A new energy vehicle power battery pack temperature control method

Abstract

This invention relates to the field of new energy vehicle technology, and specifically discloses a method for controlling the temperature of a power battery pack in a new energy vehicle. The method includes: determining the current temperature anomaly level of the battery pack based on the current operating condition records of the new energy vehicle, a dynamic battery pack temperature model under each operating condition, and actual battery pack temperature records; when the current temperature anomaly level exceeds an anomaly level threshold, evaluating the current aging factor of the battery pack, determining a first battery pack temperature influence coefficient based on the current aging factor, and determining a second battery pack temperature influence coefficient based on the current ambient temperature; correcting the current temperature anomaly level of the battery pack based on the first and second battery pack temperature influence coefficients, determining a liquid cooling strategy for the battery pack, and activating liquid cooling equipment to cool the battery pack; and precisely adjusting the cooling operation according to the actual temperature anomaly to ensure effective control of the battery pack temperature.

Description

Technical Field

[0001] This invention relates to the field of new energy vehicle technology, and in particular to a method for temperature control of power battery packs in new energy vehicles. Background Technology

[0002] With increasing environmental awareness and the transformation of the energy structure, new energy vehicles have experienced rapid development. As the core component of new energy vehicles, the performance and lifespan of the power battery pack directly affect the overall performance of the vehicle. Temperature is a key factor affecting the performance and lifespan of the power battery pack; excessively high or low temperatures can lead to reduced charging and discharging efficiency, accelerated capacity decay, and even safety issues. Therefore, an efficient temperature control method for new energy vehicle power battery packs is crucial. It ensures that the battery pack operates within a suitable temperature range, improving charging and discharging performance, extending battery life, and thus enhancing the driving range and safety of new energy vehicles. This not only helps enhance the competitiveness of new energy vehicles in the market but also aligns with the concept of sustainable development, playing a significant role in promoting the healthy development of the new energy vehicle industry. With the continuous expansion of the new energy vehicle market, advanced temperature control technology will undoubtedly have broader application prospects, providing strong support for the prosperity of the new energy vehicle industry.

[0003] However, existing temperature control technologies for new energy vehicle power battery packs have some drawbacks. They cannot accurately determine the degree of temperature anomalies in the battery pack by comprehensively analyzing data from multiple sources; furthermore, when the temperature anomaly exceeds the threshold, they fail to combine battery aging factors and ambient temperature to correct the degree of temperature anomaly, making it impossible to formulate a precise and effective liquid cooling strategy. This, in turn, affects the performance and lifespan of the battery pack and threatens the safe and stable operation of new energy vehicles.

[0004] Therefore, this invention proposes a method for temperature control of power battery packs in new energy vehicles. Summary of the Invention

[0005] This invention provides a method for temperature control of power battery packs in new energy vehicles. By determining the degree of temperature anomaly in the battery pack based on current operating condition records of the new energy vehicle, a dynamic battery pack temperature model, and actual temperature records, it comprehensively considers multiple key factors to accurately identify abnormal battery pack temperature conditions, providing a precise basis for subsequent appropriate measures. When the temperature anomaly exceeds a threshold, an aging factor is evaluated based on battery pack usage records to determine a first battery pack temperature influence coefficient. Simultaneously, a second battery pack temperature influence coefficient is determined by combining the current ambient temperature. This comprehensively considers the impact of battery aging and environmental factors on temperature, making the control strategy more scientific and targeted. The degree of temperature anomaly is corrected using these two influence coefficients to obtain a more realistic and accurate assessment, effectively improving the accuracy of temperature anomaly judgment and avoiding deviations caused by single-factor judgments. Based on the corrected temperature anomaly degree, a liquid cooling strategy is determined and the liquid cooling equipment is activated. The cooling operation can be precisely controlled according to the actual temperature anomaly, ensuring effective control of the battery pack temperature while avoiding excessive cooling that would lead to energy waste and equipment damage. This protects battery pack performance and lifespan while improving the overall operating efficiency and safety of the new energy vehicle.

[0006] This invention provides a method for temperature control of a power battery pack for new energy vehicles, comprising:

[0007] S1: Based on the current operating condition records of new energy vehicles, the dynamic battery pack temperature model under each operating condition, and the actual temperature record data of the battery pack, determine the current temperature anomaly level of the battery pack.

[0008] 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 records 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. At the same time, the second battery pack temperature influence coefficient is determined based on the current ambient temperature.

[0009] S3: Based on the temperature influence coefficient of the first battery pack and the temperature influence coefficient of the second battery pack, the current temperature anomaly of the battery pack is corrected to obtain the corrected temperature anomaly of the battery pack.

[0010] S4: Determine the liquid cooling strategy for the battery pack based on the degree of temperature anomaly correction, and start the liquid cooling equipment to cool the battery pack based on the liquid cooling strategy.

[0011] Preferably, S1: Based on the current operating condition records 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 anomaly level of the battery pack, including:

[0012] Based on the current operating condition records of new energy vehicles, the sequence of operating condition types experienced by the new energy vehicles during the current complete continuous working period and the duration of each operating condition type are determined.

[0013] The sequence of operating conditions experienced by the new energy vehicle during the current complete continuous working period and the duration of each operating condition are substituted into the dynamic battery pack temperature model under all operating conditions to determine the ideal temperature recording data of the battery pack during the current complete continuous working period.

[0014] Based on partial actual temperature records during the current complete continuous operating period and ideal temperature records during the current complete continuous operating period, the degree of current temperature anomaly of the battery pack is determined.

[0015] Preferably, based on partial actual temperature record data within the current complete continuous operating period and ideal temperature record data of the battery pack within the current complete continuous operating period, the current temperature anomaly level of the battery pack is determined, including:

[0016] Filter out all abnormal temperature values ​​in the actual temperature record data of the battery pack within the current complete continuous working period, and determine the time period in which all abnormal temperature values ​​occurred;

[0017] The degree of the first temperature anomaly of the battery pack was determined based on all abnormal temperature values ​​and their corresponding time periods.

[0018] The deviation between the actual temperature record data of the battery pack during the current complete continuous working period and the ideal temperature record data of the battery pack during the current complete continuous working period is calculated as the second degree of temperature anomaly of the battery pack.

[0019] The current temperature anomaly level of the battery pack is determined based on the first and second temperature anomaly levels of the battery pack.

[0020] Preferably, the current aging factor of the battery pack is assessed based on the usage records of the battery pack in the new energy vehicle, including:

[0021] Based on the usage records of the battery packs of new energy vehicles, the number of charge and discharge cycles, total usage time, environmental data recorded during each use, and the depth of charge and discharge and charge and discharge rate of each charge and discharge process are determined.

[0022] Based on a pre-set aging assessment model, the current aging factor of the battery pack is determined by integrating and calculating the number of charge and discharge cycles, total usage time, environmental data recorded during each use, charge and discharge depth and charge and discharge rate of each charge and discharge process.

[0023] Preferably, the temperature influence coefficient of the first battery pack is determined based on the current aging factor of the battery pack, including:

[0024] Based on the collected usage records 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 were evaluated.

[0025] Based on the 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 environmental data over a period of time, a functional model of the aging factor of battery pack and the influence coefficient of battery pack temperature of current models of new energy vehicles is established under each working condition and environmental data.

[0026] The current aging factor of the battery pack is substituted into the function model of the aging factor of the battery pack and the temperature influence coefficient of the current model of new energy vehicle battery pack under the current operating conditions and current environmental data of new energy vehicle, and the temperature influence coefficient of the first battery pack is determined.

