree.

[0162] The beneficial effects of the above technology are as follows: Align the continuous aging factor sequence and the continuous environmental temperature sequence, and summarize the relevant data as the basis for single-group instance retrieval. This integration method makes data processing more systematic and logical, and can comprehensively consider the influence of the combination of multiple factors such as battery aging and environmental temperature on the temperature change of the battery pack, providing a solid data foundation for accurately determining the actual degree of temperature anomaly correction subsequently. Determine the actual degree of temperature anomaly correction through the actual temperature change data of the battery pack, ensuring that the evaluation results are closely related to the actual working conditions of the battery pack, and improving the accuracy and reliability of the evaluation. Calibrate the coordinates of the point to be corrected and the anchor point coordinates in the preset two-dimensional coordinate system, and determine the two-dimensional correction vector, which quantifies the correction direction and degree of the temperature anomaly based on the aging factor and environmental temperature in an intuitive geometric way, making the correction process visual and facilitating understanding and analysis. Calculate the interpolation distance of the two-dimensional correction vector, and calculate the two-dimensional correction vector under specific conditions based on this. This refined calculation method fully considers the differences in the correction vectors under different data combinations, and can adjust the first and second correction anomaly degrees more accurately according to the actual situation. Finally, obtain the corrected temperature anomaly degree of the battery pack based on these two-dimensional correction vectors, comprehensively and accurately integrating the influence of factors such as aging and environmental temperature on the temperature anomaly judgment, providing a highly accurate basis for formulating a more scientific and reasonable battery pack temperature control strategy, helping to further ensure the stable operation of the battery pack, extend its service life, and improve the overall performance and safety of new energy vehicles.

[0163] Embodiment 9:

[0164] Based on the two-dimensional correction vectors under the first correction anomaly degree and the second correction anomaly degree on the basis of Embodiment 8, obtain the corrected temperature anomaly degree of the battery pack, referring to Figure 3 , including:

[0165] Based on the two-dimensional correction vectors under the first correction anomaly degree and the second correction anomaly degree and the point coordinates calibrated in the preset two-dimensional coordinate system with the first correction anomaly degree and the second correction anomaly degree as the abscissa value and the ordinate value respectively, fit the corresponding anchor point coordinates;

[0166] Judge whether the horizontal and vertical coordinate deviation degrees of the corresponding anchor point coordinates are less than the preset deviation degree threshold. If so, take the average value of the horizontal and vertical coordinates of the corresponding anchor point coordinates as the corrected temperature anomaly degree of the battery pack. Otherwise, obtain a new continuous aging factor sequence and a new continuous environmental temperature sequence, and determine new two-dimensional correction vectors under the first correction anomaly degree and the second correction anomaly degree based on the new continuous aging factor sequence and the new continuous environmental temperature sequence, until the horizontal and vertical coordinate deviation degrees of the new anchor point coordinates determined based on the new two-dimensional correction vectors do not exceed the preset deviation degree threshold, then take the average value of the horizontal and vertical coordinates of the corresponding new anchor point coordinates as the corrected temperature anomaly degree of the battery pack.

[0167] In this embodiment, based on the two-dimensional correction vectors under the first correction anomaly degree and the second correction anomaly degree, and the point coordinates calibrated in the preset two-dimensional coordinate system with the first correction anomaly degree and the second correction anomaly degree as the abscissa value and the ordinate value respectively, the corresponding anchor point coordinates are fitted. Assume that the first correction anomaly degree is x1 and the second correction anomaly degree is y1, and the point (x1, y1) is calibrated in the coordinate system. The two-dimensional correction vector is (Δx, Δy), and a new point coordinate is obtained by adding the vector to the point coordinate, that is, the corresponding anchor point coordinates (x2, y2), where x2 = x1 + Δx and y2 = y1 + Δy.

[0168] In this embodiment, the horizontal and vertical coordinate deviation degrees of the anchor point coordinates are used to measure the difference degree between the abscissa and the ordinate of the anchor point coordinates. It can be represented by calculating the ratio of the absolute value of the difference between the horizontal and vertical coordinates to the abscissa value, that is, the deviation degree = ∣x2 - y2∣÷x2. This value reflects the difference in the influence of temperature anomaly degrees in two dimensions after correction based on aging and ambient temperature.

