Control method and system for increasing low-temperature driving range of vehicle and vehicle

Abstract

The invention discloses a control method and system for increasing the low-temperature driving range of a vehicle and the vehicle, and the method comprises the steps: in a low-temperature environment, when the SOC of a power battery reaches a preset heating initial SOC, taking a preset temperature as a heating target temperature to heat the power battery, and controlling an air blower to operate at a preset gear. Wherein the preset heating starting SOC, the preset temperature and the preset gear are parameters which are obtained through calibration and correspond to the maximum vehicle driving range in the low-temperature environment. During calibration, alpha sample data are obtained through simulation; training, testing and verifying the alpha sample data to obtain a neural network model; then different three-parameter combinations are traversed through gridding and substituted into the neural network model, and the corresponding vehicle driving mileage under the standard working condition is predicted; and finally, a three-parameter combination corresponding to the maximum vehicle driving range is screened out to serve as a parameter in a heating control strategy. The driving range of the vehicle can be effectively prolonged.

Description

Technical Field

[0001] The present invention belongs to the technical field of thermal management of electric vehicle power batteries, and specifically relates to a control method, system and vehicle for improving the low-temperature driving range of a vehicle. Background Art

[0002] With the rapid development and popularization of electric vehicles, improving vehicle driving range in low-temperature environments has become increasingly important. It is also a key direction for improving user experience and market competitiveness. At present, the main way to improve vehicle driving range in low-temperature environments is to optimize thermal management strategies. In a specific thermal management architecture, the thermal management optimization strategy in low-temperature environments mainly involves power battery heating strategies and air conditioning operation strategies (such as blower gear). However, the existing power battery thermal management control strategy (i.e., power battery heating strategy) has the following limitations: (1) Low parameter optimization efficiency: relying on exhaustive methods or fixed thresholds for heating, without considering the complex impact of multi-parameter dynamic coupling on driving range; (2) Local optimal limitations: single parameter adjustment is prone to fall into local optimal solutions, making it difficult to achieve global driving range maximization; (3) Poor dynamic adaptability: no accurate mapping model between thermal management strategy parameters and driving range has been established, resulting in insufficient strategy robustness. Summary of the Invention

[0003] The purpose of the present invention is to provide a control method, system and vehicle for improving the low-temperature driving range of a vehicle, so as to effectively increase the driving net discharge capacity of the power battery in a low-temperature environment and extend the vehicle's driving range.

[0004] In a first aspect, the present invention provides a control method for improving a vehicle's low-temperature driving range, comprising: in a low-temperature environment, when a power battery SOC reaches a preset heating start SOC, heating the power battery using a preset temperature as a heating target temperature, and controlling a blower to operate at a preset gear; wherein the preset heating start SOC, the preset temperature, and the preset gear are parameters corresponding to the maximum vehicle driving range in a low-temperature environment obtained through calibration.

[0005] Preferably, the method for obtaining the preset heating start SOC, the preset temperature, and the preset gear position by calibration includes:

[0006] S1. Obtain α sample data through simulation; each sample data includes a three-parameter combination consisting of a battery heating starting SOC, a battery heating target temperature, and a blower gear, as well as the corresponding vehicle range under standard operating conditions.

[0007] S2. Use the α sample data to perform training, testing, and verification to obtain a neural network model; wherein, during training, testing, and verification, a three-parameter combination (i.e., battery heating starting SOC, battery heating target temperature, and blower gear) is used as input, and the corresponding vehicle range under standard operating conditions is used as output.

[0008] S3. Perform a grid traversal of the battery heating starting SOC (i.e., the SOC when the power battery begins to heat) and the battery heating target temperature, and combine them with the blower gear to form β three-parameter combinations; where β is much larger than α.

[0009] S4. Substitute β three-parameter combinations into the neural network model respectively to predict the corresponding vehicle driving range under standard working conditions, and obtain β corresponding vehicle driving ranges under standard working conditions.

[0010] S5. Filter the maximum value among β corresponding vehicle driving ranges under standard operating conditions (i.e., filter the maximum vehicle driving range among the β corresponding vehicle driving ranges), and use the battery heating start SOC in the three-parameter combination corresponding to the maximum value (i.e., the maximum vehicle driving range) as the preset heating start SOC, the battery heating target temperature as the preset temperature, and the blower gear as the preset gear.

