Battery thermal management method and device based on battery working condition estimation, medium and equipment

Abstract

The invention belongs to the technical field of battery management, and provides a battery thermal management method and device based on battery working condition estimation, a medium and equipment, and the method comprises the steps: judging whether to trigger dynamic correction or not and determine a cooling mode and a target battery temperature based on a battery operation working condition at a future moment; determining a target coolant temperature based on the cooling mode; calculating target cooling power based on the temperature difference between the target battery temperature and the current battery temperature; calculating a target cooling liquid flow based on the target cooling power; and on the basis of the target battery temperature, the target cooling liquid temperature, the target cooling power and the target cooling liquid flow, through a PID controller, adjusting variables of the cooling liquid temperature, the cooling power and the cooling liquid flow are obtained. The response time and energy consumption of battery thermal management are reduced, and accurate adjustment is realized.

Description

Technical Field

[0001] The present invention belongs to the technical field of battery management, and in particular relates to a battery thermal management method, device, medium and equipment based on battery operating condition estimation. Background Art

[0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art.

[0003] Thermal management of batteries is critical in electric vehicles and other applications that require efficient battery management. To optimize battery efficiency and life, the battery pack must be kept at an appropriate temperature under different operating conditions.

[0004] Traditional thermal management methods have hysteresis and are difficult to adjust the temperature in real time. Moreover, traditional battery thermal management methods only rely on the difference between the target battery temperature and the current battery temperature, and the thermal management effect is poor; moreover, since the target battery temperature is mostly a fixed value, it is difficult to achieve hierarchical adjustment. Summary of the invention

[0005] In order to solve the technical problems existing in the above-mentioned background technology, the present invention provides a battery thermal management method, device, medium and equipment based on battery operating condition estimation, which reduces the response time and energy consumption of battery thermal management by predicting the battery operating condition in advance, and adds the errors of coolant temperature, cooling power and coolant flow rate to PID control, thereby improving the stability of battery thermal management and achieving precise regulation.

[0006] In order to achieve the above object, the present invention adopts the following technical solution:

[0007] A first aspect of the present invention provides a battery thermal management method based on battery operating condition estimation.

[0008] The battery thermal management method based on battery operating condition prediction includes:

[0009] Obtain the battery operating conditions at several consecutive moments, and predict the battery operating conditions at future moments through a neural network model;

[0010] Based on the battery operating conditions at the future moment, determine whether to trigger dynamic correction and determine the cooling mode and target battery temperature;

[0011] Based on the cooling mode, determine the target coolant temperature; based on the temperature difference between the target battery temperature and the current battery temperature, calculate the target cooling power; based on the target cooling power, calculate the target coolant flow rate;

[0012] Based on the target battery temperature, the target coolant temperature, the target cooling power and the target coolant flow rate, the adjustment amounts of the coolant temperature, the cooling power and the coolant flow rate are obtained through the PID controller.

[0013] Furthermore, the target cooling power is:

[0014] P cool =Q / t

[0015] Wherein, Q=mc△T, m is the mass of the battery pack, c is the specific heat capacity of the battery material, △T represents the temperature difference between the target battery temperature and the current battery temperature, and t is the time.

[0016] Furthermore, the target coolant flow rate is:

[0017]

[0018] Where c represents the specific heat capacity of the coolant, △R coolant Indicates the coolant temperature difference, P cool Indicates the target cooling power.

[0019] Furthermore, the cooling mode is determined based on:

[0020] When the battery temperature is within the first range at a future time, the heating mode is activated without triggering dynamic correction;

[0021] When the battery temperature is within the second range at a future moment, no additional intervention is required and no dynamic correction is triggered;

[0022] When the battery temperature is in the third range at a future moment, the low-speed cooling mode is activated, triggering dynamic correction;

[0023] When the battery temperature is in the fourth range at a future moment, the medium-speed cooling mode is activated, triggering dynamic correction;

[0024] When the battery temperature is in the fifth range at a future moment, the high-speed cooling mode is activated, and the charge and discharge currents are limited, triggering dynamic correction;

[0025] When the battery temperature is within the sixth range at a future time, the emergency cooling mode is activated and the battery operation is stopped urgently.