[0169] In this embodiment, the preset deviation degree threshold is a pre-set standard value used to determine whether the horizontal and vertical coordinate deviation degrees of the anchor point coordinates are within an acceptable range. For example, the preset deviation degree threshold is set to 0.1, which is determined based on factors such as the accuracy requirements of the battery pack temperature control and actual experience, and is used as the basis for judging whether further adjustment is needed.

[0170] In this embodiment, the average value of the abscissa and ordinate of the anchor point coordinates is (x2 + y2)÷2. When the horizontal and vertical coordinate deviation degrees of the anchor point coordinates are less than the preset deviation degree threshold, this average value is regarded as the corrected temperature anomaly degree of the battery pack, comprehensively considering the influence of aging and ambient temperature on the temperature anomaly degree, and serving as a relatively balanced representative value.

[0171] In this embodiment, a new continuous aging factor sequence and a continuous ambient temperature sequence are obtained, and a new two-dimensional correction vector under the first correction anomaly degree and the second correction anomaly degree is determined based on the new continuous aging factor sequence and the continuous ambient temperature sequence. If the horizontal and vertical coordinate deviation degrees of the anchor point coordinates are greater than the preset deviation degree threshold, it indicates that the current correction of the temperature anomaly degree based on the aging factor and the ambient temperature is not accurate enough. At this time, a new continuous aging factor sequence (such as changing the generated step size or range) and a continuous ambient temperature sequence (the generation method can also be adjusted) are regenerated. Then, according to the method of determining the two-dimensional correction vector before, that is, recalculating the first and second influence coefficient sequences based on the new sequences, and combining the first and second correction anomaly degrees, a new two-dimensional correction vector under the first correction anomaly degree and the second correction anomaly degree in the new sequence is determined to further optimize the correction of the temperature anomaly degree of the battery pack.

[0172] The beneficial effects of the above technology are as follows: By using two-dimensional correction vectors and relevant coordinates to fit the coordinates of the anchor points, comprehensively integrating the information of the aging factor and the environmental temperature on the correction of the temperature anomaly degree, the result is more scientific and comprehensive. By judging the deviation degree of the horizontal and vertical coordinates of the anchor point coordinates from the preset deviation degree threshold, when the deviation degree is less than the threshold, the average value of the horizontal and vertical coordinates of the anchor point coordinates is used as the corrected temperature anomaly degree, ensuring the accuracy and reliability of the result. If the deviation degree does not meet the requirements, a new sequence is obtained to re-determine the two-dimensional correction vector until the deviation degree of the new anchor point coordinates meets the standard. Through iterative optimization, the final result accurately reflects the actual condition of the battery, providing an accurate basis for the temperature control of the battery pack, ensuring the stable operation of the battery pack, extending the service life, and improving the safety and reliability of new energy vehicles.

[0173] Example 10:

[0174] On the basis of Example 1, S4: Determine the liquid cooling cooling strategy of the battery pack based on the corrected temperature anomaly degree of the battery pack, and start the liquid cooling equipment to cool the battery pack based on the liquid cooling cooling strategy of the battery pack, including:

[0175] Build a liquid cooling cooling strategy generation model for the battery pack;

[0176] Input the corrected temperature anomaly degree of the battery pack into the liquid cooling cooling strategy generation model of the battery pack to obtain the liquid cooling cooling strategy of the battery pack;

[0177] Start the liquid cooling equipment to cool the battery pack based on the liquid cooling cooling strategy of the battery pack.

[0178] In this embodiment, building a liquid cooling cooling strategy generation model for the battery pack refers to constructing a mathematical model or algorithm system for generating a suitable liquid cooling cooling strategy according to the relevant parameters of the battery pack (such as the corrected temperature anomaly degree). This involves machine learning algorithms. For example, first collect a large amount of operation data of new energy vehicle battery packs under different working conditions and environments, including the corrected temperature anomaly degree, environmental factors, aging factors, previous liquid cooling cooling strategies and cooling effects, etc. Then, use the collected large amount of historical battery temperature data, environmental data, aging factor data, working condition data, usage records, etc. to determine the corresponding corrected temperature anomaly degree of the corresponding battery pack according to the previous calculation logic. Then, use the liquid cooling cooling strategy of the battery pack determined by professionals based on the historical battery temperature data, environmental data, aging factor data, working condition data, usage records as training samples for machine learning to obtain this model, which can output the corresponding liquid cooling cooling strategy according to the input corrected temperature anomaly degree of the battery pack.