[0011] First, α sample data are obtained through simulation. This data is then used for training, testing, and verification to obtain a neural network model. Different three-parameter combinations are then gridded and substituted into the neural network model to predict the corresponding vehicle range under standard operating conditions. Finally, the three-parameter combination corresponding to the maximum vehicle range is selected as the parameter in the power battery heating control strategy. Because simulation requires a lot of time and computing power, while the neural network model establishes a precise mapping relationship between the three-parameter combination and vehicle range and reduces prediction time, this method of first simulating and then using the neural network model to predict the three-parameter combination corresponding to the maximum vehicle range is more efficient than the method of only simulating the three-parameter combination. The three-parameter combination corresponding to the maximum vehicle range is more accurate, thereby improving the robustness of the power battery heating control strategy, maximizing the net drive discharge of the power battery in low-temperature environments, and extending the vehicle's range.

[0012] Preferably, the method of obtaining α sample data through simulation in S1 includes:

[0013] S11, define the thermal management parameter boundaries; wherein the thermal management parameter boundaries include: the minimum SOC value of the battery heating start SOC min , the maximum SOC of the battery heating starting SOC max , the minimum value of the battery heating target temperature T min, the maximum value of the battery heating target temperature T max , z blower gears.

[0014] S12, in SOC min With SOC max Select x1 values between the two as x1 battery heating starting SOC; at T min With T max Select y1 values as y1 battery heating target temperatures; combine with z blower gears to form α three-parameter combinations; where α = x1*y1*z.

[0015] S13, the battery heating starting SOC, battery heating target temperature and blower gear in the i-th three-parameter combination are substituted into the one-dimensional thermal management simulation model for simulation calculation to obtain the battery net discharge capacity Q corresponding to the i-th three-parameter combination i , a high-voltage load power curve and a low-voltage load power curve; wherein, i takes all integers from 1 to α in sequence, the high-voltage load power curve is a curve showing that the high-voltage load power changes with time, and the low-voltage load power curve is a curve showing that the low-voltage load power changes with time.

[0016] S14, calculate the net discharge capacity Q of the battery corresponding to the i-th three-parameter combination i , high-voltage load power curve and low-voltage load power curve are substituted into the power economy simulation model, and simulation under standard working conditions is performed, and the vehicle driving range D corresponding to the i-th three-parameter combination under standard working conditions is calculated. i .

[0017] S15, combining the i-th three-parameter combination with the vehicle's driving range D i Then combine to form the α sample data.

[0018] By defining thermal management parameter boundaries and selecting α three-parameter combinations for step-by-step simulation calculations, simulation efficiency and accuracy were improved. Key indicators were first calculated using a one-dimensional thermal management simulation model and then substituted into the power economy simulation model to determine vehicle range, ensuring reliable results. The resulting sample data correlates thermal management parameters with range, providing strong data support for optimizing thermal management strategies, guiding experimental design and practical application, and reducing resource waste. Furthermore, the sample data's clear structure facilitates subsequent analysis and processing, and the solution's scalability allows for flexible adaptation to diverse needs, significantly contributing to improved vehicle thermal management performance and range.

[0019] Preferably, the simulation under standard working conditions is performed in S14, and D is calculated. i The specific method is:

[0020] The accelerator pedal signal APS(t) at time t is calculated using formula (1).

[0021]

[0022] Among them, K P , K I , K D Respectively represent the proportional gain, integral gain and differential gain used to calculate the accelerator pedal signal; v target (t-Δt) represents the target vehicle speed at time t-Δt, v actual (t-Δt) represents the actual vehicle speed at time t-Δt, v target (t-Δt) is obtained from the vehicle speed time curve corresponding to the standard working condition. The actual vehicle speed at the initial moment is 0, and Δt represents the preset calculation period; are known parameters, the corresponding v target (τ) represents the target vehicle speed at time τ, v actual (τ) represents the actual vehicle speed at time τ, v target (τ) is obtained through the vehicle speed time curve corresponding to the standard working condition.

[0023] Use formula (2) to calculate the motor speed n at time t-Δt actual (t-Δt).

[0024]

[0025] Where k represents the reduction ratio and r represents the wheel radius; k and r are known parameters.

[0026] According to n actual (t-Δt), APS(t) queries the corresponding relationship table of the preset speed, accelerator pedal signal and electric drive assembly required torque to obtain the electric drive assembly required torque T at time t need (t).

[0027] The vehicle road load F(t) at time t is calculated using formula (3).