[0026] Furthermore, the battery operating conditions include battery temperature, voltage, current and SOC.

[0027] A second aspect of the present invention provides a battery thermal management device based on battery operating condition estimation.

[0028] The battery thermal management device based on battery operating condition estimation includes:

[0029] The operating condition prediction module is configured to: obtain the battery operating conditions at a number of consecutive moments, and predict the battery operating conditions at a future moment through a neural network model;

[0030] A cooling mode determination module is configured to: determine whether to trigger dynamic correction and determine a cooling mode and a target battery temperature based on the battery operating condition at a future moment;

[0031] A target calculation module is configured to: determine a target coolant temperature based on a cooling mode; calculate a target cooling power based on a temperature difference between a target battery temperature and a current battery temperature; and calculate a target coolant flow rate based on the target cooling power;

[0032] The regulating module is configured to obtain the regulating amount of the coolant temperature, the cooling power and the coolant flow rate through a PID controller based on the target battery temperature, the target coolant temperature, the target cooling power and the target coolant flow rate.

[0033] Furthermore, the target cooling power is:

[0034] P cool =Q / t

[0035] Wherein, Q=mc△T, m is the mass of the battery pack, c is the specific heat capacity of the battery material, △T represents the temperature difference between the target battery temperature and the current battery temperature, and t is the time.

[0036] Furthermore, the target coolant flow rate is:

[0037]

[0038] Where c represents the specific heat capacity of the coolant, △R coolant Indicates the coolant temperature difference, P cool Indicates the target cooling power.

[0039] A third aspect of the present invention provides a computer-readable storage medium.

[0040] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the battery thermal management method based on battery operating condition estimation as described in the first aspect above.

[0041] A fourth aspect of the present invention provides a computer device.

[0042] A computer device comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the steps of the battery thermal management method based on battery operating condition estimation as described in the first aspect above are implemented.

[0043] Compared with the prior art, the present invention has the following beneficial effects:

[0044] The present invention reduces the response time and energy consumption of battery thermal management by predicting the battery operating conditions in advance, and adds errors of coolant temperature, cooling power and coolant flow rate to PID control, thereby improving the stability of battery thermal management and achieving precise regulation.

[0045] The present invention divides a plurality of cooling modes, combines the errors of coolant temperature, cooling power and coolant flow rate, and realizes graded regulation through PID control, which can be applied to a variety of application scenarios requiring high-efficiency battery management. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0047] Figure 1 is a flow chart of a battery thermal management method based on battery operating condition estimation according to Embodiment 1 of the present invention;

[0048] Figure 2 It is a structural diagram of a computer device shown in Embodiment 4 of the present invention. DETAILED DESCRIPTION

[0049] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0050] It should be noted that the following detailed descriptions are all illustrative and intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art to which the present invention belongs.

[0051] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit exemplary embodiments according to the present invention. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprising" and / or "including" are used in this specification, it indicates the presence of features, steps, operations, devices, components and / or combinations thereof.

[0052] It should be noted that the flowcharts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the methods and devices according to various embodiments of the present invention. It should be noted that each box in the flowchart or block diagram can represent a module, a program segment, or a part of the code, and the module, the program segment, or a part of the code may include one or more executable instructions for implementing the logical functions specified in the various embodiments. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in an order different from that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, or they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the flowchart and / or block diagram, and the combination of boxes in the flowchart and / or block diagram can be implemented using a dedicated hardware-based device that performs a specified function or operation, or can be implemented using a combination of dedicated hardware and computer instructions.

[0053] Embodiment 1

[0054] This embodiment provides a battery thermal management method based on battery operating condition estimation.

[0055] The battery thermal management method based on battery operating condition prediction provided in this embodiment proposes a prediction algorithm based on a neural network, combined with the current battery state (SOC) estimated by multiple sensors, to adjust the battery pack temperature within the next five minutes, thereby achieving more efficient thermal management.

