Thermal management system and method for electric commercial vehicle

Abstract

The invention discloses a thermal management system and method for an electric commercial vehicle. The thermal management system comprises a data acquisition module, a data processing module and a thermal management control module, relates to the technical field of vehicle thermal management, and solves the technical problem of low thermal management efficiency of an electric commercial vehicle in the prior art. According to the method, the initial temperature of the battery pack and the weather forecast information of the target charging station are collected, the predicted charging time is calculated in combination with the driving data and the charging distance, the charging environment temperature is determined, and accurate thermal management of the battery pack is achieved. According to the system, the charging efficiency and safety of the battery in different environments are effectively improved, energy loss and battery aging caused by temperature discomfort are reduced, and the service life of the battery is prolonged. Meanwhile, based on the thermal management mode adjusted in real time, it can be ensured that the battery is always in the optimal working temperature range, and the charging efficiency and the vehicle running stability are improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of vehicle thermal management, and particularly relates to a thermal management system and method for electric commercial vehicles. Background Art

[0002] In order to improve the economy of vehicle operation, electric commercial vehicles require large battery capacity and fast charging speed. Therefore, fast-charging batteries are increasingly widely used in the commercial vehicle field.

[0003] The prior art realizes battery thermal management of commercial vehicles to a certain extent by obtaining the real-time temperature of the vehicle battery pack in real time, comparing the real-time temperature with a preset temperature threshold, and adjusting the temperature of the vehicle battery pack according to the comparison result. However, in actual situations, it is difficult for the prior art to accurately estimate the time required for temperature adjustment of the battery pack in advance. Premature temperature adjustment will cause additional power consumption, and too late temperature adjustment will cause the temperature of the battery pack not to reach the optimal charging temperature range when the commercial vehicle arrives at the charging station, resulting in low thermal management efficiency of electric commercial vehicles.

[0004] The present invention provides a thermal management system and method for electric commercial vehicles to solve the above technical problems. Summary of the Invention

[0005] The present invention aims to solve at least one of the technical problems existing in the prior art; for this purpose, the present invention provides a thermal management system and method for electric commercial vehicles, which are used to solve the technical problem that it is difficult for the prior art to accurately calculate the time required for temperature adjustment of the battery pack, resulting in low thermal management efficiency of electric commercial vehicles.

[0006] To achieve the above object, a first aspect of the present invention provides a thermal management system for electric commercial vehicles, including: a data processing module, and a data acquisition module and a thermal management control module connected thereto;

[0007] The data acquisition module: is used to collect the initial temperature of the battery pack in the target vehicle and the weather forecast information of the target charging station; obtain the driving information and charging distance of the target vehicle; wherein, the driving information includes the average vehicle speed and the remaining battery power; the charging distance is the navigation distance between the target vehicle and the target charging station;

[0008] The data processing module: is used to calculate the driving duration to reach the target charging station based on the driving information and charging distance of the target vehicle; determine the charging environment temperature of the target vehicle based on the estimated charging time and the weather forecast information; wherein, the estimated charging time is obtained by adding the current time to the driving duration;

[0009] The thermal management control module: is used to perform thermal management on the target vehicle based on the initial temperature of the battery pack, the driving duration, and the charging ambient temperature.

[0010] Preferably, calculating the driving duration to reach the target charging station based on the driving information and charging distance of the target vehicle includes:

[0011] Extracting the average vehicle speed, remaining battery power, and charging distance from the driving information of the target vehicle; determining the cruising range of the target vehicle based on the remaining battery power; determining whether the cruising range is less than the charging distance; if so, navigating to the nearest charging station within the cruising range; if not, calculating the ratio of the charging distance to the average vehicle speed to obtain the driving duration.

[0012] Preferably, determining the cruising range of the target vehicle based on the remaining battery power includes:

[0013] Obtaining the remaining battery power and average power consumption of the target vehicle, calculating the ratio of the remaining battery power to the average power consumption, and obtaining the cruising range of the target vehicle.