[0028] F(t)=A+Bv actual (t-Δt)+C[v actual (t-Δt)] 2 (3)

[0029] Among them, A, B, and C represent the constant term coefficient, linear term coefficient, and quadratic term coefficient respectively obtained from the sliding resistance test fitting; A, B, and C are known parameters.

[0030] Use formula (4) to calculate the actual vehicle speed v at time t actual (t) and store.

[0031]

[0032] Where δ represents the vehicle rotation mass conversion coefficient, m represents the vehicle mass, and η' represents the reducer efficiency; δ, m, and η' are known parameters; T need (τ) represents the required torque of the electric drive assembly at time τ, and F(τ) represents the vehicle road load at time τ.

[0033] Use formula (5) to calculate the electric drive assembly driving power P at time t D (t).

[0034]

[0035] Among them, η represents the driving efficiency of the electric drive assembly, n actual (t) represents the motor speed at time t; η is a known parameter,

[0036] Use formula (6) to calculate the power battery power P corresponding to the i-th three-parameter combination at time t: i (t).

[0037]

[0038] Among them, η DCDC Indicates the efficiency of the DC converter connecting the power battery and the low-voltage load, P h_i (t) represents the high-voltage load power corresponding to the i-th three-parameter combination at time t, P l_i (t) represents the low-voltage load power corresponding to the i-th three-parameter combination at time t, η DCDC is a known parameter, P h_i (t) Obtained through the high voltage load power curve, P l_i (t) is obtained through the low voltage load power curve.

[0039] Use formula (7) to calculate the power battery SOC value SOC corresponding to the i-th three-parameter combination at time t i (t), and in SOC i When (t) = 0, the simulation ends and the simulation time t corresponding to the i-th three-parameter combination is output. i_SOC .

[0040]

[0041] Among them, U represents the rated voltage of the power battery; U is a known parameter, P i (τ) represents the power battery power corresponding to the i-th three-parameter combination at time τ.

[0042] The vehicle range D corresponding to the i-th three-parameter combination under standard working conditions is calculated using formula (8): i .

[0043]

[0044] The above simulation method greatly improves the accuracy and reliability of simulation calculations, provides strong support for vehicle performance evaluation and improvement, and is of great significance for optimizing vehicle thermal management strategies and extending vehicle driving range.

[0045] Preferably, the starting SOCs of the x1 batteries for heating are: SOC min , SOC min +△SOC1, SOC min +2*△SOC1,…,SOC max ; Among them, △SOC1=(SOC max -SOC min ) / (x1-1). The target heating temperatures of the y1 batteries are: T min 、T min +△T1、T min +2*△T1,…,T max ; Among them, △T1=(T max -T min ) / (y1-1).

[0046] Preferably, in S2, 70% (i.e., 0.7α sample data) of the α sample data are used for training, 15% (i.e., 0.15α sample data) are used for testing, and 15% (i.e., 0.15α sample data) are used for verification to obtain a neural network model; during verification, if the 0.15α relative errors are all less than or equal to 2%, it means that the neural network model is qualified, otherwise it means that the neural network model is unqualified and needs to be trained, tested, and verified again until the neural network model is qualified. Among them, the jth relative error = (jth neural network predicted driving range - jth sample data driving range) / jth sample data driving range; the jth neural network predicted driving range is the vehicle driving range corresponding to the standard working condition obtained by substituting the jth three-parameter combination in the 0.15α sample data used for verification into the neural network model; the jth sample data driving range is the vehicle driving range corresponding to the jth three-parameter combination under the standard working condition in the 0.15α sample data used for verification; j takes all integers from 1 to 0.15α in sequence.

[0047] Preferably, the method of performing grid traversal on the battery heating starting SOC (i.e., the SOC when the battery starts to heat) and the battery heating target temperature in S3, and forming β three-parameter combinations in combination with the blower gear, includes: min With SOC max Select x2 values between them as x2 battery heating starting SOC; at T min With Tmax Select y2 values as y2 battery heating target temperatures; combine with z blower gears to form the β three-parameter combinations; where β = x2*y2*z, x2 is much larger than x1, and y2 is much larger than y1.

[0048] Preferably, the starting SOCs of the x2 batteries for heating are: SOC min , SOC min +△SOC2, SOC min +2*△SOC2,…,SOC max ; Among them, △SOC2=(SOC max -SOC min ) / (x2-1). The target heating temperatures of the y2 batteries are: T min 、T min +△T2、T min +2*△T2,…,T max ; Among them, △T2=(T max -T min ) / (y2-1).