[0027] Preferably, the temperature influence coefficient of the second battery pack is determined based on the current ambient temperature, including:

[0028] Based on a large amount of collected data on battery pack temperature changes of current new energy vehicle models under the same operating conditions, the same aging factor, and all environmental data except ambient temperature that correspond to the current environmental data of the new energy vehicle but with different ambient temperatures, a function model is established for the influence coefficient of ambient temperature on the battery pack temperature of current new energy vehicle models under each operating condition, each aging factor, and all environmental data except ambient temperature that correspond to the current environmental data of the new energy vehicle.

[0029] The temperature influence coefficient of the second battery pack is determined by substituting the current ambient temperature into a function model that corresponds to the current operating conditions, current aging factor, and all environmental data except ambient temperature with the current environmental data of the new energy vehicle.

[0030] Preferably, S3: Based on the temperature influence coefficient of the first battery pack and the temperature influence coefficient of the second battery pack, the current temperature anomaly level of the battery pack is corrected to obtain the corrected temperature anomaly level of the battery pack, including:

[0031] A continuous aging factor sequence is generated based on the current aging factors. Each aging factor in the continuous aging factor sequence is then substituted into the function model of the aging factor of the battery pack and the temperature influence coefficient of the current model of the new energy vehicle battery pack under the current operating conditions and current environmental data of the new energy vehicle to obtain the first influence coefficient sequence.

[0032] A continuous ambient temperature sequence is generated based on the current ambient temperature. Each ambient temperature in the continuous ambient temperature sequence is then substituted into a function model that corresponds to the current operating conditions, current aging factor, and all other environmental data except ambient temperature with the current environmental data of the new energy vehicle, to obtain a second influence coefficient sequence.

[0033] The current temperature anomaly level of the battery pack is corrected based on the temperature influence coefficient of the first battery pack to obtain the first corrected anomaly level.

[0034] The current temperature anomaly level of the battery pack is corrected based on the temperature influence coefficient of the second battery pack to obtain the second corrected anomaly level.

[0035] The corrected temperature anomaly degree of the battery pack is obtained based on the first influence coefficient sequence, the second influence coefficient sequence, the first correction anomaly degree, and the second correction anomaly degree.

[0036] Preferably, the corrected temperature anomaly degree of the battery pack is obtained based on the first influence coefficient sequence, the second influence coefficient sequence, the first correction anomaly degree, and the second correction anomaly degree, including:

[0037] Align the continuous aging factor sequence and the continuous ambient temperature sequence, and summarize all environmental data except ambient temperature that correspond to the current operating conditions and the current environmental data of new energy vehicles in each aligned aging factor and ambient temperature as the basis for single instance retrieval.

[0038] Based on the battery pack temperature change data of the current model of new energy vehicle that meets the retrieval criteria for each set of instances, the actual degree of temperature anomaly correction is determined.

[0039] The first and second influence coefficients with the same sorting value in the first and second influence coefficient sequences are used as the horizontal and vertical coordinate values, respectively, and the coordinates of the point to be corrected corresponding to each sorting value are marked in the preset two-dimensional coordinate system.

[0040] At the same time, each actual corrected temperature anomaly degree determined by the same sorting value in the continuous aging factor sequence and the continuous ambient temperature sequence is used as the horizontal and vertical coordinate values, and the anchor point coordinates corresponding to each sorting value are marked in the preset two-dimensional coordinate system.

[0041] The vector pointing from the coordinates of the point to be corrected for each sorted value to the coordinates of the corresponding anchor point is used as a two-dimensional correction vector;

[0042] Based on the starting coordinates of all two-dimensional correction vectors and the coordinates of the points marked in the preset two-dimensional coordinate system using the first correction anomaly degree and the second correction anomaly degree as the abscissa and ordinate values ​​respectively, the interpolation distance of each two-dimensional correction vector is determined.

[0043] The two-dimensional correction vectors under the first and second correction anomalies are calculated based on all two-dimensional correction vectors and the corresponding interpolation distances.

[0044] The corrected temperature anomaly level of the battery pack is obtained based on the two-dimensional correction vector under the first and second correction anomaly levels.

[0045] Preferably, the corrected temperature anomaly level of the battery pack is obtained based on a two-dimensional correction vector under the first and second corrected anomaly levels, including:

[0046] Based on the two-dimensional correction vectors under the first and second correction anomalies, and the point coordinates calibrated in the preset two-dimensional coordinate system using the first and second correction anomalies as the abscissa and ordinate values ​​respectively, the corresponding anchor point coordinates are fitted.

[0047] If the deviation of the horizontal and vertical coordinates of the corresponding anchor point is less than a preset deviation threshold, then the average value of the horizontal and vertical coordinates of the corresponding anchor point is taken as the corrected temperature anomaly degree of the battery pack. Otherwise, a new continuous aging factor sequence and a continuous ambient temperature sequence are obtained, and a new two-dimensional correction vector is determined based on the new continuous aging factor sequence and the continuous ambient temperature sequence under the first correction anomaly degree and the second correction anomaly degree. This process continues until the deviation of the horizontal and vertical coordinates of the new anchor point determined based on the new two-dimensional correction vector does not exceed the preset deviation threshold. Then, the average value of the horizontal and vertical coordinates of the corresponding new anchor point is taken as the corrected temperature anomaly degree of the battery pack.

[0048] Preferably, S4: Based on the degree of temperature anomaly correction of the battery pack, a liquid cooling strategy for the battery pack is determined, and based on the liquid cooling strategy, the liquid cooling equipment is activated to cool the battery pack, including:

[0049] Build a model for generating liquid cooling strategies for battery packs;

[0050] The corrected temperature anomaly level of the battery pack is input into the liquid cooling strategy generation model of the battery pack to obtain the liquid cooling strategy of the battery pack.

[0051] The liquid cooling strategy for battery packs is used to activate liquid cooling equipment to cool the battery packs.

[0052] The beneficial effects of this invention compared to existing technologies are as follows: By determining the degree of temperature anomaly in the battery pack based on the current operating condition records of the new energy vehicle, the dynamic battery pack temperature model, and actual temperature record data, this invention can comprehensively consider multiple key factors and accurately identify abnormal battery pack temperature conditions, providing an accurate basis for subsequent reasonable measures. When the degree of temperature anomaly exceeds a threshold, the aging factor is evaluated based on the battery pack usage records to determine the first battery pack temperature influence coefficient. Simultaneously, a second battery pack temperature influence coefficient is determined by combining the current ambient temperature. This comprehensively considers the impact of battery aging and environmental factors on temperature, making the control strategy more scientific and targeted. Using these two influence coefficients to correct the degree of temperature anomaly yields a more realistic and accurate result, effectively improving the accuracy of temperature anomaly judgment and avoiding deviations caused by single-factor judgments. Based on the corrected degree of temperature anomaly, a liquid cooling strategy is determined and the liquid cooling equipment is activated. The cooling operation can be precisely controlled according to the actual temperature anomaly, ensuring effective control of the battery pack temperature while avoiding excessive cooling that could lead to energy waste and equipment damage. This protects battery pack performance and lifespan while improving the overall operating efficiency and safety of the new energy vehicle.

[0053] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in this application.

[0054] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0055] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0056] Figure 1 This is a schematic flowchart of a method for controlling the temperature of a power battery pack for a new energy vehicle, as described in an embodiment of the present invention.

[0057] Figure 2 This is a schematic diagram illustrating the implementation process of step S1 in an embodiment of the present invention;

[0058] Figure 3 This is a schematic diagram of the line-of-sight flow for step S3 in an embodiment of the present invention. Detailed Implementation

[0059] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0060] Example 1:

[0061] This invention provides a method for temperature control of power battery packs in new energy vehicles, with reference to... Figure 1 ,include:

[0062] S1: Based on the current operating condition records of new energy vehicles, the dynamic battery pack temperature model under each operating condition, and the actual temperature record data of the battery pack, determine the current temperature anomaly level of the battery pack.

[0063] 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 records 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. At the same time, the second battery pack temperature influence coefficient is determined based on the current ambient temperature.