[0014] Preferably, determining the charging ambient temperature of the target vehicle based on the estimated charging time and weather forecast information includes:

[0015] Extracting the estimated charging time of the target vehicle and the weather forecast information of the target charging station; wherein, the weather forecast information is temperature data that changes over time; obtaining the temperature data corresponding to the estimated charging time, and marking the temperature data at the corresponding time as the charging ambient temperature.

[0016] Preferably, performing thermal management on the target vehicle based on the initial temperature of the battery pack, the driving duration, and the charging ambient temperature includes:

[0017] X1: Extracting the initial temperature of the battery pack, the driving duration, and the charging ambient temperature; obtaining the average vehicle speed and remaining battery power of the target vehicle; calculating the sum of the current time and the driving duration and marking it as the estimated charging time;

[0018] X2: Inputting the initial temperature of the battery pack, the estimated charging time, the average vehicle speed, and the remaining battery power of the target vehicle into the battery pack temperature prediction model to obtain the predicted battery temperature of the battery pack at the estimated charging time; wherein, the battery pack temperature prediction model is constructed based on an artificial intelligence model;

[0019] X3: Obtaining the corresponding temperature adjustment rate based on the temperature change data of the target vehicle in different temperature control modes; wherein, the temperature control modes include the heating mode and the cooling mode;

[0020] X4: Calculating the temperature control adjustment duration based on the predicted battery temperature, the charging ambient temperature, and the temperature adjustment rate;

[0021] X5: Determine whether the temperature control adjustment duration is greater than the driving duration; if yes, immediately activate the thermal management device to adjust the temperature of the battery pack of the target vehicle; if no, calculate the difference between the estimated charging time and the temperature control adjustment duration to obtain the temperature control adjustment start time;

[0022] X6: Determine whether the current time is equal to the temperature control adjustment start time; if yes, automatically activate the thermal management device to adjust the temperature of the battery pack of the target vehicle; if no, temporarily do not adjust the temperature of the battery pack of the target vehicle.

[0023] Preferably, the battery pack temperature prediction model is constructed based on an artificial intelligence model, including:

[0024] Extract the initial temperature, remaining power, average vehicle speed of the battery pack in the target vehicle, and the actual temperature of the battery pack after the driving duration of the target vehicle; integrate the initial temperature, remaining power, average vehicle speed, and driving duration of the battery pack in the target vehicle into a standard input data set, and integrate the actual temperature of the battery pack after the driving duration of the target vehicle into a standard output data set; train the artificial intelligence model according to the standard input data set and the standard output data set, and mark the trained artificial intelligence model as the battery pack temperature prediction model; wherein, the artificial intelligence model includes a BP neural network model or an RBF neural network model.

[0025] Preferably, the obtaining of the corresponding temperature adjustment rate based on the temperature change data of the target vehicle under different temperature control modes includes:

[0026] Respectively obtain the temperature change data of the target vehicle in the heating mode and the cooling mode; linearly fit the temperature change data in the heating mode to obtain the heating adjustment rate; linearly fit the temperature change data in the cooling mode to obtain the cooling adjustment rate; integrate the heating adjustment rate and the cooling adjustment rate into the temperature adjustment rate.

[0027] Preferably, the calculation of the temperature control adjustment duration based on the predicted battery temperature, charging environment temperature, and temperature adjustment rate includes:

[0028] T1: Extract the predicted battery temperature, charging environment temperature, and temperature adjustment rate;

[0029] T2: Input the charging environment temperature into the charging temperature database for matching to obtain the corresponding optimal charging temperature range;

[0030] T3: Determine whether the predicted battery temperature is greater than the upper limit of the optimal charging temperature range; if yes, calculate the difference between the predicted battery temperature and the upper limit and mark it as the temperature difference; if no, jump to T4;

[0031] T4: Determine whether the predicted battery temperature is less than the lower limit of the optimal charging temperature range; if so, calculate the difference between the predicted battery temperature and the lower limit and label it as the temperature difference; if not, set the temperature control adjustment duration to 0;

[0032] T5: Calculate the temperature control adjustment duration WTS through the formula where WDC is the temperature difference, SWS is the heating adjustment rate, JWS is the cooling adjustment rate; k1 and k2 are both proportionality coefficients greater than 0; ln() is the natural logarithm function.

