An integrated control method for energy-saving driving and energy-saving refrigeration of a pure electric vehicle

Abstract

The application discloses an energy-saving driving and energy-saving refrigeration integrated control method for a pure electric vehicle, and comprises the following steps: S1, constructing a transfer equation of a vehicle motion state in a space domain; S2, fitting a torque boundary of a motor based on motor calibration data; S3, calibrating and fitting temperature characteristics and energy consumption characteristics of a vehicle-mounted air conditioner; S4, constructing and solving an energy-saving vehicle speed planning problem in a rolling time domain; and S5, constructing and solving an air conditioner refrigeration optimization problem in the rolling time domain. The energy-saving driving and energy-saving refrigeration are integrated controlled, the obtained planning vehicle speed sequence can guide the refrigeration optimization of the air conditioner, and based on a variable cost function of an optimization target, the energy-saving refrigeration effect of the air conditioner under a second-level planning time domain length is improved, and the adaptability of the integrated control method to an expressway and an urban road is promoted.

Description

Technical Field

[0001] This invention relates to the field of energy-saving control technology for electric vehicles, and in particular to an integrated control method for energy-saving driving and energy-saving cooling of pure electric vehicles. Background Technology

[0002] The drive motor and the vehicle air conditioning system are the most energy-consuming components in the powertrain and auxiliary systems of pure electric vehicles, respectively. Therefore, optimizing their operation to improve their energy efficiency will effectively reduce the overall energy consumption of pure electric vehicles and increase their driving range. Since the air ram speed of the air conditioning condenser increases with vehicle speed, thereby improving the air conditioning's heat dissipation efficiency and overall energy efficiency, dynamically adjusting the air conditioning's cooling intensity based on the vehicle's future driving speed is an effective way to achieve energy-saving cooling. However, compared to the vehicle's motion, the temperature change in the cabin is relatively slow. Therefore, air conditioning cooling optimization is usually performed in a planning time domain on the order of minutes. This optimization process requires guidance from the vehicle's speed information for the corresponding future time period. For road scenarios with stable traffic conditions, the vehicle's future driving speed can be obtained by fitting the average speed of the road traffic flow. However, for road scenarios with dynamically changing traffic conditions or traffic lights, the driving speed differences between individual vehicles are significant. The future speed predicted by the aforementioned methods cannot accurately reflect the actual driving speed of the vehicle, making it unable to effectively guide the energy-saving cooling optimization process. In the field of energy-saving driving, vehicles travel at a planned speed to reduce the energy consumption of the drive motor while meeting driving objectives. Therefore, the energy-saving planned vehicle speed calculated based on traffic constraints and driving objectives can serve as future vehicle speed information to guide the optimization of air conditioning cooling. Thus, energy-saving driving and energy-saving cooling constitute an integrated optimization problem. However, since the error in predicting the speed of vehicles ahead usually increases with the number of prediction steps, the planning time domain for vehicle speed planning is generally set to the second level. This cannot meet the requirements of conventional energy-saving cooling methods for the length of the planning time domain, making it difficult to apply existing integrated optimization technologies for vehicle speed planning and air conditioning control to dynamic traffic environments with traffic lights and vehicles ahead. Summary of the Invention

[0003] This invention addresses the shortcomings of existing technologies by providing an integrated control method for energy-saving driving and energy-saving cooling in pure electric vehicles, thus resolving the deficiencies in the prior art.

[0004] To achieve the above-mentioned objectives, the technical solution adopted by the present invention is as follows:

[0005] An integrated control method for energy-saving driving and energy-saving cooling of pure electric vehicles includes: construction of a vehicle control model, energy-saving speed planning, and air conditioning cooling optimization;

[0006] Vehicle control model construction: A transition equation for the vehicle's motion state is constructed in the spatial domain. Based on motor calibration data, the maximum torque of the motor is fitted as a polynomial function of the motor speed. Under stationary and constant-speed driving conditions, the control quantity of the onboard air conditioning is adjusted to periodically change in a sinusoidal form. Relevant temperature state variables and energy consumption changes of various air conditioning components are collected and recorded to calibrate the dynamic temperature characteristics of the onboard air conditioning and the passenger cabin, as well as the energy consumption characteristics of the onboard air conditioning. Based on the calibration data of the onboard air conditioning, state transition equations for the passenger cabin temperature and evaporator temperature, as well as energy consumption equations for various air conditioning components, are fitted.

[0007] Energy-saving speed planning: Energy-saving speed planning aims to optimize driving energy consumption, ensure driving safety, and maintain driving speed. It takes into account the constraints of vehicles ahead, traffic lights, and motor torque boundaries. Based on the current vehicle and traffic conditions, it optimizes the driving speed in the planning time domain using an online rolling optimization method to obtain the desired driving speed sequence and guide the vehicle's driving.