[0056] The battery thermal management method based on battery operating condition prediction provided in this embodiment aims to solve the response lag and inaccuracy problems existing in traditional battery thermal management systems, and achieves real-time and accurate control of battery pack temperature by introducing neural network-based operating condition prediction and multi-sensor data fusion. Traditional methods are difficult to respond quickly to dynamically changing operating conditions, resulting in poor battery temperature control, which affects battery performance and life.

[0057] The battery thermal management method based on battery operating condition prediction provided in this embodiment predicts the battery operating condition in the next five minutes in advance and combines the current SOC data to dynamically adjust the working state of the cooling system to ensure that the battery pack always operates within the optimal temperature range, thereby improving battery efficiency and extending battery life.

[0058] The battery thermal management method based on battery operating condition prediction provided in this embodiment adopts a neural network-based algorithm to predict the operating conditions in the next five minutes and adjust the temperature of the battery pack in advance in combination with the current SOC. The battery pack and parameters are as follows: Model: a certain PHEV battery pack; Battery type: lithium-ion battery; Battery capacity: 60kWh; Voltage range: 320V-450V; Rated current: maximum 250A; Operating temperature range: -20℃-60℃; Cooling method: water cooling.

[0059] The battery thermal management method based on battery operating condition prediction provided in this embodiment is as follows: Figure 1 As shown, the following steps are included:

[0060] Step 1: Data collection and preprocessing.

[0061] Step 101: Multi-sensor data collection.

[0062] Sensor type: temperature sensor, current sensor, voltage sensor. Voltage sensor accuracy: ±0.1V; current sensor accuracy: ±0.5A; temperature sensor accuracy: ±1℃.

[0063] Sensor location: inside the battery pack, on the surface and in the cooling system.

[0064] Data collection frequency: once per second.

[0065] (1) Real-time monitoring: The battery’s temperature, voltage, current, SOC and other data are monitored in real time through multiple sensors such as temperature sensors and SOC sensors.

[0066] (2) Data transmission: The data is preliminarily processed using the on-board controller and edge computing nodes, and then transmitted to the cloud server via the wireless network.

[0067] (3) Data storage: The received data is stored in the cloud database.

[0068] Step 102: Data cleaning and preprocessing: Filter out abnormal data using anomaly detection algorithms and normalize the data.

[0069] Anomaly detection method: Z-value filtering based on statistical methods.

[0070] Normalization method: Min-Max normalization.

[0071] Step 2: Current SOC estimation.

[0072] Step 201, multi-sensor fusion: combine the voltage, current, temperature and other multi-sensor data to perform real-time estimation of SOC through Kalman filter. Kalman filter parameters:

[0073] x k =F k-1 x k-1 +B k-1 u k-1 +w k-1

[0074] z k =H k xk+v k

[0075] Among them, Xk is the state vector, uk is the control vector, Wk and Vk are process noise and observation noise respectively.

[0076] State transfer matrix: SOC changes based on battery model.

[0077] Observation matrix: the mapping relationship between measured voltage and current.

[0078] Step 202, data fusion and correction: use the SOC estimation result to correct the neural network model to improve the accuracy of the prediction.

[0079] Correction method: Iterative least squares (ILS).

[0080] Step 3: The neural network model predicts future operating conditions. The pre-processed battery temperature, voltage, current, SOC and other data are input into the LSTM model, and the LSTM model is used to predict the battery operating conditions in the next 5 minutes, including changes in battery temperature, voltage, current, SOC, etc.

[0081] Neural network model: The long short-term memory network (LSTM) model is used to predict the working conditions in the next 5 minutes. The input data includes historical data such as voltage, current, SOC, and temperature. LSTM model parameters: input data length: 100 time steps; feature dimension: 4 (voltage, current, SOC, temperature); number of LSTM layers: 3 layers, 128 units per layer; output layer: SOC, temperature, voltage, and current predictions for the next five minutes.

[0082] Model training and optimization:

[0083] Training data: obtained from actual vehicle operation data, covering a variety of working conditions;

[0084] Training parameters: batch size is 32, learning rate is 0.001, and mean square error (MSE) is used as the loss function;

[0085] Optimization method: Adam optimizer, initial learning rate 0.001.

[0086] Step 4: Battery thermal management.