[0033] Preferably, the charging temperature database is constructed in the following manner:

[0034] Obtain the charging speed of the battery temperature data of the target vehicle at different charging ambient temperatures, mark the maximum battery temperature when the charging speed is equal to the preset rated charging speed as the upper temperature limit, and mark the minimum battery temperature when the charging speed is equal to the preset rated charging speed as the lower temperature limit; integrate the lower temperature limit and the upper temperature limit into the optimal charging temperature range of the target vehicle under the corresponding ambient temperature, and save the mapping relationship between several optimal charging temperature ranges and the corresponding charging ambient temperatures to the charging temperature database.

[0035] The second aspect of the present invention provides a thermal management method for electric commercial vehicles, including:

[0036] S1: Collect the initial temperature of the battery pack in the target vehicle and the weather forecast information of the target charging station;

[0037] S2: Obtain the driving information and charging distance of the target vehicle;

[0038] S3: Calculate the driving duration to reach the target charging station based on the driving information and charging distance of the target vehicle;

[0039] S4: Determine the charging ambient temperature of the target vehicle based on the estimated charging time and weather forecast information;

[0040] S5: Perform thermal management on the target vehicle based on the initial temperature of the battery pack, the driving duration, and the charging ambient temperature.

[0041] Compared with the prior art, the beneficial effects of the present invention are:

[0042] 1. The present invention collects the initial temperature of the battery pack and the weather forecast information of the target charging station, calculates the estimated charging time by combining the driving data and the charging distance, and determines the charging ambient temperature, so as to achieve precise thermal management of the battery pack. This system effectively improves the charging efficiency and safety of the battery in different environments, reduces the energy loss and battery aging caused by inappropriate temperature, and extends the service life of the battery. At the same time, based on the thermally managed mode adjusted in real time, it can ensure that the battery is always within the optimal operating temperature range, improving the charging efficiency and the vehicle operation stability.

[0043] 2. The present invention extracts key parameters such as the initial temperature of the battery pack, the driving duration, and the charging ambient temperature and calculates the estimated charging time. Using an advanced battery pack temperature prediction model, it accurately estimates the battery temperature at a certain future moment. Based on the temperature adjustment rate under different temperature control modes, combined with the predicted temperature and the charging ambient temperature, it precisely calculates the temperature control adjustment duration, and makes an intelligent decision based on this result whether to immediately start or delay the activation of the thermal management system until a specific time point. This method ensures that the battery pack reaches the optimal operating temperature before entering the charging state, effectively avoiding the low charging efficiency and potential safety hazards caused by inappropriate temperature. At the same time, this strategy prevents unnecessary power consumption caused by premature temperature adjustment of the battery pack, thus significantly improving the efficiency and economy of the battery thermal management of electric commercial vehicles. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0045] Figure 1 is the overall flowchart of the thermal management method for electric commercial vehicles of the present invention;

[0046] Figure 2 is the schematic diagram of the principle of the thermal management system for electric commercial vehicles of the present invention;

[0047] Figure 3 is the flowchart of the present invention for thermally managing the target vehicle according to the initial temperature, the estimated charging time, and the charging ambient temperature. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0048] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative work fall within the protection scope of the present invention.