[0049] In a second aspect, the present invention provides a control system for improving the low-temperature driving range of a vehicle, which includes a controller programmed to execute the above-mentioned control method for improving the low-temperature driving range of a vehicle.

[0050] In a third aspect, the present invention provides a vehicle comprising the above-mentioned control system for improving the low-temperature driving range of the vehicle.

[0051] The present invention comprehensively considers the heating timing of the power battery in a low-temperature environment (i.e., the heating starting SOC), the heating target temperature of the power battery, and the blower gear, thereby effectively improving the driving net discharge capacity of the power battery in a low-temperature environment and maximizing the vehicle's cruising range. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 This is a flow chart of a control method for improving a vehicle's low-temperature driving range in an embodiment of the present invention.

[0053] Figure 2 This is a flow chart of a method for obtaining a preset heating start SOC, a preset temperature, and a preset gear position through calibration in an embodiment of the present invention.

[0054] Figure 3 Flowchart of a method for obtaining α sample data through simulation in an embodiment of the present invention. DETAILED DESCRIPTION

[0055] In order to enable a more detailed understanding of the features and technical contents of the embodiments of the present invention, the implementation of the embodiments of the present invention is described in detail below with reference to the accompanying drawings. The accompanying drawings are for reference only and are not intended to limit the embodiments of the present invention.

[0056] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which the present invention pertains. The terms used herein are for the purpose of describing embodiments of the present invention only and are not intended to limit the present invention.

[0057] In the following description, reference is made to “some embodiments”, which describes a subset of all possible embodiments, but it will be understood that “some embodiments” may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict.

[0058] like Figure 1 As shown, the control method for improving the low-temperature driving range of a vehicle in an embodiment of the present invention includes:

[0059] The first step is to determine whether the vehicle is in a low temperature environment. If so, execute the second step, otherwise end.

[0060] In some embodiments, if the ambient temperature of the vehicle is less than or equal to a first temperature threshold, it indicates that the vehicle is in a low temperature environment. As an example, the first temperature threshold is -7°C.

[0061] Step 2: Determine whether the power battery SOC reaches the preset heating start SOC. If yes, execute step 3; otherwise, end.

[0062] Step 3: Heat the power battery using the preset temperature as the heating target temperature, control the blower to operate at the preset gear, and then end. The preset heating start SOC, preset temperature, and preset gear are parameters corresponding to the maximum vehicle range in a low-temperature environment obtained through calibration.

[0063] like Figure 2 As shown, the method for obtaining a preset heating start SOC, a preset temperature, and a preset gear position by calibration in an embodiment of the present invention includes:

[0064] S1. Acquire α sample data through simulation. Each sample data includes a three-parameter combination consisting of a battery heating start SOC, a battery heating target temperature, and a blower gear position, as well as the corresponding vehicle range under standard operating conditions.

[0065] As an example, the standard operating condition is the China Light Vehicle Cycle (CLTC), which defines the time-varying relationship between the target vehicle speed and the vehicle's speed. During simulation, the speed-time curve corresponding to the standard operating condition is the time-varying relationship between the target vehicle speed and the vehicle's speed as defined by the CLTC (a known parameter).

[0066] In some embodiments, as Figure 3 As shown, the method for obtaining α sample data through simulation in S1 includes:

[0067] S11, define the thermal management parameter boundaries. The thermal management parameter boundaries include: the minimum SOC value of the battery heating start SOC min , the maximum SOC of the battery heating starting SOC max , the minimum value of the battery heating target temperature T min , the maximum value of the battery heating target temperature T max As an example, there are three blower gears, that is, z=3, and the three blower gears are gear 1, gear 2, and gear 3 respectively.

[0068] S12, in SOC min With SOC max Select x1 values between the two as x1 battery heating starting SOC; at T min With T max Select y1 values as y1 battery heating target temperatures, and combine them with z blower gears to form α three-parameter combinations. Where α = x1*y1*z.

[0069] In some embodiments, the starting SOCs of x1 battery heating are: SOC min , SOC min +△SOC1, SOC min +2*△SOC1,…,SOC max ; Among them, △SOC1=(SOC max -SOC min ) / (x1-1). The target heating temperatures of y1 batteries are: T min 、T min +△T1、T min +2*△T1,…,T max ; Among them, △T1=(T max -T min ) / (y1-1).