[0087] Based on the forecast results of the working conditions in the next 5 minutes and monitoring the real-time temperature, it is determined whether the predicted temperature reaches the preset temperature threshold and triggers dynamic correction.

[0088] (1) Threshold control measures:

[0089] When the temperature in the next 5 minutes is in the first range, i.e. less than 20°C, the heating mode is started (dynamic correction);

[0090] When the temperature is between 20°C and 35°C (second range) in the next 5 minutes, no additional intervention is required;

[0091] When the temperature is between 35℃-40℃ (the third range) in the next 5 minutes, the low-speed cooling mode (dynamic correction) is activated;

[0092] When the temperature is between 40℃-45℃ (the fourth range) in the next 5 minutes, the medium-speed cooling mode (dynamic correction) is started;

[0093] When the temperature is between 45℃-50℃ (fifth range) in the next 5 minutes, the high-speed cooling mode is activated and the charge and discharge current is limited (dynamic correction);

[0094] When the temperature exceeds 50°C (in the sixth range) in the next 5 minutes, the battery operation is stopped urgently and the emergency cooling mode is activated.

[0095] (2) Cooling system control, determine the target coolant temperature.

[0096] Low-speed cooling mode: adjust the coolant flow rate to 2L / min-5L / min, and the target coolant temperature to 15℃-25℃;

[0097] Medium-speed cooling mode: adjust the coolant flow rate to 5L / min-10L / min, and the target coolant temperature to 10℃-20℃;

[0098] High-speed cooling mode: adjust the coolant flow rate to 10L / min-15L / min, the target coolant temperature to 5℃-15℃, and limit the charge and discharge current;

[0099] Emergency cooling mode: Maximizes coolant flow, coolant temperature can be as low as 0°C, and stops charging and discharging operations.

[0100] (3) If dynamic correction is triggered, the required cooling power, i.e., the target cooling power, needs to be calculated. The detailed calculation steps are as follows:

[0101] Get the current battery temperature T battery : Use the temperature sensor to measure the current battery pack temperature in real time. Assume that the measured value is T battery =40℃;

[0102] Determine the target battery temperature T target : The target battery temperature is set within the optimal operating temperature range of the battery, for example, T target =30℃;

[0103] Calculate the temperature difference Delta_T: Delta_T T=T battery -T target =10℃;

[0104] Calculate the required cooling power P cool , the target cooling power calculation formula is:

[0105]

[0106] Where Q is the amount of heat to be removed and t is the time. The heat Q to be removed is calculated as: Q = m·c·ΔT, where m is the mass of the battery pack (assumed to be 200 kg) and c is the specific heat capacity of the battery material (assumed to be 0.9 kJ / (kg*K)).

[0107] For example, calculate Q = 1800 kJ, assuming the cooling time t is 5 minutes (300 seconds), P cool =1800kJ / 300s=6kW.

[0108] (4) Determine the coolant flow rate.

[0109] As an implementation mode, the method in (2) is adopted to determine the target coolant flow rate according to the cooling mode.

[0110] As another embodiment, the required coolant flow rate, i.e., the target coolant flow rate, is calculated based on the relationship between the coolant flow rate and the heat removal rate. The target coolant flow rate calculation formula is:

[0111]

[0112] Where c is the specific heat capacity of the coolant (assumed to be 4.18 kJ / (kg·K)), △R coolant Indicates the coolant temperature difference (assuming the inlet and outlet temperature difference is 10°C).

[0113]

[0114] Step 5: The battery thermal management temperature control method is PID:

[0115]

[0116] Among them, e(t) is the error, Kp, Ki, Kd are proportional, integral, and differential gains respectively.

[0117] Among them, PID input includes: target battery temperature, target coolant temperature, target cooling power, target coolant flow rate; observed quantities: battery temperature, V, I, SOC, etc.

[0118] Among them, PID output includes: coolant temperature adjustment, coolant flow, current power and other adjustment quantities.

[0119] The errors include: the difference between the target battery temperature and the output battery temperature, the difference between the target coolant temperature and the output coolant temperature, the difference between the target cooling power and the output cooling power, and the difference between the target coolant flow and the output coolant flow.