[0049] Please refer to Figures 1-3 , an embodiment of the first aspect of the present invention provides a thermal management system for an electric commercial vehicle, including: a data processing module, and a data acquisition module and a thermal management control module connected thereto;

[0050] The data acquisition module: is used to collect the initial temperature of the battery pack in the target vehicle, as well as the weather forecast information of the target charging station; obtain the driving information and charging distance of the target vehicle; wherein, the driving information includes the average vehicle speed and the remaining battery power; the charging distance is the navigation distance between the target vehicle and the target charging station;

[0051] The data processing module: is used to calculate the driving duration to reach the target charging station based on the driving information and charging distance of the target vehicle; determine the charging environment temperature of the target vehicle based on the estimated charging time and the weather forecast information; wherein, the estimated charging time is obtained by adding the current time to the driving duration;

[0052] The thermal management control module: is used to perform thermal management on the target vehicle based on the initial temperature of the battery pack, the driving duration, and the charging environment temperature.

[0053] In this embodiment, calculating the driving duration to reach the target charging station based on the driving information and charging distance of the target vehicle includes:

[0054] Extract the average vehicle speed, remaining battery power, and charging distance in the driving information of the target vehicle; determine the cruising range of the target vehicle based on the remaining battery power; determine whether the cruising range is less than the charging distance; if so, navigate to the nearest charging station within the cruising range; if not, calculate the ratio of the charging distance to the average vehicle speed to obtain the driving duration.

[0055] Exemplarily, set the average vehicle speed of the target vehicle to 80 km / h, the remaining battery power to 25 kWh, and the charging distance to. According to the remaining battery power, the cruising range of the target vehicle is determined to be; since the cruising range is greater than the charging distance of 150 km, the ratio of the charging distance to the average vehicle speed is calculated to obtain a driving duration of 1.875 h, that is, 1 hour 52 minutes and 30 seconds.

[0056] In this embodiment, determining the cruising range of the target vehicle based on the remaining battery power includes:

[0057] Set the remaining power of the target vehicle to 25 kWh and the average power consumption to 12 kWh / 100 km. Calculate the ratio of the remaining power to the average power consumption to obtain the cruising range of the target vehicle, which is 208.33 km.

[0058] In this embodiment, determining the charging environment temperature of the target vehicle based on the estimated charging time and weather forecast information includes:

[0059] Extract the estimated charging time of the target vehicle and the weather forecast information of the target charging station; among them, the weather forecast information is temperature data that changes over time; obtain the temperature data corresponding to the estimated charging time, and mark the temperature data at the corresponding time as the charging environment temperature.

[0060] In this embodiment, performing thermal management on the target vehicle based on the initial temperature of the battery pack, the driving duration, and the charging environment temperature includes:

[0061] X1: Extract the initial temperature of the battery pack, the driving duration, and the charging environment temperature; obtain the average vehicle speed and remaining power of the target vehicle; calculate the sum of the current time and the driving duration and mark it as the estimated charging time;

[0062] X2: Input the initial temperature of the battery pack, the estimated charging time, the average vehicle speed, and the remaining power of the target vehicle into the battery pack temperature prediction model to obtain the predicted battery temperature of the battery pack at the estimated charging time; among them, the battery pack temperature prediction model is constructed based on an artificial intelligence model;

[0063] X3: Obtain the corresponding temperature adjustment rate based on the temperature change data of the target vehicle in different temperature control modes; among them, the temperature control modes include the heating mode and the cooling mode;

[0064] X4: Calculate the temperature control adjustment duration based on the predicted battery temperature, the charging environment temperature, and the temperature adjustment rate;

[0065] X5: Determine whether the temperature control adjustment duration is greater than the driving duration; if yes, immediately turn on the thermal management device to adjust the temperature of the battery pack of the target vehicle; if not, calculate the difference between the estimated charging time and the temperature control adjustment duration to obtain the temperature control adjustment start time;

[0066] X6: Determine whether the current time is equal to the temperature control adjustment start time; if yes, automatically turn on the thermal management device to adjust the temperature of the battery pack of the target vehicle; if not, temporarily do not adjust the temperature of the battery pack of the target vehicle.