[0008] Air Conditioning Cooling Optimization: To improve the energy consumption performance of air conditioning cooling optimization within the second-level planning time domain, a target function controlled by a continuously differentiable switching function is constructed as the cost function for air conditioning cooling optimization. This allows the objective of air conditioning cooling optimization to switch between adjusting the cabin temperature and reducing air conditioning energy consumption. Combining the control and state constraints of air conditioning control, and based on the desired driving speed sequence given by vehicle speed planning, an online rolling optimization problem for air conditioning cooling is constructed, and the desired air conditioning cooling control sequence is solved.

[0009] A vehicle control model is established based on calibrated vehicle powertrain and air conditioning system data. This model serves as the equality constraint in speed planning and air conditioning cooling optimization. Speed ​​planning considers traffic light constraints and vehicle spacing constraints to provide a reference driving speed for the vehicle, reducing wheel-end power demand. The solved planned speed sequence will guide the energy-saving cooling optimization of the air conditioning system. The energy-saving cooling optimization employs a cost function with a variable optimization objective, adaptively optimizing control commands based on the planned vehicle speed to solve the energy-saving cooling problem within the second-level planning time domain.

[0010] The specific steps are as follows:

[0011] S1. Construct the transition equations for the vehicle's motion state in the spatial domain;

[0012] S2. Fit the motor torque boundary based on motor calibration data;

[0013] S3. Calibrate and fit the temperature and energy consumption characteristics of the vehicle air conditioner;

[0014] S4. Construct and solve the energy-saving vehicle speed planning problem in the rolling time domain;

[0015] S5. Construct and solve the air conditioning refrigeration optimization problem in the rolling time domain;

[0016] Furthermore, S1 specifically refers to:

[0017] S11: Obtain historical movement information of vehicles ahead and road traffic information of downstream sections.

[0018] The historical motion information of the vehicle ahead includes: the vehicle's position, speed, and acceleration;

[0019] Downstream road traffic information includes: downstream road speed limit, downstream average vehicle speed, downstream traffic light location, downstream traffic light phase and remaining time;

[0020] S12: Predict the future speed of the vehicle ahead.

[0021] The future speed of the vehicle ahead can be predicted using the constant acceleration assumption, that is, the vehicle ahead maintains the current acceleration in the prediction time domain until the predicted speed reaches the minimum or maximum speed limit of the road.

[0022] The future speed prediction of vehicles ahead can also be combined with road traffic information of downstream sections and predicted using machine learning methods such as multiple regression and sequence generation models.

[0023] S13: Select the vehicle spacing, vehicle speed, and travel time per unit distance as state variables, and the output torque of the power system as the control variable. Considering the driving resistance, construct a discrete state transition equation for the state variables with respect to the control variable in the spatial domain.

[0024] Driving resistance includes: air resistance, rolling resistance, and gradient resistance.

[0025] Furthermore, S2 specifically refers to:

[0026] S21: Based on the calibration data of the drive motor, draw the motor characteristic curve and find the motor speed corresponding to the boundary point between the constant torque region and the constant power region of the motor on the motor characteristic curve.

[0027] S22: In the constant torque region, the maximum torque of the motor is fixed; in the constant power region, the maximum torque of the motor is fitted as a function of the motor speed.

[0028] Furthermore, S3 specifically refers to:

[0029] S31: When the vehicle is stationary, the control quantity of the air conditioning is adjusted to change periodically in a sinusoidal form, and the changes of the corresponding air conditioning temperature state quantity and the cockpit temperature state quantity are recorded.

[0030] Vehicle air conditioning systems widely employ a layered control method. Therefore, the control quantities mentioned here include the blower's air intake volume and the evaporator's target temperature. The operating power of each component is adjusted by the manufacturer's built-in underlying algorithm based on the set values ​​of the above two control quantities.

[0031] Air conditioning temperature status parameters include: evaporator temperature.

[0032] The cockpit temperature status parameters include: cockpit temperature, cockpit intake air temperature, cockpit inner wall temperature, and cockpit outer shell temperature.

[0033] S32: Control the vehicle to travel at a constant speed at different vehicle speeds, and at each vehicle speed, adjust the control quantity of the air conditioner to change periodically in a sinusoidal form, and record the power change of the corresponding air conditioner components.

[0034] The power of air conditioning components includes the power of the compressor and the power of the blower.

[0035] S33: The state transition equations for the air conditioning state variables and the cockpit temperature state variables are fitted using a polynomial fitting method.

[0036] The cockpit intake air temperature is used as an intermediate variable and is fitted as a polynomial function of the evaporator temperature and the cockpit temperature.