[0070] As an example, x1=10, y1=5, in the case of z=3, α=150.

[0071] S13, the battery heating starting SOC, battery heating target temperature and blower gear in the i-th three-parameter combination are substituted into the one-dimensional thermal management simulation model for simulation calculation to obtain the battery net discharge capacity Q corresponding to the i-th three-parameter combination i , high-voltage load power curve, and low-voltage load power curve. Here, i is an integer from 1 to α. The high-voltage load power curve is a curve showing the high-voltage load power changing over time, and the low-voltage load power curve is a curve showing the low-voltage load power changing over time.

[0072] As an example, the one-dimensional thermal management simulation model is a system-level thermal simulation framework built based on the fluid network and heat conduction model. It is built using the commercial software AMESim and is used to analyze the net discharge capacity and power requirements of the battery of the entire vehicle under different thermal conditions.

[0073] S14, calculate the net discharge capacity Q of the battery corresponding to the i-th three-parameter combination i , high-voltage load power curve and low-voltage load power curve are substituted into the power economy simulation model, and simulation under standard working conditions is performed, and the vehicle driving range D corresponding to the i-th three-parameter combination under standard working conditions is calculated. i .

[0074] As an example, the power economy simulation model is a forward simulation model, which is built using the commercial software Matlab / Simulink.

[0075] In some embodiments, simulation under standard working conditions is performed in S14, and D is calculated. i The specific methods include:

[0076] The accelerator pedal signal APS(t) at time t is calculated using formula (1).

[0077]

[0078] Among them, K P , K I , K D Respectively represent the proportional gain, integral gain and differential gain used to calculate the accelerator pedal signal; v target (t-Δt) represents the target vehicle speed at time t-Δt, v actual (t-Δt) represents the actual vehicle speed at time t-Δt, v target (t-Δt) is obtained from the vehicle speed time curve corresponding to the standard working condition. The actual vehicle speed at the initial moment is 0, and Δt represents the preset calculation period; are known parameters, the corresponding v target (τ) represents the target vehicle speed at time τ, v actual (τ) represents the actual vehicle speed at time τ, v target(τ) is obtained through the vehicle speed time curve corresponding to the standard working condition.

[0079] Use formula (2) to calculate the motor speed n at time t-Δt actual (t-Δt).

[0080]

[0081] Among them, k represents the reduction ratio, r represents the wheel radius, and k and r are known parameters.

[0082] According to n actual (t-Δt), APS(t) queries the corresponding relationship table of the preset speed, accelerator pedal signal and electric drive assembly required torque to obtain the electric drive assembly required torque T at time t need (t).

[0083] The vehicle road load F(t) at time t is calculated using formula (3), where F(t) is obtained from the actual vehicle sliding resistance test.

[0084] F(t)=A+Bv actual (t-Δt)+C[v actual (t-Δt)] 2 (3)

[0085] Among them, A, B, and C represent the constant term coefficient, linear term coefficient, and quadratic term coefficient respectively obtained from the sliding resistance test fitting, and A, B, and C are known parameters.

[0086] Use formula (4) to calculate the actual vehicle speed v at time t actual (t) and store.

[0087]

[0088] Where δ represents the vehicle rotation mass conversion coefficient, m represents the vehicle mass, and η' represents the reducer efficiency; δ, m, and η' are known parameters; T need (τ) represents the required torque of the electric drive assembly at time τ, and F(τ) represents the vehicle road load at time τ.

[0089] Use formula (5) to calculate the electric drive assembly driving power P at time t D (t).

[0090]

[0091] Among them, η represents the driving efficiency of the electric drive assembly, n actual (t) represents the motor speed at time t; η is a known parameter,

[0092] Use formula (6) to calculate the power battery power P corresponding to the i-th three-parameter combination at time t: i (t).

[0093]

[0094] Among them, η DCDC Indicates the efficiency of the DC converter connecting the power battery and the low-voltage load, P h_i (t) represents the high-voltage load power corresponding to the i-th three-parameter combination at time t, P l_i (t) represents the low-voltage load power corresponding to the i-th three-parameter combination at time t, η DCDC is a known parameter, P h_i (t) Obtained by the high-voltage load power curve corresponding to the i-th three-parameter combination, P l_i (t) is obtained through the low-voltage load power curve corresponding to the i-th three-parameter combination.