[0067] Exemplarily, set the initial temperature of the battery pack to 30°C, the driving duration to 1 hour 52 minutes and 30 seconds, and the charging ambient temperature; add the driving duration of 1.875 h to the current time of 9 o'clock to obtain the estimated charging time of 10:52:30; set the average speed of the target vehicle to 80 km / h and the remaining battery power to 25 kWh; input the initial temperature of the battery pack, the estimated charging time, the average speed of the target vehicle, and the remaining battery power into the battery pack temperature prediction model to obtain the predicted battery temperature of the battery pack at the estimated charging time as 45°C;

[0068] Set the temperature control adjustment duration of the target vehicle to 49.05 minutes; since the temperature control adjustment duration is less than the driving duration; therefore, calculate the difference between the estimated charging time and the temperature control adjustment duration to obtain the temperature control adjustment start time of 10:03:27; when the temperature control adjustment start time is equal to the current time, automatically turn on the thermal management device to adjust the temperature of the battery pack of the target vehicle.

[0069] In the present invention, relevant parameters are extracted and the estimated charging time is calculated, and then the predicted battery temperature at a future moment is obtained by using the battery pack temperature prediction model. Subsequently, according to the temperature adjustment rate under different temperature control modes, the temperature control adjustment duration is calculated by combining the predicted temperature and the charging ambient temperature, and based on this, it is determined whether to immediately start or delay until a specific time to turn on the thermal management system. This method can ensure that the battery pack reaches the optimal working temperature before entering the charging state, avoid the low charging efficiency or safety hazards caused by inappropriate temperature, and avoid the additional power consumption caused by premature temperature adjustment of the battery pack of commercial vehicles, thereby being beneficial to improving the efficiency of thermal management of the battery of electric commercial vehicles.

[0070] In this embodiment, the battery pack temperature prediction model is constructed based on an artificial intelligence model, including:

[0071] Extract the initial temperature, remaining battery power, and average speed of the battery pack in the target vehicle, as well as the actual temperature of the battery pack in the target vehicle after the driving duration; integrate the initial temperature, remaining battery power, average speed, and driving duration of the battery pack in the target vehicle into a standard input data set, and integrate the actual temperature of the battery pack in the target vehicle after the driving duration into a standard output data set; train the artificial intelligence model according to the standard input data set and the standard output data set, and mark the trained artificial intelligence model as the battery pack temperature prediction model; wherein, the artificial intelligence model includes a BP neural network model or an RBF neural network model.

[0072] The present invention uses the initial temperature, remaining power, average vehicle speed of the battery pack in the target vehicle, and the actual temperature of the battery pack after the driving duration to train an artificial intelligence model, and marks the trained artificial intelligence model as a battery pack temperature prediction model; enabling the battery pack temperature prediction model to obtain the external environmental temperature when the target vehicle arrives at the target charging station in advance, facilitating subsequent determination of the corresponding optimal charging temperature range according to the external environmental temperature of the target charging station, thereby being beneficial to improving the charging speed of the target vehicle.

[0073] In this embodiment, obtaining the corresponding temperature adjustment rate based on the temperature change data of the target vehicle under different temperature control modes includes:

[0074] Respectively obtain the temperature change data of the target vehicle in the heating mode and the cooling mode; linearly fit the temperature change data in the heating mode to obtain the heating adjustment rate; linearly fit the temperature change data in the cooling mode to obtain the cooling adjustment rate; integrate the heating adjustment rate and the cooling adjustment rate into the temperature adjustment rate.