[0037] The change in cockpit temperature per unit time is fitted as a polynomial function of the cockpit inner wall temperature, cockpit outer shell temperature, cockpit intake air temperature, and blower intake air volume.

[0038] The change in evaporator temperature per unit time is fitted as a polynomial function of the rate of change of the target evaporator temperature, ambient temperature, and blower intake air volume.

[0039] S34: The compressor power and blower power of the vehicle in a stationary state are fitted using a polynomial fitting method.

[0040] The compressor power is fitted as a polynomial function of the intake air volume, evaporator temperature, and target evaporator temperature.

[0041] The blower power is fitted as a polynomial function of the blower intake volume.

[0042] S35: Calculate the ratio of compressor power at each constant vehicle speed to compressor power when the vehicle is stationary, and fit it as a polynomial function with respect to vehicle speed.

[0043] Furthermore, S4 specifically refers to,

[0044] S41: Construct a cost function for speed planning with the goal of reducing wheel-end power demand and maintaining vehicle speed.

[0045] S42: Constructing equation constraints for vehicle speed planning based on vehicle motion state transition equations in the spatial domain.

[0046] S43: Considering motor torque and speed constraints, safe vehicle spacing constraints, road speed limits and traffic light constraints, construct inequality constraints for vehicle speed planning.

[0047] S44: Set the unit step size and the number of planned steps to construct the optimal control problem of vehicle speed planning in the spatial domain.

[0048] S45: Solve the constructed vehicle speed planning problem to obtain the desired vehicle speed sequence, and use the first value of the speed sequence as the target vehicle speed for the next moment.

[0049] S46: Set the update frequency for vehicle speed planning, and execute S41-S45 on a rolling basis.

[0050] Furthermore, the cost function constructed in S41 consists of three parts: the first part is the output torque of the electric drive system, which is used to reduce the vehicle's wheel-end power demand; the second part is the difference between the vehicle spacing at the end of the planning time domain and the vehicle spacing at the beginning of the planning time domain, which is used to maintain the vehicle speed; and the third part is the difference between the two when the vehicle spacing is less than the ideal vehicle spacing, which is used to ensure driving safety.

[0051] Furthermore, for the weights of the cost function constructed in S41, the weight values ​​of the third part are set to be several orders of magnitude larger than the weight values ​​of the first part in order to emphasize driving safety; the weight values ​​of the first two parts are specifically set according to the emphasis on energy consumption and driving speed in actual use.

[0052] Furthermore, in S43, when setting constraints for vehicle speed planning: the driving speed is constrained based on the motor speed range and road speed limit; the motor torque is constrained based on the fitted motor torque boundary value; and the minimum safe distance between vehicles is set to constrain the vehicle spacing.

[0053] Furthermore, in S43, traffic light constraints are set according to the following rules: if the distance to the traffic light is less than the current planned spatial domain, then the vehicle can pass through the traffic light in the current traffic light cycle based on the vehicle spacing and road speed limit. If the vehicle can pass through the traffic light in the current traffic light cycle, then the vehicle is constrained to arrive at the traffic light at a time less than the green light end time or greater than the red light end time. If the vehicle cannot pass through the traffic light in the current traffic light cycle, then the vehicle is constrained to stop in front of the traffic light.

[0054] Furthermore, in the optimal control problem of vehicle speed planning constructed in S44, the state variables include: vehicle spacing, vehicle speed, and travel time; the control variable includes: power system output torque.

[0055] Furthermore, when solving the vehicle speed planning problem in S45, the optimal control problem is first transformed into a nonlinear optimization problem using the shooting method or pseudospectral method. Then, one of the algorithms, including sequential quadratic programming, interior point method, and genetic algorithm, is used to solve the nonlinear optimization problem.

[0056] Furthermore, S5 specifically refers to,

[0057] S51: Use interpolation to convert the distance-domain-based planned vehicle speed sequence into a time-domain-based planned vehicle speed sequence.

[0058] S52: Construct a continuous and differentiable switching function with speed as the variable or with speed and cockpit temperature as the variables.

[0059] S53: Based on the constructed switching function, a cost function is constructed for the energy-saving cooling optimization problem with the goal of reducing air conditioning energy consumption and maintaining cockpit temperature.

[0060] S54: Constructing equality constraints for the energy-saving cooling optimization problem based on the state transition equations of air conditioning temperature and cockpit temperature.

[0061] S55: Construct inequality constraints for the energy-saving cooling optimization problem based on the upper and lower limits of air conditioning control quantities and the upper and lower limits of cockpit temperature.

[0062] S56: Set the unit step size and the number of planned steps to construct the optimal control problem for energy-saving cooling in the time domain.

[0063] S57: Solve the constructed energy-saving cooling optimal control problem to obtain the expected sequence of air conditioning control quantities, and use the first value of the control sequence as the air conditioning control command for the next moment.