[0095] Use formula (7) to calculate the power battery SOC value SOC corresponding to the i-th three-parameter combination at time t i (t), and in SOC i When (t) = 0 (i.e. the power battery storage power is exhausted), the simulation ends and the simulation time t corresponding to the i-th three-parameter combination is output. i_SOC .

[0096]

[0097] Among them, U represents the rated voltage of the power battery; U is a known parameter, P i (τ) represents the power battery power corresponding to the i-th three-parameter combination at time τ.

[0098] The vehicle range D corresponding to the i-th three-parameter combination under standard working conditions is calculated using formula (8) (i.e., actual vehicle speed integral): i .

[0099]

[0100] S15, combining the i-th three-parameter combination with the vehicle's driving range D i Then combine to form α sample data.

[0101] S2. Use α sample data to perform training, testing, and validation to obtain a neural network model. During training, testing, and validation, a three-parameter combination (i.e., battery heating start SOC, battery heating target temperature, and blower gear position) is used as input, and the corresponding vehicle range under standard operating conditions is used as output.

[0102] In some embodiments, the neural network model is a feedforward neural network with one hidden layer, which mainly includes three parts: input layer, hidden layer and output layer. It is created by the built-in function train in the commercial software Matlab, where the number of neurons in the hidden layer is set to 8.

[0103] In some embodiments, in S2, 70% (i.e., 0.7α sample data) of the α sample data are used for training, 15% (i.e., 0.15α sample data) are used for testing, and 15% (i.e., 0.15α sample data) are used for verification to obtain a neural network model. During verification, if the 0.15α relative errors are all less than or equal to 2%, it means that the neural network model is qualified; otherwise, it means that the neural network model is unqualified and needs to be trained, tested, and verified again until the neural network model is qualified. Among them, the jth relative error = (jth neural network predicted driving range - jth sample data driving range) / jth sample data driving range; the jth neural network predicted driving range is the vehicle driving range corresponding to the standard working condition obtained by substituting the jth three-parameter combination in the 0.15α sample data used for verification into the neural network model; the jth sample data driving range is the vehicle driving range corresponding to the jth three-parameter combination under the standard working condition in the 0.15α sample data used for verification; j takes all integers from 1 to 0.15α in sequence.

[0104] S3. Perform a grid traversal of the battery heating starting SOC (i.e., the SOC when the power battery begins heating) and the battery heating target temperature, and combine them with the blower gear to form β three-parameter combinations. β is much larger than α.

[0105] In some embodiments, the method of performing grid traversal of the battery heating start SOC and the battery heating target temperature in S3 and combining them with the blower gear to form β three-parameter combinations includes:

[0106] In SOC min With SOC max Select x2 values between them as x2 battery heating starting SOC; at T min With T max Select y2 values as y2 battery heating target temperatures; combine with z blower gears to form β three-parameter combinations. Where β = x2*y2*z, x2 is much larger than x1, and y2 is much larger than y1.

[0107] In some embodiments, the starting SOCs of x2 battery heating are: SOC min , SOC min +△SOC2, SOC min +2*△SOC2,…,SOC max ; Among them, △SOC2=(SOCmax -SOC min ) / (x2-1). The target heating temperatures of y2 batteries are: T min 、T min +△T2、T min +2*△T2,…,T max ; Among them, △T2=(T max -T min ) / (y2-1).

[0108] As an example, x2=50, y2=25, in the case of z=3, β=3750.

[0109] S4. Substitute β three-parameter combinations into the neural network model respectively to predict the corresponding vehicle driving range under standard working conditions, and obtain β corresponding vehicle driving ranges under standard working conditions.

[0110] S5. Filter the maximum value among β corresponding vehicle driving ranges under standard operating conditions (i.e., filter the maximum vehicle driving range among the β corresponding vehicle driving ranges), and use the battery heating start SOC in the three-parameter combination corresponding to the maximum value (i.e., the maximum vehicle driving range) as the preset heating start SOC, the battery heating target temperature as the preset temperature, and the blower gear as the preset gear.

[0111] For example, the neural network model calculated a maximum vehicle range of 310.6 km, corresponding to the following parameter combinations: battery heating start SOC: 26.2%, battery heating target temperature: 31°C, and blower gear: 3. The neural network model calculates the range in just a few seconds, significantly improving computational efficiency compared to simply finding the three-parameter combination that maximizes the vehicle's range through simulation.