[0075] In this embodiment, calculating the temperature control adjustment duration based on the predicted battery temperature, charging environment temperature, and temperature adjustment rate includes:

[0076] T1: Extract the predicted battery temperature, charging environment temperature, and temperature adjustment rate;

[0077] T2: Input the charging environment temperature into the charging temperature database for matching to obtain the corresponding optimal charging temperature range;

[0078] T3: Determine whether the predicted battery temperature is greater than the upper temperature limit of the optimal charging temperature range; if so, calculate the difference between the predicted battery temperature and the upper temperature limit and mark it as the temperature difference; if not, jump to T4;

[0079] T4: Determine whether the predicted battery temperature is less than the lower temperature limit of the optimal charging temperature range; if so, calculate the difference between the predicted battery temperature and the lower temperature limit and mark it as the temperature difference; if not, set the temperature control adjustment duration to 0;

[0080] T5: Through the formula Calculate the temperature control adjustment duration WTS; where, WDC is the temperature difference, SWS is the heating adjustment rate, JWS is the cooling adjustment rate; k1 and k2 are both proportionality coefficients greater than 0; ln() is the natural logarithm function.

[0081] Exemplarily, set the proportionality coefficients k1 = 2 and k2 = 1.8; the predicted battery temperature is 45°C, the charging environment temperature is 30°C, the heating adjustment rate in the temperature adjustment rate is SWS = 1°C / min, and the cooling adjustment rate JWS = 0.8°C / min; input the charging environment temperature into the charging temperature database for matching, and obtain the corresponding optimal charging temperature range of 18°C - 28°C; since the predicted battery temperature is greater than the upper limit of the optimal charging temperature range, calculate the difference between the predicted battery temperature and the upper limit and mark it as the temperature difference WDC = 17°C; calculate the temperature control adjustment duration WTS ≈ 49.05 min through the formula.

[0082] The present invention extracts the predicted battery temperature, the charging environment temperature, and the temperature adjustment rate, and matches the charging environment temperature with the charging temperature database to obtain the optimal charging temperature range. According to the relationship between the predicted battery temperature and the optimal charging temperature range, it is calculated whether temperature control adjustment is required and the specific temperature control adjustment duration. If the predicted battery temperature exceeds the optimal range, calculate the temperature difference and determine the precise duration required for heating or cooling accordingly, ensuring that the battery is charged at the optimal temperature and improving the charging efficiency and safety, thereby facilitating the improvement of the efficiency of thermal management of the battery for commercial vehicles.

[0083] In this embodiment, the charging temperature database is constructed in the following manner:

[0084] Obtain the charging speed of the battery temperature data of the target vehicle at different charging environment temperatures, mark the maximum battery temperature when the charging speed is equal to the preset rated charging speed as the upper limit of the temperature, and mark the minimum battery temperature when the charging speed is equal to the preset rated charging speed as the lower limit of the temperature; integrate the lower limit of the temperature and the upper limit of the temperature into the optimal charging temperature range of the target vehicle at the corresponding environmental temperature, and save the mapping relationship between several optimal charging temperature ranges and the corresponding charging environment temperatures to the charging temperature database.

[0085] Exemplarily, the data of the charging temperature database is shown in the following table:

[0086]

[0087] An embodiment of the second aspect of the present invention provides a thermal management method for an electric commercial vehicle, including:

[0088] S1: Collect the initial temperature of the battery pack in the target vehicle and the weather forecast information of the target charging station;

[0089] S2: Obtain the driving information and charging distance of the target vehicle;

[0090] S3: Calculate the driving duration to reach the target charging station based on the driving information and charging distance of the target vehicle;

[0091] S4: Determine the charging ambient temperature of the target vehicle based on the predicted charging time and weather forecast information;

[0092] S5: Perform thermal management on the target vehicle based on the initial temperature of the battery pack, the driving duration, and the charging ambient temperature.

[0093] Some of the data in the above formula are taken as their numerical values after removing the dimension. The formula is obtained by software simulation of a large amount of collected data to get a formula closest to the actual situation; the preset parameters and preset thresholds in the formula are set by those skilled in the art according to the actual situation or obtained through simulation of a large amount of data.