[0064] S58: Set the update frequency for air conditioning cooling optimization, and execute S51-S57 on a rolling basis.

[0065] Furthermore, the switching function in S52 is used in the cost function. When speed is the variable, a speed threshold is first set. When the vehicle speed is significantly less than the speed threshold, the switching function is set to 0. When the vehicle speed is significantly greater than the speed threshold, the switching function is set to 1. Near the speed threshold, the switching function switches smoothly.

[0066] Furthermore, when the switching function in S52 uses speed and cabin temperature as variables, two sub-switching functions are set based on speed and cabin temperature respectively; a speed threshold is set, and when the vehicle speed is significantly less than the speed threshold, the sub-switching function is set to 0, and when the vehicle speed is significantly greater than the speed threshold, the sub-switching function is set to 1. Near the speed threshold, the switching function transitions smoothly; a cabin temperature threshold is set, and when the cabin temperature is less than the temperature threshold, the switching function is set to 1, and when the cabin temperature is greater than the temperature threshold, the switching function is set to 0. Near the cabin temperature threshold, the switching function transitions smoothly; the output value of the switching function is the product of the above two sub-switching functions.

[0067] Furthermore, the cost function constructed in S53 consists of three parts: the first part is the sum of the blower power and the compressor power; the second part is the magnitude of the deviation of the cockpit temperature from the ideal cockpit temperature range; and the third part is the deviation of the evaporator target temperature from the evaporator target temperature guide value.

[0068] Furthermore, in S53, the guide value for the evaporator target temperature is set according to the vehicle speed. When the vehicle is stationary, the guide value for the evaporator target temperature is set to the upper limit of the evaporator target temperature. When the vehicle speed is the maximum speed limit of the road, the guide value for the evaporator target temperature is set to the lower limit of the evaporator target temperature. When the vehicle speed is in the middle range, the guide value for the evaporator target temperature is obtained by interpolation.

[0069] Furthermore, in S53, when setting the weights of the cost function: the order of magnitude of the three parts is normalized by the weight coefficients of the first part, and the weight coefficients of the second and third parts are controlled by the switching function constructed in S52.

[0070] Furthermore, in the energy-saving refrigeration optimal control problem constructed in S56, the state variables include: evaporator temperature, cockpit temperature, and blower intake air volume; the control variables include: target evaporator temperature and rate of change of blower intake air volume.

[0071] Furthermore, when solving the optimal control problem for energy-saving refrigeration in S57, the optimal control problem is first transformed into a nonlinear optimization problem using the shooting method or pseudospectral method. Then, one of the algorithms, including sequential quadratic programming, interior point method, and genetic algorithm, is used to solve the nonlinear optimization problem.

[0072] Compared with the prior art, the advantages of the present invention are as follows:

[0073] By integrating and optimizing vehicle speed planning with air conditioning cooling control, the dual objectives of energy-efficient driving and energy-efficient cooling are coordinated within the rolling planning time domain. Vehicle speed planning reduces wheel-end power demand when dealing with disturbances from vehicles ahead and traffic lights, and provides future vehicle speed information for air conditioning cooling optimization. Air conditioning cooling optimization, based on the planned future vehicle speed sequence, improves energy-efficient cooling performance within the second-level planning time domain through a designed cost function with variable optimization objectives. With the combined contribution of these two optimized control components, the energy efficiency of the pure electric vehicle is improved, enabling it to adapt to various road scenarios, including highways and urban roads. Attached Figure Description

[0074] Figure 1 This is a schematic diagram of an integrated control architecture for energy-saving driving and energy-saving cooling of a pure electric vehicle according to the present invention.

[0075] Figure 2 This is an example diagram of the control quantity of the sinusoidal periodic change when calibrating the vehicle air conditioner according to the present invention. Detailed Implementation

[0076] To make the objectives, technical solutions, and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings and examples.

[0077] like Figure 1 As shown, an integrated control method for energy-saving driving and energy-saving cooling of a pure electric vehicle includes the following steps:

[0078] S1. Construct the transition equations for the vehicle's motion state in the spatial domain:

[0079] First, obtain the current speed and acceleration information of the vehicle ahead, obtain the road speed limit information of the downstream road section, and obtain the traffic light distance, phase and remaining phase time information of the downstream road section;

[0080] Secondly, the assumption of uniform acceleration is used to predict the future speed sequence of vehicles ahead;

[0081] Finally, the vehicle spacing, vehicle speed, and travel time per unit distance are selected as state variables, and the output torque of the power system is selected as the control variable. Considering the driving resistance, a discrete state transition equation for the state variables with respect to the control variable is constructed in the spatial domain.