[0064] S3: Based on the temperature influence coefficient of the first battery pack and the temperature influence coefficient of the second battery pack, the current temperature anomaly of the battery pack is corrected to obtain the corrected temperature anomaly of the battery pack.

[0065] S4: Determine the liquid cooling strategy for the battery pack based on the degree of temperature anomaly correction, and start the liquid cooling equipment to cool the battery pack based on the liquid cooling strategy.

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

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

[0068] In this embodiment, the actual temperature data recorded for the battery pack is the temperature data actually recorded during the battery pack's operation.

[0069] In this embodiment, the current temperature anomaly of the battery pack is determined by combining the actual temperature and ideal temperature recorded data of the battery pack, reflecting the degree to which the current temperature deviates from the normal state.

[0070] In this embodiment, the abnormality threshold is a pre-set standard value used to determine whether the current temperature abnormality of the battery pack exceeds the normal range.

[0071] In this embodiment, the battery pack usage record includes the number of times the battery pack is charged and discharged, the total usage time, the environmental and operating conditions recorded for each use, the depth of charge and discharge and the rate of charge and discharge, etc.

[0072] In this embodiment, the current aging factor of the battery pack is derived from the battery pack usage records and is calculated and analyzed by a preset aging assessment model, reflecting the degree of performance degradation of the battery pack.

[0073] In this embodiment, the temperature influence coefficient of the first battery pack is calculated based on the current aging factor of the battery pack through an established function model, and is a coefficient that measures the influence of battery aging on the degree of temperature anomaly.

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

[0075] In this embodiment, the temperature influence coefficient of the second battery pack is determined based on the current ambient temperature through an established function model, and reflects the influence of ambient temperature on the degree of temperature anomaly of the battery pack.

[0076] In this embodiment, the corrected temperature anomaly level of the battery pack is a value that is corrected for the current temperature anomaly level by combining the temperature influence coefficients of the first and second battery packs, thus more accurately reflecting the temperature anomaly situation. Because the actual temperature anomaly of the battery pack is usually more severe than the calculated surface value (i.e., the current temperature anomaly level) when the battery pack is aging or the ambient temperature is too high, the actual temperature anomaly of the battery pack is usually more severe than the calculated surface value (i.e., the current temperature anomaly level). In this case, it is necessary to increase cooling measures. However, if liquid cooling control is applied according to the current temperature anomaly level, it will slow down the cooling effect of the battery pack. Therefore, when determining the liquid cooling strategy, the influence of battery aging and ambient temperature on the calculated surface value (i.e., the current temperature anomaly level) needs to be considered. Based on this logic, the corrected temperature anomaly level of the battery pack is determined. The liquid cooling strategy determined based on the corrected temperature anomaly level of the battery pack will also result in better temperature control of the battery pack. Conversely, when the ambient temperature is too low, the actual temperature anomaly of the battery pack (here, the anomaly of excessively high temperature) will be less severe than the calculated surface value (i.e., the current temperature anomaly level) because the ambient temperature will have a cooling effect. Therefore, the liquid cooling strategy determined based on the corrected temperature anomaly level of the battery pack, taking this influence into account, can avoid excessive cooling that would lead to energy waste and equipment damage.

[0077] In this embodiment, the liquid cooling strategy for the battery pack is generated based on the degree of temperature anomaly correction of the battery pack, and is used to control the liquid cooling equipment to cool the battery pack.

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

[0079] In this embodiment, the liquid cooling equipment is activated to cool the battery pack based on the liquid cooling strategy of the battery pack, that is, the liquid cooling equipment is activated to implement cooling according to the generated liquid cooling strategy.

[0080] Example 2:

[0081] Based on Example 1, S1: Based on the current operating condition records 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 degree of current temperature anomaly of the battery pack, referring to... Figure 2 ,include:

[0082] Based on the current operating condition records of new energy vehicles, the sequence of operating condition types experienced by the new energy vehicles during the current complete continuous working period and the duration of each operating condition type are determined.

[0083] The sequence of operating conditions experienced by the new energy vehicle during the current complete continuous working period and the duration of each operating condition are substituted into the dynamic battery pack temperature model under all operating conditions to determine the ideal temperature recording data of the battery pack during the current complete continuous working period.

[0084] Based on partial actual temperature records during the current complete continuous operating period and ideal temperature records during the current complete continuous operating period, the degree of current temperature anomaly of the battery pack is determined.

[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's working state is continuous and uninterrupted.

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

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

[0088] In this embodiment, the sequence of operating conditions experienced by the new energy vehicle during the current complete continuous working period and the duration of each operating condition are substituted into the dynamic battery pack temperature model under all operating conditions to determine the ideal temperature recording data of the battery pack during the current complete continuous working period. The specific process is as follows: Assuming the operating condition sequence is acceleration-constant speed-deceleration, with acceleration lasting 5 minutes, constant speed lasting 10 minutes, and deceleration lasting 3 minutes, this information is input into the dynamic model describing the change of battery pack temperature over time under all operating 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 the current complete continuous working period of 18 minutes based on the characteristics and duration of each operating condition, thereby obtaining the ideal temperature recording data.

[0089] In this embodiment, the ideal temperature recording data refers to the data recording of the temperature that the battery pack should theoretically be at over time, calculated based on the dynamic battery pack temperature model and the sequence of operating conditions and the duration of each operating condition during the current complete continuous working period of the vehicle.

[0090] The beneficial effects of the above technologies are as follows: By recording the current operating conditions of new energy vehicles, the sequence of operating conditions and the duration of each condition within the current complete and continuous working period are clearly defined, accurately reflecting the dynamic changes in the vehicle's operating status and providing a core basis for subsequent simulation of battery pack temperature. Substituting this operating condition information into the dynamic battery pack temperature model yields ideal temperature recording data, simulating theoretical temperature changes and providing a reasonable reference standard for judging actual temperature anomalies. Combining partial actual temperature recording data and ideal temperature recording data within the current complete and continuous working period, and comprehensively considering both actual and theoretical temperatures, the determination of the degree of temperature anomaly is more scientific and accurate, and the degree of deviation of the battery pack temperature from the normal state is more sensitively detected. This significantly improves the accuracy of judging battery pack temperature anomalies, lays a solid foundation for taking appropriate temperature control measures, effectively ensures the stable operation of new energy vehicle power battery packs under different operating conditions, extends battery pack lifespan, and comprehensively improves the safety and reliability of new energy vehicles.

[0091] Example 3:

[0092] Based on Example 2, and using partial actual temperature recordings from the battery pack's actual temperature records during the current complete continuous operating period, along with ideal temperature recordings from the battery pack's actual temperature records during the current complete continuous operating period, the current temperature anomaly level of the battery pack is determined, with reference to... Figure 2 ,include:

[0093] Filter out all abnormal temperature values ​​in the actual temperature record data of the battery pack within the current complete continuous working period, and determine the time period in which all abnormal temperature values ​​occurred;

[0094] The degree of the first temperature anomaly of the battery pack was determined based on all abnormal temperature values ​​and their corresponding time periods.

[0095] The deviation between the actual temperature record data of the battery pack during the current complete continuous working period and the ideal temperature record data of the battery pack during the current complete continuous working period is calculated as the second degree of temperature anomaly of the battery pack.

[0096] The current temperature anomaly level of the battery pack is determined based on the first and second temperature anomaly levels of the battery pack.

[0097] In this embodiment, filtering out all abnormal temperature values ​​in the actual temperature records of the battery pack within the current complete continuous working period means identifying those temperature values ​​that significantly deviate from the normal range from the actual recorded battery pack temperature data within the current complete continuous working period. For example, if the normal temperature range is set to 20℃-30℃, actual recorded temperatures such as 35℃ and 15℃ would be considered abnormal temperature values.