[0094] The working principle of the present invention:

[0095] The present invention collects the initial temperature of the battery pack in the target vehicle and the weather forecast information of the target charging station; obtains the driving information and charging distance of the target vehicle; calculates the driving duration to reach the target charging station based on the driving information and charging distance of the target vehicle; determines the charging ambient temperature of the target vehicle based on the predicted charging time and weather forecast information; performs thermal management on the target vehicle based on the initial temperature of the battery pack, the driving duration, and the charging ambient temperature.

[0096] The above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical method of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A thermal management system for an electric commercial vehicle, comprising: A data processing module, as well as a data acquisition module and a thermal management control module connected thereto; characterized in that The data acquisition module: is used to collect the initial temperature of the battery pack in the target vehicle and the weather forecast information of the target charging station; obtain the driving information and charging distance of the target vehicle; wherein, the driving information includes the average vehicle speed and the remaining battery power; the charging distance is the navigation distance between the target vehicle and the target charging station. The data processing module: is used to calculate the driving duration to reach the target charging station based on the driving information and charging distance of the target vehicle; determine the charging environment temperature of the target vehicle based on the estimated charging time and the weather forecast information; wherein, the estimated charging time is obtained by adding the current time and the driving duration. The thermal management control module: is used to perform thermal management on the target vehicle based on the initial temperature of the battery pack, the driving duration, and the charging environment temperature.

2. The thermal management system for an electric commercial vehicle according to claim 1, wherein, The calculating the driving duration to reach the target charging station based on the driving information and charging distance of the target vehicle includes: Extract the average vehicle speed, remaining battery power, and charging distance from the driving information of the target vehicle; determine the cruising range of the target vehicle based on the remaining battery power; judge whether the cruising range is less than the charging distance; if so, navigate to the nearest charging station within the cruising range; if not, calculate the ratio of the charging distance to the average vehicle speed to obtain the driving duration.

3. A thermal management system for an electric commercial vehicle according to claim 2, characterized in that, The determining the cruising range of the target vehicle based on the remaining battery power includes: Obtain the remaining battery power and average power consumption of the target vehicle, calculate the ratio of the remaining battery power to the average power consumption, and obtain the cruising range of the target vehicle.

4. A thermal management system for an electric commercial vehicle according to claim 1, characterized in that, The determining the charging environment temperature of the target vehicle based on the estimated charging time and the weather forecast information includes: Extract the estimated charging time of the target vehicle and the weather forecast information of the target charging station; wherein, the weather forecast information is temperature data that changes with time; obtain the temperature data corresponding to the estimated charging time, and mark the temperature data at the corresponding time as the charging environment temperature.

5. A thermal management system for an electric commercial vehicle according to claim 1, characterized in that, The performing thermal management on the target vehicle based on the initial temperature of the battery pack, the driving duration, and the charging environment temperature includes: X1: Extract the initial temperature of the battery pack, the driving duration, and the charging environment temperature; obtain the average vehicle speed and the remaining battery power of the target vehicle; calculate the sum of the current time and the driving duration and mark it as the estimated charging time. X2: Input the initial temperature of the battery pack, the estimated charging time, the average vehicle speed, and the remaining battery power of the target vehicle into the battery pack temperature prediction model to obtain the predicted battery temperature of the battery pack at the estimated charging time; wherein, the battery pack temperature prediction model is constructed based on an artificial intelligence model. X3: Obtain the corresponding temperature adjustment rate based on the temperature change data of the target vehicle under different temperature control modes; wherein, the temperature control modes include a heating mode and a cooling mode. X4: Calculate the temperature control adjustment duration based on the predicted battery temperature, the charging environment temperature, and the temperature adjustment rate. X5: Judge whether the temperature control adjustment duration is greater than the driving duration. If so, immediately turn on the thermal management device to adjust the temperature of the battery pack of the target vehicle. If not, calculate the difference between the estimated charging time and the temperature control adjustment duration to obtain the temperature control adjustment start time. X6: Determine whether the current time is equal to the start time of temperature control adjustment. If so, automatically turn on the thermal management device to adjust the temperature of the battery pack of the target vehicle. If not, temporarily do not adjust the temperature of the battery pack of the target vehicle.