[0179] In this embodiment, the liquid cooling temperature reduction strategy of the battery pack is a series of instructions for controlling the liquid cooling equipment obtained through the liquid cooling temperature reduction strategy generation model based on the corrected temperature anomaly degree of the battery pack. These instructions include, but are not limited to, the adjustment of the flow rate and velocity of the coolant, the power regulation of the refrigeration equipment, etc., with the aim of restoring the temperature of the battery pack to an appropriate range. For example, the strategy may stipulate that when the corrected temperature anomaly degree is within a certain range, the coolant flow rate is set to 5 liters per minute and the power of the refrigeration equipment is adjusted to 3 kilowatts.

[0180] The beneficial effects of the above technology are as follows: By building a liquid cooling temperature reduction strategy generation model, it provides an effective framework for generating scientific and reasonable temperature reduction strategies, making the strategy formulation follow certain rules and avoiding blindness. Taking the corrected temperature anomaly degree of the battery pack as the model input ensures that the generated liquid cooling temperature reduction strategy closely fits the actual temperature anomaly situation of the battery pack, with stronger pertinence, and can accurately match corresponding temperature reduction strategies according to different degrees of temperature anomalies. Based on the generated strategy, the liquid cooling equipment is started for temperature reduction operations, realizing an efficient connection from evaluation to execution, enabling the battery pack to be appropriately cooled in a timely manner, and ensuring its stable operation within an appropriate temperature range. This method of determining and implementing the temperature reduction strategy based on accurate evaluation can effectively prevent the battery from performance degradation and shortened lifespan due to excessive temperature, greatly improving the reliability and durability of the battery pack, providing a strong guarantee for the stable operation of new energy vehicles, and thus enhancing the safety and performance of the whole vehicle.

[0181] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the present invention and its equivalent technologies, the present invention also intends to include these changes and modifications.

Claims

1. A temperature control method for a power battery pack of a new energy vehicle, characterized in that: include: S1: Determine the current abnormal temperature of the battery pack based on the current operating condition record of the new energy vehicle, the dynamic battery pack temperature model under each operating condition, and the actual temperature record data of the battery pack; S2: When the current temperature abnormality of the battery pack exceeds the abnormality threshold, the current aging factor of the battery pack is evaluated based on the usage record of the battery pack of the new energy vehicle, and the first battery pack temperature influence coefficient is determined based on the current aging factor of the battery pack, and at the same time, the second battery pack temperature influence coefficient is determined based on the current ambient temperature; S3: correcting the current temperature abnormality of the battery pack based on the first battery pack temperature influence coefficient and the second battery pack temperature influence coefficient to obtain a corrected temperature abnormality of the battery pack; S4: determining a liquid cooling strategy for the battery pack based on the corrected temperature abnormality of the battery pack, and starting a liquid cooling device to cool the battery pack based on the liquid cooling strategy for the battery pack.

2. The temperature control method of the new energy vehicle power battery pack according to claim 1, characterized in that: S1: Based on the current operating condition record of the new energy vehicle, the dynamic battery pack temperature model under each operating condition, and the actual temperature record data of the battery pack, determine the current temperature abnormality of the battery pack, including: Based on the current working condition record of the new energy vehicle, determine the working condition type sequence and the duration of each working condition type experienced by the new energy vehicle in the current complete continuous working period; Substitute the sequence of operating condition types experienced by the new energy vehicle in the current complete continuous working period and the duration of each operating condition type into the dynamic battery pack temperature model under all operating conditions to determine the ideal temperature recording data of the battery pack in the current complete continuous working period; Based on a portion of the actual temperature record data of the battery pack in the current complete continuous working period and the ideal temperature record data of the battery pack in the current complete continuous working period, the current temperature abnormality degree of the battery pack is determined.