[0082] d(s+1)=d(s)+(v p t Δs -Δs) (1)

[0083] v(s+1) 2 -v(s) 2 =2aΔs (2)

[0084] t Δs=Δs / v(s) (3)

[0085] In the formula, s represents the location index, d represents the vehicle spacing, and v p Δs represents the predicted speed of the vehicle ahead, Δs represents the unit step size, and t represents the predicted speed of the vehicle ahead. Δs This represents the travel time per unit distance, where v represents the vehicle speed and a represents the acceleration. The acceleration is determined by the output torque of the powertrain and the resistance force.

[0086] a=(F T -F r -F b ) / m (4)

[0087] In the formula, F T F represents the driving force output by the drive system. b This represents the mechanical braking force output by the braking system. It only works when the maximum braking force of the drive system is less than the vehicle's required braking force. (F) r This represents driving resistance, which includes air resistance, rolling resistance, and gradient resistance.

[0088] F r =μmgcos(θ)+mgsin(θ)+C d ρ air Av 2 / 2 (5)

[0089] In the formula, m represents the vehicle mass, g represents the acceleration due to gravity, θ represents the road surface slope, μ represents the rolling resistance coefficient, and ρ represents the rolling resistance coefficient. air Indicates air density, A represents the frontal area, C represents ... d This represents the air drag coefficient.

[0090] Specifically, when predicting the future speed of a vehicle ahead using the assumption of uniform acceleration:

[0091] Assuming the vehicle maintains its current acceleration within the prediction time domain, the speed value for each prediction step is calculated sequentially based on the prediction step size. If the vehicle speed calculated in a prediction step exceeds or falls below the road speed limit, the predicted vehicle speed for this prediction step and subsequent prediction steps is set to the road speed limit.

[0092] For roads with traffic lights, if the speed sequence calculated by the uniform acceleration assumption violates the red light constraint when the distance to the traffic light is less than the predicted range, then it is assumed that the vehicle will begin a uniform deceleration mode 30 meters before the traffic light until it stops in front of the traffic light.

[0093] S2. Fitting the motor's torque boundary based on motor calibration data:

[0094] First, obtain the motor characteristic curve based on the calibration data of the drive motor, and find the inflection point between the constant torque region and the constant power region of the motor on the motor characteristic curve.

[0095] Secondly, for the maximum torque value at each speed in the constant power region, a polynomial fitting method is used to fit the torque boundary value as a function of the speed. In the constant torque region, the motor has a fixed torque boundary value. Therefore, the motor's torque boundary can be fitted as a function of the speed;

[0096] T ub (ω)=min(T max ,a1ω 2 +a2ω+a3) (6)

[0097] In the formula, T ub This represents the upper boundary value of the motor torque, and the lower boundary value of the motor torque is set to its opposite.

[0098] S3. Calibrate and fit the temperature and energy consumption characteristics of the vehicle air conditioning system:

[0099] First, when the vehicle is stationary, such as Figure 2 As shown, the control quantity for adjusting the air conditioning changes periodically in a sinusoidal form, and the changes in the corresponding air conditioning temperature state quantity and cockpit temperature state quantity are recorded.

[0100] Vehicle air conditioning systems widely employ a layered control method. Therefore, the control quantities mentioned here include the blower's air intake volume and the evaporator's target temperature. The operating power of each component is adjusted by the manufacturer's built-in underlying algorithm based on the set values ​​of the above two control quantities.

[0101] Secondly, control the vehicle to travel at a constant speed at different speeds, while simultaneously... Figure 2 As shown, the control quantity of the air conditioner is adjusted periodically in a sinusoidal form at various vehicle speeds, and the power changes of the corresponding air conditioner blower and compressor are recorded.

[0102] Secondly, based on the calibration data of the vehicle air conditioner, the state transition equations of the air conditioner temperature state quantity and the cockpit temperature state quantity are fitted using a polynomial.

[0103] K ai (k)=β1+β2K evap (k)+β3K cab (k) (7)

[0104]

[0105]

[0106] In the formula, K ai This indicates the intake air temperature of the cockpit, in Kelvin.evap This indicates the evaporator temperature, in Kelvin (K). cab Indicates cockpit temperature, K ci This indicates the temperature of the cockpit's interior walls, in Kelvin (K). cs This indicates the outer shell temperature of the cockpit, in Kelvin. env Indicates ambient temperature, m bl This indicates the air intake volume of the blower. The target temperature of the evaporator is represented by Δm. bl β represents the rate of change of the blower's intake air volume. i Describing the fitting coefficients of the polynomial

[0107] Based on the calibration data of the vehicle air conditioner, the compressor power and blower power in the vehicle's stationary state are fitted using a polynomial.

[0108] P b =α1+α2·m bl 2 +α3·m bl (10)

[0109]

[0110] In the formula, P b P represents the power of the blower. c Indicates compressor power, α i The coefficients represent the fitting coefficients of the polynomial.