[0098] In this embodiment, determining the first degree of temperature anomaly in the battery pack based on all abnormal temperature values ​​and their corresponding occurrence times means quantifying one dimension of battery pack temperature anomalies by analyzing the selected abnormal temperature values ​​and their specific occurrence times. For example, a basic parameter value related to temperature anomalies is first set, such as 1 point for every 1°C deviation of the abnormal temperature from the normal range, and 0.5 points for every minute the abnormal temperature lasts. Assume the selected 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 occurring from 10:00 AM for 10 minutes. Then, the calculation yields: temperature deviation score = 5 × 1 = 5 points, duration score = 10 × 0.5 = 5 points, and the sum of these two scores gives a first degree of temperature anomaly of 10 points. Here, the first degree of temperature anomaly is determined by comprehensively considering the deviation magnitude and duration of the abnormal temperature, reflecting one aspect of the battery pack temperature anomaly. In actual calculations, the scoring rules will be formulated more scientifically and reasonably based on battery characteristics and actual needs.

[0099] In this embodiment, the deviation between a portion of the actual temperature records within the current complete continuous operating period and the ideal temperature records within the same period is calculated and used as the second degree of temperature anomaly of the battery pack. Specifically, the average of the ratios of the absolute values ​​of the differences between the actual and ideal temperatures at all corresponding time points to the ideal temperature is taken as the second degree of temperature anomaly. This deviation reflects the overall degree to which the actual temperature deviates from the ideal temperature.

[0100] In this embodiment, the current temperature anomaly level of the battery pack is determined based on a first temperature anomaly level and a second temperature anomaly level. For example, the first temperature anomaly level is determined to be 3 (an assumed quantified value) based on the abnormal temperature value and the time period in which it occurred. The second temperature anomaly level is 2.25 as calculated above. Using a preset algorithm, such as a simple weighted average (assuming all weights are 0.5), the current temperature anomaly level = 3 × 0.5 + 2.25 × 0.5 = 2.625. By combining the anomaly levels of these two dimensions, the deviation of the battery pack's current temperature from its normal state can be measured more comprehensively and accurately.

[0101] The beneficial effects of the above technology are as follows: It filters abnormal temperature values ​​and their occurrence periods from actual temperature records, focuses on abnormal temperature fluctuations in the battery pack, and accurately locates the specific circumstances of temperature deviations from normal, providing crucial details for judging the degree of abnormality. Based on this, a first degree of temperature abnormality is determined, directly reflecting the impact of the actual abnormal temperature on the battery pack's temperature anomaly. The deviation between the actual and ideal temperature records is calculated as a second degree of temperature abnormality, comprehensively measuring the deviation between the actual temperature and theoretical expectations, and fully considering the difference between the temperature change trend and the ideal state. Combining the first and second degrees of temperature abnormality determines 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 precise judgment helps to take timely and accurate temperature control measures, ensuring stable operation of the battery pack, guaranteeing the safe and reliable operation of new energy vehicles, while extending the battery pack's lifespan and reducing maintenance costs.

[0102] Example 4:

[0103] Based on Example 1, the current aging factor of the battery pack is assessed based on the usage records of the battery pack in the new energy vehicle, including:

[0104] Based on the usage records of the battery packs of new energy vehicles, the number of charge and discharge cycles, total usage time, environmental data recorded during each use, and the depth of charge and discharge and charge and discharge rate of each charge and discharge process are determined.

[0105] Based on a pre-set aging assessment model, the current aging factor of the battery pack is determined by integrating and calculating the number of charge and discharge cycles, total usage time, environmental data recorded during each use, charge and discharge depth and charge and discharge rate of each charge and discharge process.

[0106] In this embodiment, the number of charge / discharge cycles of the battery pack refers to the number of cycles from full charge to full discharge, or from full discharge to full charge; the total usage time is the cumulative working time of the battery pack from the start of use to the current moment; environmental data recorded during each use includes information such as ambient temperature and humidity; the depth of charge / discharge in each charge / discharge process refers to the proportion of the amount of electricity released or charged by the battery during the charge / discharge process to the total battery capacity; and the charge / discharge rate refers to the ratio of the current value output or input by the battery when it releases its rated capacity within a specified time to its rated capacity. For example, the number of charge / discharge cycles is 500, the total usage time is 500 hours, the ambient temperature during a certain use is 25°C, the humidity is 60%, the operating condition is constant speed driving, the depth of charge / discharge is 80%, and the charge / discharge rate is 1C.

[0107] In this embodiment, the above data are integrated and analyzed based on a preset aging assessment model to determine the current aging factor of the battery pack. The preset aging assessment model is assumed to be: Aging Factor = 0.3 × Number of Charge / Discharge Cycles / 1000 + 0.2 × Total Usage Time / 1000 + 0.2 × (Average Ambient Temperature - 25)² / 25 + 0.1 × Average Depth of Charge / Discharge + 0.2 × Average Charge / Discharge Rate (this is an example model). If the number of charge-discharge cycles is 500, the total usage time is 500 hours, the average ambient temperature during all usage cycles is 28℃, the depth of charge-discharge during all charge-discharge cycles is 80%, and the charge-discharge rate during all charge-discharge cycles is 1C, substituting these values ​​into the model, we can obtain: 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, which means the current aging factor is 0.602, thus quantifying the degree of aging of the battery pack.

[0108] The beneficial effects of the above technologies are as follows: Example 4, by assessing the current aging factor based on battery pack usage records, brings multiple positive effects. Comprehensive data collection includes the number of charge / discharge cycles, total usage time, environmental and operating condition records for each use, as well as information on the depth and rate of charge / discharge for each cycle. This data comprehensively reflects the battery pack's usage history and conditions. By integrating and analyzing this data using a pre-set aging assessment model, various factors affecting battery aging can be comprehensively considered, avoiding the one-sidedness of single-factor assessments and making the determination of aging factors more scientific and accurate. Accurate assessment of the current aging factor helps the system more precisely determine the degree of battery pack performance degradation, providing a reliable basis for adjusting temperature control strategies based on aging conditions. This helps to more rationally regulate the battery pack temperature, slow down the aging rate, ensure stable operation of the battery pack at different aging stages, thereby improving the overall performance and safety of new energy vehicles, extending battery pack lifespan, and reducing vehicle operating costs.

[0109] Example 5:

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

[0111] Based on the collected usage records 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 were evaluated.

[0112] Based on the 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 environmental data over a period of time, a functional model of the aging factor of battery pack and the influence coefficient of battery pack temperature of current models of new energy vehicles is established under each working condition and environmental data.

[0113] The current aging factor of the battery pack is substituted into the function model of the aging factor of the battery pack and the temperature influence coefficient of the current model of new energy vehicle battery pack under the current operating conditions and current environmental data of new energy vehicle, and the temperature influence coefficient of the first battery pack is determined.

[0114] In this embodiment, the aging factor of battery packs from a large number of current-model new energy vehicles is evaluated at different times based on collected usage records. This means that by acquiring usage record information such as the number of charge / discharge cycles, total usage time, environmental data for each use, charge / discharge depth, and rate of many battery packs of the same model, a preset aging assessment model is used to calculate the aging factor values ​​of these battery packs at different usage times. For example, if usage records of battery packs from 100 vehicles of the same model are collected, the records for different usage stages of each vehicle are analyzed to obtain the aging factor for each stage.