[0120] Performance evaluation: Use test data to evaluate the performance of the model and cooling system to ensure the effectiveness of the temperature control strategy.

[0121] Adaptive adjustment: Adaptively adjust the LSTM model and cooling system parameters according to new operating condition data to improve the adaptability and stability of the system.

[0122] The present invention achieves accurate prediction of the future working conditions of the battery pack and real-time temperature control by introducing LSTM neural network and multi-sensor fusion technology, which has significant beneficial effects. By adjusting the working state of the cooling system in advance, the battery pack is ensured to operate within the optimal temperature range, thereby effectively improving the working efficiency and service life of the battery. The Kalman filter algorithm is used to estimate the current SOC in real time, combined with the precise adjustment of the PID controller, which reduces the response time and energy consumption of battery thermal management, improves the overall stability and adaptability of the system, and is suitable for a variety of application scenarios that require high-efficiency battery management, such as electric vehicles and energy storage equipment.

[0123] Embodiment 2

[0124] This embodiment provides a battery thermal management device based on battery operating condition estimation.

[0125] The operating condition prediction module is configured to: obtain the battery operating conditions at a number of consecutive moments, and predict the battery operating conditions at a future moment through a neural network model;

[0126] A cooling mode determination module is configured to: determine whether to trigger dynamic correction and determine a cooling mode and a target battery temperature based on the battery operating condition at a future moment;

[0127] A target calculation module is configured to: determine a target coolant temperature based on a cooling mode; calculate a target cooling power based on a temperature difference between a target battery temperature and a current battery temperature; and calculate a target coolant flow rate based on the target cooling power;

[0128] The regulating module is configured to obtain the regulating amount of the coolant temperature, the cooling power and the coolant flow rate through a PID controller based on the target battery temperature, the target coolant temperature, the target cooling power and the target coolant flow rate.

[0129] Furthermore, the target cooling power is:

[0130] P cool =Q / t

[0131] Wherein, Q=mc△T, m is the mass of the battery pack, c is the specific heat capacity of the battery material, △T represents the temperature difference between the target battery temperature and the current battery temperature, and t is the time.

[0132] Furthermore, the target coolant flow rate is:

[0133]

[0134] Where c represents the specific heat capacity of the coolant, △R coolant Indicates the coolant temperature difference, P cool Indicates the target cooling power.

[0135] It should be noted that the battery thermal management device based on battery operating condition estimation provided in the above embodiment is only illustrated by the division of the above functional modules. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the electronic device is divided into different functional modules to complete all or part of the functions described above. In addition, the battery remaining cycle life calculation device provided in the above embodiment and the battery remaining cycle life calculation method embodiment belong to the same concept. The specific implementation process is detailed in the method embodiment and will not be repeated here.

[0136] Embodiment 3

[0137] This embodiment provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the steps of the battery thermal management method based on battery operating condition estimation as described in the first embodiment above are implemented.

[0138] Embodiment 4

[0139] This embodiment provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the steps of the battery thermal management method based on battery operating condition estimation as described in the first embodiment are implemented.

[0140] Figure 2 The schematic diagram of the structure of a computer device provided by an exemplary embodiment of the present application is shown. The computer device includes: a processor and a memory.

[0141] The processor may include one or more processing cores, such as a 4-core processor, an 8-core processor, etc. The processor may be implemented in at least one hardware form of DSP (Digital Signal Processing), FPGA (Field Programmable Gate Array), and PLA (Programmable Logic Array). The processor may also include a main processor and a coprocessor. The main processor is a processor for processing data in an awake state, also known as a CPU (Central Processing Unit); the coprocessor is a low-power processor for processing data in a standby state. In some embodiments, the processor may be integrated with a GPU (Graphics Processing Unit), which is responsible for rendering and drawing the content that needs to be displayed on the display screen. In some embodiments, the processor may also include an AI (Artificial Intelligence) processor, which is used to process computing operations related to machine learning.

[0142] The memory may include one or more computer-readable storage media, which may be non-transitory. The memory may also include a high-speed random access memory, and a non-volatile memory, such as one or more disk storage devices, flash memory storage devices. In some embodiments, the non-transitory computer-readable storage medium in the memory is used to store at least one computer program, which is used to be used by the processor to implement the vehicle occupant detection method provided in the method embodiment of the present application.