[0111] Finally, considering that the efficiency of the air conditioning compressor varies with the vehicle speed, the ratio of the compressor power at each constant speed to the compressor power when the vehicle is stationary is calculated and fitted as a polynomial function with respect to vehicle speed.

[0112] η=c1(v-v0)+c2 (12)

[0113] In the formula, η represents the efficiency value of the compressor at different vehicle speeds, and c i The coefficients represent the fitting coefficients of the polynomial.

[0114] S4. Construct and solve the energy-saving vehicle speed planning problem in the rolling time domain:

[0115] First, vehicle spacing, vehicle speed, and travel time are selected as the state variables for vehicle speed planning, and the output torque of the power system is selected as the control variable for vehicle speed planning.

[0116] x=[d,v,t s (13)

[0117] u=[T] (14)

[0118] In the formula, t s The value represents the travel time, and T represents the output torque of the power system.

[0119] Based on this, a cost function for vehicle speed planning is constructed with the goal of reducing wheel-end power demand and maintaining vehicle speed.

[0120]

[0121] In the formula, F v This represents the cost function value. The time-domain steps represent the number of planning steps, W, Q, and R represent the weights of the cost function, and S represents the weights of the cost function. d This represents the difference between the two when the distance between vehicles is less than the ideal distance between vehicles;

[0122] S d =d-(t) th v+d min (16)

[0123] In the formula, t th d represents the minimum headway. min This indicates the minimum vehicle spacing.

[0124] Specifically, for the weights in the aforementioned cost function, R is set to be several orders of magnitude larger than W and Q to emphasize driving safety; the values ​​of W and Q are specifically set according to the actual emphasis on energy consumption and driving speed during use.

[0125] Secondly, based on the transfer equation of the vehicle's motion state, we construct the equality constraints of the optimal control problem, and based on the boundary values ​​of motor speed and torque, vehicle spacing, road speed limit, distance to traffic lights, traffic light status, and remaining time of the current phase, we construct the inequality constraints of the optimal control problem.

[0126] ceq(x)=0 (17)

[0127] c(x,u)≤0 (18)

[0128] t lb ≤t s (s trl )≤t ub (19)

[0129] In the formula, ceq(x) represents the equality constraint, c(x,u) represents the inequality constraint, and s trl The index t represents the planning step index corresponding to the position of the traffic light. lb Indicates the lower boundary of the travel time, t ub Indicates the upper limit of the passage time.

[0130] Specifically, regarding traffic light constraints: if the distance to the traffic light is less than the current planned spatial domain, firstly, based on the predicted speed of the preceding vehicle, it is determined whether the preceding vehicle can pass through the traffic light in the current traffic light cycle. Then, based on the vehicle spacing, the distance to the traffic light, and the road speed limit, it is determined whether the controlled vehicle can pass through the traffic light in the current traffic light cycle. If the controlled vehicle can pass through the traffic light in the current traffic light cycle, the time for the vehicle to arrive at the traffic light is constrained to be less than the end time of the green light or greater than the end time of the red light. If the vehicle cannot pass through the traffic light in the current traffic light cycle, the vehicle is constrained to stop in front of the traffic light.

[0131] Secondly, by setting the unit step size and the number of planned steps, the optimal control problem of vehicle speed planning is constructed in the spatial domain.

[0132] Secondly, the optimal control problem concerning vehicle speed planning is transformed into a nonlinear optimization problem by using the multiple target method, and then the interior point method is used to solve it to obtain the optimal vehicle speed sequence.

[0133] Finally, the update frequency for the vehicle speed planning is set, and the construction and solution steps of the above vehicle speed planning problem are executed in a rolling manner.

[0134] S5. Construct and solve the air conditioning refrigeration optimization problem in the rolling time domain:

[0135] First, a linear interpolation method is used to convert the distance-domain-based planned vehicle speed sequence into a time-domain-based planned vehicle speed sequence.

[0136] Secondly, the evaporator temperature, cockpit temperature and blower air intake volume are selected as state variables, and the rate of change of the target evaporator temperature and blower air intake volume are control variables.

[0137] x AC =[m ai ,K evap ,K cab (20)

[0138]

[0139] In the formula, m ai K represents the air intake volume of the blower. evap This indicates the evaporator temperature, in Kelvin (K). cab Indicates the cockpit temperature, Δm ai This indicates the rate of change in the blower's air intake volume. This indicates the target temperature of the evaporator.

[0140] Secondly, construct a continuous and differentiable switching function with velocity as the variable;

[0141]

[0142] In the formula, χ represents the value of the switching function, and vth This indicates the set switching speed threshold, v plan Indicates the speed of the plan.