6. The thermal management system for an electric commercial vehicle according to claim 5, characterized in that, The battery pack temperature prediction model is constructed based on an artificial intelligence model and includes: Extract the initial temperature, remaining power, average vehicle speed of the battery pack in the target vehicle, and the actual temperature of the battery pack after the driving duration of the target vehicle. Integrate the initial temperature, remaining power, average vehicle speed, and driving duration of the battery pack in the target vehicle into a standard input data set, and integrate the actual temperature of the battery pack after the driving duration of the target vehicle into a standard output data set. Train the artificial intelligence model according to the standard input data set and the standard output data set, and mark the trained artificial intelligence model as the battery pack temperature prediction model. Among them, the artificial intelligence model includes a BP neural network model or an RBF neural network model.

7. A thermal management system for an electric commercial vehicle according to claim 5, characterized in that, The obtaining of the corresponding temperature adjustment rate based on the temperature change data of the target vehicle under different temperature control modes includes: Respectively obtain the temperature change data of the target vehicle in the heating mode and the cooling mode. Linearly fit the temperature change data in the heating mode to obtain the heating adjustment rate. Linearly fit the temperature change data in the cooling mode to obtain the cooling adjustment rate. Integrate the heating adjustment rate and the cooling adjustment rate into the temperature adjustment rate.

8. A thermal management system for an electric commercial vehicle according to claim 5, characterized in that, The calculation of the temperature control adjustment duration based on the predicted battery temperature, charging environment temperature, and temperature adjustment rate includes: T1: Extract the predicted battery temperature, charging environment temperature, and temperature adjustment rate. T2: Input the charging environment temperature into the charging temperature database for matching to obtain the corresponding optimal charging temperature range. T3: Determine whether the predicted battery temperature is greater than the upper limit of the temperature of the optimal charging temperature range. If so, calculate the difference between the predicted battery temperature and the upper limit and mark it as the temperature difference. If not, jump to T4. T4: Determine whether the predicted battery temperature is less than the lower limit of the temperature of the optimal charging temperature range. If so, calculate the difference between the predicted battery temperature and the lower limit and mark it as the temperature difference. If not, set the temperature control adjustment duration to 0. T5: Calculate the temperature control adjustment duration WTS through the formula where WDC is the temperature difference, SWS is the heating adjustment rate, JWS is the cooling adjustment rate; k1 and k2 are both proportionality coefficients greater than 0; ln() is the natural logarithm function.

9. A thermal management system for an electric commercial vehicle according to claim 8, characterized in that, The charging temperature database is constructed in the following way: Obtain the charging speed of the battery temperature data of the target vehicle under different charging environment temperatures. Mark the maximum battery temperature when the charging speed is equal to the preset rated charging speed as the upper limit of the temperature, and mark the minimum battery temperature when the charging speed is equal to the preset rated charging speed as the lower limit of the temperature. Integrate the lower limit of the temperature and the upper limit of the temperature into the optimal charging temperature range of the target vehicle under the corresponding environmental temperature, and save the mapping relationship between several optimal charging temperature ranges and the corresponding charging environment temperatures to the charging temperature database.

10. A thermal management method for an electric commercial vehicle, which operates based on the thermal management system for an electric commercial vehicle according to any one of claims 1-9, characterized in that, It includes: Collect the initial temperature of the battery pack in the target vehicle and the weather forecast information of the target charging station. Obtain the driving information and charging distance of the target vehicle. Calculate the driving duration to reach the target charging station based on the driving information and charging distance of the target vehicle. Determine the charging environment temperature of the target vehicle based on the estimated charging time and weather forecast information. Perform thermal management on the target vehicle based on the initial temperature of the battery pack, driving duration, and charging ambient temperature.

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