[0112] In addition, an embodiment of the present invention further provides a control system for improving the low-temperature driving range of a vehicle, which includes a controller programmed to execute the above-mentioned control method for improving the low-temperature driving range of a vehicle.

[0113] In addition, an embodiment of the present invention further provides a vehicle, which includes the above-mentioned control system for improving the low-temperature driving range of the vehicle.

[0114] The above description is only a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by any technician familiar with this technical field within the technical scope disclosed in the present invention should be covered by the scope of protection of the present invention.

Claims

1. A control method for improving vehicle low-temperature driving range, characterized in that: include: In a low-temperature environment, when the power battery SOC reaches a preset heating start SOC, the preset temperature is used as the heating target temperature to heat the power battery, and the blower is controlled to operate at a preset gear; wherein the preset heating start SOC, the preset temperature, and the preset gear are parameters corresponding to the maximum vehicle range in a low-temperature environment obtained through calibration.

2. The control method for improving vehicle low-temperature driving range according to claim 1, characterized in that: The method for obtaining the preset heating start SOC, the preset temperature, and the preset gear position by calibration includes: S1. Acquire α sample data through simulation; each sample data includes a three-parameter combination consisting of a battery heating start state of charge (SOC), a battery heating target temperature, and a blower gear position, as well as the corresponding vehicle range under standard operating conditions; S2. Using the α sample data to perform training, testing, and verification to obtain a neural network model; wherein, during training, testing, and verification, a three-parameter combination is used as input, and the corresponding vehicle range under standard operating conditions is used as output; S3. Performing a grid traversal of the battery heating starting SOC and the battery heating target temperature, combined with the blower gear position, to form β three-parameter combinations; where β is much larger than α; S4, respectively substituting β three-parameter combinations into the neural network model to predict the corresponding vehicle driving range under standard operating conditions, thereby obtaining β corresponding vehicle driving ranges under standard operating conditions; S5. Filter the maximum value of β corresponding vehicle driving ranges under standard operating conditions, and use the battery heating start SOC in the three-parameter combination corresponding to the maximum value as the preset heating start SOC, the battery heating target temperature as the preset temperature, and the blower gear as the preset gear.

3. The control method for improving vehicle low-temperature driving range according to claim 2, characterized in that: The method for obtaining α sample data through simulation in S1 includes: S11, define the thermal management parameter boundaries; wherein the thermal management parameter boundaries include: the minimum SOC value of the battery heating start SOC min , the maximum SOC of the battery heating starting SOC max , the minimum value of the battery heating target temperature T min , the maximum value of the battery heating target temperature T max , z blower gears; S12, in SOC min With SOC max Select x1 values between the two as x1 battery heating starting SOC; at T min With T max Select y1 values as y1 battery heating target temperatures; combine with z blower gears to form α three-parameter combinations; where α = x1*y1*z; S13, the battery heating starting SOC, battery heating target temperature and blower gear in the i-th three-parameter combination are substituted into the one-dimensional thermal management simulation model for simulation calculation to obtain the battery net discharge capacity Q corresponding to the i-th three-parameter combination i , high-voltage load power curve and low-voltage load power curve; wherein i takes all integers from 1 to α in sequence; S14, calculate the net discharge capacity Q of the battery corresponding to the i-th three-parameter combination i , high-voltage load power curve and low-voltage load power curve are substituted into the power economy simulation model, and simulation under standard working conditions is performed, and the vehicle driving range D corresponding to the i-th three-parameter combination under standard working conditions is calculated. i ; S15, combining the i-th three-parameter combination with the vehicle's driving range D i Then combine to form the α sample data.