[0143] Based on this, a cost function for the air conditioning cooling optimization problem is constructed. To optimize the slowly changing cockpit temperature within a second-level planning time domain, a cost function based on a continuously differentiable switching function is designed.

[0144]

[0145] In the formula, This represents the cost function value. W1 and W2 represent the number of planning steps in the time domain, and W1 and W2 represent the weight coefficients of the cost function. This indicates the target temperature of the evaporator.

[0146] Specifically, the cost function constructed above is divided into three parts: the first part is the power of the blower and the power of the compressor; the second part is the magnitude of the deviation of the cockpit temperature from the ideal cockpit temperature range, so as to guide the cockpit temperature to be within the set temperature; and the third part is the deviation between the evaporator target temperature and the evaporator target temperature guidance value.

[0147] Specifically, the guide value for the evaporator target temperature is set according to the vehicle speed. When the vehicle is stationary, the guide value for the evaporator target temperature is set to the upper limit of the evaporator target temperature. When the vehicle speed is the road speed limit, the guide value for the evaporator target temperature is set to the lower limit of the evaporator target temperature. For vehicle speeds in the intermediate range, the guide value for the evaporator target temperature is obtained by interpolation.

[0148] Secondly, the optimal control problem of air conditioning refrigeration control is transformed into a nonlinear optimization problem by using the multiple target method, and the interior point method is used to solve it to obtain the optimal control sequence of air conditioning refrigeration.

[0149] Finally, the update frequency for air conditioning cooling optimization is set, and the above steps for constructing and solving the air conditioning cooling optimization problem are executed continuously.

[0150] Those skilled in the art will recognize that the embodiments described herein are intended to help the reader understand the implementation methods of the present invention, and should be understood that the scope of protection of the present invention is not limited to such specific statements and embodiments. Those skilled in the art can make various other specific modifications and combinations based on the technical teachings disclosed in this invention without departing from the spirit of the invention, and these modifications and combinations are still within the scope of protection of the present invention.

Claims

1. An integrated control method for energy-saving driving and energy-saving cooling of a pure electric vehicle, characterized in that, Includes the following steps: S1. Construct the transition equations for the vehicle's motion state in the spatial domain; specifically including the following sub-steps: S11: Obtain historical movement information of vehicles ahead and road traffic information of downstream sections; The historical motion information of the vehicle ahead includes: the vehicle's position, speed, and acceleration; Downstream road traffic information includes: downstream road speed limit, downstream average vehicle speed, downstream traffic light location, downstream traffic light phase and remaining time; S12: Predict the future speed of the vehicle ahead; The future speed of the vehicle ahead is predicted using the constant acceleration assumption, that is, the vehicle ahead maintains the current acceleration in the prediction time domain until the predicted speed reaches the minimum or maximum speed limit of the road. Alternatively, the future speed of vehicles ahead can be predicted using machine learning methods, combined with road traffic information from downstream sections. S13: Select the vehicle spacing, vehicle speed, and travel time per unit distance as state variables, and the output torque of the power system as the control variable. Considering the driving resistance, construct a discrete state transition equation for the state variables with respect to the control variable in the spatial domain. Driving resistance includes: air resistance, rolling resistance, and gradient resistance; S2. Fit the motor torque boundary based on motor calibration data; S3. Calibrate and fit the temperature and energy consumption characteristics of the vehicle air conditioner; S4. Construct and solve the energy-saving vehicle speed planning problem in the rolling time domain; S5. Construct and solve the air conditioning refrigeration optimization problem in the rolling time domain; including the following sub-steps: S51: Use interpolation to convert the distance-domain-based planned vehicle speed sequence into a time-domain-based planned vehicle speed sequence; S52: Construct a continuous and differentiable switching function with speed as the variable or with speed and cockpit temperature as the variables; S53: Based on the constructed switching function, a cost function for the energy-saving cooling optimization problem is constructed with the goal of reducing air conditioning energy consumption and maintaining cockpit temperature; The cost function constructed in S53 consists of three parts: the first part is the sum of the blower power and the compressor power; the second part is the magnitude of the deviation of the cockpit temperature from the ideal cockpit temperature range; and the third part is the deviation of the evaporator target temperature from the evaporator target temperature guide value. The weights of the second and third parts are controlled by the switching function constructed in S52. S54: Constructing equality constraints for the energy-saving cooling optimization problem based on the state transition equations of air conditioning temperature and cockpit temperature; S55: Construct inequality constraints for the energy-saving cooling optimization problem based on the upper and lower limits of air conditioning control quantities and the upper and lower limits of cockpit temperature; S56: Set the unit step size and the number of planned steps to construct the optimal control problem for energy-saving cooling in the time domain; S57: Solve the constructed energy-saving cooling optimal control problem, obtain the expected sequence of air conditioning control quantities, and use the first value of the control sequence as the air conditioning control command at the next moment. S58: Set the update frequency for air conditioning cooling optimization, and execute S51-S57 on a rolling basis.