[0143] Those skilled in the art will appreciate that the illustrated structure does not constitute a limitation on the computer device, and may include more or fewer components than illustrated, or combine certain components, or adopt a different component arrangement.

[0144] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A battery thermal management method based on battery operating condition prediction, characterized in that: include: Obtain the battery operating conditions at several consecutive moments, and predict the battery operating conditions at future moments through a neural network model; Based on the battery operating conditions at the future moment, determine whether to trigger dynamic correction and determine the cooling mode and target battery temperature; Based on the cooling mode, determining a target coolant temperature; Calculating a target cooling power based on a temperature difference between a target battery temperature and a current battery temperature; Based on the target cooling power, a target coolant flow rate is calculated; Based on the target battery temperature, the target coolant temperature, the target cooling power and the target coolant flow rate, the adjustment amounts of the coolant temperature, the cooling power and the coolant flow rate are obtained through the PID controller.

2. The battery thermal management method based on battery operating condition estimation according to claim 1, characterized in that: The target cooling power is: P cool =Q / t Wherein, Q=mc△T, m is the mass of the battery pack, c is the specific heat capacity of the battery material, △T represents the temperature difference between the target battery temperature and the current battery temperature, and t is the time.

3. The battery thermal management method based on battery operating condition estimation according to claim 1, characterized in that: The target coolant flow rate is: Where c represents the specific heat capacity of the coolant, △R coolant Indicates the coolant temperature difference, P cool Indicates the target cooling power.

4. The battery thermal management method based on battery operating condition estimation according to claim 1, characterized in that: The cooling mode is determined based on: When the battery temperature is within the first range at a future time, the heating mode is activated without triggering dynamic correction; When the battery temperature is within the second range at a future moment, no additional intervention is required and no dynamic correction is triggered; When the battery temperature is in the third range at a future moment, the low-speed cooling mode is activated, triggering dynamic correction; When the battery temperature is in the fourth range at a future moment, the medium-speed cooling mode is activated, triggering dynamic correction; When the battery temperature is in the fifth range at a future moment, the high-speed cooling mode is activated, and the charge and discharge currents are limited, triggering dynamic correction; When the battery temperature is within the sixth range at a future time, the emergency cooling mode is activated and the battery operation is stopped urgently.

5. The battery thermal management method based on battery operating condition estimation according to claim 1, characterized in that: The battery operating conditions include battery temperature, voltage, current and SOC.

6. A battery thermal management device based on battery operating condition estimation, characterized in that: include: The operating condition prediction module is configured to: obtain the battery operating conditions at a number of consecutive moments, and predict the battery operating conditions at a future moment through a neural network model; A cooling mode determination module is configured to: determine whether to trigger dynamic correction and determine a cooling mode and a target battery temperature based on the battery operating conditions at a future time; A target calculation module is configured to: determine a target coolant temperature based on a cooling mode; Calculating a target cooling power based on a temperature difference between a target battery temperature and a current battery temperature; Based on the target cooling power, a target coolant flow rate is calculated; The regulating module is configured to obtain the regulating amount of the coolant temperature, the cooling power and the coolant flow rate through a PID controller based on the target battery temperature, the target coolant temperature, the target cooling power and the target coolant flow rate.

7. The battery thermal management device based on battery operating condition estimation according to claim 6, characterized in that: The target cooling power is: P cool =Q / t Wherein, Q=mc△T, m is the mass of the battery pack, c is the specific heat capacity of the battery material, △T represents the temperature difference between the target battery temperature and the current battery temperature, and t is the time.

8. The battery thermal management device based on battery operating condition estimation according to claim 6, characterized in that: The target coolant flow rate is: Where c represents the specific heat capacity of the coolant, △R coolant Indicates the coolant temperature difference, P cool Indicates the target cooling power.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the battery thermal management method based on battery operating condition estimation as described in any one of claims 1 to 5 are implemented.

10. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the steps in the battery thermal management method based on battery operating condition estimation as described in any one of claims 1 to 5 are implemented.

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