[0115] In this embodiment, based on a large amount of collected aging factor data of battery packs of current models of new energy vehicles at different times and corresponding battery pack temperature change data of all current models of new energy vehicles under the same operating conditions and environmental data over a time period, a functional model is established for the relationship between the aging factor of the battery pack and the temperature influence coefficient of the battery pack of the current model of new energy vehicles under each operating condition and environmental data. Specifically, assuming that under constant speed operating conditions and an ambient temperature of 25°C, the aging factors at different times are collected as 0.2, 0.3, and 0.4, respectively, the corresponding battery pack temperature changes are increases of 2°C, 3°C, and 4°C, respectively. With the aging factor as the independent variable x and the temperature influence coefficient (which can be obtained through the relationship between temperature change and a certain benchmark value, such as temperature influence coefficient = temperature change value / 10) as the dependent variable y, a simple functional model y = 10x can be established. In practice, data under various operating conditions (acceleration, deceleration, etc.) and environmental data (different temperatures, humidity, etc.) will be collected to establish a more complex and general functional model to describe the relationship between the aging factor and the temperature influence coefficient.

[0116] The beneficial effects of the above technologies are as follows: A large amount of usage records of current new energy vehicle battery packs are collected, and aging factors at different times are evaluated, accumulating a rich and comprehensive data foundation. This makes research on battery pack aging more universal and representative. Using this aging factor data, combined with battery pack temperature change data under the same operating conditions and environments, a function model is established. This model accurately reflects the intrinsic relationship between aging factors and temperature influence coefficients under different operating conditions and environments, providing a scientific basis for the subsequent accurate calculation of the temperature influence coefficient. Substituting the current aging factors of the battery pack into the function model under specific operating conditions and environments yields the first battery pack temperature influence coefficient. This allows for precise measurement of the degree of aging's impact on temperature based on the real-time aging status of the battery pack. This helps the system to more rationally adjust temperature control strategies based on the battery pack's aging status, achieving refined management of battery pack temperature, ensuring stable battery pack operation, extending battery life, and improving the overall performance and reliability of new energy vehicles.

[0117] Example 6:

[0118] Based on Example 1, the temperature influence coefficient of the second battery pack is determined based on the current ambient temperature, including:

[0119] Based on a large amount of collected data on battery pack temperature changes of current new energy vehicle models under the same operating conditions, the same aging factor, and all environmental data except ambient temperature that correspond to the current environmental data of the new energy vehicle but with different ambient temperatures, a function model is established for the influence coefficient of ambient temperature on the battery pack temperature of current new energy vehicle models under each operating condition, each aging factor, and all environmental data except ambient temperature that correspond to the current environmental data of the new energy vehicle.

[0120] The temperature influence coefficient of the second battery pack is determined by substituting the current ambient temperature into a function model that corresponds to the current operating conditions, current aging factor, and all environmental data except ambient temperature with the current environmental data of the new energy vehicle.

[0121] In this embodiment, the environmental data remaining besides ambient temperature refers to other environmental factors that may affect the battery pack temperature, such as ambient humidity and air pressure. For example, ambient humidity may affect battery heat dissipation, and air pressure may also affect the internal chemical reactions of the battery to some extent, thus affecting the temperature. These data, together with ambient temperature, constitute the environmental conditions for battery operation, but this specifically refers to other environmental factors excluding ambient temperature.

[0122] In this embodiment, based on a large amount of collected battery pack temperature change data for current models of new energy vehicles under the same operating conditions, the same aging factor, and with all environmental data except ambient temperature consistent with the current environmental data of the new energy vehicles but with different ambient temperatures, a function model is established for the influence coefficient of ambient temperature on the battery pack temperature of the current models of new energy vehicles under each operating condition, each aging factor, and with all environmental data except ambient temperature consistent with the current environmental data of the new energy vehicles. For example, assuming the current operating condition is constant speed driving, the aging factor is 0.5, and the ambient humidity is 60% and the air pressure is standard atmospheric pressure, data on battery pack temperature changes under these environmental conditions at different ambient temperatures (such as 20℃, 25℃, 30℃, etc.) are collected. Using ambient temperature as the independent variable and the temperature influence coefficient calculated based on battery pack temperature changes as the dependent variable (for example, the temperature influence coefficient is determined by comparing the ratio of battery pack temperature changes under different ambient temperatures to temperature changes under a fixed standard state), a functional model is established by analyzing a large amount of such data. This model reflects the relationship between ambient temperature and the temperature influence coefficient under specific operating conditions, aging factors, and other fixed environmental data. For example, the functional relationship might be y = 0.2x + 1 (this is just an example; the actual relationship would be more complex). This model is then used to determine the temperature influence coefficient based on the current ambient temperature.

[0123] The beneficial effects of the above technology are as follows: It collects a large amount of data on battery pack temperature changes under specific conditions, focusing solely on ambient temperature variations in current new energy vehicle battery packs. This targeted data collection method highlights the impact of ambient temperature on battery pack temperature, providing detailed evidence for accurate modeling. Based on this, a function model of the influence coefficient between ambient temperature and battery pack temperature is established under various operating conditions, aging factors, and other environmental data. This model comprehensively covers different situations and accurately depicts the complex relationship between 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 degree of influence of the current ambient temperature on the battery pack temperature in real time and accurately. This allows for full consideration of ambient temperature factors when regulating battery pack temperature, improving the accuracy and effectiveness of temperature control, optimizing the battery pack's working environment, ensuring stable battery pack operation, further extending battery pack lifespan, and guaranteeing the performance and safety of new energy vehicles in different environments.

[0124] Example 7:

[0125] Based on Example 1, S3: The current temperature anomaly level of the battery pack is corrected based on the temperature influence coefficients of the first and second battery packs to obtain the corrected temperature anomaly level of the battery pack, with reference to… Figure 3 ,include:

[0126] A continuous aging factor sequence is generated based on the current aging factors. Each aging factor in the continuous aging factor sequence is then substituted into the function model of the aging factor of the battery pack and the temperature influence coefficient of the current model of the new energy vehicle battery pack under the current operating conditions and current environmental data of the new energy vehicle to obtain the first influence coefficient sequence.

[0127] A continuous ambient temperature sequence is generated based on the current ambient temperature. Each ambient temperature in the continuous ambient temperature sequence is then substituted into a function model that corresponds to the current operating conditions, current aging factor, and all other environmental data except ambient temperature with the current environmental data of the new energy vehicle, to obtain a second influence coefficient sequence.

[0128] The current temperature anomaly level of the battery pack is corrected based on the temperature influence coefficient of the first battery pack to obtain the first corrected anomaly level.

[0129] The current temperature anomaly level of the battery pack is corrected based on the temperature influence coefficient of the second battery pack to obtain the second corrected anomaly level.

[0130] The corrected temperature anomaly degree of the battery pack is obtained based on the first influence coefficient sequence, the second influence coefficient sequence, the first correction anomaly degree, and the second correction 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 according to a certain rule based on the currently obtained battery pack aging factor, forming a sequence. For example, assuming the current aging factor is 0.3, the sequence is generated with a step size of 0.05: [0.25, 0.3, 0.35, 0.4].

[0132] In this embodiment, each aging factor in the continuous aging factor sequence is substituted into the function model of the battery pack's aging factor and the temperature influence coefficient of the current model of the new energy vehicle battery pack under the current operating conditions and environmental data of the new energy vehicle, to obtain the first influence coefficient sequence. That is, for each value in the above-generated continuous aging factor sequence, such as 0.25, 0.3, 0.35, and 0.4, it is substituted 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) to calculate the corresponding temperature influence coefficient value, forming a new sequence. Substituting 0.25, we get y = 2 × 0.25 + 0.5 = 1; substituting 0.3, we get 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, generating a continuous ambient temperature sequence based on the current ambient temperature means using the current ambient temperature as the intermediate value and generating a set of continuously changing ambient temperature values ​​according to certain rules to form a sequence. For example, if the current ambient temperature is 25℃, a sequence is generated with 2℃ intervals: [21℃, 23℃, 25℃, 27℃, 29℃].