4. The control method for improving the low-temperature driving range of a vehicle according to claim 3, characterized in that: In the S14, simulation under standard working conditions is performed and D is calculated. i The specific methods include: Calculate the accelerator pedal signal APS(t) at time t using formula (1); Among them, K P , K I , K D Respectively represent the proportional gain, integral gain and differential gain used to calculate the accelerator pedal signal; v target (t-Δt) represents the target vehicle speed at time t-Δt, v actual (t-Δt) represents the actual vehicle speed at time t-Δt, v target (t-Δt) is obtained from the vehicle speed time curve corresponding to the standard working condition. The actual vehicle speed at the initial moment is 0, and Δt represents the preset calculation period; Use formula (2) to calculate the motor speed n at time t-Δt actual (t-Δt); Among them, k represents the reduction ratio, and r represents the wheel radius; According to n actual (t-Δt), APS(t) queries the corresponding relationship table of the preset speed, accelerator pedal signal and electric drive assembly required torque to obtain the electric drive assembly required torque T at time t need (t); Calculate the vehicle road load F(t) at time t using formula (3); F(t)=A+Bv actual (t-Δt)+C[v actual (t-Δt)] 2 (3) Among them, A, B, and C represent the constant term coefficient, linear term coefficient, and quadratic term coefficient obtained from the sliding resistance test fitting, respectively; Use formula (4) to calculate the actual vehicle speed v at time t actual (t) and store; Wherein, δ represents the vehicle rotation mass conversion coefficient, m represents the vehicle mass, and η' represents the reducer efficiency; Use formula (5) to calculate the electric drive assembly driving power P at time t D (t); Among them, η represents the driving efficiency of the electric drive assembly, n actual (t) represents the motor speed at time t; Use formula (6) to calculate the power battery power P corresponding to the i-th three-parameter combination at time t: i (t); Among them, η DCDC Indicates the efficiency of the DC converter connecting the power battery and the low-voltage load, P h_i (t) represents the high-voltage load power corresponding to the i-th three-parameter combination at time t, P l_i (t) represents the low-voltage load power corresponding to the i-th three-parameter combination at time t; Use formula (7) to calculate the power battery SOC value SOC corresponding to the i-th three-parameter combination at time t i (t), and in SOC i When (t) = 0, the simulation ends and the simulation time t corresponding to the i-th three-parameter combination is output. i_SOC ; Wherein, U represents the rated voltage of the power battery; The vehicle range D corresponding to the i-th three-parameter combination under standard working conditions is calculated using formula (8): i ; 5. The control method for improving vehicle low-temperature driving range according to claim 3, characterized in that: The starting SOCs of the x1 battery heating are: SOC min , SOC min +△SOC1, SOC min +2*△SOC1,…,SOC max ; Among them, △SOC1=(SOC max -SOC min ) / (x1-1); The target temperatures of the y1 battery heating are: T min 、T min +△T1、T min +2*△T1,…,T max ; Among them, △T1=(T max -T min ) / (y1-1).

6. The control method for improving the low-temperature driving range of a vehicle according to any one of claims 2 to 5, characterized in that: In S2, 70% of the α sample data are used for training, 15% for testing, and 15% for verification to obtain a neural network model; during verification, if 0.15α relative errors are all less than or equal to 2%, it means that the neural network model is qualified; otherwise, it means that the neural network model is unqualified and needs to be trained, tested, and verified again until the neural network model is qualified; Among them, the jth relative error = (jth neural network predicted driving range - jth sample data driving range) / jth sample data driving range; the jth neural network predicted driving range is the vehicle driving range corresponding to the standard working condition obtained by substituting the jth three-parameter combination in the 0.15α sample data used for verification into the neural network model; the jth sample data driving range is the vehicle driving range corresponding to the jth three-parameter combination under the standard working condition in the 0.15α sample data used for verification; j takes all integers from 1 to 0.15α in sequence.

7. The control method for improving the low-temperature driving range of a vehicle according to any one of claims 3 to 5, characterized in that: The method of performing grid traversal of the battery heating start SOC and the battery heating target temperature in S3 and forming β three-parameter combinations in combination with the blower gear position includes: In SOC min With SOC max Select x2 values between them as x2 battery heating starting SOC; at T min With T max Select y2 values as y2 battery heating target temperatures; combine with z blower gears to form the β three-parameter combinations; where β = x2*y2*z, x2 is much larger than x1, and y2 is much larger than y1.

8. The control method for improving vehicle low-temperature driving range according to claim 7, characterized in that: The starting SOCs of the x2 batteries for heating are: SOC min , SOC min +△SOC2, SOC min +2*△SOC2,…,SOC max ; Among them, △SOC2=(SOC max -SOC min ) / (x2-1); The y2 battery heating target temperatures are: T min 、T min +△T2、T min +2*△T2,…,T max ; Among them, △T2=(T max -T min ) / (y2-1).

9. A control system for improving vehicle low-temperature driving range, comprising a controller, characterized in that: The controller is programmed to execute the control method for improving the low-temperature driving range of a vehicle according to any one of claims 1 to 8.

10. A vehicle, characterized in that: The invention comprises a control system for improving the low-temperature driving range of a vehicle as claimed in claim 9.

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