3. The temperature control method of the new energy vehicle power battery pack according to claim 2, characterized in that: Based on a portion of the actual temperature record data of the battery pack in the current complete continuous working period and the ideal temperature record data of the battery pack in the current complete continuous working period, the current temperature abnormality of the battery pack is determined, including: Filter out all abnormal temperature values ​​in a portion of the actual temperature record data of the battery pack within the current complete continuous working period, and determine the period in which all abnormal temperature values ​​appear; Determining a first temperature abnormality degree of the battery pack based on all abnormal temperature values ​​and corresponding occurrence time periods; Calculate the deviation between a portion of the actual temperature record data of the battery pack in the current complete continuous working period and the ideal temperature record data of the battery pack in the current complete continuous working period as the second temperature abnormality degree of the battery pack; The current temperature abnormality degree of the battery pack is determined based on the first temperature abnormality degree and the second temperature abnormality degree of the battery pack.

4. The temperature control method of the new energy vehicle power battery pack according to claim 1, characterized in that: Based on the usage records of the battery pack of the new energy vehicle, the current aging factor of the battery pack is evaluated, including: Based on the usage records of the battery pack of the new energy vehicle, determine the number of charge and discharge times of the battery pack, the total usage time, the environmental record data during each use, the charge and discharge depth and charge and discharge rate of each charge and discharge process; Based on the preset aging assessment model, the number of charge and discharge times of the battery pack, the total usage time, the environmental recording data during each use, the charge and discharge depth and the charge and discharge rate of each charge and discharge process are integrated and calculated and analyzed to determine the current aging factor of the battery pack.

5. The temperature control method of the new energy vehicle power battery pack according to claim 1, characterized in that: Determining a first battery pack temperature influence coefficient based on a current aging factor of the battery pack includes: Based on the collected usage records of the battery packs of a large number of current models of new energy vehicles, the aging factors of the battery packs of a large number of current models of new energy vehicles at different times are evaluated; Based on the collected aging factors of battery packs of a large number of current models of new energy vehicles at different times and the corresponding battery pack temperature change data of all current models of new energy vehicles under the same working conditions and the same environmental data in a time period, a function model of the battery pack aging factor and the temperature influence coefficient of the battery pack of the current model of new energy vehicles under each working condition and each environmental data is established; Substitute the current aging factor of the battery pack into the function model of the aging factor of the battery pack and the temperature influence coefficient of the battery pack of the current model of new energy vehicle under the current operating conditions and current environmental data of the new energy vehicle to determine the first battery pack temperature influence coefficient.

6. The temperature control method of the new energy vehicle power battery pack according to claim 1, characterized in that: Determining the temperature influence coefficient of the second battery pack based on the current ambient temperature includes: Based on a large number of collected battery pack temperature change data of current models of new energy vehicles under the same operating conditions, the same aging factors, and all the remaining environmental data except the ambient temperature are consistent with the current environmental data of the new energy vehicle, and the ambient temperature is different, a function model of the ambient temperature and the temperature influence coefficient of the battery pack of the current model of new energy vehicles under each operating condition, each aging factor, and all the remaining environmental data except the ambient temperature are consistent with the current environmental data of the new energy vehicle is established; Substitute the current ambient temperature into the function model of the ambient temperature and the temperature influence coefficient of the battery pack of the current model of new energy vehicle under the current operating conditions, the current aging factor, and all other environmental data except the ambient temperature corresponding to the current environmental data of the new energy vehicle to determine the second battery pack temperature influence coefficient.

7. The temperature control method of the new energy vehicle power battery pack according to claim 1, characterized in that: S3: Correcting the current temperature abnormality of the battery pack based on the first battery pack temperature influence coefficient and the second battery pack temperature influence coefficient to obtain a corrected temperature abnormality of the battery pack, including: Generate a continuous aging factor sequence based on the current aging factor, and substitute each aging factor contained in the continuous aging factor sequence into a function model of the aging factor of the battery pack and the temperature influence coefficient of the battery pack of the current model of the new energy vehicle under the current working condition and current environmental data of the new energy vehicle to obtain a first influence coefficient sequence; Generate a continuous ambient temperature sequence based on the current ambient temperature, and substitute each ambient temperature in the continuous ambient temperature sequence into a function model of the ambient temperature and the temperature influence coefficient of the battery pack of the current model of the new energy vehicle under the current working condition, the current aging factor, and all the remaining environmental data except the ambient temperature and the current environmental data of the new energy vehicle, to obtain a second influence coefficient sequence; Correcting the current temperature abnormality degree of the battery pack based on the first battery pack temperature influence coefficient to obtain a first corrected abnormality degree; Correcting the current temperature abnormality degree of the battery pack based on the second battery pack temperature influence coefficient to obtain a second corrected abnormality degree; Based on the first influence coefficient sequence, the second influence coefficient sequence, the first corrected abnormality degree, and the second corrected abnormality degree, a corrected temperature abnormality degree of the battery pack is obtained.