[0134] In this embodiment, each ambient temperature in the continuous ambient temperature sequence is substituted into a function model that corresponds to the current operating conditions, current aging factor, and all environmental data other than ambient temperature with the current environmental data of the new energy vehicle battery pack, to obtain a second influence coefficient sequence. For each temperature value in the generated continuous ambient temperature sequence, such as 21℃, 23℃, etc., it is substituted into a specific function model reflecting the relationship between ambient temperature and temperature influence coefficient (assuming 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℃, we get y = 0.1 × 21 - 0.5 = 1.6, and substituting 23℃, we get y = 0.1 × 23 - 0.5 = 1.8, thus obtaining the second influence coefficient sequence [1.6, 1.8, 2, 2.2, 2.4].

[0135] In this embodiment, the current temperature anomaly level of the battery pack is corrected based on the temperature influence coefficient of the first battery pack to obtain a first corrected anomaly level. Assuming the current temperature anomaly level is 0.8 and the temperature influence coefficient of the first battery pack is 1.2, the first corrected anomaly level is obtained by using a preset correction algorithm (e.g., multiplication) as 0.8 × 1.2 = 0.96. This value initially considers the impact of battery aging on the current temperature anomaly level.

[0136] In this embodiment, the current temperature anomaly level of the battery pack is corrected based on the temperature influence coefficient of the second battery pack to obtain a second corrected anomaly level. Similarly, assuming the current temperature anomaly level is 0.8 and the temperature influence coefficient of the second battery pack is 1.5, according to a preset correction algorithm (such as multiplication), the second corrected anomaly level is obtained as 0.8 × 1.5 = 1.2. This value initially considers the influence of ambient temperature on the current temperature anomaly level.

[0137] The beneficial effects of the above technologies are as follows: Generating a continuous aging factor sequence and substituting it into a function model to obtain the first influence coefficient sequence allows for dynamic analysis of the changes in the temperature influence coefficient under different aging degrees, fully considering the dynamic impact of battery aging on temperature anomaly judgment and improving 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 changes in the temperature influence coefficient under different ambient temperatures, making the temperature anomaly judgment more consistent with actual environmental changes. The current temperature anomaly degree is corrected based on the first and second battery pack temperature influence coefficients respectively, resulting in the first and second corrected anomaly degrees. This provides an initial adjustment to the anomaly degree from two key dimensions: aging and ambient temperature, laying the foundation for final precise correction. Combining the first and second influence coefficient sequences and the two corrected anomaly degrees to obtain the corrected temperature anomaly degree takes into account the combined effects of battery aging and ambient temperature changes on temperature anomaly judgment, greatly optimizing the assessment of temperature anomaly degree. This provides a reliable basis for more reasonable and accurate formulation of battery pack temperature control strategies, effectively ensuring stable battery pack operation, extending its service life, and improving the safety and reliability of new energy vehicles.

[0138] Example 8:

[0139] Based on Example 7, the corrected temperature anomaly degree of the battery pack is obtained based on the first influence coefficient sequence, the second influence coefficient sequence, the first correction anomaly degree, and the second correction anomaly degree, with reference to... Figure 3 ,include:

[0140] Align the continuous aging factor sequence and the continuous ambient temperature sequence, and summarize all environmental data except ambient temperature that correspond to the current operating conditions and the current environmental data of new energy vehicles in each aligned aging factor and ambient temperature as the basis for single instance retrieval.

[0141] Based on the battery pack temperature change data of the current model of new energy vehicle that meets the retrieval criteria for each set of instances, the actual degree of temperature anomaly correction is determined.

[0142] The first and second influence coefficients with the same sorting value in the first and second influence coefficient sequences are used as the horizontal and vertical coordinate values, respectively, and the coordinates of the point to be corrected corresponding to each sorting value are marked in the preset two-dimensional coordinate system.

[0143] At the same time, each actual corrected temperature anomaly degree determined by the same sorting value in the continuous aging factor sequence and the continuous ambient temperature sequence is used as the horizontal and vertical coordinate values, and the anchor point coordinates corresponding to each sorting value are marked in the preset two-dimensional coordinate system.

[0144] The vector pointing from the coordinates of the point to be corrected for each sorted value to the coordinates of the corresponding anchor point is used as a two-dimensional correction vector;

[0145] Based on the starting coordinates of all two-dimensional correction vectors and the coordinates of the points marked in the preset two-dimensional coordinate system using the first correction anomaly degree and the second correction anomaly degree as the abscissa and ordinate values ​​respectively, the interpolation distance of each two-dimensional correction vector is determined.

[0146] The two-dimensional correction vectors under the first and second correction anomalies are calculated based on all two-dimensional correction vectors and the corresponding interpolation distances.

[0147] The corrected temperature anomaly level of the battery pack is obtained based on the two-dimensional correction vector under the first and second correction anomaly levels.

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

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

[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 for each set of instances, the actual corrected temperature anomaly level is determined. That is, by analyzing and processing these battery pack temperature change data that meet the conditions, a value reflecting the actual temperature anomaly level is obtained according to certain rules (possibly a preset algorithm, such as calculation combining ideal temperature range, temperature change rate, etc.). For example:

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

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

[0153] Assuming that it took 10 minutes for the temperature to rise from 28℃ to 32℃, the calculated rate of temperature change is 0.4℃ / minute.

[0154] Set a baseline rate, such as 0.2℃ / minute, and add 0.1 points for every 0.1℃ / minute exceeding the baseline rate (points are deducted here because an excessively rapid temperature rise may indicate a more serious anomaly).

[0155] The portion exceeding the baseline rate is 0.2℃ / minute, corresponding to a deduction of 0.2 ÷ 0.1 × 0.1 = 0.2 points. Therefore, the score for the rate of temperature change is 0.2 points.

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

[0157] Substituting the above calculation results, the actual corrected temperature anomaly degree = 1.0 + 0.4 + 0.2 = 1.6.

[0158] In this embodiment, the preset two-dimensional coordinate system is a pre-defined two-dimensional coordinate system 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, the specific physical meaning of which is set according to actual needs. Here, the horizontal axis may represent influencing factors related to aging, and the vertical axis may represent influencing factors related to ambient temperature (or vice versa).

[0159] In this embodiment, each actual corrected temperature anomaly degree determined by the same sorting value in the continuous aging factor sequence and the continuous ambient temperature sequence is used as both the horizontal and vertical coordinate values. The anchor point coordinates corresponding to each sorting value are then marked in a preset two-dimensional coordinate system. For example, in the continuous aging factor sequence [0.25, 0.3, 0.35] and the continuous ambient temperature sequence [21℃, 23℃, 25℃], for a sorting value of 1, the actual corrected temperature anomaly degree determined by the aging factor 0.25 and ambient temperature 21℃ is 1.2. Therefore, the coordinate point (1.2, 1.2) is marked in the preset two-dimensional coordinate system as the anchor point coordinates. Similarly, other sorting values ​​are marked.

[0160] In this embodiment, the interpolation distance of each two-dimensional correction vector is determined based on the starting coordinates of all two-dimensional correction vectors and the coordinates of the point marked in a preset two-dimensional coordinate system using the first correction anomaly degree and the second correction anomaly degree as the x-coordinate and y-coordinate values, respectively. Assuming the first correction anomaly degree is 0.9 and the second correction anomaly degree is 1.0, the point (0.9, 1.0) is marked in the coordinate system. The starting coordinates of the two-dimensional correction vector are (0.5, 0.6). The distance between the two points is calculated using the distance formula between two points (such as the Euclidean distance formula), thus obtaining the interpolation distance of the two-dimensional correction vector.