2. The integrated control method for energy-saving driving and energy-saving cooling of a pure electric vehicle according to claim 1, characterized in that: S3 specifically includes the following sub-steps: S31: When the vehicle is stationary, the control quantity of the air conditioning is adjusted to change periodically in a sinusoidal form, and the changes of the corresponding air conditioning temperature state quantity and the cockpit temperature state quantity are recorded. Air conditioning control parameters include: blower air intake volume and evaporator target temperature; Air conditioning temperature status parameters include: evaporator temperature; The cockpit temperature status parameters include: cockpit temperature, cockpit intake air temperature, cockpit inner wall temperature, and cockpit outer shell temperature; S32: Control the vehicle to travel at a constant speed at different vehicle speeds, and at each vehicle speed, adjust the control quantity of the air conditioner to change periodically in a sinusoidal form, and record the power change of the corresponding air conditioner components. The power of air conditioning components includes: compressor power and blower power; S33: The state transition equations for the air conditioning state variables and the cockpit temperature state variables are fitted using a polynomial fitting method. The cockpit intake air temperature is used as an intermediate variable and is fitted as a polynomial function of the evaporator temperature and the cockpit temperature. The change in cockpit temperature per unit time is fitted as a polynomial function of the cockpit inner wall temperature, cockpit outer shell temperature, cockpit intake air temperature, and blower intake air volume. The change in evaporator temperature per unit time is fitted as a polynomial function of the target evaporator temperature, ambient temperature, and the rate of change of blower intake air volume; S34: The compressor power and blower power of the vehicle in a stationary state are fitted using a polynomial fitting method; The compressor power is fitted as a polynomial function of the intake air volume, evaporator temperature, and target evaporator temperature. The blower power is fitted as a polynomial function of the blower intake volume; S35: Calculate the ratio of compressor power at each constant vehicle speed to compressor power when the vehicle is stationary, and fit it as a polynomial function with respect to vehicle speed.

3. The integrated control method for energy-saving driving and energy-saving cooling of a pure electric vehicle according to claim 1, characterized in that: S4 specifically includes the following sub-steps: S41: Construct a cost function for speed planning with the goal of reducing wheel-end power demand and maintaining vehicle speed; S42: Constructing equation constraints for vehicle speed planning based on vehicle motion state transition equations in the spatial domain; S43: Considering motor torque and speed constraints, safe vehicle spacing constraints, road speed limits and traffic light constraints, construct inequality constraints for vehicle speed planning; S44: Given a unit step size and a planned number of steps, construct the optimal control problem for vehicle speed planning in the spatial domain; S45: Solve the constructed vehicle speed planning problem to obtain the desired vehicle speed sequence, and use the first value of the speed sequence as the target vehicle speed at the next moment. S46: Set the update frequency for vehicle speed planning, and execute S41-S45 on a rolling basis.

4. The integrated control method for energy-saving driving and energy-saving cooling of a pure electric vehicle according to claim 3, characterized in that: The cost function constructed in S41 consists of three parts: the first part is the output torque of the electric drive system, which is used to reduce the vehicle's wheel-end power demand; the second part is the difference between the vehicle spacing at the end of the planning time domain and the vehicle spacing at the beginning of the planning time domain, which is used to maintain the vehicle speed; and the third part is the difference between the two when the vehicle spacing is less than the ideal vehicle spacing, which is used to ensure driving safety.

5. The integrated control method for energy-saving driving and energy-saving cooling of a pure electric vehicle according to claim 1, characterized in that: The continuous and differentiable switching function in S52 is constructed based on a type B function such as the Sigmoid function or the hyperbolic tangent function. When the switching function takes speed as the variable, the switching function is set with speed as the independent variable, and the center of the type B function is set according to the set speed threshold. When the switching function takes speed and cockpit temperature as variables, two sub-switching functions are set with speed and cockpit temperature as independent variables respectively, and the center of the type B function is set according to the set speed threshold and cockpit temperature threshold. The output value of the switching function is the product of the two sub-switching functions.

6. The integrated control method for energy-saving driving and energy-saving cooling of a pure electric vehicle according to claim 1, characterized in that: In S53, the guide value for the evaporator target temperature is set according to the vehicle speed. When the vehicle is stationary, the guide value for the evaporator target temperature is set to the upper limit of the evaporator target temperature. When the vehicle speed is the maximum speed limit of the road, the guide value for the evaporator target temperature is set to the lower limit of the evaporator target temperature. When the vehicle speed is in the middle range, the guide value for the evaporator target temperature is obtained by interpolation.

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