8. The temperature control method of the new energy vehicle power battery pack according to claim 7, characterized in that: Obtaining a corrected temperature abnormality degree of the battery pack based on the first influence coefficient sequence, the second influence coefficient sequence, the first corrected abnormality degree, and the second corrected abnormality degree, includes: Align the continuous aging factor sequence and the continuous ambient temperature sequence, and summarize all the remaining environmental data except the ambient temperature that are consistent with each set of aligned aging factors, ambient temperature and current working conditions and the current environmental data of the new energy vehicle as a single set of instance retrieval basis; Determine the actual corrected temperature anomaly degree based on the battery pack temperature change data of the current model of the new energy vehicle that meets the retrieval criteria of each set of instances; The first influence coefficient and the second influence coefficient with the same ranking value in the first influence coefficient sequence and the second influence coefficient sequence are respectively regarded as the horizontal coordinate value and the vertical coordinate value, and the coordinates of the point to be corrected corresponding to each ranking value are calibrated in the preset two-dimensional coordinate system; At the same time, each actual corrected temperature anomaly degree determined by the same ranking value corresponding to the continuous aging factor sequence and the continuous ambient temperature sequence is used as the horizontal coordinate value and the vertical coordinate value at the same time, and the anchor point coordinates corresponding to each ranking value are calibrated in the preset two-dimensional coordinate system; The vector from the coordinates of the to-be-corrected point of each sorted value to the coordinates of the corresponding anchor point is regarded as a two-dimensional correction vector; Based on the starting point coordinates of all two-dimensional correction vectors and the point coordinates calibrated in a preset two-dimensional coordinate system using the first correction abnormality degree and the second correction abnormality degree as the abscissa value and the ordinate value, an interpolation distance of each two-dimensional correction vector is determined; Calculate the two-dimensional correction vectors at the first correction abnormality degree and the second correction abnormality degree based on all the two-dimensional correction vectors and the corresponding interpolation distances; A corrected temperature abnormality degree of the battery pack is obtained based on the two-dimensional correction vectors at the first corrected abnormality degree and the second corrected abnormality degree.

9. The temperature control method of the new energy vehicle power battery pack according to claim 8, characterized in that: Obtaining a corrected temperature abnormality degree of the battery pack based on the two-dimensional correction vectors under the first corrected abnormality degree and the second corrected abnormality degree includes: Fitting the corresponding anchor point coordinates based on the two-dimensional correction vectors under the first correction abnormality degree and the second correction abnormality degree and the point coordinates calibrated in the preset two-dimensional coordinate system using the first correction abnormality degree and the second correction abnormality degree as the abscissa value and the ordinate value respectively; Determine whether the deviation of the horizontal and vertical coordinates of the corresponding anchor point coordinates is less than the preset deviation threshold. If so, take the average value of the horizontal and vertical coordinates of the corresponding anchor point coordinates as the corrected temperature abnormality of the battery pack. Otherwise, obtain a new continuous aging factor sequence and a continuous ambient temperature sequence, and determine a new two-dimensional correction vector under the first corrected abnormality and the second corrected abnormality based on the new continuous aging factor sequence and the continuous ambient temperature sequence, until the deviation of the horizontal and vertical coordinates of the new anchor point coordinates determined based on the new two-dimensional correction vector does not exceed the preset deviation threshold, then take the average value of the horizontal and vertical coordinates of the corresponding new anchor point coordinates as the corrected temperature abnormality of the battery pack.

10. The temperature control method of the new energy vehicle power battery pack according to claim 1, characterized in that: S4: determining a liquid cooling strategy for the battery pack based on the corrected temperature abnormality of the battery pack, and starting a liquid cooling device to cool the battery pack based on the liquid cooling strategy for the battery pack, including: Build a liquid cooling strategy generation model for battery packs; Inputting the corrected temperature abnormality of the battery pack into a liquid cooling strategy generation model of the battery pack to obtain a liquid cooling strategy of the battery pack; Based on the liquid cooling strategy of the battery pack, the liquid cooling device is started to cool the battery pack.

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