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

[0162] The beneficial effects of the above technologies are as follows: Aligning continuous aging factor sequences and continuous ambient temperature sequences, and summarizing relevant data as the basis for single-set instance retrieval, this integration method makes data processing more systematic and logical. It can comprehensively consider the impact of multiple factors such as battery aging and ambient temperature on battery pack temperature changes, providing a solid data foundation for accurately determining the actual corrected temperature anomaly level. Determining the actual corrected temperature anomaly level through actual battery pack temperature change data ensures that the evaluation results are closely related to the actual working condition of the battery pack, improving the accuracy and reliability of the evaluation. Marking the coordinates of the point to be corrected and the anchor point coordinates in a preset two-dimensional coordinate system, and determining the two-dimensional correction vector, quantifies the direction and degree of correction based on aging factors and ambient temperature in an intuitive geometric way, making the correction process visual and easy to understand and analyze. Calculating the interpolation distance of the two-dimensional correction vector, and calculating the two-dimensional correction vector under specific conditions based on this, this refined calculation method fully considers the differences in correction vectors under different data combinations, enabling more precise adjustments to the first and second correction anomaly levels according to actual conditions. Ultimately, the degree of temperature anomaly of the battery pack is obtained based on these two-dimensional correction vectors. This comprehensively and accurately integrates the influence of factors such as aging and ambient temperature on the judgment of temperature anomalies, providing a highly accurate basis for formulating more scientific and reasonable battery pack temperature control strategies. This helps 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] Example 9:

[0164] Based on Example 8, the corrected temperature anomaly level of the battery pack is obtained based on the two-dimensional correction vector under the first and second corrected anomaly levels, with reference to... Figure 3 ,include:

[0165] Based on the two-dimensional correction vectors under the first and second correction anomalies, and the point coordinates calibrated in the preset two-dimensional coordinate system using the first and second correction anomalies as the abscissa and ordinate values ​​respectively, the corresponding anchor point coordinates are fitted.

[0166] If the deviation of the horizontal and vertical coordinates of the corresponding anchor point is less than a preset deviation threshold, then the average value of the horizontal and vertical coordinates of the corresponding anchor point is taken as the corrected temperature anomaly degree of the battery pack. Otherwise, a new continuous aging factor sequence and a continuous ambient temperature sequence are obtained, and a new two-dimensional correction vector is determined based on the new continuous aging factor sequence and the continuous ambient temperature sequence under the first correction anomaly degree and the second correction anomaly degree. This process continues until the deviation of the horizontal and vertical coordinates of the new anchor point determined based on the new two-dimensional correction vector does not exceed the preset deviation threshold. Then, the average value of the horizontal and vertical coordinates of the corresponding new anchor point is taken as the corrected temperature anomaly degree of the battery pack.

[0167] In this embodiment, based on the two-dimensional correction vectors under the first and second correction anomaly degrees, and the point coordinates marked in a preset two-dimensional coordinate system using the first and second correction anomaly degrees as the abscissa and ordinate values ​​respectively, the corresponding anchor point coordinates are fitted. Assuming the first correction anomaly degree is x1 and the second correction anomaly degree is y1, the point (x1, y1) is marked in the coordinate system. The two-dimensional correction vector is (Δx, Δy). By adding the vector to the point coordinates, a new point coordinate is obtained, i.e., the corresponding anchor point coordinates (x2, y2), where x2 = x1 + Δx, and y2 = y1 + Δy.

[0168] In this embodiment, the deviation of the anchor point coordinates is used to measure the degree of difference between the horizontal and vertical coordinates of the anchor point. It can be expressed by calculating the ratio of the absolute value of the difference between the horizontal and vertical coordinates to the horizontal coordinate value, i.e., deviation = |x2-y2| ÷ x2. This value reflects the magnitude of the difference in the impact of temperature anomalies on the two dimensions after correction based on aging and ambient temperature.

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

[0170] In this embodiment, the average value of the horizontal and vertical coordinates of the anchor point is (x2+y2)÷2. When the deviation of the horizontal and vertical coordinates of the anchor point is less than a preset deviation threshold, this average value is used as the corrected temperature anomaly degree of the battery pack, taking into account 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 new two-dimensional correction vectors are determined based on the new continuous aging factor sequence and the continuous ambient temperature sequence under the first correction anomaly degree and the second correction anomaly degree. If the deviation of the horizontal and vertical coordinates of the anchor point is greater than a preset deviation threshold, it indicates that the current correction of the temperature anomaly degree based on the aging factor and ambient temperature is not accurate enough. At this time, a new continuous aging factor sequence is regenerated (e.g., by changing the generation step size or range) and a continuous ambient temperature sequence (the generation method can also be adjusted). Then, following the method for determining the two-dimensional correction vector previously, that is, recalculating the first and second influence coefficient sequences based on the new sequences, and combining the first and second correction anomaly degrees, new two-dimensional correction vectors are determined under the first correction anomaly degree and the second correction anomaly degree under the new sequences, so as to further optimize the correction of the battery pack temperature anomaly degree.

[0172] The beneficial effects of the above technology are as follows: By fitting anchor point coordinates with a two-dimensional correction vector and relevant coordinates, the information on the correction of temperature anomalies by aging factors and ambient temperature is comprehensively integrated, making the results more scientific and holistic. The deviation of the anchor point coordinates' horizontal and vertical axes is judged against a preset deviation threshold. If the deviation is less than the threshold, the average value of the anchor point coordinates' horizontal and vertical axes is used as the corrected temperature anomaly level, ensuring accurate and reliable results. If the deviation does not meet the requirements, a new sequence is obtained to re-determine the two-dimensional correction vector until the new anchor point coordinate deviation meets the standard. Through iterative optimization, the final result accurately reflects the actual condition of the battery, providing a precise basis for battery pack temperature control, ensuring stable battery pack operation, extending service life, and improving the safety and reliability of new energy vehicles.

[0173] Example 10:

[0174] Based on implementation 1, S4: Determine the liquid cooling strategy for the battery pack based on the degree of temperature anomaly correction, and activate the liquid cooling equipment to cool the battery pack based on the liquid cooling strategy, including:

[0175] Build a model for generating liquid cooling strategies for battery packs;

[0176] The corrected temperature anomaly level of the battery pack is input into the liquid cooling strategy generation model of the battery pack to obtain the liquid cooling strategy of the battery pack.

[0177] The liquid cooling strategy for battery packs is used to activate liquid cooling equipment to cool the battery packs.

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

[0179] In this embodiment, the liquid cooling strategy for the battery pack is based on the degree of temperature anomaly correction of the battery pack. A series of commands for controlling the liquid cooling equipment are generated through a liquid cooling strategy generation model. These commands include, but are not limited to, adjustments to the coolant flow rate and velocity, and power adjustments to the cooling equipment, with the aim of restoring the battery pack temperature to a suitable range. For example, the strategy might specify that when the degree of temperature anomaly correction is within a certain range, the coolant flow rate is set to 5 liters / minute, and the cooling equipment power is adjusted to 3 kilowatts.

[0180] The beneficial effects of the above technologies are as follows: Establishing a liquid cooling strategy generation model provides an effective framework for generating scientifically sound cooling strategies, ensuring that strategy formulation is systematic and avoids haphazard approaches. Using the degree of temperature anomaly correction of the battery pack as model input ensures that the generated liquid cooling strategy closely matches the actual temperature anomaly conditions of the battery pack, making it more targeted and able to accurately match corresponding cooling strategies based on different degrees of temperature anomalies. Activating the liquid cooling equipment based on the generated strategy achieves efficient integration from evaluation to execution, enabling the battery pack to receive timely and appropriate cooling treatment, ensuring its stable operation within a suitable temperature range. This method of determining and executing cooling strategies based on precise evaluation effectively prevents battery performance degradation and shortened lifespan due to excessive temperature, greatly improving the reliability and durability of the battery pack, providing strong support for the stable operation of new energy vehicles, and thus improving the safety and performance of the entire vehicle.

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

Claims

1. A method for temperature control of a power battery pack for new energy vehicles, characterized in that, include: S1: Based on the current operating condition records of new energy vehicles, the dynamic battery pack temperature model under each operating condition, and the actual temperature record data of the battery pack, determine the current temperature anomaly level 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 records 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. At the same time, the second battery pack temperature influence coefficient is determined based on the current ambient temperature. S3: Based on the temperature influence coefficients of the first and second battery packs, the current temperature anomaly level of the battery pack is corrected to obtain the corrected temperature anomaly level of the battery pack, including: A continuous aging factor sequence is generated based on the current aging factors. Each aging factor in the continuous aging factor sequence is then substituted into the function model of the aging factor of the battery pack and the temperature influence coefficient of the current model of the new energy vehicle battery pack under the current operating conditions and current environmental data of the new energy vehicle to obtain the first influence coefficient sequence. A continuous ambient temperature sequence is generated based on the current ambient temperature. Each ambient temperature in the continuous ambient temperature sequence is then substituted into a function model that corresponds to the current operating conditions, current aging factor, and all other environmental data except ambient temperature with the current environmental data of the new energy vehicle, to obtain a second influence coefficient sequence. The current temperature anomaly level of the battery pack is corrected based on the temperature influence coefficient of the first battery pack to obtain the first corrected anomaly level. The current temperature anomaly level of the battery pack is corrected based on the temperature influence coefficient of the second battery pack to obtain the second corrected anomaly level. Align the continuous aging factor sequence and the continuous ambient temperature sequence, and summarize all environmental data except ambient temperature that correspond to the current operating conditions and the current environmental data of new energy vehicles in each aligned aging factor and ambient temperature as the basis for single instance retrieval. Based on the battery pack temperature change data of the current model of new energy vehicle that meets the retrieval criteria for each set of instances, the actual degree of temperature anomaly correction is determined. The first and second influence coefficients with the same sorting value in the first and second influence coefficient sequences are used as the horizontal and vertical coordinate values, respectively, and the coordinates of the point to be corrected corresponding to each sorting value are marked in the preset two-dimensional coordinate system. At the same time, each actual corrected temperature anomaly degree determined by the same sorting value in the continuous aging factor sequence and the continuous ambient temperature sequence is used as the horizontal and vertical coordinate values, and the anchor point coordinates corresponding to each sorting value are marked in the preset two-dimensional coordinate system. The vector pointing from the coordinates of the point to be corrected for each sorted value to the coordinates of the corresponding anchor point is used as a two-dimensional correction vector; Based on the starting coordinates of all two-dimensional correction vectors and the coordinates of the points marked in the preset two-dimensional coordinate system using the first correction anomaly degree and the second correction anomaly degree as the abscissa and ordinate values ​​respectively, the interpolation distance of each two-dimensional correction vector is determined. The two-dimensional correction vectors under the first and second correction anomalies are calculated based on all two-dimensional correction vectors and the corresponding interpolation distances. The corrected temperature anomaly level of the battery pack is obtained based on the two-dimensional correction vector under the first and second correction anomaly levels. S4: Determine the liquid cooling strategy for the battery pack based on the degree of temperature anomaly correction, and start the liquid cooling equipment to cool the battery pack based on the liquid cooling strategy.

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

3. The temperature control method for a new energy vehicle power battery pack according to claim 2, characterized in that, Based on partial actual temperature records from the battery pack's actual temperature data during the current complete continuous operating period and ideal temperature records from the battery pack's actual temperature data during the current complete continuous operating period, the current degree of temperature anomaly in the battery pack is determined, including: Filter out all abnormal temperature values ​​in the actual temperature record data of the battery pack within the current complete continuous working period, and determine the time period in which all abnormal temperature values ​​occurred; The degree of the first temperature anomaly of the battery pack was determined based on all abnormal temperature values ​​and their corresponding time periods. The deviation between the actual temperature record data of the battery pack during the current complete continuous working period and the ideal temperature record data of the battery pack during the current complete continuous working period is calculated as the second degree of temperature anomaly of the battery pack. The current temperature anomaly level of the battery pack is determined based on the first and second temperature anomaly levels of the battery pack.

4. The temperature control method for a new energy vehicle power battery pack according to claim 1, characterized in that, The current aging factor of the battery pack is assessed based on the usage records of the battery pack in new energy vehicles, including: Based on the usage records of the battery packs of new energy vehicles, the number of charge and discharge cycles, total usage time, environmental data recorded during each use, and the depth of charge and discharge and charge and discharge rate of each charge and discharge process are determined. Based on a pre-set aging assessment model, the current aging factor of the battery pack is determined by integrating and calculating the number of charge and discharge cycles, total usage time, environmental data recorded during each use, charge and discharge depth and charge and discharge rate of each charge and discharge process.

5. The temperature control method for a new energy vehicle power battery pack according to claim 1, characterized in that, The temperature influence coefficient of the first battery pack is determined based on the current aging factor of the battery pack, including: Based on the collected usage records 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 were evaluated. Based on the 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 environmental data over a period of time, a functional model of the aging factor of battery pack and the influence coefficient of battery pack temperature of current models of new energy vehicles is established under each working condition and environmental data. The current aging factor of the battery pack is substituted into the function model of the aging factor of the battery pack and the temperature influence coefficient of the current model of new energy vehicle battery pack under the current operating conditions and current environmental data of new energy vehicle, and the temperature influence coefficient of the first battery pack is determined.

6. The temperature control method for a new energy vehicle power battery pack according to claim 1, characterized in that, The temperature influence coefficient of the second battery pack is determined based on the current ambient temperature, including: Based on a large amount of collected data on battery pack temperature changes of current new energy vehicle models under the same operating conditions, the same aging factor, and all environmental data except ambient temperature that correspond to the current environmental data of the new energy vehicle but with different ambient temperatures, a function model is established for the influence coefficient of ambient temperature on the battery pack temperature of current new energy vehicle models under each operating condition, each aging factor, and all environmental data except ambient temperature that correspond to the current environmental data of the new energy vehicle. The temperature influence coefficient of the second battery pack is determined by substituting the current ambient temperature into a function model that corresponds to the current operating conditions, current aging factor, and all environmental data except ambient temperature with the current environmental data of the new energy vehicle.

7. The method for temperature control of a new energy vehicle power battery pack according to claim 1, characterized in that, The corrected temperature anomaly level of the battery pack is obtained based on a two-dimensional correction vector under the first and second correction anomaly levels, including: Based on the two-dimensional correction vectors under the first and second correction anomalies, and the point coordinates calibrated in the preset two-dimensional coordinate system using the first and second correction anomalies as the abscissa and ordinate values ​​respectively, the corresponding anchor point coordinates are fitted. If the deviation of the horizontal and vertical coordinates of the corresponding anchor point is less than a preset deviation threshold, then the average value of the horizontal and vertical coordinates of the corresponding anchor point is taken as the corrected temperature anomaly degree of the battery pack. Otherwise, a new continuous aging factor sequence and a continuous ambient temperature sequence are obtained, and a new two-dimensional correction vector is determined based on the new continuous aging factor sequence and the continuous ambient temperature sequence under the first correction anomaly degree and the second correction anomaly degree. This process continues until the deviation of the horizontal and vertical coordinates of the new anchor point determined based on the new two-dimensional correction vector does not exceed the preset deviation threshold. Then, the average value of the horizontal and vertical coordinates of the corresponding new anchor point is taken as the corrected temperature anomaly degree of the battery pack.

8. The method for temperature control of a new energy vehicle power battery pack according to claim 1, characterized in that, S4: Determine the liquid cooling strategy for the battery pack based on the degree of temperature anomaly correction, and activate the liquid cooling equipment to cool the battery pack based on the liquid cooling strategy, including: Build a model for generating liquid cooling strategies for battery packs; The corrected temperature anomaly level of the battery pack is input into the liquid cooling strategy generation model of the battery pack to obtain the liquid cooling strategy of the battery pack. The liquid cooling strategy for battery packs is used to activate liquid cooling equipment to cool the battery packs.

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